Lab 3: Open Star Cluster Age and Distance Determination

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1 Name:... Astro 101: Observational Astronomy Fall 2009 Lab 3: Open Star Cluster Age and Distance Determination 1 Observations 1.1 Objectives During this lab you will integrate some of the skills you learned in the previous two labs. You will use PINTO 14-inch telescope in New Mexico to make photometric observations of a distant star cluster. You will take and analyze images taken through multiple optical bandpasses and use this multi-color information to create a Hertzsprung-Russell color-magnitude plot (HR diagram) of the cluster. These data will be compared to theoretical stellar evolution isochrones to determine the age and distance of your cluster. This lab is considerably more involved than the first two, so prepare yourselves. It will involve the following steps: 1) remote observing with PINTO telescope; 2) source extraction and photometry with IRAF; 4) photometric calibration of your data using observations of known standard stars and 5) analysis of your calibrated data. The observations will be done from the Astronomy Lab on Thursday, October 15. You will be working in pairs, as usual. However, you will have only one hour to take your data, so being prepared for your observation is absolutely essential. If you do not finish in your allotted time, you may obtain the missing data from other groups, however, this will count against you when the final grade for this lab is assigned. 1.2 Preparation Target For this lab you will be observing NGC7654 (M52), an open star cluster. You can find some basic information about this cluster at the SEDS Messier Catalog website ( There you will be able to find a summary and history of this object. Another page you might find useful is WEBDA, a page dedicated to open cluster research ( This target has been chosen based on its intermediate stellar density. High density globular clusters would require PSF fitting to disentangle overlapping stars. On the other hand, extended low density open clusters are easy targets for aperture photometry, but would require multiple pointings. M52 is a good compromise. Object Name Type RA Dec M 52 Open Cluster 23h 24.8m Table 1: M52 / NGC7654 Open Star Cluster Since you will have very little time to obtain your data, you have to familiarize yourself with the telescope ahead of time. All the information about operating the telescope that you will need is found here: The camera mounted on PINTO is an SBIG ST-10XME; its specs can be found at Before lab, prepare a good finding chart for this target. Do not forget to invert colormap on ds9 before printing your chart! PINTO FOV is approximately 10.9 by

2 1.2.2 Standard Stars In addition to images of your cluster field, you will want to take flux calibration images of a photometric standard star. Since the brightness of these standard stars is well known you will use this observation to set the flux scale for your science exposures. In principle, if sky conditions never changed and the telescope sensitivity were completely stable, this would only need to be done once. In reality of course, sky conditions can vary throughout the course of a single night, so we will want to observe this standard field throughout the night. A good practice is to observe a standard star before and after every new science pointing. If you plan on sitting on a field for multiple hours, then you should take standard star frames intermittently. For this lab, a much simpler approach is to take advantage of the fact that your target overlaps with a Stetson standard star field. Got to: and click on Photometric Standard Fields. The file NGC7654.pos has positions of the standard stars and NGC7654.pho lists the photometry information. Keep in mind that PINTO camera does not have much sensitivity in the I band, so plan to use the other three (B, V and R). 1.3 Observing with PINTO telescope You can connect to the telescope via VNC (the command vncviewer on astro machines) or via the web interface at Prof. Hoard will supply the username and password. Professor Hoard will focus the camera at the beginning of the night. Your goal will be to find your target, take a short (10 sec) trial exposure in each filter to determine the best integration time, and obtain the data you need. The telescope is controlled with The Sky and the camera with CCDSoft. Both programs should be familiar to you from Brackett. For this lab you do not need to take calibration images. The skies at the telescope location are too dark to take night flats in a reasonable amount of time and we do not control the time of opening for the dome, so twilight flats are not always possible to take. However, the telescope facility has a library of flat, dark and bias images and will do the preliminary data reduction for you. For this to be possible you must use 2x2 binning when taking your data and select the Bias,Dark,Flat reduction mode. Reduced image is displayed on screen in CCDsoft window, and raw and reduced images are almost immediately available for download at There are many files in this directory and they are listed alphabetically. It will be prudent to start the names of all your images with a101 followed by your names to make sure they float up to the beginning of the list. Having reduced images makes it very straight-forward to estimate the desired exposure time for your target, since the background is due to the sky and scales linearly with the integration time. Take a test 10 sec exposure in each filter and calculate the optimal exposure time per frame. This telescope tracks quite well, so you will be limited by the saturation limit, which is about 77,000 electrons. With a stated gain of 1.3e /ADU, this corresponds to 59,000 counts. Remember that you do not want to exceed about 2/3 of the full well capacity. Look at the relative depths of your exposures in different filters and settle on an observing strategy that will result in co-added images that are of comparable depth (i.e. roughly equal number of counts in each filter). It will be a waste of time if your V-band image is 2 magnitudes deeper than your B-band image. Once you have determined the optimum exposure times, you will need to decide how many exposures to take. As a rule, you should always take a minimum of 3 exposures to remove cosmic rays. In this case I suggest taking a minimum of 5 exposures per filter per pointing so that you will have the freedom to discard any bad frames. As usual, keep a careful observing log. You will be changing between exposure times and filters so a good log will make organizing your data for analysis a lot easier. The image headers contain detailed information 2

