Radio Observations of Supernova Remnants at 31 Ghz with the Cosmic

RADIO OBSERVATIONS OF SUPERNOVA REMNANTS AT 31GHZ WITH THE COSMIC BACKGROUND IMAGER

A thesis submitted to the University of Manchester for the degree of Master of Science in the Faculty of Engineering and Physical Sciences

2015

By Shweta Agarwal School of Physics and Astronomy Contents

Abstract 10

Declaration 12

Acknowledgements 14

1 Introduction 15 1.1 Radio Observations ...... 15 1.2 Diﬀuse Radiation ...... 16 1.2.1 Synchrotron ...... 17 1.2.2 Free-Free ...... 20 1.2.3 Thermal Dust ...... 22 1.2.4 Anomalous Microwave Emission (AME) ...... 24 1.3 Supernova Remnants ...... 24 1.3.1 Galactic Supernova Remnants ...... 25 1.3.2 Radio Properties of Supernova Remnants ...... 29 1.3.3 Distances to Supernova Remnants ...... 30 1.4 Interferometry and Synthesis Imaging ...... 31 1.4.1 Imaging ...... 33 1.5 Thesis Outline ...... 34

2 Cosmic Background Imager and Data Analysis 36 2.1 The Cosmic Background Imager ...... 36 2.2 Observations ...... 40

2 2.3 Data reduction ...... 43 2.3.1 Flagging ...... 46 2.3.2 Calibration ...... 48 2.4 Imaging ...... 53

3 Radio Observation of SNRs 56 3.1 Description ...... 56 3.1.1 G20.0 0.2 ...... 57 − 3.1.2 G312.5 3.0 ...... 59 − 3.2 Data and Observations ...... 60 3.3 Subtracting the UCHii region from G20.0 0.2 ...... 64 − 3.4 Multi-frequency Maps ...... 68 3.4.1 Data description ...... 68 3.4.2 The visual analysis ...... 69

4 Analysis 73 4.1 Photometry ...... 73 4.1.1 Aperture Photometry ...... 73 4.1.2 Map Unit Conversion ...... 74 4.2 Uncertainties in the Measurements ...... 77 4.2.1 Total Uncertainty ...... 79 4.2.2 Modelling the Ancillary Data with the CBI (u,v) Cov- erage ...... 80 4.3 Flux Density and Spectral Index ...... 81 4.3.1 SED of G20.0 0.2 ...... 82 − 4.3.2 SED of G312.5 3.0 ...... 84 − 4.3.3 Discussion ...... 86

5 Conclusions 88 5.1 Summary and Conclusions ...... 88 5.2 Future Prospects ...... 90

Appendices 90

3 A Some deﬁnitions 91

B Scripts 94

Bibliography 108

Word count: 29309

4 List of Tables

1.1 Optical properties of supernovae ...... 26 1.2 Comparison between a single dish, and an interferometer. .. 31

2.1 Speciﬁcations for CBI1 and CBI2...... 40 2.2 The supernova remnants observed by CBI ...... 42

3.1 Observational details of G20.0 0.2...... 61 − 3.2 Observational details of G312.5 3.0...... 61 − 3.3 Flux density of sub-components of the UCHii region at 15GHz and 5GHz, as given by Wood & Churchwell (1989)...... 67

4.1 Various methods applied on G20.0 0.2 to estimate the con- − tribution from ground and background contamination. .... 78 4.2 Photometry results for G20.0 0.2. The value is parentheses − is after subtracting UCHii regions...... 80 4.3 Results for G312.5 0.3. The values below 31GHz are taken − from Kane & Vaughan (2003)...... 80 4.4 Final corrected flux densities for G20.0 0.2 (top) and G312.5 − − 0.3 (bottom). For G20.0 0.2 the flux was calculated using − the Effelsberg data at 2.7GHz, and for G312.5 0.3 using the − Parkes 5GHz data...... 81

5 List of Figures

1.1 Atmospheric absorption by O2 and H2O molecules...... 16 1.2 Diffuse emission spectra at 1◦ resolution over the high latitude sky (not including the Galactic plane) as as a function of frequency. Synchrotron dominates below 10GHz, dust above ∼ 100GHz, free-free between 50GHz to 70GHz and spinning ∼ dust dominates at frequencies 30GHz. Figure reproduced ∼ from Planck Collaboration et al. (2015)...... 17 1.3 The synchrotron spectrum of a single electron, pointed as flux density (F(x)) against frequency (x) on a linear scale. The details are given in (Pacholczyk, 1973). The critical frequency ν , is equal to 2πω , as shown in the figure at x 1. ω is c c ≈ c defined in Eq. 1.2. Image taken from http://www.cv.nrao.edu/. 19 1.4 Full-sky radio map at 408MHz (Haslam et al., 1982) in Moll- weide projection. Bright point-like sources have been removed. The emission in this map is dominated by diffuse Galactic synchrotron radiation. Note that the colour-scale has been histogram equalized to highlight both bright and faint features Remazeilles et al. (2015). Synchrotron radiation dominates at low frequencies and is not a major contributor at frequencies 10GHz for most lines-of-sight...... 20 ≥ 1.5 Full sky composite Hα map at the 1◦ scale. It can be used to infer limits on free-free emission from ionized gas (Finkbeiner, 2003). The Hα intensity is in units of Rayleighs and there has been no correction made for extinction effects...... 22

6 1.6 857GHz all-sky map of thermal dust emission from the Planck (Planck Collaboration et al., 2014a) ...... 23

1.7 At the end of its life, the central core of a massive star collapses to form a neutron star. This collapse releases a tremendous amount of energy, powering a supernova explosion. Fig. taken from http://chandra.harvard.edu/ ...... 26

1.8 A forward and a reverse shock are created when a supernova shock wave interacts with the ISM. The forward shock continues to expand into the ISM, the reverse shock travels back into the freely expanding supernova ejecta. Image taken from http://chandra.harvard.edu/...... 27

1.9 Geometry convention for a two-element interferometer. Figure taken from (Burke & Graham-Smith, 2014)...... 33

2.1 CBI and CBI2 ...... 37

2.2 The primary beam pattern of CBI. (a) 26 GHz, (b) 31 GHz, (c) 36 GHz. The main beam can be well approximated by a Gaussian with FWHM (44 31 )...... 39 × ν 2.3 Flowchart showing the procedure used to obtain calibrated data, and CLEANed image. The calibrated data are written to .uvf (uv-FITS) ﬁles...... 43

2.4 This ﬁgure is of a total-power plot for channel 4 (all receivers) within the CBICAL program. Receiver 11 shows amplitude that deviates from the mean by more than 10%, so we ﬂagged it out. The receiver should have a power reading of 1, but ∼− sometimes was lower than 0.3 ...... 44 − 2.5 CBICAL visibility plot ...... 45

2.6 Primary calibrator ...... 49

7 2.7 Plot of the visibility statistics of the calibration source (Jupiter). The observations were made on the 25 September 2007. Each colour represents a channel and each individual point is a different baseline. In this plot Jupiter is calibrated from another observation of Jupiter and thus the phase is not exactly equal to zero...... 51

3.1 VLA map of G20.0 0.2 with a maximum at 0.92Jy/beam − and a minimum at 0.00433Jy/beam. Image produced from − MAGPI survey (Helfand et al., 2006)...... 58

3.2 The Parkes 4850MHz survey map of G312.5 3.0. The map − shows the morphology of the object at a resolution of 4.3arcmin. Image produced by (Griﬃth & Wright, 1993) data...... 60

3.3 CLEANED CBI map of G20.0 0.2 before subtracting the − ﬂux for UCHII region...... 62

3.4 CBI image of G312.5 3.0 having a maximum brightness of − 0.07 Jy/beam...... 63

3.5 The CBI map of the G20.0 0.2 after subtracting the UCHii − region. The extended component in the south is G19.61 0.23, − which is a complex Hii region with RA = 18h 27m 38s and Dec = -11h 56m 40s (Wood & Churchwell, 1989)...... 65

3.6 Image of the UCHii region at (left) 2cm and (right) 6cm. Sub-components of the region are marked as A,B and C. The three regions are studied separately. A is the brightest region, followed by B and then C. Taken from Wood & Churchwell (1989)...... 66

3.7 The free-free model for the three sources within the UCHii region. The data has been taken from Wood & Churchwell (1989)...... 67

8 3.8 Multi-frequency maps of G20.0 0.2 with the contours from − the CBI map at (0.1, 0.2, 0.5, 0.7, 0.9)*0.99Jy/beam. (a) is the Parkes 5GHz map, (b) is the IRAS 12 µ m map, (c) is the Planck map at 545GHz map, (d) is the Eﬀelsberg 100m map...... 70 3.9 Multi-frequency maps of G312.5 3.0 with CBI contours from − the CBI map at (0.1, 0.2, 0.5, 0.7, 0.9)*0.07Jy/beam. (a) is the Planck map at 545GHz, (b) is the IRAS map at 12 µm and the (c) is the Parkes map at 5 GHz...... 71

4.1 Spectral energy distribution of G20.0 0.2. The fit was ob- − tained using the data points as discussed in Table 4.2. (a) shows the power-law fit. (b) is the fit for the spectral break at 5 GHz...... 83 ∼ 4.2 Spectral energy distribution of G312.5 3.0. The power law − was calculated using the methodology discussed in Table 4.3. . 85 4.3 Distribution of spectral indices of Galactic SNRs compiled by Green (2009). Figure taken from Reynoso & Walsh (2015). .. 86

9 Abstract

The explosion of a supernova releases almost instantaneously about 1051 ergs of mechanical energy, an event that irreversibly changes the physical and chemical properties of large regions of their host galaxy. A supernova remnant (SNR) consists of three components: the stellar ejecta, the nebula resulting from the powerful shock waves, and sometimes a compact stellar remnant. They can radiate their energy across the whole electromagnetic spectrum, but the great majority are strongest in the radio regime. Almost 70 years after the first detection of radio emission coming from a SNR, great progress has been made in fully comprehending their physical characteristics and evolution. The radio spectra of the synchrotron emission from the SNRs has not been studied much at high frequencies ( few GHz), hence the ≥ motivation behind this research. In this thesis we analyse data from the Cosmic Background Imager (CBI) at 31GHz. These data were modified and calibrated using the CBI data reduction pipeline. The culled data was then imaged and deconvolved using the CLEAN algorithm. It was decided that we would observe two SNRs, namely G20.0 0.2 and G312.5 3.0. These two SNRs have different mor- − − phologies, as well as spectral properties. To estimate the flux density of the SNRs we used an aperture photometry technique. Using this technique the flux densities of G20.0 0.2 and G312.5 3.0 at 31GHz were found to be − − 3.56 0.2 Jy and 0.49 0.07 Jy, respectively. We estimated various uncer- ± ± tainties, and by taking multi-frequency maps from literature it was possible to fit a power-law spectra and estimate the spectral indices. The value of the spectral indices for G20.0 0.2 after fitting a power law was α = 0.36 − − 0.05 and α = 0.8 0.1 after fitting a spectral break at 5 GHz. For ± − ± ∼ 10 G312.5 3.0 it was estimated to be α = 0.51 0.07. Our results show − − ± that the spectrum of G20.0 0.2 was flat at low frequencies, similar to Crab − SNR, and showing the steepening at high frequency. While the spectrum for G312.5 3.0 has a typical value which agrees with most of the SNRs’ − spectra.

11 Declaration

I declare that no portion of the work referred to in this thesis has been submitted in support of an application for another degree or qualiﬁcation of this or any other university or other institute of learning.

Shweta Agarwal

University of Manchester

Jodrell Bank Center of Astrophysics

Oxford Road

Manchester

Supervisor: Clive Dickinson Co-supervisor: Mat´ıas Vidal

12 Copyright i. The author of this thesis (including any appendices and/or schedules to this thesis) owns certain copyright or related rights in it (the Copyright) and s/he has given The University of Manchester certain rights to use such Copyright, including for administrative purposes. ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. iii. The ownership of certain Copyright, patents, designs, trade marks and other intellectual property (the Intellectual Property) and any reproductions of copyright works in the thesis, for example graphs and tables (Reproductions), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and commercialisation of this thesis, the Copyright and any Intellectual Property and/or Reproductions described in it may take place is available in the University IP Policy (download pdf ﬁle from http://documents.manchester.ac.uk/DocuInfo .aspx?DocID=487), in any relevant Thesis restriction declarations deposited in the University Library, The University Librarys regulations (see http://www.manchester.ac.uk/ library/aboutus/regulations) and in The Universitys policy on presentation of The- ses.

13 Acknowledgements

This thesis would not have been possible without the hard work of my supervisor and co-supervisor. I am so grateful to them for being consistently kind and helpful throughout. They have motivated to, against all hurdles, persevere with my work. It was both a pleasure and an inspiration to work alongside individuals so passionate about their work. I am grateful to Papa, Ankita and Amma for their love and support. I would like to thank all my friends in Manchester for welcoming me with an open heart and making this time one of the best times of my life. I also owe a big thanks to Sundar who was always up for oﬀering profound emotional encouragement and computing support. Lastly, thanks LATEX for this beautiful thesis.

14 Chapter 1

Introduction

We begin with a brief introduction to radio interferometry in Section 1.1 and discuss how it is used to observe various Galactic and non Galactic objects. The chapter gives a brief description of the four diﬀuse Galactic emission mechanisms in Section 1.2 which are used to study various Galactic objects. The astronomical topic which comprises the focus of this thesis is that of Supernova Remnants, a major source of synchrotron emission. The detailed physics of supernova remnants are discussed in Section 1.2.1.

1.1 Radio Observations

The radio window (λ 15m 0.1mm) was the ﬁrst spectral range available ≈ − to astronomy outside of the optical window, later followed by far infrared and then ultraviolet (Wilson et al., 2009). This window lies between absorption by the ionosphere and absorption by quantized molecular rotations. Radio waves are absorbed by the atmosphere mainly by water vapour and oxygen, as shown in Fig 1.1. The strong water vapour bands are at 22.2GHz and 183GHz whereas oxygen exhibits an exceedingly strong band at 60 GHz. At high frequencies the atmosphere is not transparent, and at frequencies below the plasma frequency ( 9MHz) the radiation is also absorbed. Therefore ≈ the atmosphere plays a critical role in an astronomer’s choice of frequency range. Astronomy can bypass most atmospheric absorption if studied from

15 16 CHAPTER 1. INTRODUCTION satellites/balloons above the earth’s ionosphere.

Figure 1.1: A visualization of the absorption by O2 and H2O molecules as a function of frequency. The radio absorption occurs because of the rotational transitions in O2 and H2O molecules. Image taken from the classroom radio astronomy notes by Peter Wilkinson

We study the sky by detecting signals in the form of electromagnetic radiation emitted by charged particles when they are accelerated. Ultimately, the signals we detect depend on the state of the charged particle from which the photon was released. Thermal emission (e.g. free-free) is produced by particles in thermal equilibrium. Conversely, non-thermal emission arises from particles that are not in thermal equilibrium, synchrotron radiation is produced by such particles and follows a power law, discussed in Section 1.2.1. Galactic diﬀuse radiation is principally a mixture of synchrotron radiation, free-free radiation, thermal radiation from cold interstellar dust and anomalous microwave emission.

1.2 Diﬀuse Radiation

Understanding diffuse Galactic emission is interesting not only in its own right, but also as a tool for minimizing the foreground contamination of cosmological measurements. The study of diffuse emission could help us study various objects in the Galaxy by studying their radio spectra. We will 1.2. DIFFUSE RADIATION 17 now discuss four components (at cm wavelengths) in detail. As shown in Fig. 1.2 different emission mechanisms dominate at different frequencies.

