Working with CG4+SA101

This page is meant to be used in conjunction with the Working with L1688 page, and was developed specifically for the 2010 CG4 team visit.

Downloading the data

Using the SHA, a concrete example is already tuned to this project. There are bugs in the SHA! Let's work through this one together, as a big group.

Big picture goal: Get you comfortable enough to search for your own favorite target, understand what to do with the search results, and download data.

More specific shorter term goals: Search on our targets. Understand the difference between the observations. Understand why I chose to use the observations that I did.

Questions for you:

1. Compare the IRAC AORs for sa101 from pid 20714 and for cg4 from pid 462. How are they the same/different?
2. Figure out how to get the instrument parameters. Does this help you assess how the AORs are the same/different? (For advanced folks - how come I'm not using the AOR from pid 20714?)
3. Where is SA101 with respect to CG4? How much overlap is there in these observations?

Making the mosaics

In the generic case for most targets, you can probably use the online mosaics that come as PBCD (Level 2) mosaics (or, later, Level 3 delivered products, if they exist for the region you want). In this case, we have some special circumstances: one of the observations was taken with cluster-mode targets, and there are some very bright stars here (bright at all Spitzer bands) which prompt some instrumental effects that require some special handling.

Big picture goal: Recognize at a glance what is an instrumental artifact and what is real.

More specific shorter term goals: Look at the online mosaics and the mosaics I created. Understand the differences. Understand why I had to make the mosaics for us and why we could not use the online mosaics in this case.

Questions for you:

1. Compare the online mosaics with mine. Did I do a better job of creating good mosaics?
2. What is image artifact and what is real sky? What is saturated?
3. How does the short frame differ from the long frame? Why do we do this?
4. Notice the pixel scale. What is the real pixel scale of IRAC (and MIPS)? What are the pixel scales of the images? Does that actually change the resolution? (for advanced folks - why did I do this?)

Getting data from other wavelengths

You have already made some progress on this in your literature search this Spring. (See Putting CG4 in Context Homework Page).

Big picture goal: Understand how to use the various archives to find non-Spitzer data.

More specific shorter term goals: Go get data for SA101 and CG4 for comparison to our Spitzer data.

Questions for you:

1. If you have not yet, figure out where CG4+SA101 are in the sky. How do they fit in to the larger Gum Nebula?
2. What wavelength did you pick and why?
3. What coordinate system did you pick and why?
4. Get a smaller-scale image too, on the same scale as the Spitzer images.
5. What band did you pick and why? How is the resolution different? (You may need to do the next section before you can answer this.)
6. Figure out how to get a 2MASS catalog. I've done this for you, but you should be able to get this yourself too.

Investigating the mosaics

It is "real astronomy" to spend a lot of time staring at the mosaics and understanding what you are looking at. Don't dismiss this step as not "real astronomy" just because you are not making quantitative measurements. This is time well-spent. Start with my mosaics, as opposed to the online mosaics.

Big picture goal: Understand what is seen at each Spitzer band.

More specific shorter term goals: Recognize how the images differ between SA101 and CG4, and among the various bands.

Questions for you:

1. How does the number of stars differ across the bands? Which band has the most stars? The fewest? (Bonus question: why?) The most nebulosity? The least? (Bonus question: why?) Are there more stars in the regions of nebulosity, or less? Why?
2. Do the star counts differ between SA101 and CG4? Why?
3. Which objects are saturated, in which bands?
4. How big are any of the features in the image (nebulosity, galaxy, space between objects)? (What do I mean by big?) in pixels, arcseconds, parsecs, and/or light years? (Hint: you need to know how far away the thing is. If it helps, there are 3.26 light years in a parsec.)
5. Make a three-color image. What happens when you include a MIPS-24 mosaic in as one of the three colors with IRAC as the other two? Do the stars match up? Does the resolution matter? Can you tell from just a glance at the three-color mosaic which stars are bright at MIPS wavelengths?
6. Which features are found across multiple wavelengths, beyond the Spitzer regime? Why?
7. Obtain IRAS images from the web (via the SHA or other methods) and line them up with the Spitzer images of comparable wavelengths (e.g., 8 um with 12 um, 25 um with 24 um). How much more detail do you see with Spitzer that was missed by IRAS? Do you see more texture in the nebulosity? More point sources?

Previously identified sources

You've already done this. There is a table in our proposal.

Big picture goal: Understand what has already been studied and what hasn't in the image.

More specific shorter term goals: Determine if the previously-known objects are saturated or not.

Questions for you:

1. For each of the known objects, you have the RA/Dec - what are the pixel coordinates in the image? Does it change among the IRAC bands? In the MIPS band?

Doing photometry

OK, this step is doing to take the longest, be the most complex, involve the most steps and the most math.