3 about each exposure but you should still log the file name, approximate start time, filter, and exposure time for each image, as well as note any problems or features (e.g., target off-center to north, bad tracking, etc). Having a page in your notebook that is already set-up with columns to record this information will streamline this process and will help you avoid loosing time at the telescope. 3

4 2 Data Analysis Start by downloading your reduced data from You can also access them from an ftp server at ftp:// as described on PINTO website. 2.1 Image Alignment / Co-addition Once you have your reduced images, before you can combine them, you will need to align them using imalign. imalign will take as input: a) a list of images to shift, b) a reference image, and c) a reference coordinates file that it will use to compute the shifts. Display all images that you plan to align and choose one that has a good PSF to use as your reference image. Next, use imexamine with this images to create a log of about bright non-saturated stars. This will serve as your reference coordinates file. If you didn t make any large moves between exposures, the default centering box sizes should be sufficient to compute the shift sizes. For larger shifts, you can increase the centering box size. The upper limit is dependent on the stellar density of your field, and the number of stars in your reference coordinate list. For large shifts you can also provide an initial shift file, but hopefully this won t be necessary for this data set. Once you have your images aligned, you will want to co-add them by filter (often referred to as stacking). Use imcombine to average your images. Assuming your exposure times were all the same, you will want to make certain that scaling is turned off. Also, be sure to use some type of sigma clipping algorithm here to reject cosmic rays. avsigclip tends to work well if you have a large number of exposures (N 10). You can find info about the other clipping algorithms in the imcombine HELP pages. 2.2 Multi-object Photometry There are a few different approaches you can adopt to perform multi-object photometry. The most straightforward would be to use imexamine, manually identify all sources that you want to measure and use the a keystroke interactively to perform aperture photometry. This is a reasonable approach if you are measuring 2-3 stars per image, but if you want to measure hundreds of stars in a cluster, this is obviously impractical. You will instead use diaofind, a source detection code to identify stars in your image and then phot to perform aperture photometry on the identified stars. Both of these tasks can be found in the digiphot.daophot IRAF package that you can load by typing: > digi > daophot Before doing anything else, take a look at the HELP pages for these IRAF tasks as well as their parameter files Source detection with diaofind Type lpar diaofind; you will see two blank parameters datapars and findpars. These are actually the names of parameter lists that diaofind uses, if left blank (they should be) diaofind will use default parameter files that you can access/edit by typing: > epar datapars > epar findpars on the IRAF command line. An alternative is to type epar diaofind and enter :e for the parameter list you want to edit. This will bring you to that list. Once you have made your desired edits, :q will bring you back to the diaofind parameter list. Both datapars and findpars have HELP files associated with them. 4