Figure 1.2: Diﬀuse emission spectra at 1◦ resolution over the high latitude sky (not including the Galactic plane) as as a function of frequency. Synchrotron dominates below 10GHz, dust above 100GHz, free-free between 50GHz to 70GHz and spinning∼ dust dominates∼ at frequencies 30GHz. Figure reproduced from Planck Collaboration et al. (2015). ∼

1.2.1 Synchrotron

Synchrotron radiation as shown in Fig. 1.4, also known as magneto-Bremsstrahlung radiation, is emitted by highly relativistic cosmic ray electrons moving in the interstellar magnetic ﬁeld. The electrons are accelerated as they spiral around the magnetic ﬁeld lines and in turn generate electromagnetic radiation. The total power radiated by a single electron is given by (Wilson et al., 2009):

2e4ν2 B2E2 P = ⊥ = σ γ2cυ (1.1) 3m4c7 T B where γ is the Lorentz factor, B is the magnetic field strength, E is the total energy of the electron, υ = B2/4π, σ = 6.65 10−25 cm2 is the Thomson B T × cross section, and the rest of the symbols have their usual meaning. Thus, synchrotron radiation depends on the energy spectrum of the electrons as well as on the magnetic field intensity. Synchrotron emission dominates at lower frequencies (ν 10GHz), due to the steep spectral index, as shown in Fig. ≤ 1.2. Synchrotron emission has a negative correlation between flux density 18 CHAPTER 1. INTRODUCTION and frequency. Unlike Free-free, synchrotron radiation exhibits significant spectral variation with typical values in the range α = 0.3 to 1.0 (Plank − − Collaboration et al., 2014). α is related to the electron energy distribution power law index δ by α = 1 δ/21 at large angular scales. The spectral index observed from − Galactic emission is flattest near the star forming regions ( 0.3) (Gold ≈ − et al., 2011) and steepest in the halo, where it has a value of 0.7. This ∼ − is because, in the star forming region cosmic ray electrons are produced, whereas in the plane and the halo, they diffuse and lose their energy in the form of synchroton (radiatively) and inverse Compton emission (scattering) (Bennett et al., 2003). The radiation of the relativistic electrons is beamed in the direction of their motion, and this beam is seen to be elongated in the observer’s frame. A pulse of radiation is observed when an electron’s velocity vector lies within an angle of 1/γ with respect to the observer’s ± line of sight (Longair, 1992). As shown in Fig. 1.3, the resulting power spectrum of electrons with energy E is quite sharply peaked near the critical frequency ωc (Eq.1.2), and is much narrower than the breadth of the electron energy spectrum. Therefore, to a good approximation, it may be assumed that all the radiation of an electron of energy E is radiated at the critical frequency ωc, which we can estimate as: (Rybicki & Lightman, 1979)

2 ωc =3/2γ ωB sin α (1.2)

where ωB is the rotation frequency of the relativistic electron, and α is the pitch angle of the electron. The energy spectrum of cosmic ray electrons in the frequency range from ωc to ωc + dωc is often represented by a power-law distribution (Casadei & Bindi, 2004):

N(E)dE = KE−δdE (1.3) where N(E)dE is the number of electrons per unit volume with energies from

1Spectral indices are S να unless otherwise stated in the thesis. Note that Green (2009) uses the convention∝ S ν−α. ∝ 1.2. DIFFUSE RADIATION 19

Figure 1.3: The synchrotron spectrum of a single electron, pointed as flux density (F(x)) against frequency (x) on a linear scale. The details are given in (Pacholczyk, 1973). The critical frequency νc, is equal to 2πωc, as shown in the figure at x 1. ωc is defined in Eq. 1.2. Image taken from http://www.cv.nrao.edu/. ≈

E to E+dE, and δ is the electron power law index. The emission coeﬃcient of synchrotron radiation for cosmic ray electrons at a frequency ν is given by: ǫ Bδ+1/2ν1−δ/2 (1.4) ν ∝

For a regular and ordered magnetic field, it can be shown that synchrotron δ+1 emission is linearly polarised with fractional polarizationΠ= 7 which im- δ+ 3 plies a value of α = 0.5(Wilson et al., 2009). A typical value of the energy − spectral index is δ = 2.5 (Fermi, 1949), which gives the intrinsic fractional polarization as 72%. At frequencies 50GHz synchrotron radiation is the ≤ most polarized mechanism observed (Vidal et al., 2014). We can therefore study the magnetic field at a point in the Galaxy by knowing the polarization of the synchrotron radiation. The interstellar gas is permeated by a Galac- tic magnetic field such that when linearly polarized radio emission passes through it, the electric vector is rotated in the direction of the magnetic field. This phenomenon is known as Faraday rotation. Faraday rotation is dependent on the wavelength of the radiation, δχ = RM λ2. RM is the × 20 CHAPTER 1. INTRODUCTION rotation measure (defined as the strength of the effect, which depends on the electron density and the magnetic field along the line-of-sight) and χ is the polarization angle. For details see (Mancuso & Spangler, 2000; Longair, 1992).

Figure 1.4: Full-sky radio map at 408MHz (Haslam et al., 1982) in Mollweide projection. Bright point-like sources have been removed. The emission in this map is dominated by diﬀuse Galactic synchrotron radiation. Note that the colour-scale has been histogram equalized to highlight both bright and faint features Remazeilles et al. (2015). Synchrotron radiation dominates at low frequencies and is not a major contributor at frequencies 10GHz for most lines-of-sight. ≥

1.2.2 Free-Free

Free-free (or Bremsstrahlung) radiation is an important contribution to Galac- tic diﬀuse emission, which is caused by the acceleration of free electrons. A photon is emitted every time an electron passes an ion on account of the coulombic interaction between the electron and the nuclei. This emission occurs in ionized regions, as these regions have atomic hydrogen ionized by the ultra-violet radiation from OB stars. The energy radiated through free-free 1.2. DIFFUSE RADIATION 21 emission is given by:

− − −hν ǫff =6.82 10 38T 1/2Z2n n e kT < g > (1.5) ν × e e i ff

−3 −1 −1 The units are in ergcm s Hz , and < gff > is the velocity averaged Gaunt factor which relates quantum mechanical expressions to their classical analogues, ni and ne are the densities of ions and electrons respectively, and Z is the charge (Rybicki & Lightman, 1979). Free-free emission is most dominant in the frequency range 10-100GHz (see Fig. 1.2) at low Galactic latitude (Dickinson et al., 2003). However, at higher latitudes free-free is weak compared to other types of emissions (Davies et al., 2006). At high frequencies ( 10GHz) free-free emission has a S να spectrum with α 0.15. At ∼ ∝ ≃ − low frequencies ( 1GHz) optically thick self absorption occurs. The free-free ∼ spectral index α is a slow function of frequency and ﬂux (Dickinson et al., 2003). An expression of the free-free emission spectral index for 20 – 100 GHz frequencies is given by (Bennett et al., 2003):

1 αff = (1.6) − 10.48+1.5ln Te ln(ν) 8000 − ! 22 CHAPTER 1. INTRODUCTION

Figure 1.5: Full sky composite Hα map at the 1◦ scale. It can be used to infer limits on free-free emission from ionized gas (Finkbeiner, 2003). The Hα intensity is in units of Rayleighs and there has been no correction made for extinction eﬀects.

Hα surveys provide most of the information on free-free emission as it is otherwise difficult to separate synchrotron and free-free just from the continuum. The diffuse Hα is a good tracer for diffuse free-free emission and it gives an independent estimate see Fig. 1.5. Emission from the thermal Bremsstrahlung process does not show any polarization. Emission from individual electrons is polarized, but because of their random distribution the total polarization cancels out. Thomson scattering occurs at the edge of HII regions, with a maximum of 10% fractional polarization possible in principle (Davies & Wilkinson, 1999; Rybicki & Lightman, 1979; Keating et al., 1998).

1.2.3 Thermal Dust

Dust grains are well mixed with the ambient gas; the ratio of gas to dust is roughly 100 : 1 (Planck Collaboration et al., 2014a). They are good tracer for the interstellar medium. Thermal dust emission arises from bigger dust 1.2. DIFFUSE RADIATION 23 grains on the order of 10 µm, which are in thermal equilibrium with the ∼ ambient radiation ﬁeld (Planck Collaboration et al., 2014b). Thermal dust has been mapped over the full sky in several infrared bands by COBE, IRAS and the Planck mission. Fig. 1.6 shows the Planck map. The emission has a peak at 140 µm, which corresponds to a dust tem- ∼ perature of 20K, and deviates signiﬁcantly from a pure black body spec- ∼ trum. The emissivity function is related to dust temperature and frequency by I B (T )ν2 as given in (Draine & Lee, 1984). With the dust tem- ν ∝ ν plate variations, the spectral index β is between 1.5–1.7 at lower frequencies (Planck Collaboration et al., 2014a), with a break at 500 GHz (Finkbeiner ∼ & Schlegel, 1999; Planck Collaboration et al., 2014d). As can be seen in Fig. 1.2 the emission is weak at 30GHz and below.

Figure 1.6: 857GHz all-sky map of thermal dust emission from the Planck (Planck Collaboration et al., 2014a) .

Polarization of the thermal dust emission becomes signiﬁcant at frequencies <100 GHz (Benoˆıt et al., 2004), where synchroton emission dominates. The level of polarization typically varies from 5% to 10% and, can be as high 24 CHAPTER 1. INTRODUCTION as 18 % (Planck Collaboration et al., 2014b). ∼

1.2.4 Anomalous Microwave Emission (AME)

Anomalous microwave emission (AME) was conclusively first observed by Kogut et al. (1996) in COBE DMR data, but was not understood at the − time. Another detection was later recorded by Leitch et al. (1997), who noted that it could not be accounted for by synchrotron, free-free, thermal dust or CMB radiation. The source of AME is thought to be electric dipole radiation from small spinning dust grains (Planck Collaboration et al., 2014d), theoretically, thought to be produced by the polycyclic aromatic hydrocarbons (PAHs). AME is most prevalent in the frequency range 10 - 60GHz, rising below 10GHz and falling above 30GHz (see Fig. 1.2). The spectral en- ∼ ∼ ergy distribution of AME peaks at 30GHz which agrees with the spinning ∼ dust hypothesis (Draine & Lazarian, 1998). The emissivity of AME is defined as the ratio of the optical depth to that of dust (Planck Collaboration et al., 2014d) and appears to be negatively correlated with column density (Vidal et al., 2011). AME is detected in HII regions (Dickinson et al., 2007), as well as dust clouds (Scaife et al., 2008), but has only been observed in one bright supernova remnant (3C396) to date (Scaife et al., 2007). However, this could be due to the flattening of the SNR’s spectral index. AME is not strongly polarized ( 2%) (Dickinson ≤ et al., 2011).

1.3 Supernova Remnants

Supernova remnants (SNRs) are the objects produced at the end of a massive star’s lifetime. The energy released by these explosions has a major eﬀect on the interstellar medium2. Also, at the time of the Big Bang little material besides hydrogen and helium were produced, yet our planet is composed of various other elements which are believed to be produced inside stars during

2The region between stars having low densities and consisting mainly of gas (99%) and dust. 1.3. SUPERNOVA REMNANTS 25 later red giant stages to supernova explosions. There are 10,000 Galactic ∼ SNRs expected at any given time but they are diﬃcult to detect, so far there are 295 SNRs detected as listed in (Green, 2009), mostly identiﬁed by radio/X-ray observations. The following section describes the basic physics of the supernova remnant in details.

1.3.1 Galactic Supernova Remnants

Supernovae are characterized by the instantaneous release of 1051 erg of ∼ energy, which can occur via either the catastrophic collapse of a massive star, or runaway nuclear burning on the surface of a white dwarf (dense stellar remnant). A small fraction of energy is released in the visible band, which can make it seem as though a new star has appeared in the sky (Phillips, 1999). Astronomers study the spectrum of the supernova, by which they classify them as either Type II, should they exhibit hydrogen Balmer lines, or Type I, otherwise (see Table 1.1 below). Supernova remnants in a galaxy are the remains of a supernova explosion. They heat up the interstellar medium, and accelerate cosmic rays. The heat in the early universe caused nuclear fusion which resulted in the formation of the light elements including H, He. The formation of heavy elements required extra energy, which was provided by supernovae (Reynoso & Walsh, 2015). 26 CHAPTER 1. INTRODUCTION

Figure 1.7: At the end of its life, the central core of a massive star collapses to form a neutron star. This collapse releases a tremendous amount of energy, powering a supernova explosion. Fig. taken from http://chandra.harvard.edu/

Type I Type II (No Hydrogen Lines) (Hydrogen Lines) Type I-a Type I-b Type I-c Type II-P Type II-L Type IIb Si lines no Si no Si Light Curve Light Curve resembles but He no He has reached a displays a type I-b ‘plateau’ linear decrease

Table 1.1: Optical properties of supernovae. Type I-a are the pure shell type remnants of the thermonuclear explosion of massive stars, in which the whole star is destroyed (releasing energy of 1.5 1051 erg/sec). Type II/Type I-b/c comprise the composite type remnants∼ × of massive star core collapse. In this explosion a rotating neutron star is left behind. The energy release is in the range 1049 1051 erg/sec. The light curve is the plot of the magnitude of a supernovae∼ − as a function of time. Fig. taken from http://chandra.harvard.edu/.

In stars, the nuclear fusion of hydrogen to helium takes place in the core, producing thermal radiation which acts against gravity and prevents the star from collapsing. In the later stages of evolution, synthesis continues until an iron nucleus is produced. Iron is the most stable element and core fusion 1.3. SUPERNOVA REMNANTS 27 stops. The electron repulsive forces in the iron atoms support the star against gravitational forces until the iron core mass exceeds the Chandrasekhar limit. The core then collapses and the electrons and the protons are now pushed inwards, so close that they merge to form neutrons. The core is now called a neutron star (Plank Collaboration et al., 2014) which is gravitationally stable (see Fig. 1.7). 99% of the supernova’s energy is lost in the formation of energetic neutrons, and the remainder causes the stellar material to accelerate outwards at a speed greater than that of sound. This causes a ‘shock wave’ to move outwards from the central star. The magnetic ﬁeld of the ambient medium increases by the compressed post shock gas (see Fig. 1.8) which causes the relativistic acceleration of the electrons, thus culminating in the release of synchrotron radiation.

Figure 1.8: A forward and a reverse shock are created when a supernova shock wave interacts with the ISM. The forward shock continues to expand into the ISM, the reverse shock travels back into the freely expanding supernova ejecta. Image taken from http://chandra.harvard.edu/.

The evolution of SNR comprises of three major phases (Chevalier, 1977):

1. Free Expansion Phase - Initially, the ejecta expands outwards from the star and compresses the ambient ISM, which gives the area around the ejecta a low density interior. The amount of gas swept up is much lower than the mass of the stellar ejector. The expansion is not aﬀected by 28 CHAPTER 1. INTRODUCTION

the outer gas, and keeps its initial speed and energy. This phase can last anywhere from 90 years to over 300 years (e.g. Cassiopeia A).

2. Adiabatic Phase or Sedov Phase - When the mass of the gas swept up becomes larger than that of the stellar material, the kinetic energy of the stellar material is transferred to the swept up gas, causing heating. The period during which the swept up gas is not releasing energy is called the adiabatic phase. This phase can last anywhere from 100 – 100,000 years (e.g. Crab Nebula).

3. Snowplow or Radiative Expansion Phase (Cioﬃ et al., 1988) - As the expansion proceeds, the shock front begins to lose energy (per unit volume per unit time) via radiative cooling. When the radiative cooling time of the gas becomes shorter than the expansion time, the outward expansion stops. This phase can last hundreds of thousands of years (e.g. North Polar Spur).

