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Robper: an R Package to Calculate Periodograms for Light Curves Based on Robust Regression
RobPer: An R Package to Calculate Periodograms for Light Curves Based on Robust Regression Anita M. Thieler Roland Fried Jonathan Rathjens TU Dortmund University TU Dortmund University TU Dortmund University Abstract An important task in astroparticle physics is the detection of periodicities in irregularly sampled time series, called light curves. The classic Fourier periodogram cannot deal with irregular sampling and with the measurement accuracies that are typically given for each observation of a light curve. Hence, methods to fit periodic functions using weighted regression were developed in the past to calculate periodograms. We present the R package RobPer which allows to combine different periodic functions and regression techniques to calculate periodograms. Possible regression techniques are least squares, least absolute deviations, least trimmed squares, M-, S- and τ-regression. Measurement accuracies can be taken into account including weights. Our periodogram function covers most of the approaches that have been tried earlier and provides new model-regression-combinations that have not been used before. To detect valid periods, RobPer applies an outlier search on the periodogram instead of using fixed critical values that are theoretically only justified in case of least squares regression, independent periodogram bars and a null hypothesis allowing only normal white noise. Finally, the package also includes a generator to generate artificial light curves. Keywords: periodogram, light curves, period detection, irregular sampling, robust regression, outlier detection, Cramér-von-Mises distance minimization, time series analysis, beta distri- bution, measurement accuracies, astroparticle physics, weighted regression, regression model. 1. Introduction We introduce the R (R Core Team 2015) package RobPer (Thieler, Rathjens, and Fried 2015), which can be used to calculate periodograms and detect periodicities in irregularly sampled time series. -
Variable Star Classification and Light Curves Manual
Variable Star Classification and Light Curves An AAVSO course for the Carolyn Hurless Online Institute for Continuing Education in Astronomy (CHOICE) This is copyrighted material meant only for official enrollees in this online course. Do not share this document with others. Please do not quote from it without prior permission from the AAVSO. Table of Contents Course Description and Requirements for Completion Chapter One- 1. Introduction . What are variable stars? . The first known variable stars 2. Variable Star Names . Constellation names . Greek letters (Bayer letters) . GCVS naming scheme . Other naming conventions . Naming variable star types 3. The Main Types of variability Extrinsic . Eclipsing . Rotating . Microlensing Intrinsic . Pulsating . Eruptive . Cataclysmic . X-Ray 4. The Variability Tree Chapter Two- 1. Rotating Variables . The Sun . BY Dra stars . RS CVn stars . Rotating ellipsoidal variables 2. Eclipsing Variables . EA . EB . EW . EP . Roche Lobes 1 Chapter Three- 1. Pulsating Variables . Classical Cepheids . Type II Cepheids . RV Tau stars . Delta Sct stars . RR Lyr stars . Miras . Semi-regular stars 2. Eruptive Variables . Young Stellar Objects . T Tau stars . FUOrs . EXOrs . UXOrs . UV Cet stars . Gamma Cas stars . S Dor stars . R CrB stars Chapter Four- 1. Cataclysmic Variables . Dwarf Novae . Novae . Recurrent Novae . Magnetic CVs . Symbiotic Variables . Supernovae 2. Other Variables . Gamma-Ray Bursters . Active Galactic Nuclei 2 Course Description and Requirements for Completion This course is an overview of the types of variable stars most commonly observed by AAVSO observers. We discuss the physical processes behind what makes each type variable and how this is demonstrated in their light curves. Variable star names and nomenclature are placed in a historical context to aid in understanding today’s classification scheme. -
Gaia Data Release 2 Special Issue