Never just trust that the computer has done it right. It probably did what you asked it to do correctly, but you asked it to do the wrong thing. Always make some plots to test and see if the photometry seems correct.

Questions:

Big picture goal: Understand what photometry is, and what the steps are to accomplish it. Understand the units of Spitzer images. Understand how to assess if your photometry makes sense.

More specific shorter term goals: Do photometry on a set of mosaics for the same sources. Assess whether your photometry seems right.

Questions for you:

1. Compare the source extractions I did from just one band with their corresponding image. Is it getting fooled by detector artifacts?
2. Compare the source extractions from, say, IRAC band 3 with the image from IRAC band 1, or the source extractions from MIPS-24 with image from IRAC band 1. Are there a lot of stars (or other objects) in common? How does the nebulosity affect it?
3. Why did this happen?

Bandmerging the photometry

Now that you have a measurement of the brightness of each object in each frame, you need to match the same object across multiple bands, called "bandmerging." Maybe, if you used APT, you kept track of that manually. If you did the photometry automatically, you need to have the computer match the sources. If you did it automatically, some of the sources you extracted might not even be real - this is a common problem, and you will have to tune the source extraction... or get rid of the false sources in the bandmerging step, since the false sources will generally not have counterparts at other bands. Of course, some of the most interesting real sources also can be single-band detections...

There is no GUI tool to bandmerge the photometry.

The code I use is written in IDL (expensive to have a site license, even for academics), and for each band, starting with 2MASS JHK, looks for matches within 1 arcsecond in IRAC-1, then IRAC-2, then IRAC-3, then IRAC-4, and then within 2 arcseconds in MIPS-1, and then within 10 arcseconds in MIPS-2. (Bonus questions: Why should I start with the 2MASS data and work up in wavelength? Why should this matching distance change?)

RESULTS: Bandmerged photometry (including 2MASS data!) is included in the tarball above if you wish to short-cut this process, and I promise I won't think any less of you.

Questions:

1. Make sure that I've merged the right sources across several bands by spotchecking a few of them. (Find an object that the catalog says is detected in at least 3 bands and then overlay the images in a 3-color image or Spot to see if there is really a source there, at exactly that spot, in all bands, or if it's a cluster of objects, or different objects getting bright at different bands.

Working with the data tables

I've provided the data tables in something called "IPAC table format" in the tarball above. There is a long version with many columns, and a short version with many fewer columns. Spot, Leopard, and IPAC's Skyview all understand IPAC table format. You can also import the file into Excel to play with it there. NOTE THAT there are a LOT of sources in this relatively small mosaic. You will want the computer to do as much work for you as possible - you don't want to be counting sources manually.

You can use Spot, Leopard, or IPAC's Skyview to overplot the sources onto an image of your choice. NOTE AGAIN THAT there are a LOT of sources in this relatively small mosaic. Skyview (and maybe Spot/Leopard) don't understand the full table, with all those columns; that's why I provided the "catshort" version with just positions.

Import the data table into Excel, as that's probably the easiest way for you to work with it. (DO I NEED TO WRITE A TUTORIAL ON THIS?) There should even be a (contrived) way to get Excel to cough back up a file that is close enough to IPAC table format that you can manually edit it to be compatible - you might wish to do this for example, if you want to take just the IRAC-4 detections and overlay them on an IRAC-1 image. (see questions in 'photometry' above.)

A few words on working with IPAC's skyview

(skyview is also free and available for a variety of platforms. There is a manual too.) Get into Skyview and "paint" ("pa") the image into a window. Use the left mouse button to pull up a "zoom window" in the upper left. Click the right button to zoom in and the left button to zoom out. Click the middle button to reposition the view. Read in the table and mark every source with a red circle of diameter 6 px. Use the "gpi" ("graphics pick") feature to click on individual sources and have the parameters from the tbl file printed in the terminal window. (Left button to select source, right button to quit out of it.) Use "er" to erase window.

      unix% skyview
      skyview version  02/28/00  (R3.4)
        Copyright (C) 1992, California Institute of Technology.
        U.S. Government Sponsorship under NASA Contract NAS7-918 is acknowledged.
      > pa mosaic3.6long.fits
       reading file . . done
       scanning for max min . . done
       building histogram . . done
       redoing histogram . . done
       stretching from -2.190281e+00 to 2.686462e+00
      > zoom_pan
     Centered on RA = 5h07m35.69s  Dec = -6d20m44.4s
      > table extract3.6long.tbl mark $RA $Dec red circle 6
      > gpi
      mark came from line 145 of file [...]
      > er

Questions and things to try

The final catalog should include only real detections, dropping false detections that are due to cosmic rays or other instrumental effects. (If you are constructing your own catalog, you will probably want to drop objects seen in just one band.)