Radio supernova remnants can be distinguished as follows (Sakhibov & Smirnov, 1982; Longair, 1992):

Shell Type Remnants - As the shock wave from the supernova travels • through space it heats up the surroundings thus producing a big shell of hot material. This is seen as a ring and astronomers sometimes refer to this phenomenon as limb brightening (e.g., SN1006 (Green, 2009)).

Filled-Centered, Plerion-Type, Crab-like, or Pulsar Wind Nebula - • Characterized by an energetic wind of particles and a strong magnetic ﬁeld sustained by a central pulsar (e.g., Crab Nebula).

Composite Type - Has emission originating in the centre as well as in • the shell (e.g., IC443) (Green, 2009).

Fermi (1949) proposed a method through which particles colliding with clouds can be accelerated to high energies. He posited that in diffuse cosmic ray acceleration, collisions occurring through magnetic mirroring (between charged particles and interstellar clouds) could result in the production of 1.3. SUPERNOVA REMNANTS 29 a suprathermal power law population. He further proved that the energy gain in one collision (between a particle and the magnetic perturbation) is (u/v)2, where u is the velocity of the magnetic perturbation and v is ∝ the velocity of the particle, which was later termed the second order Fermi acceleration. Diffusive Shock Acceleration (DSA) is the first order Fermi acceleration where energy gain is (u/v) (Bell, 1978). DSA is seen as the ∝ mechanism behind SNR emision. The ejected stellar material expands freely (as its density is much larger than the density of the interstellar gas) sweeping the magnetic field. The swept magnetic field collects, along with the gas, by the outward moving shock front (Draine, 2011). The main Galactic sources of cosmic rays are considered to be supernova remnants, one of the reasons why it is important to study their properties.

1.3.2 Radio Properties of Supernova Remnants

The magnetic field of the ISM is enhanced in the compressed post-shock gas, and accelerates charged particles to relativistic speeds. These relativistic charged particles emit synchrotron radiation. At radio frequencies this synchrotron radiation is the hallmark of a remnant (Plank Collaboration et al., 2014). The radio-frequency spectra of supernova remnants are studied to understand their physics. Their spectra are observed to be varying over the lifetime of a SNR (Lazendic et al., 2000). In young supernovae, particle acceleration is efficient and SNR exhibit free expansion in the early Sedov phase (Pavlovićet al., 2013). Green (2009) clearly showed that young SNRs have steep radio spectral indices of the order = 0.5. Theoretically, the spectrum should steepen with time, as shock ∝ − waves lose significant amounts of energy. However in young SNR contradic- tory results were observed. Bell et al. (2013) explained that the orientation of the magnetic field, which was assumed to be parallel in Bell (1978), is actually quasi perpendicular in young SNRs. This geometry is responsible for the steepening of the spectral index (Uroˇsević, 2014). As SNR age, the effect of particle acceleration on their mechanism gradually decreases. For evolved SNRs the expected spectral index value is 0.5<α< 0.6. We see − − 30 CHAPTER 1. INTRODUCTION that it gets steeper as the SNR ages. Especially at high frequencies the value of spectral index can be α 1.0 or steeper. The steepening is explained ∼ − through radiative losses. With age, the associated supernova weaken, resulting in a decrease in the compression ratio and corresponding steepening of the radio spectra. The filled centre SNRs have flatter spectra (α < 0.5) − (Green, 2009), which contradicts what we would expect from First Order Fermi acceleration .

1.3.3 Distances to Supernova Remnants

The estimate of the distance to SNRs is important towards identifying their physical parameters and understanding their nature. A common statistical method used to calculate the distance to a SNR is the radio surface-brightness-to-diameter (Σ D) relation (Case & Bhat- − tacharya, 1998). The radio surface brightness, Σ, is deﬁned as:

S Σ =1.505 10−19 ν Wm−2Hz−1sr−1 (1.7) ν × Θ2 where S is in Jy and Θ is in arcmin. The Σ D relation for Galactic shell ν − remnant observed at 1GHz is given by (Case & Bhattacharya, 1998):

Σ =2.07 10−17 D(−2.38±0.26) Wm−2Hz−1sr−1, (1.8) 1 Ghz × × where the distance D is in pc. This method assumes that:

The radio luminosity of all ‘shell type’ remnants are dependent on the • linear diameter in exactly the same manner.

All remnants have the same supernova explosion mechanism and en- • ergy.

All remnants are expanding into mediums of identical physical proper- • ties.

Green (1984) concluded that this method is limited and can have uncertainties up to a factor of 3. Other methods to estimate the distance are: 1.4. INTERFEROMETRY AND SYNTHESIS IMAGING 31

Kinematic method: This method is based on constructing an HI ab- • sorption spectrum. The radial velocity of the absorption peak is used to calculate the distances.

X-ray observations method: This method assumes that the SNR shell • is in its adiabatic expansion phase. In this case the distance is derived as a function of the initial energy.

The observations of the SNRs were made using the radio intereferometry techniques discussed in the following section.

1.4 Interferometry and Synthesis Imaging

For a signal from the sky we want the collecting area to be maximum, while the noise is minimum. This depends on a telescope’s design, as well as on the receiver’s characteristics. It is also an important criterion to have the best possible angular resolution which depends on the wavelength of the radiation, as well as on the telescope’s diameter. In the case of a single dish, the diameter of the aperture has some practical limits depending on the technical and ﬁnancial means available. This limit can be overcome by combining the signal received by two or more small telescopes separated by a given distance. The angular resolution in this case is given by λ/D, where D is the distance between the telescopes. Comparison between a single dish and an interferometer can be seen in table 1.2.

Single Dish Interferometer Low angular resolution High angular resolution Low sidelobes Multiple beams Large area surveys Imaging of limited areas

Table 1.2: Comparison between a single dish, and an interferometer.

A single dish telescope reflects the light it collects off a parabolic surface into its detector. Due to the parabolic shape of the dish there is a differential delay in the signals (arriving across the aperture). This brings the signals to 32 CHAPTER 1. INTRODUCTION the focal point at the same point. This can also be accomplished by synthe- sizing many apertures, assuming the path length to the focus is standardized for all signals and then combining the data. The output of the telescopes can be coherently added through a process known as interferometry. The further combination of interferometric pairs over a range of projected separations is aperture synthesis. The two-element interferometer (see Fig.1.9) is a direct analogue of ‘Young’s slit experiment’ in optics. The signals are received by antennas, amplified by radio receivers, and then correlated to give the total power (voltage) as a function of time. These signals interfere constructively and destructively, which modulates into quasi-sinusoidal oscillations and gives a fringe pattern. The fringe pattern is represented by a complex number compromising of the signal’s amplitude and phase (Burke & Graham-Smith, 2014). If, instead of adding, the signals are multiplied, then the two voltage/power terms do not correlate. Thus the output will be insensitive to receiver gain variation, as well as variations in atmospheric noise. When using an interferometer some important considerations include:

The size of the individual elements deﬁnes the ﬁeld of view of the • interferometer’s primary beam3.

The sky signal is not monochromatic, which lowers the visibility of • sources off-centre with respect to the phase centre of the interferometer. This is called ‘bandwidth smearing’. Reducing the bandwidth is not a solution in this case, as it will reduce the energy collected by the antenna and will make the data noisier. So, to overcome this, the radiation is split into channels of narrower bandwidth. For this we should have ∆ν θbeam where θ is the synthesized beamwidths. ν ≪ dθ As a result of the earth’s rotation and the fact that the integration • time per data point is non-zero we can experience a phase offset and a reduction in the field-of-view. This is known as ‘time smearing’.

3The primary beam is the power pattern of the response from the antenna, usually a Gaussian. 1.4. INTERFEROMETRY AND SYNTHESIS IMAGING 33

Figure 1.9: Geometry convention for a two-element interferometer. Figure taken from (Burke & Graham-Smith, 2014).

The total output of an interferometer (after correlating the individual signals) is known as the spatial coherence function, or the ‘visibility function’ (Taylor et al., 1999):

V = A(σ)I(σ)e−2iπν(b·σ)/cdΩ (1.9) Z where A(σ) is the normalized reception pattern of the interferometer, b is the projected baseline length, I(σ) is the observed intensity of the sky, σ is the oﬀset of a point of interest in source from its source centre and the other symbols have their usual meanings (see Fig.1.9). This is the van Cittert- Zernike theorem, for which a proof can be found in (Burke & Graham-Smith, 2014). In summary, the visibility is the Fourier transform of the intensity distribution convolved with the primary beam. The inverse Fourier transform of the visibility function (Eq.1.9) gives the brightness distribution for a given source.

1.4.1 Imaging

Practically, the correlator response (or the visibility function), is measured in the (u,v) plane where u and v are the components of the projected baseline vector in the east-west and north-south directions respectively. All distances are expressed in terms of the relevant wavelength (Wilson et al., 2009). The 34 CHAPTER 1. INTRODUCTION

Visibility function (see Eq. 1.9) is sampled at discrete points along elliptical tracks. Some regions are missed in this sampling, either due to missing short spacing or missing angular wedges in the form of the projected baseline vector. The Fourier transform of the (u,v) sampling function is referred to as the ‘dirty beam’, and when the dirty beam is convolved with the true brightness we get the ‘dirty map’. The true image can be obtained from the dirty map by using various deconvolution schemes. These remove the side lobes present in the image plane, and estimate the visibility function in the un-sampled part of the (u,v) plane. The most widely used is the CLEAN algorithm (Hogbom & Brouw, 1974), which assumes the sky to be composed of a small number of point sources and uses simple iterative procedures to ﬁnd the positions, as well as strengths of these sources. The CLEAN algorithm is less suited for extended sources, but as we will see in Chapter 2 we optimize the gain to account (partially) for this. This provides a dirty image with many points. The maximum entropy method (MEM) is a good for the extended sources, as it assumes the sky to be smooth and produces a similarly smooth model (Burke & Graham-Smith, 2014).

1.5 Thesis Outline

The main goal of this project is to study the radio spectra of supernova remnants in order to understand their behaviour at radio frequencies up to 31GHz. The supernova remnants have not been previously studied at frequencies 15GHz and we would like to see if they follow power laws ≥ at radio frequencies up to 40GHz. We map the remnants in intensity and measure their integrated ﬂux density at 31GHz. Chapter 2 discusses the instrumentation used for the observations, with a detailed explanation of the data reduction and calibration procedure. Chapter 3 summarizes the supernova remnants studied, as well as the challenges faced while calculating the ﬂux densities. Chapter 4 describes the methods used for the calculations and discusses the uncertainties. Chapter 5 concludes the thesis and provides various discussions about the project and future work required to complete 1.5. THESIS OUTLINE 35 the programme. Chapter 2

Cosmic Background Imager and Data Analysis

This Chapter discusses the generation of maps from the available data recorded using the Cosmic Background Imager (CBI) in Section 2.1, followed by an overview of its suitability for the observations of Galactic sources in Section 2.2. In Section 2.3 the procedure of data calibration and editing is summarized, while Section 2.4 outlines the process of imaging and deconvolution.

2.1 The Cosmic Background Imager

The Cosmic Background Imager (Padin et al., 2001) was a 13-element interferometer. It was located at an altitude of 5080m in the Chilean Andes at the Chajnantor Observatory. The location was chosen to reduce the impact of atmospheric emission by being above the majority of water vapour. As discussed in Section 1.1 the presence of water and oxygen results in contaminated and noisy data. It was primarily designed to measure the CMB fluctuations on angular scales in the range 5 arcmins to 0.5◦, the SZ1 effect, ∼ and Galactic sources (Casassus et al., 2004). It was also the first instrument

1Sunyaev-Zel’dovich eﬀect: high energy electrons distorts the CMB through inverse Compton scattering, resulting in the increase in the energy of low energy CMB photons. This eﬀect is mainly observed in massive cluster of galaxies

36 2.1. THE COSMIC BACKGROUND IMAGER 37 to detect the ‘damping tail’ of the CMB at a scale of 10arcmin (Pear- ∼ son et al., 2003), and one of the ﬁrst to detect the E-mode CMB polarization (Readhead et al., 2004). The instrument also detected an excess power in the CMB power spectrum at high ℓ-values (Mason et al., 2003) and observed the Sunyaev-Zel’dovich eﬀect in nearby clusters of galaxies (Udomprasert et al., 2004).

Figure 2.1: Photos of the cosmic background imager 1 (left) and cosmic background imager 2 (right) in the Chajnantor plateau. The 13 Cassegrain antennas, as shown in the image of CBI1, had cylindrical shields to reduce the signal overlap between antennas. Image from www.astro.caltech.edu.

The receivers of the instrument operated in ten frequency channels rang- ing from 26 to 36 GHz. This frequency range was chosen to minimize the contribution from foreground sources at lower frequencies, as well as from atmospheric noise at higher frequencies. Each receiver measures either left (L) or right (R) circular polarization. Each antenna was 0.9 m in diameter. The primary beam (hereafter referred to as PB) corresponds to a 44 arcmin full-width half maximum (FWHM) at 31GHz. The antennas were mounted on an alt-azimuthal planar rigid platform that could be rotated about the optical axis, as shown in Fig. 2.1, such that the orientation of each baseline could be varied. This platform allowed for the easy detection and removal of false signals produced by the instrumentation. This is because the spurious signals rotate with the array while the sky signal does not. Moreover, the additional rotation axis can be used to increase the (u,v) coverage. The baselines ranges from 1 to 5.5 m corresponding to 38CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS angular scales of 0.5◦ 6arcmin. The pointing model is calculated from ∼ − observations of stars using a 15cm refractor telescope incorporated in the antenna platform.

The rms pointing accuracy of CBI is 0.5arcmin. The instantaneous field- of-view is set by the primary beam of the antennas. Larger fields can be imaged by assembling data from multiple pointings (called “mosaicing”). Dickinson et al. (2007), for example, used standard imaging techniques to create an image observation covering a total 2◦ 2◦ field with rms sensitivity × 2.4 mJy/beam.

In 2006, CBI was upgraded to CBI2 (Taylor et al., 2011), (see Fig. 2.1) with larger antennas of 1.4m diameter. This increased the eﬀective collecting area, which allowed for the use of longer baselines to improve the resolution without aﬀecting the surface brightness. The primary beam of CBI2 detected at a frequency of 31GHz had a FWHM 28.2 arcmin, whereas for CBI it had been 44 arcmin. CBI and CBI2 are compared in Table 2.1. In Fig. 2.2 the primary beam pattern of the CBI (averaged over the 13 elements) can be seen. The primary beam is well approximated by a beam of FWHM (28.2 31 )arcmin within a central 15 arcmin. × ν ≈ 2.1. THE COSMIC BACKGROUND IMAGER 39

Figure 2.2: The primary beam pattern of CBI. (a) 26 GHz, (b) 31 GHz, (c) 36 GHz. The main beam can be well approximated by a Gaussian with FWHM (44 31 ). × ν The CBI signal processing can be divided into three main stages: 1. The receivers where the signal is detected and ampliﬁed.

2. The down converter where the 2-12 GHz output for each of the 13 CBI antennas is converted into ten 1-10 GHz signals, and the signal is further ampliﬁed to give an integrated bandpass power of +16 dBm (as required by the correlator).

3. The correlator where the signal is split into real and imaginary parts, that can then be digitised and stored in the CBI Archive. The data can then be read in and analyzed, as will be discussed in Section 2.3. 40CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS

Description CBI1 CBI2 Period of operation 1999-2006 2006-2008 Location Chilean Andes-Atacama desert Altitude (m) 5000 Observing frequency (GHz) 26-36 (wavelength 1cm) ≈ Channels 10 channels, each 1GHz wide Number of antennas 13 Number of baselines 78 Antenna size (m) 0.9 1.4 Primary beam, FWHM (arcmin) at 31GHz 44 28.2 Polarization LL or RR or (RL,LR) System temperature (K) 20

Table 2.1: Speciﬁcations for CBI1 and CBI2.