A&A 623, A110 (2019) Astronomy https://doi.org/10.1051/0004-6361/201833304 & © ESO 2019 Astrophysics Gaia Data Release 2 Special issue Gaia Data Release 2 Variable stars in the colour-absolute magnitude diagram?,?? Gaia Collaboration, L. Eyer1, L. Rimoldini2, M. Audard1, R. I. Anderson3,1, K. Nienartowicz2, F. Glass1, O. Marchal4, M. Grenon1, N. Mowlavi1, B. Holl1, G. Clementini5, C. Aerts6,7, T. Mazeh8, D. W. Evans9, L. Szabados10, A. G. A. Brown11, A. Vallenari12, T. Prusti13, J. H. J. de Bruijne13, C. Babusiaux4,14, C. A. L. Bailer-Jones15, M. Biermann16, F. Jansen17, C. Jordi18, S. A. Klioner19, U. Lammers20, L. Lindegren21, X. Luri18, F. Mignard22, C. Panem23, D. Pourbaix24,25, S. Randich26, P. Sartoretti4, H. I. Siddiqui27, C. Soubiran28, F. van Leeuwen9, N. A. Walton9, F. Arenou4, U. Bastian16, M. Cropper29, R. Drimmel30, D. Katz4, M. G. Lattanzi30, J. Bakker20, C. Cacciari5, J. Castañeda18, L. Chaoul23, N. Cheek31, F. De Angeli9, C. Fabricius18, R. Guerra20, E. Masana18, R. Messineo32, P. Panuzzo4, J. Portell18, M. Riello9, G. M. Seabroke29, P. Tanga22, F. Thévenin22, G. Gracia-Abril33,16, G. Comoretto27, M. Garcia-Reinaldos20, D. Teyssier27, M. Altmann16,34, R. Andrae15, I. Bellas-Velidis35, K. Benson29, J. Berthier36, R. Blomme37, P. Burgess9, G. Busso9, B. Carry22,36, A. Cellino30, M. Clotet18, O. Creevey22, M. Davidson38, J. De Ridder6, L. Delchambre39, A. Dell’Oro26, C. Ducourant28, J. Fernández-Hernández40, M. Fouesneau15, Y. Frémat37, L. Galluccio22, M. García-Torres41, J. González-Núñez31,42, J. J. González-Vidal18, E. Gosset39,25, L. P. Guy2,43, J.-L. Halbwachs44, N. C. Hambly38, D. -
Downloads/ Astero2007.Pdf) and by Aerts Et Al (2010)
This work is protected by copyright and other intellectual property rights and duplication or sale of all or part is not permitted, except that material may be duplicated by you for research, private study, criticism/review or educational purposes. Electronic or print copies are for your own personal, non- commercial use and shall not be passed to any other individual. No quotation may be published without proper acknowledgement. For any other use, or to quote extensively from the work, permission must be obtained from the copyright holder/s. i Fundamental Properties of Solar-Type Eclipsing Binary Stars, and Kinematic Biases of Exoplanet Host Stars Richard J. Hutcheon Submitted in accordance with the requirements for the degree of Doctor of Philosophy. Research Institute: School of Environmental and Physical Sciences and Applied Mathematics. University of Keele June 2015 ii iii Abstract This thesis is in three parts: 1) a kinematical study of exoplanet host stars, 2) a study of the detached eclipsing binary V1094 Tau and 3) and observations of other eclipsing binaries. Part I investigates kinematical biases between two methods of detecting exoplanets; the ground based transit and radial velocity methods. Distances of the host stars from each method lie in almost non-overlapping groups. Samples of host stars from each group are selected. They are compared by means of matching comparison samples of stars not known to have exoplanets. The detection methods are found to introduce a negligible bias into the metallicities of the host stars but the ground based transit method introduces a median age bias of about -2 Gyr. -
Appendix: Spectroscopy of Variable Stars
Appendix: Spectroscopy of Variable Stars As amateur astronomers gain ever-increasing access to professional tools, the science of spectroscopy of variable stars is now within reach of the experienced variable star observer. In this section we shall examine the basic tools used to perform spectroscopy and how to use the data collected in ways that augment our understanding of variable stars. Naturally, this section cannot cover every aspect of this vast subject, and we will concentrate just on the basics of this field so that the observer can come to grips with it. It will be noticed by experienced observers that variable stars often alter their spectral characteristics as they vary in light output. Cepheid variable stars can change from G types to F types during their periods of oscillation, and young variables can change from A to B types or vice versa. Spec troscopy enables observers to monitor these changes if their instrumentation is sensitive enough. However, this is not an easy field of study. It requires patience and dedication and access to resources that most amateurs do not possess. Nevertheless, it is an emerging field, and should the reader wish to get involved with this type of observation know that there are some excellent guides to variable star spectroscopy via the BAA and the AAVSO. Some of the workshops run by Robin Leadbeater of the BAA Variable Star section and others such as Christian Buil are a very good introduction to the field. © Springer Nature Switzerland AG 2018 M. Griffiths, Observer’s Guide to Variable Stars, The Patrick Moore 291 Practical Astronomy Series, https://doi.org/10.1007/978-3-030-00904-5 292 Appendix: Spectroscopy of Variable Stars Spectra, Spectroscopes and Image Acquisition What are spectra, and how are they observed? The spectra we see from stars is the result of the complete output in visible light of the star (in simple terms). -
