PHYS2220: Galaxy Classification and Spectral Analysis project The purpose of this project is to put into practice some of the procedures covered in the galaxies’ lectures of the Foundations of Astronomy course. The project is broken into three parts which cover morphological classification, deriving physical properties from galaxy spectra and exploring online galaxy databases. You are expected to provide a neat project report which answers the questions posed, shows your working and reasoning, and provides appropriate descriptions and explanations where required. PART 1: MORPHOLOGICAL CLASSIFICATION Although galaxy classification against the Hubble tuning fork (HTF) is gradually going out of fashion, it is still in common usage and therefore important for astronomers to be able to identify galaxies according to their Hubble type. In this exercise we want you to classify 20 bright nearby galaxies onto the Hubble Sequence. The 20 galaxies are shown at the URL below. Note the last line are slightly fainter galaxies drawn from the GAMA survey. See WebCT, Foundations of Astronomy, Project, galaxies montage.pdf Draw a grid in your project report to mimic the galaxy layout and within each box write the Hubble classification for the galaxy and any particular observations you might wish to make (e.g., red, asymmetric, nearby companion, dust lane etc). Before starting the exercise you should review the online notes for lecture 8 available on WebCT. PART 2: GALAXY SPECTRA You are provided on WebCT with a galaxy image and spectrum in the Project folder within the Foundations of Astronomy Folder. In your project report describe the appearance of the galaxy and what you might be able to infer from the image alone as to the galaxies age, star-formation, likely mass, and formation history. See WebCT, Foundations of Astronomy, Project, G321075 image.pdf See WebCT, Foundations of Astronomy, Project, G321075 spectrum.pdf Galaxy spectra are extremely useful for many purposes, in particular one can derive a galaxy’s distance, star-formation rate, and even its mass. Using the spectrum provided above and the notes from Lecture 9 identify as many spectral features as you can for the spectrum provided. Using the lines you’ve identified we now wish to measure both the distance to the galaxy and its mass. DISTANCE: Because of the expansion of the Universe the further away a galaxy is the more the vrec ∆λ lines are shifted to longer wavelengths according to the standard Doppler formulae: c = λo . 5 Where vrec is the recession velocity caused by the expansion, c is the speed-of-light (3 × 10 km/s - note irregular units), λo is the wavelength one expects the line feature to occur at in the lab (i.e., at rest w.r.t us), and ∆λ is the wavelength offset (i.e., λobserved − λo). 1 Using as many line features as possible estimate the recession velocity of your galaxy. Identify any erroneous line classifications and correct your line list. Combine your measurements to provide the average recession velocity and an appropriate error. At fairly low distances we can assume the expansion of the Universe is constant and use the H v latest value of the Hubble constant to calculate the distance to your galaxy ( o = d = 75 km/s/Mpc, note funny units) in mega-parsecs (Mpc). DYNAMICAL MASS: We can also extract important information on the chemical compo- sition (metallicity), age in Gyrs, and the current star-formation rate of the galaxy as well as dynamical information by modeling the shape, amplitude and ratio of the various spectral lines. We now want to extract an approximate measurement of the dynamical mass of the galaxy. For this to work we are assuming that the lines are being smeared (broadened) because of the galaxy’s rotation as some of the stars are rotating towards us and some rotating away. Other effects can also cause this broadening and in particular instrumental effects. In reality one needs to model the line quite carefully to overcome these effects. However, if we assume that the broadening is entirely due to the rotation we can derive a dynamical mass: Measure the half-width at half maximum (HWHM) for one spectral line and one absorption feature. One can then use a similar formulae as above to determine an approximate line-of-sight v ∆λ rotational velocity ( c = λc ). Note that when using this formulae one now wants to use the line centre λc as the reference wavelength on the denominator and not the laboratory value. This is because we want to measure the velocity relative to the galaxy’s axis of rotation and not relative to us. The rotational velocity that we see is the vector component of the full rotational velocity along the line-of-sight. To correct this for the galaxy’s inclination we need to measure the aspect ratio i b and use the relation between axis ratio and inclination (cos( ) = a ). Use a ruler to measure the approximate axis ratio, estimate the axis ratio, and correct the l-o-s velocity to the true v vlos rotational velocity ( rot = sin(i) ). M v2r Assuming the Virial Theorem you can now derive the galaxy’s dynamical mass ( = G ) in units of solar mass. NB: in order to get the radius you’ll need to use your distance to convert the major axis length to a diameter. STELLAR MASS Returning to the image we have measured the magnitude of this galaxy to be 12.0 mag in the r band. Use the distance magnitude relation to convert this apparent magnitude (m), to an absolute magnitude (M). Assuming that the galaxy is comprised entirely of solar mass stars (Absolute magnitude M⊙,r = +4.71 mag) calculate its stellar mass in solar units. MASS-TO-LIGHT RATIO We can now derive the mass-to-light ratio for this galaxy by simply dividing the dynamical mass by the stellar mass. A value close to unity implies that the mass inferred from the motions of the stars can be explained by the mass in stars alone, a value significantly less than unity suggests the stars must have significantly lower mass than solar type stars, while a value significantly above unity implies that either the average star is much heavier than solar mass or that some additional “dark” matter component is required. 2 PART 3: GALAXY DATABASES These days we build very large samples of galaxies so we can study galaxy properties in a statistically robust way. Here we wish to introduce you to four galaxy databases: Galaxy And Mass Assembly (GAMA) — this is a database of almost 0.5 million galaxies which we’re building here at UWA in collaboration with the University of St Andrews in the UK by combining data derived from a variety of facilities (three space missions and five ground-based telescopes covering all wavelengths). The Public face of the survey is at http://www.gama- survey.org. The galaxy you have studied is G321075 (also known as NGC5740). Select the “Data Access” tab, “Database Inspection Tools” and then GAMAID. Type in the ID (without the G prefix) to get access to more information on this galaxy and check the lines identified on the spectra against those you identified earlier. Enter the following ID numbers and for each object write a brief description from the image and spectrum as to the properties of each galaxy, what is its type, is it star-forming etc. [8487, 16828, 22741, 30911, 30916, 40278] Galaxy Zoo — The volume of data we’re dealing with is huge often into the Terabytes and many automated algorithms fail or have a high error rate. Galaxy Zoo is a project in which the power of the Public is being harnessed to provided eyeball classifications for millions of galaxies. Enter the website at: http://www.galaxyzoo.org/ and hit the classify galaxies tab. Follow the instructions and classify 10 or so galaxies. Describe your impression of the strengths and weaknesses of Galaxy Zoo. The NASA Extragalactic Database (NED) — This is one of the professional workhorse websites and should be able to provide the answers to your original galaxy classifications. Enter the website at: http://nedwww.ipac.caltech.edu and select the “by name search” in the “Object” column. Enter the name of each of your galaxies from Part I and scroll down to the box on classifications to see what the professionals have classified your galaxies as. Discuss why your classifications differ from those listed in NED. The HST Heritage Project — The Hubble Space Telescope was launched over 20 years ago and has collected some of the most spectacular astronomical images from the very near to very far. The Hubble Heritage Project is an attempt to firmly place these images into the Public domain to satisfy our curiosity and inspire the next generation (i.e., you guys). Enjoy looking through some of the galleries at the images. As you do try to metnally interpret what the visual information in the image is telling you in terms of the galaxy type, age, star-formation rate and likely formation history and how you might image the spectra to look like, emission features, absorption features 4000Abreak˚ etc. Attach a copy of your favourite image and provide a brief description of it. 3.