2.2 Observations

There were 24 SNRs observed by the CBI as listed in Table 2.2. Most of the SNRs observed could be imaged with a single pointing of CBI. The SNRs have been summarized in various astronomical catalogs. For our purposes we primarily used that of Dave Green (Green, 2009). In addition to deep CMB and SZ observations, Galactic observations were also carried out when time/weather allowed. As well:

1. Only bright SNRs were chosen.

2. The declination was above 70◦ and below +23◦. − 3. Angular size was chosen to be preferably less than 30arcmin, as to not exceed the ﬁeld-of-view.

4. The ﬂux density of selected sources was & 300mJy at 1GHz. This corresponds to 25mJy at 31GHz assuming the spectral index of -0.7. ≈ This was readily detectable by CBI.

The observations were made from 25 May 2000 to 10 June 2008 using CBI and CBI2. CBI2 observations started in 2006 and ended in 2008. Most 2.2. OBSERVATIONS 41 of the SNRs were observed by CBI2. The observations were generally carried out at night, although some were made during sunlight hours. The contamination from the sun was thought to only be significant for deep CMB and SZ observations. The observation times for SNRs ranged from a few minutes to a few hours. We generally chose the calibrator that was closest (in time) to the source observations. For these observations, this happened to be our primary calibrator, Jupiter, to which our absolute calibration scale is tied to. It was used as the main calibrator because it is less affected by flux loss and does not require accurate knowledge of the primary beam shape. Jupiter is a point source, and its model flux density is more precise because it is calibrated from WMAP data (therefore accurate to 0.5%). Therefore, ∼ Jupiter was observed shortly before or after the main source. Also, it was typically observed near (in time) to our observations, thus minimising any phase offsets/gradients. Tau-A could have been used however, it is extended, resolved at longer baselines, and also requires an accurate model which made Jupiter a better choice.

We carried out the radio observations of SNRs at 31GHz so as to minimize the error in the derived spectral index, which is given as:

1 ∆S 2 ∆S 2 ∆α = 1 + 2 , (2.1) ln ν1 s S1 S2 ν2 Where, ∆S1 and ∆S2 are the errors in the ﬂuxes, ν1 and ν2 are the frequencies and S1 and S2 are the ﬂux densities at ν1 and ν2 respectively. Choosing a high value of ν1, allows us to minimize the error ∆α, thus our observations at 31GHz were useful. 42CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS

Name Size Flux density Type R.A./Dec. α (arcmin) 1GHz (Jy) J2000 (S να) ∝ G189.1+3.0 45 160 C 06 17 00 +22 34 0.36 − G263.9 3.3 255 1750 C 08 34 00 45 50 varies − − G290.1 0.8 19 14 42 S 11 01 00 60 40 0.4 − × − − G291.0 0.1 15 13 16 C 11 11 54 60 38 0.29 − × − − G292.0+1.8 12 8 15 C 11 24 36 59 16 0.4 × − − G293.8+0.6 20 5? C 11 35 00 60 54 0.6? − − G299.2 2.9 18 11 0.5? S 12 15 13 65 30 ? − × − G308.8 0.1 30 20? 15? C? 13 42 30 62 23 0.4? − × − − G312.5 3.0 20 18 3.5? C 14 21 00 65 12 ? − × − G315.4 2.3 42 49 S 14 43 00 62 30 0.6 − − − G322.5 0.1 15 1.5 C 15 23 23 57 06 0.4 − − − G327.1 1.1 18 7? C 15 54 25 55 09 ? − − G328.4+0.2 5 15 F 15 55 30 53 17 0.0 − G326.3 1.8 38 145 C 15 53 00 56 10 varies − − G327.6+14.6 30 19 S 15 02 50 41 56 0.6 − − G332.4 0.4 10 28 S 16 17 33 51 02 0.5 − − − G341.2+0.9 22 16 1.5? C 16 47 35 43 47 0.6? × − − G344.7 0.1 8 2.5? C? 17 03 51 41 42 0.3? − − − G351.7+0.8 18 10 S 17 21 00 35 27 0.5? × − − G351.2+0.1 7 5? C? 17 22 27 36 11 0.4 − − G354.1+0.1 15 3? ? C? 17 30 28 33 46 varies × − G20.0 0.2 10 10 F 18 28 07 11 35 0.1 − − G34.7 0.4 35 27 250 C 18 56 00 +01 22 0.37 − × − G49.2 0.7 30 160? S? 19 23 50 +14 06 0.3? − − Table 2.2: The supernova remnants observed by CBI. The Galactic coordinates are quoted conventionally. The angular size of the circular remnants is quoted as a single value representing their diameter, whereas for remnants modelled as ellipses there are two values representing the major and minor axes. The flux density is deduced from the radio-frequency spectrum. The type is ‘S’, ‘F’, and ‘C’ for shell-type, filled-centre and composite type, respectively. Right Ascension (R.A.) and Declination (Dec.) are deduced from the radio-maps. The spectral index α is deduced from the power law, which is inadequate to describe the radio-spectra of many SNRs. Hence ‘varies’ is written in such cases (Green, 2009). Many values are uncertain, and thus marked with a ‘?’ in D.Green’s catalogue. These fluxes either have data with errors or the analysis was done with problems. 2.3. DATA REDUCTION 43

2.3 Data reduction

Figure 2.3: Flowchart showing the procedure used to obtain calibrated data, and CLEANed image. The calibrated data are written to .uvf (uv-FITS) ﬁles. 44CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS

Figure 2.4: This ﬁgure is of a total-power plot for channel 4 (all receivers) within the CBICAL program. Receiver 11 shows amplitude that deviates from the mean by more than 10%, so we ﬂagged it out. The receiver should have a power reading of 1, but sometimes was lower than 0.3 ∼− . −

The CBI team developed several packages to calibrate the data. For our purposes we employ CBICAL, which is used to read the raw visibilities from the CBI archive, and provides a suite of tools for calibrating, inspecting and ﬂagging the visibilities (Pearson et al., 2003). We perform a series of operations on the data using CBICAL, as shown in Fig. 2.3. Typically the process goes as follows:

1. Read in the data from the CBI archive. For e.g., read 25-sep-2007:22:05 12:00.

2. Examine the data. Using the plot command we get a plot such as the one shown in Fig. 2.5, which can help us to ﬁnd bad/good data. The data were also checked by plotting the total power for all 13 inputs as can be seen in Fig. 2.4 , as well as the noise calibrator’s power (using the tpplot command). The channels showing a power disturbance of more than 5% were ﬂagged. 2.3. DATA REDUCTION 45

Figure 2.5: The top panel shows the amplitude (real) part of the visibility function. The middle panel shows data from baseline 2 (antenna 0-3) as a function of time. Results from September 25th, 2008. Jupiter was chosen as the calibrator, as it has an amplitude considerably higher than the source we wish to study, and also has a well deﬁned phase. The grey data points have been ﬂagged. The bottom panel shows other information about the telescope, including azimuth, elevation, declination, coordinates, and diagnostics. 46CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS

3. Edit the data. The data editing can be done manually as discussed in Section 2.3.1 or by CBICAL using the flag and delete commands.

4. Calibrate the data. This includes noise calibration and astronomical calibrations, using the quad, ncal and antcal command. See Section 2.3.2 for details.

5. Finally, we export the data in FITS format using the export command.

CBICAL has a limitation in that it can only read upto a maximum of 15000 frames into memory. However, this many frames are suﬃcient for one night of observation.

2.3.1 Flagging

Automatic Flagging: The visibilities are read into the CBICAL which then provides some algorithms to correct specific irregularities, such as pointing errors. This shown in Fig. 2.3. The telescope control system flags data that are unreliable, such as if a receiver was warm, the local oscillator was not phase locked, or the total power of a receiver was outside the normal range. It does this by assigning a zero weighting to the data. Automatic flagging is generally reliable, but sometimes it may skip some data or flag data which could be useful. For this reason manual flagging/unflagging is necessary, and is discussed below. The following commands were used before reading the data to modify the automatic flagging that is applied:

lockignore - to prevent the ﬂagging of a channel due to lock errors. • psignore - to prevent the ﬂagging of an antenna due to phase-shifter • errors.

warmignore - to stop the ﬂagging of an antenna due to temperature • errors.

threshold - to set thresholds for total-power and power-meter ﬂagging. • tracking - to specify non-standard treatment of tracking errors. • 2.3. DATA REDUCTION 47

Manual Editing: Sometimes the calibrations executed by the software were not as precise as desired, and we thus had to clip out some data. The observer’s log – a series of notes on the telescope’s day-to-day functioning – was used as a reference for the weather on a particular day. It also provided a record of whether or not each receiver was working fine or not. Typically 2% of data were removed on the basis of these specifications. Occasionally, ∼ we still saw signals from instrumental glitches or from the atmosphere during less optimal weather. Generally, manual editing is not recommended as we can lose some good data with it, but it is preferrable to have less data over bad data. Other ways of deciding which receiver data to clip is by looking at the total power plots of the receiver, an example of which is shown in Fig. 2.4. Receiver 11 shows amplitude that deviates from the mean by more than 10%, so we flagged it out. The receiver should have a power reading of 1, but sometimes was lower than 0.3. We accepted values which varied ∼− − up-to a factor of 2 (expected due to some variations in atmosphere and elevations). Also, the amount of automated flagged data helped in deciding whether or not to delete a receiver. Next, we plotted the visibilites as shown in Fig. 2.5. This was mainly done to identify any non-astronomical signals which could be due to instrumental problems or ground contamination. Some scans were flagged as they were highly contaminated by ground spillover or instrument problems. As expected, the phase of the data was almost random noise unless a strong astronomical source was observed. This random signal could also be from ground contamination. This was confirmed by plotting data from different days and different deck angles. Ground contamination was a problem, and was seen to dominate for shorter baselines. For some observations we clipped shorter baselines in order to have a better idea of the nature of this contamination.

Another point we took into consideration was the closure errors which appear on the .log ﬁle and can be a decisive factor towards deciding which receiver to delete. This is because they represent problems with particular channels. Those with errors greater than 10% in amplitude or 10◦ in phase were deleted. 48CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS

Glitch: These are points which were showing much higher or lower amplitudes than expected, this could be due to the interference of some ground signal, or instrumental problems. These points were taken care of through the use of the glitch command. This command calculates the mean and rms values of the amplitude (weighted by integration time) for each scan, and then ﬂags the points that show excessive (more than 5 times) divergence from the rms.

2.3.2 Calibration

Data calibration was necessary to correct the complex gain, which is composed of amplitude and phase gains. To maintain the desired accuracy over time and to minimize any measurement uncertainty, it is important to put the complex gain into a well-defined intensity (flux-density) scale. The CBI observed calibrators for roughly 15% of the total time of each observing session. A number of calibration procedures must be carried out for each observing session, beginning with creating a pointing model for the observation. The main effect of pointing errors is that they introduce a phase error in the data, and thus limit the accuracy with which we can subtract point sources from our observations. The pointing calibration is followed by quadrature calibration, which helps to measure the gains of the correlator’s real and imaginary channels, as well as to orient them orthogonally. 2.3. DATA REDUCTION 49

Figure 2.6: CLEANed map of the primary calibrator, Jupiter, which was used as the calibrator for most of the observations. It is a bright point source, and in the image it is clear that there were minimal sidelobes and artifacts. The data were calibrated using Tau-A. The CBI contour levels are [0.1, 0.2, 0.5, 0.7, 0.9]

Quadrature calibration: In CBICAL quadrature calibration is car- • ried out using the quad command. The main purpose of performing quadrature calibration is to maintain the orthogonality of the real and imaginary components of the correlator’s signal, which is accomplished by ﬂipping the noise diode. Quad decides upon a correction and applies it to the imaginary part of the signal. The quadrature corrections are 50CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS

typically 5% in amplitude and 3◦ in phase, with variations a con- ∼ ∼ sequence of the injected noise diode. The quadrature corrections can be quite large, at times 10% in amplitude and 6◦ in phase, but ∼ ∼ they are stable and easy to measure. The command quad would fail if the amplitude of the signal was low, if there is little data, or if the errors were too large.

Internal noise source calibration: This is the comparison of the • receiver’s output to a source of constant temperature. The calibration procedure removes slow gain variations due to any changes in the system’s temperature. The system’s temperature could fluctuate due to atmosphere, instrumentation issues, or ground temperature changes. The temperature of the noise calibration source is difficult to stabilize, so measurements from all baselines are averaged over the course of a night to give an identical response (Mason et al., 2003). Noise calibration (NCAL) removes the antenna-based gain and phase calibration errors. An antenna based error can be removed by primary flux density calibration.

The noise calibration command takes into account the gain changes across each noise diode. The ﬂuctuations across each noise diode are averaged, which allows us to calculate a time-dependant correction factor.

Antenna calibration: The ANTCAL command performs the antenna • calibration for the CBI data. It locates the scans of the calibration source, whose ﬂux density is already known and then adjusts the antenna- based complex gain factors and, from this, the antenna correction factor. Antenna based calibration is preferred over baseline based as it is easier because of the fewer antennas and they give better signal-to- noise ratio. We can then correct all visibilities by interpolating the gain factors between scans. The last step is the absolute ﬂux calibration, in which visibility amplitudes are converted to Jy, and the phase angle is set to zero for point sources. 2.3. DATA REDUCTION 51

The accuracy of the antenna calibration can be quantified by the number of logged closure errors. A large number of closure errors implies that more bad data should be flagged. The plot of the visibilities of the calibrator is shown in Fig. 2.7, which gives an idea of the accuracy of the calibration performed. The phase of the calibrator should not deviate from zero, and thus CBICAL forced the phase of the calibrator to be zero. The amplitude fluctuations were small and the RMS noise did not vary by more than a factor of 2.

Figure 2.7: Plot of the visibility statistics of the calibration source (Jupiter). The observations were made on the 25 September 2007. Each colour represents a channel and each individual point is a diﬀerent baseline. In this plot Jupiter is calibrated from another observation of Jupiter and thus the phase is not exactly equal to zero.

We also visualized the CLEANed image of the calibrator to see the signal-to-noise ratio and check for the constant phase. Fig 2.6 shows the CLEAN image of, Jupiter, the calibrator. The image was produced after calibration to conﬁrm the accuracy of the calibration. The image shows the calibrator is point like and there is no evidence of amplitude 52CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS

and phase errors. Fig. 2.7 shows the calibration observations of the main calibrator Jupiter. The scatter is observed to be larger than for self-calibrations because there were small phase errors due to noise. In self-calibration, the gains are calibrated on a per-antenna basis and therefore the phase is essentially ﬁxed to zero (there is a slight bias because individual baselines can introduce small phase errors but we do not have enough S/N to do baseline-based gain calibration). There can also be a bias i.e. the average phase is not 0. This corresponds to additional phase oﬀsets/gradients, which was believed to be mostly due to pointing errors. The radplot for Jupiter and TauA were produced, it was noted that TauA was extended and did not show a constant phase as it was unresolved on all baselines. The model of Jupiter was based on Mason et al. (1999) corrected to WMAP value as discussed in Hill et al. (2009). The brightness temperature was taken to be 146.6 0.75K at ± 32GHz with a spectral index of +2.24.