Sigspec User's Manual
Comm. in Asteroseismology - Complementary Topics Volume 1, 2010 c Austrian Academy of Sciences Preface Jeffrey D. Scargle1 1 Space Science and Astrobiology Division, NASA Ames Research Center SigSpec is a method for detecting and characterizing periodic signals in noisy data. This is an extremely common problem, not only in astronomy but in almost every branch of science and engineering. This work will be of great interest to anyone carrying out harmonic analysis employing Fourier techniques. The method is based on the definition of a quantity called spectral signif- icance – a function of Fourier phase and amplitude. Most data analysts are used to exploring only the Fourier amplitude, through the power spectrum, ignoring phase information. The Fourier phase spectrum can be estimated from data, but its interpretation is usually problematic. The spectral sig- nificance quantity conveys more information than does the conventional amplitude spectrum alone, and appears to simplify statistical issues as well as the interpretation of phase information. arXiv:1006.5081v1 [astro-ph.IM] 25 Jun 2010 Comm. in Asteroseismology - Complementary Topics Volume 1, 2010 c Austrian Academy of Sciences SigSpec User’s Manual P. Reegen1 1 Institut f¨ur Astronomie, T¨urkenschanzstraße 17, 1180 Vienna, Austria [email protected] Abstract SigSpec computes the spectral significance levels for the DFT amplitude spec- trum of a time series at arbitrarily given sampling. It is based on the analyti- cal solution for the Probability Density Function (PDF) of an amplitude level, including dependencies on frequency and phase and referring to white noise. Using a time series dataset as input, an iterative procedure including step- by-step prewhitening of the most significant signal components and MultiSine least-squares fitting is provided to determine a whole set of signal components, which makes the program a powerful tool for multi-frequency analysis. -
Variable Star
Variable star A variable star is a star whose brightness as seen from Earth (its apparent magnitude) fluctuates. This variation may be caused by a change in emitted light or by something partly blocking the light, so variable stars are classified as either: Intrinsic variables, whose luminosity actually changes; for example, because the star periodically swells and shrinks. Extrinsic variables, whose apparent changes in brightness are due to changes in the amount of their light that can reach Earth; for example, because the star has an orbiting companion that sometimes Trifid Nebula contains Cepheid variable stars eclipses it. Many, possibly most, stars have at least some variation in luminosity: the energy output of our Sun, for example, varies by about 0.1% over an 11-year solar cycle.[1] Contents Discovery Detecting variability Variable star observations Interpretation of observations Nomenclature Classification Intrinsic variable stars Pulsating variable stars Eruptive variable stars Cataclysmic or explosive variable stars Extrinsic variable stars Rotating variable stars Eclipsing binaries Planetary transits See also References External links Discovery An ancient Egyptian calendar of lucky and unlucky days composed some 3,200 years ago may be the oldest preserved historical document of the discovery of a variable star, the eclipsing binary Algol.[2][3][4] Of the modern astronomers, the first variable star was identified in 1638 when Johannes Holwarda noticed that Omicron Ceti (later named Mira) pulsated in a cycle taking 11 months; the star had previously been described as a nova by David Fabricius in 1596. This discovery, combined with supernovae observed in 1572 and 1604, proved that the starry sky was not eternally invariable as Aristotle and other ancient philosophers had taught. -
Small Astronomy Calendar for Amateur Astronomers 2019