5 The datapars parameter file contains information about the input image so you will need to provide the image scale, PSF FWHM and the standard deviations of sky pixels. If you set scale=1.0 you can work in pixel units. Use imexamine to measure a) the PSF FWHM of a few bright unsaturated stars (keystroke a or r ) and b) the standard deviation of the sky background (keystroke m ). The images that we are using do not have read noise or gain information in their headers, so you will need to put these in by hand under readnoise and epadu. According to the camera specs, its gain is 1.3,e /ADU and its RMS read noise is 8.8 e. The findpars parameter file sets the source detection sensitivity limits. If these limits are too low, you will identify thousands of fake sources in the sky noise. If too high, you may not identify anything at all. The default settings are probably a bit too low at the moment, but run a couple of tests to see how your extraction is affected. The most critical one for this application is the threshold. There are numerous statistical tests you can perform to determine the completeness and reliability of an extracted sample, but for this lab since you won t need to dig deep into the noise, it might be enough to just do a visual inspection to make certain that all of your sources look real and that you are not missing any obvious stars. To do this, run through diaofind on your best image in terms of depth and PSF FWHM (probably V-band) and use the IRAF tasks display and tvmark to mark circles of the detected sources on the image. > display IMAGE NAME.fits 1 > tvmark 1 DAOFIND OUTPUT COORDINATE FILE mark=circle radii=7 diaofind can feel like a black box, so to understand what it is doing, take a look at the starmap and skymap images that diaofind produces and try to understand how they are being used. The HELP pages for diaofind may be useful here Aperture Photometry using phot Once you are happy with your source list, you can simply feed this list and the input image to phot to perform aperture photometry. phot has a long parameter list and also calls on multiple parameter files. Take a look at these values and make sure they seem reasonable for your application. The defaults should be OK for moderately bright, relatively uncrowded fields, but one parameter that you should adjust is apertures under photpars. This is set to 3.0 by default, but you will need to check that this is well matched to your PSFs. If your aperture is too big, you will unnecessarily include excess sky. If it is too small, you will miss some of the flux of your star. Select an aperture size and justify your choice. Another parameter that I like to adjust is zmag under photpars. This is the zeropoint of the magnitude scale that we will derive below, the default value is arbitrarily set to Since you will measure this value shortly, for now I recommend setting it to 0.0 so you can avoid having to back off this arbitrary value later. A shortcut to setting parameters is to use the following syntax on the IRAF command line: > photpars.zmag=0.0 Though you can do the source detection separately for your images in different bands, I recommend that you instead use a single source list on both images when running phot. This will give you bogus fluxes for some faint sources that show up in your detection image, but drop out in the other band; however, it will make your band merging much easier since your two catalogs will have the same number of entries. If you don t do this, you will need to position match your two catalogs. This is neither fun nor trivial, especially in crowded fields. The output data file from phot will contain a slew of information about each source, including position, shape, flux, magnitude and uncertainties. These will be a standard ASCII files that you can read into a plotting program like SuperMongo (see Software Manuals on the course webpage) or excel (ooffice on astro computers). Depending on your plotting tool, you ll probably need to make minor formatting modifications. For instance, each source will have multiple lines of data as opposed to a single long line and some of the 5