Spillover rejection: The CBI observations were taken such as a field • was tracked in a pair, a lead, and a trail, the two fields were subtracted to reject the common spillover contamination. This was done using the UVSUB command, which matches the (u,v) points in the lead with the corresponding points in the trail, and differentiates the real and imaginary parts of the associated visibilities. The root causes of error in this case are the elevation of the telescope, as well as the angle between the plane and the antennae. The ground spillover is unchanged over a period of few minutes. In cases, such as ours, where observations are not accompanied by trails, UVSUB simply averages the visibilities for each cells of (u,v) space. If they fall in the same cell, a weighted average is taken. The weight assigned is the inverse noise variance i.e. 1/σ2 where σ is the noise level for a particular visibility. UVSUB averages the multitude of raw visibilities (each with 8.4s integration), and estimates the visibility errors based on the variation in the 8.4s integration. (Pearson et al., 2003) 2.4. IMAGING 53

Combining multiple observation days: Each day of SNRs obser- • vation was calibrated individually. Then, in order to have more data and reduce the noise level the visiblities were merged using UVCON. The principal function of UVCON is that it reads the headers of all input files and checks if they have the same units, frequency channels, and coordinates. It then concatenates all the (u,v) visibilities into a single uvfits file.

2.4 Imaging

The calibrated and reduced visibilities we have arrived at represent the Fourier transform of the true images convolved with the aperture function. The program we used for the synthesis imaging of visibility data was the Difference Mapping (Difmap) package (Shepherd, 1997). A ‘difference’ or ‘residual’ map is obtained after a model of the source is subtracted from the dirty map. Then an iterative process is used to create the source model, usually through the deconvulation of multiple residual maps using the CLEAN algorithm. We can also fit a model to the observed visibilities. CLEAN keeps the residual map up to date with the changes that it makes to the model by adding and subtracting the same delta-function to the model and from the residual map respectively. Conversely, when model fitting is used to create the source model (or when a user edits either the visibilities or the model between successive iterations of CLEAN as discussed in Chapter 1), then the residual map will not reflect these changes. When this happens, Difmap automatically re-calculates the residual map. After the removal of erroneous visibilities, the artifacts remain in the model and not in the residual. When we subtract the model from the data the residual map contains the inverted artifacts. The multiple iterations can remove them from the model. This involves both positive and negative CLEAN components. Difference mapping is different from the earlier ap- proaches as it continues the deconvulation after modifying the model. It can be used to edit or calibrate the data whenever needed. Polarization: Dealing with polarization starts with loading the (u,v) 54CHAPTER 2. COSMIC BACKGROUND IMAGER AND DATA ANALYSIS visibilities into difmap and then selecting their respective polarizations. We chose the PI option, which stands for ‘pseudo-intensity’ polarization. The PI option sets the LL visibilities equal to the RR visibilities and makes it easier to decide which to use depending upon the availability. If both are available it uses a weighted mean. CBI measured the LL polarization and RR polarization data separately, and thus the PI option was the best choice. CBI was a single-mode2 instrument, so each receiver measured only one component of the incident radiation. This characteristic of CBI enable us to use the PI configuration. Gain: We set the loop gain to be 0.01 (unitless). This factor decides how much flux will be subtracted at each subtraction cycle. Seeing as the remnants are large in size (extended emission) we did not want to lose more flux, so we set it low. The default gain in the system was set to 0.05. This default value can result in the loss of flux, so we chose a low value to allow for more careful CLEANing. Weighting: Difmap allows for the use of the two most common weighting options: natural and uniform. The latter is the default and gives equal weight to short and long baselines. As discussed above, the angular resolution is inversely proportional to the baseline length: the shorter ones give lower resolution data. The number of shorter baselines is larger than the number of longer ones, so we chose to give more weighting to areas in the (u,v) plane with a larger number of visibilites, hence lower angular resolution data (and with a lower noise level). In other words, natural weighting gives more weight to shorter baselines to give a better signal-to-noise ratio at the expense of angular resolution. Primary beam correction: The primary beam correction was done by fitting a gaussian function (with 28.2 arcmin FWHM) to CBI2’s beam. This was done to enhance the flux of all objects viewed inside the primary beam. It worked on the principle that the centre of the source/beam was multiplied by the peak of the gaussian, whilst away from the beam centre the source flux densities were increased to account for the primary beam response. This helped us study the fainter sources around the remnants, as well as obtain

2Each receiver in the CBI responded to either right or left circular polarization 2.4. IMAGING 55 corrected ﬂux densities for extended sources. Chapter 3

Radio Observation of SNRs

The following Chapter describes the specific SNRs we chose and provides some context regarding their observation. Section 3.1 begins with the introduction of the SNRs G20.0 0.2 and G312.5 3.0. It summarizes the − − observations, sizes and distances of the SNRs, as well as their morphologies. Section 3.2 discusses the CBI observations of the remnants, while Section 3.3, explains the method through which we dealt with an ultra compact Hii region (UCHii) in the vicinity of the SNR G20.0 0.2. − Section 3.4 briefly explains the multi-frequency ancillary data we used for the comparison with the CBI map. The Chapter concludes with the final maps of the SNRs at different frequencies, and we also compare them to the CBI maps to understand the morphology.

3.1 Description

The main criteria for identifying a radio Galactic SNR is its spectral index. If it shows a non-thermal spectrum it can be assumed to be an SNR or a pulsar. Pulsars are usually point sources while SNRs are comparatively extended. Other indicators that the source is an SNR include the presence of linear polarization at high frequencies and the absence of recombination- line emission. We studied the radio observations of the two remnants, as will now be discussed here.

56 3.1. DESCRIPTION 57

3.1.1 G20.0 0.2 − The source G20.0 0.2, with RA = 18h 28m 07s and Declination = -11h − 35m 00s, was listed as a SNR by Downes (1971) because of a presumed non-thermal spectrum (based on the spectral index). Some sources visible in the surroundings of the remnant seem to have jet-like structures, as can be seen in Fig. 3.1. We found that there was an Ultra-Compact Hii (UCHii) in the northern region of the SNR. This is discussed in Section 3.3. The middle of the compact UCHii region coincides with a Type I OH maser as mentioned in Becker & Helfand (1985). Maser emission will not have a direct impact on the radio data. They are often seen in star forming regions, usually from the outer envelopes of stars after they have formed. The radio synchrotron emission of the remnant was found to have a complex morphology (Petriella et al., 2013). It was found to have multiple features in the surrounding region, such as bright knots, arcs and filaments. The emission is dominated by an elliptical central core of about 3.8 arcmin 2.2 arcmin, as shown in Fig. 3.1. The major axis is oriented in the di- × rection of the Galactic plane and the bright feature is surrounded by faint emission. Two arc-like filaments are located near the edges of the remnant; the north end of the SNR is delineated by a bright radio filament, suggesting an encounter with a high density ambient region. Further, the IRAS point source catalog confirms the presence of an infrared source at the same location. The infrared source was ignored in our study as it does not affect our radio flux. G20.0 0.2 has thermal emission − from its surroundings and was thus initially assumed to be an Hii region. The maximum fluxes recorded by Becker & Helfand (1985)at6and20cm were 1.2 and 12.2Jy, respectively. The flux at 6 cm is artifically low because data were insenstive to flux on scales larger than 4’. Subsequently, Caswell & Clark (1975) found fluxes of 9.6 Jy at 408MHz concluding that the SNR was an Hii region based on its flat spectral index (α = 0.03). Becker & − Helfand (1985) also showed the presence of substantial polarization at 6cm, which is not a property of thermal emission and suggests that G20.0 0.2 is − 58 CHAPTER 3. RADIO OBSERVATION OF SNRS a Crab-like SNR.

Figure 3.1: VLA map of G20.0 0.2 with a maximum at 0.92Jy/beam and a minimum at 0.00433Jy/beam.− Image produced from MAGPI survey (Helfand et al., 2006− ).

G20.0 0.2 was identified as Crab-like because: − It has a flat radio spectrum. • It shows significant linear polarization at 6cm. • 3.1. DESCRIPTION 59

It has a ﬁlled-centre brightness distribution. •

It lacks a traditional SNR shell. •

The distance to G20.0 0.2 is not known, whereas the OH maser on − the northern rim has a kinematic distance of 4kpc or 15kpc (Matthews et al., 1977). Becker & Helfand (1985) assumed the remnant to be close to the Hii region and calculated a diameter of 12pc, as well as a radio luminosity between 107 and 1011 Hz of 1.8 1034 ergs s−1 (assuming a distance × of 4kpc). The 4.875GHz observations of Altenhoﬀ et al. (1970) resolved the remnant into two components: one with a small diameter (G20.7 0.14) − and an extended component (G19.96 0.18). The extended component has − FWHM of 5arcmin and was detected by CBI. At 5GHz the UCHii region ∼ G20.07 0.14 contributes about 1.0Jy to the ﬂux density of the remnant − (Matthews et al., 1977) and is further discussed in Section 3.3.

3.1.2 G312.5 3.0 − This SNR, with RA = 14h 21m 00s and DEC = 64h 12m 00s, appears − to be faint in the middle with a ring structure around the edge. This is an indication that the object is likely to be a spherical shell, which is a typical structure for remnants produced by Type I supernovae. The extended region is in the centre, as shown in Fig. 3.2. The intensity of the SNR is higher on the western (lower RA) side. Interaction with the interstellar medium could be the reason for this asymmetry (Duncan et al., 1995). The SNR is listed in Dave Green’s catalog (Green, 2009) as a shell-type remnant with a size of 20 18arcmin. However the flux density and the spectral index are not × listed, and instead marked as ‘?’ in the catalog. This sparked our interest towards this SNR. The SNR was studied by Kane & Vaughan (2003) using the PARKES survey. They measured the flux density at different frequencies, which we used later for our estimations. With the method discussed in Section 1.3.3, the source distance and linear diameter were estimated to be 10kpc and 53pc respectively. 60 CHAPTER 3. RADIO OBSERVATION OF SNRS

Figure 3.2: The Parkes 4850MHz survey map of G312.5 3.0. The map shows the morphology of the object at a resolution of 4.3arcm− in. Image produced by (Griﬃth & Wright, 1993) data.

3.2 Data and Observations

The data for G20.0 0.2 and G312.5 3.0 were recorded by CBI2. The − − observations were mostly carried out at night, since the Sun can disturb the observations through side-lobe contamination. There were several challenges with regard to the calibration of the observations. These included 3.2. DATA AND OBSERVATIONS 61 0. 2. . . 3 0 − Comments Comments rx 10 warm − 5 0 . . nothing unusual nothing unusual nothing as usual nothing as usual rx 4 not mounted rx 4 not mounted rx 3,7 not mounted. ch 7(5-9), telescope halted many receivers not working ch 7(5-9), strong ground signal 2 . trail so did not complete the observation 0 telescope stopped while observing the source − 0 . 20 bad data possibly due to ground, ch-4(9-10) specially bad. G G312.5 - 3.0 Tau A Tau A Tau A Tau A Tau A Jupiter Jupiter Jupiter Jupiter Primary calibrator Tau A Jupiter Jupiter Jupiter Table 3.1: Observational details of G20 Table 3.2: Observational details of G312 Primary calibrator 1hr 1hr 50min 40min Duration 1hr 40min 1hr 45min 2hr 30min 2hr 30min 2hr 40min 1hr 50min 30min Duration 1hr 30min Day 11-02-2008 22-02-2008 23-02-2008 24-02-2008 25-02-2008 10-10-2007 11-10-2007 12-10-2007 13-10-2007 Day 25-09-2007 26-09-2007 27-09-2007 28-09-2007 62 CHAPTER 3. RADIO OBSERVATION OF SNRS

Figure 3.3: CLEANED CBI map of G20.0 0.2 before subtracting the ﬂux for UCHII region. − 3.2. DATA AND OBSERVATIONS 63

Figure 3.4: CBI image of G312.5 3.0 having a maximum brightness of 0.07 Jy/beam. − 64 CHAPTER 3. RADIO OBSERVATION OF SNRS instrumental hurdles, as well as technical challenges brought on by the atmosphere. The 31GHz visiblities of the two SNRs were acquired during the nights mentioned in Tables 3.1 and 3.2. These tables also includes the specific problems encountered on particular days. For example, ground spillover was a common problem, but was solved by clipping the most affected shorter baselines, as well as by subtracting lead and trail fields as discussed in Sec- tion. 2.3.2. The SNRs were observed over the course of several days, and manual record keeping could be inaccurate. To overcome this problem we developed scripts to automated this task, an example of which is shown in Appendix B. The observations made of the SNRs were short ( few hours per SNR), so ∼ we chose bright ( 20mJy/beam at 31GHz) SNR. Fig. 3.3 and 3.4 show the ≥ maps of the two SNRs as produced by CLEANing the CBI data. The resolution is 4arcmin. The CBI map of G312.5 3.0 shows its extended morphology, − with a peak flux value of 0.07Jy/beam and noise level of 0.008Jy/beam. The peak flux in the map of G20.0 0.2 was 1.6Jy/beam, and the rms − noise in the map is 0.03Jy/beam. As shown in Fig. 3.3 there was a UCHii region in the south of remnant G19.61 0.23. There was another UCHii − region in the northern rim of the SNR. As this was believed to be affecting the flux density of the SNR, we decided to subtract it. The next Section describes the subtraction procedure followed.

3.3 Subtracting the UCHii region from G20.0 0.2 − UCHii regions are often associated with extended, diﬀuse background emission (commonly found in the Galactic plane). Its presence near G20.0 0.2 is − understandable because of the shocks and dynamic motions associated with star forming regions. At radio frequencies, UCHii regions are observed as continuum sources. They are easily identiﬁed by interferometers which can resolve the internal structures of these regions. They can be detected by CBI because of its high observing frequency. To the north of the G20.0 0.2 lies the Ultra Compact Hii region with − 3.3. SUBTRACTING THE UCHII REGION FROM G20.0 0.2 65 −

Galactic coordinates (l,b)=20◦.08, 0◦.14. It was studied by Wood & Church- well (1989) using the method of dividing it into sub-regions. Figure 3.6 shows the division of UCHii into three components marked A, B and C.

Figure 3.5: The CBI map of the G20.0 0.2 after subtracting the UCHii region. The extended component in the− south is G19.61 0.23, which is a complex Hii region with RA = 18h 27m 38s and Dec = -11h− 56m 40s (Wood & Churchwell, 1989).

The UCHii region was found in the northern rim of the supernova remnant and was believed to be contributing to the ﬂux density at 31GHz. So we subtracted this unwanted contribution with the help of the publicaly available 66 CHAPTER 3. RADIO OBSERVATION OF SNRS

Figure 3.6: Image of the UCHii region at (left) 2cm and (right) 6cm. Sub- components of the region are marked as A,B and C. The three regions are studied separately. A is the brightest region, followed by B and then C. Taken from Wood & Churchwell (1989). low frequency VLA data available. In order to calculate the flux of this region, at 31GHz we first estimated the optical depth. We know that the flux density, in terms of brightness temperature (Tb), is given by :

2kT Ω S = b (3.1) λ2

The solid angle of the UCHii region at 15GHz is 4.48 10−12 sr (Wood × 4 & Churchwell, 1989). The value for Te is assumed to be 10 K (assuming the beam is uniformly ﬁlled with ionized gas). For region C the integrated ﬂux density at 15GHz is 91.0mJy. Using these values in the above equation, Tb was calculated to be 2943.8 K. Now, the optical depth (τ) related to the brightness temperature is :

τ = ln(1 Tb ) (3.2) − − Te τ =0.35 (3.3) 3.3. SUBTRACTING THE UCHII REGION FROM G20.0 0.2 67 −

This shows that region C is close to being optically thick at 6 GHz, as τ is not 1. The same analysis was carried out for regions A and B. They were ≪ found to be optically thick as well.

Region Flux density Flux density 15 GHz(mJy) 5 GHz(mJy) A 362.3 330.4 B 61.6 31.5 C 91.0 21.0

Table 3.3: Flux density of sub-components of the UCHii region at 15GHz and 5GHz, as given by Wood & Churchwell (1989).

Figure 3.7: The free-free model for the three sources within the UCHii region. The data has been taken from Wood & Churchwell (1989).