IGAEF Small Astronomy Calendar for Amateur Astronomers 2019 C A L E N D A R F O R 2019 January February March Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa 1 2 3 4 5 1 2 1 2 6 7 8 9 10 11 12 3 4 5 6 7 8 9 3 4 5 6 7 8 9 13 14 15 16 17 18 19 10 11 12 13 14 15 16 10 11 12 13 14 15 16 20 21 22 23 24 25 26 17 18 19 20 21 22 23 17 18 19 20 21 22 23 27 28 29 30 31 24 25 26 27 28 24 25 26 27 28 29 30 31 April May June Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa 1 2 3 4 5 6 1 2 3 4 1 7 8 9 10 11 12 13 5 6 7 8 9 10 11 2 3 4 5 6 7 8 14 15 16 17 18 19 20 12 13 14 15 16 17 18 9 10 11 12 13 14 15 21 22 23 24 25 26 27 19 20 21 22 23 24 25 16 17 18 19 20 21 22 28 29 30 26 27 28 29 30 31 23 24 25 26 27 28 29 30 July August September Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa 1 2 3 4 5 6 1 2 3 1 2 3 4 5 6 7 7 8 9 10 11 12 13 4 5 6 7 8 9 10 8 9 10 11 12 13 14 14 15 16 17 18 19 20 11 12 13 14 15 16 17 15 16 17 18 19 20 21 21 22 23 24 25 26 27 18 19 20 21 22 23 24 22 23 24 25 26 27 28 28 29 30 31 25 26 27 28 29 30 31 29 30 October November December Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa Su Mo Tu We Th Fr Sa 1 2 3 4 5 1 2 1 2 3 4 5 6 7 6 7 8 9 10 11 12 3 4 5 6 7 8 9 8 9 10 11 12 13 14 13 14 15 16 17 18 19 10 11 12 13 14 15 16 15 16 17 18 19 20 21 20 21 22 23 24 25 26 17 18 19 20 21 22 23 22 23 24 25 26 27 28 27 28 29 30 31 24 25 26 27 28 29 30 29 30 31 Easter Sunday: 2019 Apr 21 Phases of the Moon 2019 New Moon First Quarter Full Moon Last Quarter d h d h d h ⊕dist d h Jan 6 1.5 Jan 14 6.7 Jan 21 -
Variable Stars
VARIABLE STARS RONALD E. MICKLE Denver, Colorado 80211 ©2001 Ronald E. Mickle ABSTRACT The objective of this paper is to research the causes for variability and identify selected stars within telescope reach. In addition, individual observations of the magnitudes (Mv) of selected variable stars were made and a plan designed for more extensive work on variable stars was developed. Variable stars are stars that vary in brightness. They can range from a thousandth of a magnitude to as much as 20 magnitudes. This range of magnitudes, referred to as amplitude variation, can have periods of variability ranging from a fraction of a second to years. Algol, one of the oldest know variables, was known to ancient astronomers to vary in brightness. Today over 30,000 variable stars are known and catalogued, and thousands more are suspected. Variable stars change their brightness for several reasons. Pulsating variables swell and shrink due to internal forces, while an eclipsing binary will dim when it is eclipsed by its binary companion. (Universe 1999; The Astrophysical Journal) Today measurements of the magnitudes of variable stars are made through visual observations using the naked eye, or instruments such as a charge- coupled device (CCD). The observations made for this paper were reported to the American Association of Variable Star Observers (AAVSO) via their website. The AAVSO website lists variable star organizations in 17 countries to which observations can be reported (AAVSO). 1. IDENTIFICATION OF STAR FIELDS Observations were made from Denver, Colorado, United States. Latitude and longitude coordinates are 40ºN, 105ºW. 1.1. EYEPIECE FIELD-OF-VIEW (FOV) AAVSO charts aid in locating the target variable. -
A Heuristic Derivation of the Uncertainty of the Frequency Determination in Time Series Data
Astronomy & Astrophysics manuscript no. Kallinger˙ferror c ESO 2018 November 1, 2018 A heuristic derivation of the uncertainty of the frequency determination in time series data Thomas Kallinger, Piet Reegen and Werner W. Weiss Institute for Astronomy (IfA), University of Vienna, T¨urkenschanzstrasse 17, A-1180 Vienna Accepted 22 December 2007 ABSTRACT Context. Several approaches to estimate frequency, phase and amplitude errors in time series analyses were reported in the literature, but they are either time consuming to compute, grossly overestimating the error, or are based on empirically determined criteria. Aims. A simple, but realistic estimate of the frequency uncertainty in time series analyses. Methods. Synthetic data sets with mono- and multi–periodic harmonic signals and with randomly distributed amplitude, frequency and phase were generated and white noise added. We tried to recover the input parameters with classical Fourier techniques and investigated the error as a function of the relative level of noise, signal and frequency difference. Results. We present simple formulas for the upper limit of the amplitude, frequency