6 fainter sources will have INDEF magnitudes that you ll probably want to replace with a floating point. A simple emacs or vi macro would make quick work of these otherwise tedious edits. At this point, use your favorite plotting package to make a couple of quick plots. First, make a plot of the B-mag vs. V -mag of your sample. Is this looking reasonable? Though your magnitudes are not yet flux calibrated, you should check at this point that you are measuring magnitudes for most sources and that to first order both bands are correlated. Next, go ahead and plot an uncalibrated color-magnitude diagram (e.g. B V vs. V ) of your sample as a check that your catalogs are doing what you think they are. Hopefully you can see a main sequence and main sequence turn-off. Note: the standard way of plotting color-magnitude diagrams (CMD) is for flux to increase with positive y, and color to become more red with increasing x. To help you characterize your extracted catalog, make plots of your instrumental magnitude vs. magnitude error, as well as a histogram of your magnitude distribution. How would you define the instrumental magnitude limits of your extracted catalogs? As you should notice, you probably don t have a clean flux cut. Your detection algorithm extracted sources based on flux, spatial distribution and background noise, so defining a limiting magnitude is non-trivial. A standard method for characterizing a sample is to state a limiting magnitude along with its corresponding signal-to-noise ratio (SNR). Alternatively, since the SNR is correlated to completeness, one can instead state a limiting magnitude and the associated detection completeness at that limit. What is the signal-to-noise of the sources at your limiting magnitude? Based on the magnitude error plots what can you say about the relative depths of the B-band vs. V-band data? Did you reach comparable depths in both bands? By this I mean does the faint end of your V-band catalog have comparable magnitude errors as the faint end of you B-band catalog? 2.3 Photometric Calibration: Computing your Zeropoint Assuming that you are not going to change your apertures, the shape of your CMD will not change, so in principle, you could already make a rough age estimate for your cluster by over-plotting stellar evolution isochrones. Since you are also interested in determining the distance to this cluster, you will want to compute the apparent B & V magnitudes of your sample. To get these, you need to compute the B- & V -band zeropoints for your images. Normally, this would be done using the standard star observations that we made throughout the night. You would simply go through the basic reduction and photometry of this standard star field and the zeropoint would be the difference between the true apparent magnitude of your standard star and the magnitude as measured with phot. The above method assumes a) that sky conditions were constant between the time of your science and calibration observations and b) that the airmass difference is also negligible. If you are fortunate enough to find a standard star in your science field, you won t have to rely on this assumption. M52 is one such case. It is a well measured Stetson photometric standard star calibration field in which a large sample of stars has accurate photometry. By simply matching up a couple of stars from his catalog with yours you can derive a zeropoint that is free of uncertainties related to varying airmass and seeing conditions. Download the Stetson catalogs from and create a regions file that contains RA/Dec positions and stellar IDs (look at a region file you yourself have created in the past to get the syntax right). Since you have not computed an astrometric solution for your images, you will want to visually identify these standard stars in your field and manually compute the average magnitude offset. I recommend downloading an image from the DSS and overlaying a DS9 region file that contains the Stetson apparent magnitudes. Identify about 10 stars on the DSS image and use tvmark to label source IDs on your final image, making sure they are not saturated. Use the stellar IDs to determine their Stetson magnitudes and then compare these to your measured magnitudes to derive a zeropoint. Finally, apply this zeropoint to your entire catalog to get final apparent magnitudes. You can do this by rerunning phot, or by simply adding the determined offsets to your extracted source catalog. Since you are using more than one star to compute your zeropoint, you should be able to estimate the uncertainty in this zeropoint correction. How does it compare with the uncertainty in determining of individual magnitudes given by phot? 6

7 Is this uncertainty random or systematic? Ignoring color-term corrections that account for SED differences between the measured and an A0V star, you should now have apparent magnitude catalogs in several photometric bands. 2.4 Determining the Age and Distance to your Cluster De-Reddening As a first step in analyzing the calibrated apparent magnitude data, you need to apply the reddening correction. The mean color excess for the cluster is E(B-V)= (Danford & Thomas 1981, Pandey et al. 2001). Assume the extinction law of R v = Fitting Stellar Evolution Isochrones Use your de-reddened data to plot the final version of the CMD. Now you can overlay theoretical stellar isochrones to determine the distance and age of the cluster. There are numerous groups constantly refining their stellar models, but for this lab, you will use the Y 2 (Yonsei-Yale) Isochrones, which are an update to the Revised Yale Isochrones. You can find the full suite of isochrones for a range of metallicity, age and alpha enhancement at: To simplify things, I have downloaded the full set and pulled out a representative sample that you are welcome to use. You can find these at our website under Lab Manuals. At this point, the only thing left to do to find the isochrone that best fits the CMD that you have created. The choice of the best fit isochrone will place constrains on both the age the metallicity of the stellar population. Overlaying the YY2 isochrones on your CMD, you should be able to estimate which age isochrone (or range of isochrones) provides the best fit to your data. To determine the distance to the cluster, simply adopt the difference between your apparent magnitude and the absolute magnitude of your best-fit isochrone as the distance modulus of M52. Plot a few representative errorbars along both x and y axes on your CMD; make sure to include stars over the whole range of apparent magnitudes. Are these errors random or systematic? Is the scatter in your CMD consistent with your measurement errors or is it telling you something physical about this population? Finally, estimate the uncertainty in your fit and the corresponding uncertainties in your age, metallicity and distance. References Danford & Thomas 1981, PASP, 93, 447 Pandey et al. 2001, A&A, 374, It is actually quite non-uniform across the cluster, but we will be ignoring this here. 7

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