In order to estimate the flux density of the UCHii region, we extrapolated the fluxes at low frequencies using a free-free spectrum, which is a power-law at these frequencies (ν2) as well as at high frequencies (ν−0.1). We fitted 68 CHAPTER 3. RADIO OBSERVATION OF SNRS this spectrum to the available data using an IDL script (shown in Appendix B) and obtained the fit shown in Fig. 3.7. The data plotted at 5GHz and 15GHz were taken from Table. 3.3. This fit is given by the equation:

− T 1.35 ν −2.1 EM τ 3.82 10−7 e , (3.4) ≈ × 104K GHz pc cm−6 where EM is the emission measure, defined as the integral of the square of the electron number density along the line-of-sight, and Te is the electron temperature. The corresponding value of Tb obtained from the fit in Fig. 3.7 was used in Eq. 3.2 to calculate the 31GHz flux density. Using the aforementioned method the fluxes of the three components at 31 GHz was calculated and the integrated flux at 31GHz was found to be 1.07 Jy. Finally, using the package SRCSUB, we subtracted the UCHIIregion. The CBI map after subtracting the region is shown in Fig. 3.5.

3.4 Multi-frequency Maps

Now, having the final CBI maps for both remnants we could compare them with maps at different frequencies. In this way we studied the morphology of the SNRs and in order to understand the physics of the regions and their surroundings. The ancillary data used in this project were acquired through two different web services: NASA’s Skyview1 and the Max Planck Institute for Radio astronomy survey sampler2.

3.4.1 Data description

For our purposes we used:

Eﬀelsberg 100 m 21 cm: 21cm wavelength data (1.4GHz), with an an- • gular resolution of 9.4 arcmin. (Reich et al., 1990)

1URL: http://skyview.gsfc.nasa.gov/cgi-bin/query.pl 2URL: http://www3.mpifr-bonn.mpg.de/survey.HTML 3.4. MULTI-FREQUENCY MAPS 69

IRAS: 12 µm data (25THz) with a resolution of 2 arcmin. (Beichman • et al., 1985)

Planck: Planck 545GHz data. This survey was an all sky millimetre • survey with a resolution of 5 arcmin. (Planck Collaboration et al., 2014c)

Parkes: 4.85GHz southern sky survey data. (Condon et al., 1991) • The ﬁrst step after acquiring the data (ancillary and CBI) was to align them, using the CBI maps for reference. This was to ensure that the photometric analysis will correspond with the location of the source, and was accomplished using IDL. With the help of Skyview and MAGPI we were able to manipulate the ancillary maps such that they had the same size and coordinate system as those from CBI.

3.4.2 The visual analysis

The analysis consisted of a qualitative comparison of the CBI and ancillary (Parkes, Effelsberg, IRIS) maps. The aim was to ensure that the data reduction, alignment process and simulations that were conducted did not alter the data and cause fake sources to appear, real ones to disappear, or a shift in the observed source coordinates. This test was performed by taking each pair of aligned maps and plotting the contours of the simulated map onto the CBI map. This task was performed using two separate software packages: DS9 as well as a script written in IDL (a version of the IDL script can be found in Appendix B). The contours seem to trace the emission from the sources very well. The offset of the brightest points and the contours in both maps is considerably less than 30 arcsec. The diffuse emission is also traced by the contours. This suggests that the data reduction and alignment procedures that were carried out performed as expected. It also shows that there is a strong correlation between many of the components on both maps. This indicates that the recorded emission is likely to be either synchrotron or free-free in origin. 70 CHAPTER 3. RADIO OBSERVATION OF SNRS

(a) (b)

Figure 3.8: Multi-frequency maps of G20.0 0.2 with the contours from the CBI map at (0.1, 0.2, 0.5, 0.7, 0.9)*0.99Jy/beam.− (a) is the Parkes 5 GHz map, (b) is the IRAS 12 µ m map, (c) is the Planck map at 545GHz map, (d) is the Eﬀelsberg 100m map.

The high resolution ( 4 arcmin) map of the SNR G20.0 0.2 (Parkes ≈ − 5 GHz, CBI, IRAS and Eﬀelsberg 21 cm) is shown in Fig. 3.8, the radio and infrared maps of remnants have very similar morphologies. The UCHii 3.4. MULTI-FREQUENCY MAPS 71

(a) (b)

(c)

Figure 3.9: Multi-frequency maps of G312.5 3.0 with CBI contours from the CBI map at (0.1, 0.2, 0.5, 0.7, 0.9)*0.07Jy/beam.− (a) is the Planck map at 545GHz, (b) is the IRAS map at 12 µm and the (c) is the Parkes map at 5 GHz.

region, G19.61 0.23, is bright in all the maps. The Effelsberg map reveals − that there is a central nebula surrounded by bright sources. We observe the same in the CBI map. The source in the north is the aforementioned UCHii 72 CHAPTER 3. RADIO OBSERVATION OF SNRS region, G20.7 0.14. It is faint at most frequencies. The UCHii region − G19.61 0.23 is brighter at infrared frequencies than the SNR, as shown in − Fig. 3.8(b). This may be due to heated dust caused by UV emission from the star. As illustrated in Fig. 3.8(c), the Planck map shows bright emission surrounding the UCHii region which may be associated with heated gas. Fig. 3.8(d) shows the Effelsberg map. Fig. 3.9(c) shows the Parkes map of G312.5 3.0 at 5GHz. The morphology here is similar to the CBI map, and − shows the SNR as shell type. The SNR is not visible in Planck maps which may be due to the absence of a significant dust component. The similar non-detection was seen in IRAS maps in Fig. 3.9(b). Chapter 4

Analysis

This chapter discusses the analysis done on the maps to produce flux densities and spectral indices. Section 4.1 describes the photometric algorithms used to estimate flux densities and uncertainties. Section 4.2 describes all the sources of uncertainty in the observations and also gives the % value for each. Then the procedure used for the flux density calculation is discussed, followed by that for the spectral index. Section 4.2.2 discusses how data from other frequencies were sampled to match the CBI (u,v) coverage. The chapter concludes with Section 4.3, wherein we summarize the final values of the flux densities and spectral indices of the SNRs, followed by a discussion of the results.

4.1 Photometry

The analysis technique we used to estimate the ﬂux density is called aperture photometry and will now be summarized. For this process we calculate the integrated ﬂux density of the source in Janskys from an image that has units of brightness Jy/pixel or Jy/beam.

4.1.1 Aperture Photometry

In order to make a measurement for the integrated ﬂux density using aperture photometry, one needs to sum the values of the pixels in a circular area

73 74 CHAPTER 4. ANALYSIS centered on the source (the aperture), and then subtract the background contribution (the map units should be in Jy/pixel). The background contribution was estimated by averaging the pixel values inside a ring-shaped annulus region, defined around the aperture. Choosing the size of the aperture was a challenge as there were bright sources near the SNRs which could have contributed to the flux. We used Green (2009) for the size of the SNR. Analysis was also done by viewing the map using visualisation software (Ds9) and choosing an aperture large enough such that all the flux is encompassed. We experimented with different sizes until the flux levelled off. In the case of G20.02 0.2 there are bright sources close to it. As a result − it was difficult to choose a value for the aperture size and construct ring- shaped annulus regions around it, as these would have been contaminated by the surrounding sources. Therefore, we decided to measure the flux inside an aperture that would enclose the source, as well as a small amount of the background. To account for the background level we placed smaller circular regions adjacent to the bigger aperture, which gave us an independent estimate of the background noise. We also visually inspected the smaller circular regions and any contamination from a surrounding source was discarded. Then data from the selected regions was used to estimate the median. Finally this median value was multiplied by the total pixel number of the larger aperture. We chose the aperture of the region to be slightly larger than the SNR to account for smoothing. It is worth noting that the SNRs chosen were relatively bright, and that the background noise was relatively small. Therefore, the final results are not very sensitive to the details of the aperture.

4.1.2 Map Unit Conversion

The final step before plotting the maps of SNRs and measuring their flux was to convert all maps to units of Jy/pixel. This choice of units is necessary when using photometric analysis to calculate the integrated flux density of the SNRs. This section describes the mathematical procedure that was 4.1. PHOTOMETRY 75 followed in order to convert all maps to units of Jy/pixel. A conversion value was added to the IDL scripts used to calculate the integrated flux (see Appendix B).

Jy/Beam to Jy/pixel

To convert from units of Jy/beam to Jy/pixel one needs to divide by the number of pixels that can ﬁt in a beam area. Therefore the area of the beam and the area of each pixel on the map should be calculated. Assuming that the beam can be approximated by a 2D Gaussian, the FWHM can be found by using the following formula:

Abeam =2 πθ1θ2 , (4.1) where θ1 and θ2 are the beam’s minor and major axes respectively, and are given in units of degrees. The relation between the FWHM and the standard deviation in either the major or minor axis is given by:

FWHM =2 √2ln2σ (4.2)

By combining eq. 4.1 and 4.2 we get

8ln2 σ1σ2 = θ1θ2 . (4.3)

The area under a 1D Gaussian is given by:

∞ −(x−xo)2 A = e 2σ2 dx = √2πσ . (4.4) −∞ Z Similarly for a 2D Gaussian with a σ1 and σ2,

∞ −(x−xo)2 −(x−xo)2 ( σ2 + σ2 ) A = e 2 1 2 2 dx . (4.5) −∞ Z Based on eq. 4.4 the above integral is equal to

A = Abeam = √2πσ1√2πσ2 =2πσ1σ2 . (4.6) 76 CHAPTER 4. ANALYSIS

By using eq. 4.3 and 4.6 we have that

π A = θ θ . (4.7) beam 4ln2 1 2 The width values for the minor and major axes can be found in the FITS header of the image (given in units of degrees). The pixel area can be found by multiplying the values CDELT1 and CDELT2 (which represent the width and height of the image) also included in the FITS header of the image (given in units of degrees). Once we are able to express the number of pixels per unit area of the beam (using the values found in the FITS header of the image), we can calculate the correct conversion from Jy/beam to Jy/pixel:

Jy Jy CDELT 1 CDELT 2 I( )= I × . (4.8) pixel beam ( π ) θ θ 4ln2 × 1 2

Jy/sr to Jy/pixel

Similarly to the method followed above, in order to convert the units of a map from Jy/sr to Jy/pixel one needs to divide by the number of pixels per steradians, as follows:

Jy Jy CDELT 1 CDELT 2 I( )= I × . (4.9) pixel beam ( 180 )2 π

K to Jy/pixel

By rearranging Eq. 3.1 we get

λ2 λ2 S Jy T = B = ( ) , (4.10) 2 1.381 10−23 2 1381 Ω sr × × × where T is in Kelvins, λ in m, S in Jy and Ω in steradians. Now, by using the conversion factor calculated previously we get:

λ2 ( 180 )2 Jy T =( ) π I( ) , (4.11) 2 1381 CDELT 1 CDELT 2 pix × × 4.2. UNCERTAINTIES IN THE MEASUREMENTS 77

4.2 Uncertainties in the Measurements

The results are incomplete without estimating the associated uncertainties. These errors in the observations can be due to various factors. To better understand the accuracy of our measurements we calculate the errors as discussed below.

Absolute Calibration Error: As discussed in Section 2.3.2 the ﬂux • calibrator is used for the calibration of the amplitude and phase of the observations. For CBI2 the brightness temperature of Jupiter was taken to be 146.6 0.75K (Hill et al., 2009). This corresponds to an ± uncertainty of 0.5 percent.

Noise and Background Uncertainties: These refer to the rms noise • of the map. While conducting aperture photometry we chose an aperture with radius 8arcmin for G20.0 0.2, which was the estimated size − of the SNR using ds9. This size was also close to what is given in Dave Green’s catalog: 10arcmin (Green, 2009). The noise in the same aperture was calculated and the rms was found to be 0.037Jy/beam with centre at RA = 18h 29m 08.9s and Dec = -11◦ 36’ 04”. Then, using the script for unit conversion as discussed in Section 4.1.2, the uncertainty was calculated to be 0.04Jy. This gives a percentage error of 1.3% of ± the total ﬂux density, which is 3.13Jy. For G312.5 3.0 the same procedure was applied to estimate the radius − of the aperture, which was chosen to be 11arcmin. The size of the SNR listed in Dave Green’s catalog is 20 18arcmin. The rms noise from × Difmap was calculated to be 0.0078Jy/beam. The percentage error was thus calculated similarly, as in the case of G20.0 0.2. This error − was found to be 2.2% for the ﬂux density of 0.46Jy. The fractional error in G312.5 3.0 is larger as the source is fainter, − leading to a value of signal-to-noise that is lower.

Primary Beam Correction: The primary beam is typically a Gaus- • sian function with sidelobes as discussed in Chapter 2. The primary 78 CHAPTER 4. ANALYSIS

beam is well modelled up to the FWHM (Pearson et al., 2003; Taylor et al., 2011), which is 15arcmin in the case of CBI2. We consider the ∼ effect of the primary beam to be negligible, taking into account that the size of the SNRs studied fit well within 15 arcmin. ∼ Ground Spillover and Spurious Signal: The ground spillover, as • discussed in Section 2.3.2, was calculated by producing maps without subtracting the lead and trail fields. The flux density for G20.02 0.2, − after subtracting lead and trail fields was found to be 3.13Jy. The value obtained without subtracting them was 3.06Jy, as shown in Table 4.1. The difference in the two values was found to be small and corresponds to a percentage error of 2.2%. Then, using the same analysis, the error for G312.5 3.0 was calculated to be 13%. This is a conservative − uncertainty since this is the maximum total ground spillover.

Mis-pointing and Phase Uncertainties: The pointing of each an- • tenna of CBI2 was checked against the observations of the calibrator. CBI2 is a co-mounted interferometer. Pointing error refers to the error caused by the global pointing of the telescope. Typically, a residual pointing error of 0.5arcmin is produced (Taylor et al., 2011). Point- ∼ ing errors translate into phase errors and in the deck angle can be seen as a phase offset, which results in the loss of flux on long baselines. We estimated the contribution of pointing errors in our data by flagging the longest (most affected) baselines. The flux calculated for G20.0 0.2 − after this flagging was 3.30Jy. By comparing this value with our flux corrected value (Table 4.4) the percentage error was calculated to be 5.5%. The same value was assumed for G312.5 0.3. − Process Flux density With lead and trail 3.06 0.03Jy ± After omitting long baselines 3.30 0.03Jy ± After flagging lead and trail and including all the baselines 3.13 0.07Jy ± Table 4.1: Various methods applied on G20.0 0.2 to estimate the contribution from ground and background contamination.− 4.2. UNCERTAINTIES IN THE MEASUREMENTS 79

Atmospheric Error: The observations were affected by the atmo- • sphere of the site. The observations were made mainly during the night, with good weather, meaning when the atmosphere was approximately transparent. Any error could have been due to the fluctuations which is an issue on a bad weather day (this was mainly resolved out by the interferometer). Absorption can also affect the flux, but is corrected by the ncal command in CBICAL. It is typically an effect of a few percent at most, and therefore it is mostly negligible.

Flux Loss: Interferometers are not sensitive to all angular scales, • which leaves the possibility of losing flux. This flux loss is difficult to prevent, but can be corrected for by comparing the maps with other maps to establish the source’s morphology. Then we can correct for this during our analysis. In order to recover the correct flux we made simulations as discussed in Section 4.2.2.