1. How many stars are detected in each band? Is this about what you expected based on your answer to the questions in the mosaic section above? HINT: you can do this using Excel, you don't need to count these manually!! Ask if you need a further hint on exactly how to do this.
2. Which stars in the catalog are the stars identified in the literature?
3. Read about Spitzer units and then make sure that you understand the difference between flux and magnitudes. Check my unit conversion - or do the unit conversion yourself if you are working on your own. Make sure you understand how I got the magnitudes from the fluxes (or the fluxes from the magnitudes). You will need magnitudes for the next step, and fluxes for the step after that.

Making color-color and color-magnitude plots

Read about color-magnitude and color-color plots in general here first. Get Excel to cough up the magnitudes and learn how to make it generate a plot. (DO I NEED TO HAVE SOMEONE WRITE A TUTORIAL ON THIS?) The best way to pick which colors to plot, to be brutally honest, is to do a literature search, look at what other people did and how they interpreted it, and then make the same plot for your own data. Try some of the plots from the intro presentation, or papers mentioned in the IR section of Finding cluster members, but you should feel encouraged to branch out and try other papers that you find in the literature, since there are LOTS of color-color combinations, and they all tell you different things. Color-color plot ideas (Don't peek until you're stumped!)

1. Pick at least one color-color or color-magnitude plot to make. Figure out a way to make it ignore the -9 (no data) flags. Where are the plain stars? Where are the IR excess objects?
2. Where are the famous objects in the plot? Where are the new YSO candidates?
3. Make a new column in your Excel spreadsheet with some colors. Is there a way you can get Excel to tell you automatically which objects have an IR excess?
4. Bonus but very important question: How do you know that some of these sources aren't galaxies? Can you find something that is obviously a galaxy on the images? Can you think of a way using public data that already exist to check on the "galaxy-ness" of some of these objects? (Read about Finding cluster members.)

Making SEDs

Read about SEDs in general here first. Get Russ Laher's spreadsheet from me, or write your own. Make sure you understand the difference between plotting up a flux density as a function of wavelength versus an energy density as a function of wavelength. (You may need to read about Spitzer units first.) Note that you probably want to make it a plot of log energy vs. log wavelength, not linear values.

1. Pick some objects to plot up, maybe some of the famous ones from above would be a good place to start, or the ones you flagged above as having an IR excess.
2. What do the IR excesses look like in your plots? Do they look like the SEDs on this page or in the intro presentation?
3. Which ones of these sources have optical (or any other) data as well, from your web search above, or from the literature? Add these to your bandmerged catalog (not sure if Russ' spreadsheet can handle this...)
4. If using Russ' spreadsheet, fit some SEDs with some blackbodies. How many blackbodies does it take to make a good fit to some of the sources with the most detections (like all 3 2MASS, all 4 IRAC, and MIPS-24)?

Analyzing SEDs

Add a new column in Excel to calculate the slope between 2 and 8 microns in the log (lambda*F(lambda)) vs log (lambda) parameter space. This task only makes sense for those objects with both K band and IRAC-4 detections. (For very advanced folks: fit the slope to all available points between K and IRAC-4 or MIPS-24. How does this change the classifications?)

• if the slope > 0.3 then the class = I
• if the slope < 0.3 and the slope > -0.3 then the class = 'flat'
• if the slope < -0.3 and the slope > -1.6 then class = II
• if the slope < -1.6 then class = III

These classifications come from Wilking et al. (2001, APJ, 551, 357); yes, they are the real definitions ( read more about the classes here)!

1. How many class I, flat, II and III objects do we have in this set of extractions? Read about finding cluster members and then decide how many of the objects in this set of extractions are really cluster members, which aren't, and which ones are ambiguous.
2. Does the ratio of Class I, flat, II, and III objects change when considering just the objects you think are members? Assuming that the classes are really representative of ages, do you see how cluster membership can affect the age you estimate for a cluster?
3. Where are the objects with infrared excesses located on the images? Are all the Class Is in similar sorts of locations, but different from the Class IIIs?

For very advanced folks: suite of online models from D'Alessio et al. and suite of online models from Robitaille et al.. Compare these to the SEDs we have observed.

Writing it up!

Write up your results. Be sure to include:

1. How the data were taken.
2. How the data were reduced.
3. What the Spitzer properties are of the famous objects, including how the Spitzer observations confirm/refute/resolve/fit in context with other observations from the literature.
4. What the Spitzer properties are of other sources here, including objects you think are new YSOs (or objects you think are not), and why you think that.
5. How this region compares to other regions observed with Spitzer.

Take inspiration for other things to include from other Spitzer papers on star-forming regions in the literature.