and phase uncertainties in time–series analyses. We also demonstrate the possibility to detect frequencies which are separated by less than the classical frequency resolution and that the realistic frequency error is at least 4 times smaller than the classical frequency resolution. Key words. methods: data analysis – methods: statistical 1. Motivation based on an analytical solution for the one–sigma error of a least–squares sinusoidal fit with a rms of σ(m). The total num- In the frequency analysis of time series, a realistic estimateof the ber of data points, the total time base of the observations, the amplitude, phase and frequency uncertainties can be of special signal amplitude, phase and frequency are N, T, a, φ and f , re- interest. -
Sigspec User's Manual
Comm. in Asteroseismology Volume 163, 2011 c Austrian Academy of Sciences SigSpec User’s Manual P. Reegen† Institut f¨ur Astronomie, T¨urkenschanzstraße 17, 1180 Vienna, Austria Abstract SigSpec computes the spectral significance levels for the DFT∗ amplitude spectrum of a time series at arbitrarily given sampling. It is based on the ana- lytical solution for the Probability Density Function (PDF) of an amplitude level, including dependencies on frequency and phase and referring to white noise. Us- ing a time series dataset as input, an iterative procedure including step-by-step prewhitening of the most significant signal components and MultiSine least- squares fitting is provided to determine a whole set of signal components, which makes the program a powerful tool for multi-frequency analysis. Instead of the step-by-step prewhitening of the most significant peaks, the program is also able to take into account several steps of the prewhitening sequence simultane- ously and check for the combination associated to a minimum residual scatter. This option is designed to overcome the aliasing problem caused by periodic time gaps in the dataset. SigSpec can detect non-sinusoidal periodicities in a dataset by simultaneously taking into account a fundamental frequency plus a set of harmonics. Time-resolved spectral significance analysis using a set of intervals of the time series is supported to investigate the development of eigen- frequencies over the observation time. Furthermore, an extension is available to perform the SigSpec analysis for multiple time series input files at once. In this MultiFile mode, time series may be tagged as target and comparison data. -
Arxiv:Physics/0703160V1 [Physics.Data-An] 15 Mar 2007 Ok Nomto,Iitoueanwadubae Reliabili Unbiased and New a ( Introduce Criterion I Information, Book) W 4)
Astronomy & Astrophysics manuscript no. AA˙2006˙6597 c ESO 2018 October 15, 2018 SigSpec I. Frequency- and Phase-Resolved Significance in Fourier Space P. Reegen1 Institut f¨ur Astronomie, Universit¨at Wien, T¨urkenschanzstraße 17, 1180 Vienna, Austria, e-mail: [email protected] Received October 19, 2006; accepted March 6, 2007 ABSTRACT Context. Identifying frequencies with low signal-to-noise ratios in time series of stellar photometry and spectroscopy, and measuring their amplitude ratios and peak widths accurately, are critical goals for asteroseismology. These are also challenges for time series with gaps or whose data are not sampled at a constant rate, even with modern Discrete Fourier Transform (DFT) software. Also the False-Alarm Probability introduced by Lomb and Scargle is an approximation which becomes less reliable in time series with longer data gaps. Aims. A rigorous statistical treatment of how to determine the significance of a peak in a DFT, called SigSpec, is presented here. SigSpec is based on an analytical solution of the probability that a DFT peak of a given amplitude does not arise from white noise in a non-equally spaced data set. Methods. The underlying Probability Density Function (PDF) of the amplitude spectrum generated by white noise can be derived explicitly if both frequency and phase are incorporated into the solution. In this paper, I define and evaluate an unbiased statistical estimator, the “spectral significance”, which depends on frequency, amplitude, and phase in the DFT, and which takes into account the time-domain sampling. Results. I also compare this estimator to results from other well established techniques and assess the advantages of SigSpec, through comparison of its analytical solutions to the results of extensive numerical calculations.