4.2.1 Total Uncertainty

All the above mentioned errors contribute to the total uncertainty in the observations. Since the errors are independent of each other, we can use formula for standard propagation of uncorrelated errors. The formula for this is given as:

2 σS = σi , (4.12) s i X where σS is the error in the flux density and σi is the errors associated with each data set. Using the above equation to sum all the errors, we get, the total percentage error in the flux density of G20.02 0.2 was found to be 6.2%. The − percentage error in the result of G312.5 3.0 was calculated to be 14.8%. The − final flux densities for each source (as estimated by us and from literature) are given in Table 4.2 and 4.3. As seen in these tables, the main uncertainty is due to the phase offsets (mis-pointing) and the flux loss. Typically, they all add up to a total of 80 CHAPTER 4. ANALYSIS

Freq Flux density Source 57.5 MHz 8.5 2 Jy Odegard (1986) ± 408 GHz 9.6 0.9 Jy Caswell & Clark (1975) ± 1400 MHz 10.6 1.6 Jy Becker & Helfand (1985) ± 5 GHz 10.4 1.0 Jy Caswell & Clark (1975) ± 31 GHz 4.09 0.26Jy ( 3.13 0.2 Jy) Our calculations ± ± Table 4.2: Photometry results for G20.0 0.2. The value is parentheses is after subtracting UCHii regions. −

Freq Flux density 843 MHz 1.1 0.2Jy ± 1380 MHz 3.0 0.6Jy ± 2.378 GHz 1.3 0.3Jy ± 4850 MHz 1.2 0.2Jy ± 31GHz, Our calculations 0.46 0.07 Jy ± Table 4.3: Results for G312.5 0.3. The values below 31GHz are taken from Kane & Vaughan (2003). −

5 10%. ∼ −

4.2.2 Modelling the Ancillary Data with the CBI (u,v) Coverage

To account for any flux loss we must simulate the data and for this task we used the package MOCKCBI (Pearson et al., 2003). The main purpose of this software is to simulate the CBI data by taking a model of the source. The software replaces the visibilities of CBI data by the visibilities from a mock observation. These simulated visibilities are called ‘MOCK data’. This software calculates the flux density of the source at a different frequency by assuming a known temperature spectral index. Therefore, it sets the CMB to 0.0 and sets the foreground map to be an archived radio map, for eg. Parkes, Effelsberg, etc. MOCKCBI performs the following steps:

1. Input or read the model map, eg. Parkes, Eﬀelsberg. 4.3. FLUX DENSITY AND SPECTRAL INDEX 81

2. Multiply the map by a model of the primary beam.

3. Fourier transform the visiblities.

4. Sample the model (u,v) plane at points where (u,v) data exists.

5. Apply weights.

6. Write out the (u,v) data set.

The next step was to define the primary beam. The primary beam of CBI 2, with a FWHM of 28.2arcmin at 31GHz was thus set. Then the maps from other data (Parkes and Effelsberg) were imported and compared to the CBI map along with the CBI data (in .UVF format with Jy/beam). The visibilities of the CBI data were set to zero. The sum of the background image and the source image was multiplied by the primary beam of the CBI, centered at the pointing centre of the input visibility files, and then Fourier Transformed (convolved with the CBI synthesized beam). The result was used to derive the corresponding visibilities at the sample (u-v) points by interpolation. Finally, the modified data were written to a new .UVF file. Then a CLEANED map was obtained in order to estimate the value of flux density. The final results estimated are shown in Table 4.4 for our sources. Corrected flux for G20.0 0.2 at 31 GHz 3.56 0.2 Jy − ± Flux loss 12% Corrected flux for G312.5 0.3 at 31 GHz 0.49 0.07 Jy − ± Flux loss 6%

Table 4.4: Final corrected flux densities for G20.0 0.2 (top) and G312.5 0.3 (bottom). For G20.0 0.2 the flux was calculated− using the Effelsberg− data at 2.7GHz, and for G312− .5 0.3 using the Parkes 5GHz data. −

4.3 Flux Density and Spectral Index

To calculate the flux density and uncertainties of the SNRs we used the method of aperture photometry discussed before. Our final results for the flux 82 CHAPTER 4. ANALYSIS density are listed in Table 4.4. Next, to understand the particle acceleration occurring at the shock fronts, as well as to have an idea of SNRs as a source of Galactic cosmic rays, we estimated the spectral indices of our SNRs.

We started by ﬁtting a power-law to the spectra (i.e. ﬂux density against frequency). We used the literature available for the data and produced Fig. 4.1 and 4.2, showing the spectral energy distribution (SED1) of the two sources.

4.3.1 SED of G20.0 0.2 −

We have here fit the data for spectral index and amplitude. Fig. 4.1(a) shows the SED of G20.0 0.2 between 1.4GHz and 31GHz and (b) shows − a plot between 0.0575GHz to 31GHz. The 0.0575GHz point taken from Odegard (1986) has a flux density of 8.5 2Jy. The 9.6Jy flux density at ± 0.408GHz was calculated by Caswell & Clark (1975), who found that this value is consistent with a thermal spectrum with spectral index (α)= 0.03. − The 10Jy flux at 1GHz was taken from Green (2009), since D.Green’s value is interpolated from literature we did not fit to this value. The point at 1.4GHz was observed by Becker & Helfand (1985), who found the SNR to have a flux density of 10.6Jy after the primary beam attenuation, as well as a flat radio spectrum. They also mentioned that the 0.86Jy of flux is contributed by the northern structure. The data point at 5GHz (where the spectral break occurs) was taken from Caswell & Clark (1975). In their galactic plane radio survey at 5GHz they mentioned the flux of 10.4Jy. It can be seen that the CBI result is significantly lower than the expected value from an extrapolation of the low frequency data.

1SED is a graph of the energy emitted by an object as a function of diﬀerent frequencies. 4.3. FLUX DENSITY AND SPECTRAL INDEX 83

(a)

(b)

Figure 4.1: Spectral energy distribution of G20.0 0.2. The fit was obtained using the data points as discussed in Table 4.2. (a)− shows the power-law fit. (b) is the fit for the spectral break at 5 GHz. ∼ 84 CHAPTER 4. ANALYSIS

Altenhoff et al. (1970) states flux density to be 6Jy at 5GHz which is inconsistent to the value stated by Caswell & Clark (1975) but both are compatible with a spectral break around 5GHz. We have estimated the flux, and therefore there is the possibility of errors in the model. This can also be indicated by the value of spectral index we obtained, which is typical of a SNR. We also considered the CBI data to be low in flux density but finding the flux loss indicates that this is not a case for concern. To improve and further study the spectral behaviour we plotted another fit with more data points from literature as shown in Fig. 4.1(b). Here, to obtain an estimate of the high frequency domain, we fit a spectral break, which could have been at or above 5GHz. The SNR SED can be seen steepening at high radio frequencies. The other possibility is that the SNR is optically thick at lower frequencies which will show a spectral break between 5 and 15GHz. Our ∼ value for the spectral index at 31GHz, after the analysis and flux correction, was estimated to be α = 0.8 0.1 for the frequencies above 5GHz. The − ± spectral index calculated from the poor fit was 0.36 0.05. − ± Caswell & Clark (1975) concluded the source to be an Hii region, and assumed the emission from it to be thermal. We believe G20.0 0.2 to be a − crab-like SNR, optically thin below a spectral break around 5 GHz and our value of spectral index (α = 0.8 0.1) is consistent with the dominant − ± source being an SNR.

4.3.2 SED of G312.5 3.0 − The SED for G312.5 3.0 is shown in Fig. 4.2. This particular SNR is − interesting because the spectral index is virtually unknown; the entry of D.Green’s Green (2009) catalogue gives “?”. The SNR is 20 18arcmin and × was studied by Kane & Vaughan (2003) in detail, who also confirmed the optically thin nature of the source. We took the value of flux density at different frequencies from their study to add to our estimation at 31 GHz. We fitted these values of flux densities against the frequencies to estimate the spectral index. The plotted 843MHz point in Fig. 4.2 is from the Molonglo Observatory Synthesis Telescope (MOST), whilst the higher frequency points 4.3. FLUX DENSITY AND SPECTRAL INDEX 85 are from the Australia Telescope Compact Array (ATCA) – Kane & Vaughan (2003) point out that the short-spacing (u,v) coverage in the MOST data is significantly different from ATCA due to which the flux density of G312.5 − 3.0 – a heavily resolved SNR – measured by MOST was problematic.

Figure 4.2: Spectral energy distribution of G312.5 3.0. The power law was calculated using the methodology discussed in Table− 4.3.

To test the sensitivity of the global spectral index we tried two fits. One which included all the points we had from the literature (dotted line) and a second which excludes the value at 843MHz (solid line) as shown in Fig. 4.2. The low value at 843MHz indicates spectral flattening at low frequencies. As can be seen our results are consistent with both of the fits. To find the power-law we calculated the reduced χ2 for both of them and found the values to be 2.2 for the fit including all values, and 1.3 for the fit excluding 843MHz. Taking into consideration the degree of freedom the difference was not significant. The value of the spectral index was α = 0.51 0.07. − ± 86 CHAPTER 4. ANALYSIS

4.3.3 Discussion

The spectral index estimated for G20.0 0.2 suggests that it is a Crab-like − SNR, whereas G312.5 3.0 is similar to the majority of shell like SNRs that − have been observed. Fig. 4.3 shows the distribution of the spectral indices for 294 sources compiled by Green (2009), where 135 of them have undeﬁned or variable values. It can be seen that the spectral indices can be as extreme as 0.0 or 0.9, but most of them lie in the range 0.6 α 0.3, including − − ≤ ≤− the SNRs we studied here.

Figure 4.3: Distribution of spectral indices of Galactic SNRs compiled by Green (2009). Figure taken from Reynoso & Walsh (2015).

The SEDs provide information on the mechanism involved in particle acceleration processes, which causes the electrons to move at relativistic veloc- ities and produce synchrotron emission. The synchrotron emission produced by the electrons accelerated from the shock has of spectral index 0.6 for ≈− 1 30 GHz. As discussed in Reynolds (2008), adiabatic shocks have a the- ∼ − oretical spectral index α = 0.5, in good agreement with our measurements. − 4.3. FLUX DENSITY AND SPECTRAL INDEX 87

In the case of radiative shocks, the accelerated particles influence the shock dynamics thus increasing the compression ratio and producing a flatter spectrum. This was believed to be the case for G20.0 0.2. As summarized − in Dave Green’s catalog, 44% of shell type SNRs have poorly determined spectra, which was true for G312.5 3.0. − There are various arguments given for the flat radio spectra of the SNR. Reynolds (2011) says that it is because of the low shocks at increasing energy, which is only true for few SNRs (as we would not expect most of them to have such slow shocks). Ostrowski (1999) explains that second-order Fermi acceleration could be responsible. Reynolds & Ellison (1992) describe the non-linear shock acceleration to be the reason behind flatter spectra. As discussed in Chapter 1, particle acceleration is very efficient in young SNRs because of the free expansion phase. For a robust spectral study of a SNR three factors are usually important:

1. The intrinsic characteristics of the explosion.

2. The contribution from the surrounding. The surroundings of the SNR are often ionized because of shocks, which increases the possibility of the formation of Hii regions.

3. The observational selection eﬀects, which are mainly based on the instrument.

We were sure to take into consideration the factors mentioned above in our study of SNRs. The steep indices (< 0.5) at high frequency was seen − which was believed to be either because of the optical depth or due to the radiative losses. Chapter 5

Conclusions

5.1 Summary and Conclusions

This thesis presents a study of the radio spectra of two SNRs, namely G20.0 0.2 and G312.5 3.0. There have not previously been many mea- − − surements made at high radio frequencies ( few GHz), and as can be seen ≥ in Eq. 2.1 the error in the spectral index is reduced when using a wider frequency range. This motivated us to study the spectral index of SNRs using 31 GHz observations. This thesis began with the 31GHz observations with CBI. Considering the resolution and the primary beam size (at 31GHz) of CBI, it was a good instrument to observe bright SNRs of 10arcmin size. Looking at the ob- ∼ server log we found that most of the observations of the SNRs were carried out by CBI2. We collected the data of all SNR observations and, using the CBI data editing pipelines we calibrated the data. The data calibration was carried out by carefully examining the data visually. Non-astronomical sources, including ground spillover, contaminates the data and were therefore clipped. The effect from the ground was mini- mized by subtracting lead and trail fields using UVSUB. The data were then combined using UVCON, and the visibilities of individual days were added. This left us with the final visibilities, which we imaged using Difmap. Natu- ral weighting was selected to provide better sensitivity. We used the CLEAN

88 5.1. SUMMARY AND CONCLUSIONS 89 algorithm within Difmap to produce our CBI maps. We used an IDL script for converting the units of the maps from Jy/beam to Jy/pixel. The final CBI maps were then compared with other low frequency maps to understand their morphology. We estimated the flux loss by simulating observations with the appropriate (u,v) coverage using ancillary Parkes and Effelesberg maps. The flux loss was found to be 10% for both SNRs, which ≈ was manageable and could be corrected using a model. A UCHii region (G20.08 0.14) was observed next to G20.0 0.2 , which was contributing − − to the measured flux density of the SNR. The flux from this UCHii region at 31GHz was estimated by fitting a free-free spectrum to low frequency data from literature. The value was 1.07Jy. Using the package SRCSUB we subtracted this source from the visibilities before calculating the flux for the SNR. The next step was to calculate the flux densities of the SNRs, which we did by using an aperture photometry method. After trying several apertures we chose a suitable one around our source and, using an IDL script, calculated the flux density inside the aperture. The flux density inside the aperture was found to be 3.13Jy for G20.0 0.2 and 0.46Jy for G312.5 3.0. Residual − − phase errors were found to be the dominant source of uncertainty for G20.0 − 0.2, whereas for G312.5 3.0 the main error was due to ground spillover. − After including the uncertainties and correcting for the flux loss the final flux values were 3.56 0.20Jy and 0.49 0.07Jy for G20.0 0.2 and G312.5 3.0, ± ± − − respectively. These values, along with the values from literature, were then used to produce the SED with which we fit a power law. The spectral indices were calculated to be α = 0.36 0.05 (over the frequency range 1-31GHz by − ± fitting the power-law), α = 0.8 0.1 over 5-31GHz (fitting for the spectral − ± break) for G20.0 0.2. For G312.5 3.0 the spectral index (1.38-31GHz) was − − calculated to be α = 0.51 0.07. Our results show that the spectrum of − ± G20.0 0.2 is flat, similar to the Crab SNR, while the spectrum for G312.5 − − 3.0 is slightly steeper, similar to most of the SNRs’ spectral indices. A possibility for the flat spectrum of G20.0 0.2 is the presence of a pulsar, − which could provide additional acceleration for cosmic rays resulting in a 90 CHAPTER 5. CONCLUSIONS

ﬂatter spectral index.

5.2 Future Prospects

There are 24 SNRs observed by CBI, and future work can be based on an- alyzing all of them. This would provide a large database of well measured spectral indices for these objects. This is important as it is not clear the reason for the large dispersion of spectral indices listed in literature (as seen in Fig. 4.3). Additional future work could build upon the results of this thesis. Firstly, local variations of the spectral index within the SNR could be studied, especially for G312.5 3.0 as it is an extended source. A detailed study like − this would help us to know where the particle acceleration is taking place. The polarization of the SNRs could be studied to understand their magnetic properties, such as ﬁeld direction and ﬁeld order. Comparisons of the radio spectra with emission in other spectral ranges could be done to understand the interactions of SNRs with the ISM. Future radio telescopes such as SKA and LOFAR will provide us with spatially resolved spectral indices over a wide range of frequencies. This is imperative towards better understanding of cosmic ray physics. Appendix A

Some deﬁnitions

Brightness Temperature Tb: We know that with the exception of CMBR no body radiates as a perfect black-body. So the brightness temperature Tb is deﬁned as the temperature of a black-body which will radiate with the same intensity I(ν) in the range observed. 2kTν2 At radio wavelengths were the Plank formula is approximately Bν = c2 the brightness temperature:

T = T e−τν + T (1 e−τν) , (A.1) b b0 −

where Tb0 is the brightness temperature of the source, T is the temperature deﬁning the populations in the atomic levels involved, and τν is the optical depth.

