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.

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. Use APT to explore the various parameters. What is a curve of growth?
2. What are the best parameters to use? (RTFM to find what the instrument teams recommend.) What are the implications of those choices? What happens if you use other choices?
3. We should decide as a group which set of sources to measure, and have everyone measure the same sources. We will then compare all of our measurements among the whole group.
4. Compare the MOPEX source identifications I did from just one band with their corresponding image. Is it getting fooled by detector artifacts?
5. Compare the MOPEX source identifications 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?
6. Why did this happen?

Bandmerging the photometry

I use my own code to do this; there is no pre-existing package to do this. If you do it by hand (or semi-by-hand) using APT, you can manually merge the photometry. My merged photometry includes J through M24.

Big picture goal: Understand what this process is.

More specific shorter term goals: Do this by hand.

Questions for you:

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.
2. Have I 'lost' the instrumental artifacts you noticed in the previous section?
3. Does resolution matter? (Can you find a place where more than one IRAC source can be matched to the same MIPS source?)

Working with the data tables

Big picture goal: Understand how to work with the tables. Understand how to convert magnitudes back and forth to flux densities.

More specific shorter term goals: Import the table into excel. Program a spreadsheet to convert between mags and flux densities.

Questions for you:

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. 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

Big picture goal: Understand what plots to make. Understand the basic idea of using them to pick out certain objects.

More specific shorter term goals: Make some plots. Understand the basic approach of Gutermuth et al.

Questions for you:

1. Pick a diagnostic color-color or color-magnitude plot to make. Does my photometry seem ok?
2. 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?
3. Where are the famous objects in the plot? Where are the new YSO candidates I used the Gutermuth method to find?
4. 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? Can you implement the Gutermuth selection? (You may not be able to do so.)
5. Make the plots that go into the Gutermuth selection, including the relevant lines on the plot.
6. 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?

Making SEDs

Big picture goal: Understand what an SED is and why it matters.

More specific shorter term goals: Make at least one SED.

Questions for you:

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 you expected?

Analyzing SEDs

This is advanced, and we may not get here.

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?
2. 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?

Writing it up!

We need to write an AAS abstract!