Flux Density Sν is deﬁned as the rate of energy E that crosses an area dA, per unit bandwidth dν and is equal with the integral of surface brightness over the solid angle dω eq A.2 (Burke & Graham-Smith, 2014).

dE S = = B (Ω)dΩ . (A.2) ν dtdAdν ν Z The ﬂux density is measured in units of W.m−2Hz−1 but a more conve- nient ﬂux density has been designated, the Janskys (Jy)

1Jy = 10−26W.m−2Hz−1 . (A.3)

91 92 APPENDIX A. SOME DEFINITIONS

Optical depth τν is the integral of the absorption coeﬃcient along the line of sight eq A.4 (Burke & Graham-Smith, 2014).

z τν = ανdz . (A.4) Zzo For ionized hydrogen in the radio domain, where z=1 and ne = ni, have provide us with the expression A.5, which is valid to 5% for frequencies less than 10 GHz:

τ 8.235 10−2T −1.35ν−2.1 n2dl . (A.5) ν ≃ × e e Z The radiation is considered optically thin when τν is << 1, which means that a typical photon can traverse the medium without being absorbed while if τν is greater than 1 the photon will eventually be absorbed and it wont escape the medium (optically thick medium). 2 Spectral index: At radio wavelengths where B 2kTν , and under the ν ≃ c2 assumption of a uniform temperature distribution over the solid angle dω then eq A.2 reduces to

2kTν2 S = Ω . (A.6) c2 A more general power-law between ﬂux density and frequency is

S να , (A.7) ∝ or if we rearrange eq A.6

T νβ , (A.8) ∝ As a result a relation between α and β occurs. In the R-J limit α = β +2. For two measurements at frequencies ν1, ν2 we get:

log(S /S ) α = ν1 ν2 . (A.9) log(ν1/ν2) The spectral index is an important concept in astronomy as is used to distinguish between the diﬀerent types of emission (section 2) and hence 93 provide information about the properties of the emitting source.

Surface Brightness (or speciﬁc intensity) Bν of an object is deﬁned as the amount of power emitted per unit area, per solid angle, per unit bandwidth eq A.10 (Rybicki & Lightman, 1979). The SI units of surface brightness are W m−2sterad−1Hz−1.

dE B = . (A.10) ν dtdAdΩdν The measured visibility between two antennas in synthesis imaging can be written as (Taylor et al., 1999)

Vmn = GmnΓmn + noise , (A.11)

where Γmn is the actual visibility and Gmn denotes the complex gain arising from instrumental and observational eﬀects. Noise is present as well. In many cases the gain factors can be associated with individual antennas such that

G = g g eiθme−iθn . (A.12) mn | m|| n| Emission Measure (EM): The integral of the square of the electron density along the line of sight eq A.13 (Burke & Graham-Smith, 2014). EM is a commonly met observational parameter in plasmas such as HII regions. It is usually expressed in units of cm−6 pc.

2 EM = nedl . (A.13) Z Appendix B

Scripts

CBICAL script

lockignore 0 #ignore lock on ch 0 #tracking lax #pointing of telescope tpzero #removes tp offsets to stop them going positive thresh tp −2 −0.05 #set new threshold #thresh pm −100000 100000 #set new power meter reading read 25−sep −2007 #end early as obs. log but data available after. flag beammap #get rid of beammap data ignore rx 6 # not mounted #delete start end rx 10 ch 2 # bimodal phase − correlated signal #delete start end base 5−9 ch 7 # very strong correlated ground signal mincal 20 #@flaggroundbaselines . txt #flag ncal 1.4 glitch glitch quad ncal1 antcal export jupiter !jupiter.uvf #export G21.5 −0.9∗ ! ∗ 25−sep −2007. uvf #export G353.9 −2.0∗ ! ∗ 25−sep −2007. uvf #export G328.4+0.2∗ ! 25−sep −2007∗. uvf #export G20.0 −0.2∗ ! ∗ 25−sep −2007. uvf

#mostly bad data seems to me as ground for eg ch 4 (9 −10)

Photometry

PRO aperture , fitsfile=fitsfile ,ra0=ra0 ,dec0=dec0,rad ius=radius

;; code to calculate flux within an aperture. radius is in ;; arcmin. Coordinates are in degrees. Only works for maps in Jy/beam units . ;;

;read fits file image = readfits(fitsfile , h)

adxy, h, RA0, dec0, x center , y center ;[/print , alt=]

94 95

;18:28:07.000 − 11:34:64.000 radius/=60. ;to degree dimensions = fxpar(h, ’naxis1 ’) pixelsize = fxpar (h,’cdelt2 ’) radius pix = radius/pixelsize ;print , radius p i x ;create mask dist circle , circle , 256, x center , y center circle = reform(circle)

;within radius good = where(circle lt radius pix , ngood) ;ngood= number of pixels in the region

; creating mask mask = replicate(0,dimensions ∗ dimensions) ; mask[∗]=0 mask[ good]=1 mask =reform(mask,[256 ,256]) ; total fd = total(mask∗ image) ;print , fd

; units theta min = fxpar(h,’bmin’) ;degree theta maj= fxpar(h,’bmaj’) ;degree a beam = (!pi/(4∗ alog(2))) ∗ theta min ∗ theta m a j ∗ (!pi/180)ˆ2 ;steradian a pixel =(fxpar(h,’cdelt2 ’))ˆ2 ∗ (!pi/180)ˆ2 ;steradian

;jy/beam to jy/pixel

flux = fd ∗(a pixel/a beam)

;; error for G20= 0.037 jy/beam ;; we need to correct for the fact that the pixels are not ;; independent. for this , we will divide by sqrt(number pixe ls per ;; beam)

;; first , we calculate n beam : n beam = (a beam/ a pixel) s flux = 0.037 ∗ ( a pixel/a beam) ∗ ngood / sqrt(n beam) ;;g20 ;; 0.04 ;; s flux = 0.0078 ;;g312 ;;0.01

print, ’ ’ print , ”The flux within the aperture is: ”+string(flux ,format=’(F5.2) ’)+’+−’+string ( s flux ,format=’(F5.2) ’)+” Jy” print, ’ ’

; G20 = 3.13 Jy. ; G312 l = 0.46 Jy. ; G12 cl = 0.26 Jy. stop end

Free-free power law spectra of UC H II. function tau, te, em, freq tau = 3.28e−7 ∗ (te/1d4)ˆ( −1.35) ∗ (freq)ˆ( −2.1) ∗ em 96 APPENDIX B. SCRIPTS

return , tau end PRO ffspec

; inputs omega = 4.48d−12 ;sr te=6000d ;K freq = (findgen(1000)+1.)/10. ; GHz lambda = 2.997d8 / (freq ∗1e9) ;m tau = (0.35) em=3d8 ; cmˆ6 pc

; estimate tau ;tau = 3.28e−7 ∗ (te/1d4)ˆ( −1.35) ∗ (freq)ˆ( −2.1) ∗ em

TE = [6000.,9500.,50000] EM= [3d8 ,1.9d8, 20.3d8] tauA = tau(TE[0] ,EM[0] , freq) tauB = tau(TE[1] ,EM[1] , freq) tauC = tau(TE[2] ,EM[2] , freq)

; calculate brightness temperature tbA = te[0]∗(1 − exp(−tauA) ) tbB = te[1]∗(1 − exp(−tauB) ) tbC = te[2]∗(1 − exp(−tauC) )

; calculate flux densities in Jy fdA = 2. ∗ 1381. ∗ tbA ∗ omega / lambdaˆ2 fdB = 2. ∗ 1381. ∗ tbB ∗ omega / lambdaˆ2 fdC = 2. ∗ 1381. ∗ tbC ∗ omega / lambdaˆ2

; write results to the screen ;print , tb ;print , fd

freqs = [5.0, 15.0] fluxA = [0.021, 0.091] fluxB = [0.0315, 0.0616] fluxC = [0.3304, 0.3623] xrange = [1,100] yrange = [1e −3,1]

xtitle=’Freq [GHz] ’ ytitle=’Flux density [Jy]’

set plot , ’ps’ device , filename = ’ffspec.ps’

colors =[cgcolor(’blue ’) ,cgcolor(’green ’) ,cgcolor(’cyan’) ,cgcolor(’purple ’) ,cgcolor(’red ’) ]

;; ploting lines plot , freq , fdA, /xlog, /ylog, yrange = yrange, xrange=xrange, xstyle=1, ystyle=1,xtitle =xtitle , ytitle=ytitle oplot , freq , fdB;,linestyle=2 oplot , freq , fdC;,linestyle=3

;; overploting data points PLOTSYM, 0,1,/ fill , color=colors [0] oplot , freqs , fluxA, psym=8 ;; diamond PLOTSYM, 0,1,/ fill , color=colors [1] 97

oplot, freqs , fluxB, psym=8 ;; squares PLOTSYM, 0,1,/ fill , color=colors [2] oplot , freqs , fluxC, psym=8

al legend ,[ ’region C’,’region B’,’region A’] ,psym=[16,16, 16],/ fill ,color=colors , Position =[0.7 ,0.95] ,/norm,box=0

device , /close ; set plot , mydevice

;; flux density at 31GHz: (309 is the index that corresponds to 31GHz ;; in the ”freq” array.) print, ’ ’ print, ’ ’

;print , ’S$ {31} $ = ’+string(fdA[309],format=’(f5.2) ’) +’ Jy’ ;print, ’ ’

print, ’S 31a = ’,fdA[309],’ Jy’ print, ’ ’

print, ’S 31b = ’,fdB[309],’ Jy’ print, ’ ’

print, ’S 31c = ’,fdC[309],’ Jy’ print, ’ ’

print, ’S 31 total = ’, 1072.32, ’MJy’

STOP END

Produce the Power-law spectra

; power law function

FUNCTION POWERLAW, f r e q s , P RETURN, S = P [ 0 ] ∗ ( freqs)ˆP[1] END

PRO spectra

; values of frequencies and flux densities

freqs = [1, 1.4, 5.0, 31.0] ;ghz fluxes = [10, 10.6, 10.4, 3.13];3.13] ;jy fluxes err =[1, 1.06, 1.04, 0.55] ;; 10% to start with...

;plot the points xrange = [0.8,50] yrange = [1.0,50]

xtitle=’Freq [GHz] ’ ytitle=’Flux density [Jy]’

set plot , ’ps’ device , filename=’powerlawspectrag20.ps ’

colors =[cgcolor(’blue ’) ,cgcolor(’green ’) ,cgcolor(’cyan’) ,cgcolor(’purple ’) ,cgcolor(’red ’) ]

PLOTSYM, 0,1,/ fill , color=colors [0] 98 APPENDIX B. SCRIPTS

plot, [freqs[0]], [fluxes[0]], psym=8 ,/xlog, /ylog, yrange = yrange , xrange=xrange , xstyle=1, ystyle=1,xtitle=xtitle , ytitle=ytitle PLOTSYM, 0,1,/ fill , color=colors [1] oplot, [freqs[1]] , [fluxes[1]] , psym=8 PLOTSYM, 0,1,/ fill , color=colors [2] oplot, [freqs[2]] , [fluxes[2]] , psym=8 PLOTSYM, 3,1,/ fill , color=colors [3] oplot , [freqs [3]] , [fluxes [3]] , psym=cgSymCat(46),colo r=colors [3] ,SYMSIZE=1.6

;; plot, freqs , fluxes ,/xlog, /ylog, yrange = yrange, xrange=xrange , xstyle=1, ystyle=1, psym=4,xtitle=xtitle , ytitle=ytitle ;; ;p1= plot(freqs[0],fluxes[0],’ − r2+’) ;; plots , freqs[0], fluxes[0], psym=1 ;; plots , freqs[1], fluxes[1], psym=2 ;; plots , freqs[2], fluxes[2], psym=3 ;; ;oplot, freqs[3], fluxes[3], psym=4

oploterror , freqs , fluxes , fluxes err ,psym=5 al legend ,[ ’D.Green’,’Becker and Helfand ’,’Clark ’,’CBI’] ,psym=[16,16,16,46],/ fill , color= colors , Position=[0.7 ,0.9] ,/norm,box=0

;initialise the fitting

start = [1, 0.1]

;save the best fit values as an array r e s u l t =MPFITFUN( ’POWERLAW’ , f r e q s , f l u x e s , f l u x e s err , start ,perror=perror)

A fit = result[0] alpha fit = result[1]

;use the values to model model = A fit ∗ freqsˆ(alpha fit)

;plot the final fit oplot, freqs , model, linestyle= 0

; text value = strcompress(string(result [1] ,format=’(F5.2) ’) ,/remove all) sigma value = strcompress(string(perror [1] ,format=’(F5.2) ’) ,/remove all) text = ’\ alpha = ’+value+’\pm’+sigma value charsize=1.7 XYOUTS, 0.2 ,0.8 , textoidl(text) , /normal, charsize=char size

device , /close

STOP END

Produce the multi-frequency maps with CBI’s contours. pro multifreq , image file , contour file , out ps

;; multifreq , ’planck 545.fits ’, ’g20 cln.fits ’, ’planck 545 cnt.ps’

!p.charthick = 3.0 99

; define page size (in cm) page height = 27.94 page width = 21.59 page height = 21.0 page width = 21.0

; define position of plot ; bottom left corner (in cm) plot left = 5.0 plot bottom = 4.0

; use postscript output set plot , ’ps’ ; name the output file psname = out ps

; open the postscript file device , filename = psname, $ xsize = page width, $ ysize = page height , $ xoffset = 0, $ yoffset = 0, $ scale factor = 1.0, $ /color ,$ /portrait

; read the input image image bg = readfits(image file , hdr bg) image cnt = readfits (contour file , hdr cnt)

; expand the image hrebin , image bg, hdr bg, image bg, hdr bg, 256, 256

;image = image[64:191 ,64:191] hextract , image bg, hdr bg, image bg ,hdr bg1, 64,191, 64, 191 hextract , image cnt, hdr cnt , image cnt ,hdr cnt1, 64,191, 64, 191

; scale the input image min = min(image bg) max = max(image bg)

;; bytscale has a range from 0 to 256 (you can invert the colour table ;; by doing 256b − bytscale ... ) data = bytscl(image bg , min=min , max=max ) ;data1 = bytscl(image1 , min=min,max=max)

; determine image size tam = size(data, /dimensions)

; define plot size (in cm) xsize = 15 ysize = 15 ∗ tam[1] / tam[0] ; keep aspect ratio

;; load colour table cgloadct , 33

; plot the background image cgimage, data, /keep aspect ratio , $ position = [plot left / page width , plot bottom / page height , (plot left + xsize) / page width , (plot bottom + ysize) / page height ]

;display color bar 100 APPENDIX B. SCRIPTS

cgLOADCT, 33

;;plotthecolorbar xi yi xf yf cgCOLORBAR, POSITION= [ 0 . 2 4 , 0 . 9 2 , 0 . 9 5 , 0 . 9 6 ] ,/ top , t i t l e=’Jy/beam’ , range=[min,max]

;XYOUTS, 0.8, 0.92, ’Jy/beam’ , /norm, charsize=2 ; overplot a contour with alpha and delta labels imcontour , image cnt, hdr cnt1, $ ;levels = [0.0, 10.0, 100.0, 150.0], $ levels = [0.1,0.2,0.5, 0.7, 0.9] ∗ max( image cnt), $ /noerase , /type, $ thick = 3, $ c colors = [cgcolor(’red5’) , cgcolor(’red6’) , cgcolor(’red7’)], $ ;/ keep aspect ratio , $ position = [plot left / page width , plot bottom / page height , (plot left + xsize) / page width , (plot bottom + ysize) / page height ]

; close postscript file device ,/ close

; create a PDF file ;cgps2pdf , psname ;, /delete ps stop end ; −−−−−−−−−−−−−−−−−−−−−−−− idl code ends here Bibliography

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