Working with the BRCs

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This page is an updated version of the Working with L1688 and Working with CG4+SA101 pages, and was developed and updated specifically for the 2011 BRC team visit. Please note: NONE of these pages are meant to be used without applying your brain! They are NOT cookbooks!

Downloading the data

How do I download data from Spitzer? has a wide variety of flavors of tutorials. The second formal chapter of the professional astronomer's Data Reduction Cookbook ultimately comes from last year's NITARP project. I haven't developed one customized to your project, because this year it's easier.

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.

Relevant links: How do I download data from Spitzer?

Questions for you:

  1. Compare the various AORs you get as your search results when you search by position. How are they the same/different? Which do we want to download?

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 delivered products, if they exist for the region you want -- see "inventory search" in the SHA). In this case, we can use the online mosaics.

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. Understand what is part of the sky and what is not. Understand which I reprocessed and why.

Relevant links: What is a mosaic and why should I care? and Resolution. Why does it matter to know what is an artifact and what is not? So you don't get fooled by stuff like this.

Questions for you:

  1. Compare the mosaics across the bands. What changes? What stays the same? Why?
  2. What is saturated? What are some other instrumental effects you can see?
  3. 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 we do this?)

Getting data from other wavelengths

You have already made some progress on this in your literature search this Spring, but there are a TON more data we can mine.

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

More specific shorter term goals: Go get data for both BRCs for comparison to our Spitzer data, both images and catalogs. Specifically investigate the WISE archive.

Relevant links: How can I get data from other wavelengths to compare with infrared data from Spitzer? and Resolution Also: Access the WISE archive directly here, and see a step-by-step WISE archive tutorial from Berkeley here.

Questions for you:

  1. Figure out how to get data from Akari, WISE, 2MASS, MSX, IRAS, IPHAS, POSS, SDSS (NB: both clouds may not have hits, and some surveys might not cover both -- or either -- clouds), and anyplace else you want. Which will give you images, and which will give you catalogs (not all will give you both)? Go do it. For images, if you are using Skyview from Goddard, make sure to worry about pixel scale. Best to try to go direct to the source for these archives, rather than relying on Goddard. Get images covering about the same area as the Spitzer images so that they are easy to compare, but larger scale images might be useful to give a sense of context too.
  2. For each catalog: What wavelength is this? How is it relevant to YSOs? How is the resolution different? (You may need to do the next section before you can answer this.)

Luisa's BRC task notes (e.g., some notes on the answers I am expecting you to get! don't peek until you've tried; you might find different information than I did!)

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.

Big picture goal: Understand what is seen at each Spitzer band and all the other archival bands.

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

Relevant links: How can I make a color composite image using Spitzer and/or other data? and the questions on that page.

Questions for you, among just the Spitzer images:

  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. What other features are the same across the bands?
  3. Do the star counts differ between the two BRCs? Why?
  4. Which objects are saturated, in which bands?
  5. 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.)
  6. 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?

Questions for you, among all bands you can find:

  1. Figure out how to get imaging data from WISE, 2MASS, MSX, IRAS, POSS, and anyplace else you want. (See prior task too.) 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 or the other missions? Do you see more texture in the nebulosity? More point sources? How does the resolution and sensitivity vary?
  2. Which features are found across multiple wavelengths? Why?

Previously identified sources

You've already started to do this as part of 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.

Relevant links: How can I find out what scientists already know about a particular astronomy topic or object? and I'm ready to go on to the "Advanced" Literature Searching section

NEW (4/2011) resource: YouTube video on how to take antiquated coordinates from one of our literature papers and use 2MASS to get updated current, correct coordinates for each object.

Questions for you:

  1. For each of the known objects, you have the RA/Dec - find the objects in the image. 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. 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.

Relevant links: Units and Photometry and I'm ready to go on to a more advanced discussion of photometry and Aperture photometry using APT, specifically this, which is the closest thing to a cookbook I will give you.

NEW (5/2011) resource: YouTube video on using APT, including calculating the number APT needs. (15 min because it starts from software installation and goes through doing photometry.)

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. Compare the MOPEX source identifications I did from just one band with their corresponding image. Is it getting fooled by detector artifacts? you will need skyview for this, because you have the tbl files, as opposed to region files. Let me know if you want the reg files and I'll make you some.
  4. 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? you will need skyview for this, because you have the tbl files, as opposed to region files. Let me know if you want the reg files and I'll make you some.
  5. Why did either of these things happen when I ran automatic source detection in MOPEX? (see below)



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.

Relevant links: Resolution

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? Or are there instrumental artifacts or otherwise false sources sill in the list?
  3. Does resolution matter? (Can you find a place where more than one IRAC source can be matched to the same MIPS source?)
  4. Can you start merging in information from other bands (see tasks above)? Be very careful about resolution!!

Working with the data tables

OK, fair warning, math involved here too. And programming spreadsheets!

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.

Relevant links: Units and Skyview but lots of important words actually on the L1688 page itself, sorry.

NEW (5/2011) resource for understanding how to do this: YouTube video on what tbl files are, how to access them, and specifically how to import tbl files into xls. (10min)

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. (see Gutermuth et al. 2009, Appendix A)

Relevant links: Color-Magnitude and Color-Color plots and Finding cluster members and Color-color plot ideas and Gutermuth color selection

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 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. Of the objects I have that fit the Gutermuth criteria, are any of them false or otherwise bad sources? How can you tell?
  7. 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?

Investigating the images of the objects

Big picture goal: Understand why we need to look at the images of each of our short list of candidates.

More specific shorter term goals: Figure out how to get thumbnails and/or find these things in our images. Calibrate your eyeball for the various images/resolutions/telescopes to figure out what is extended and what isn't. Drop the bad objects off our candidate YSO list.

Relevant links: How can I get data from other wavelengths to compare with infrared data from Spitzer? and Resolution (specifically some of the concrete examples there) and IRSA finder chart

NEW (5/2011) resource for understanding how to do use finder chart to examine the images of various candidates in bands other than Spitzer: YouTube video on using Finder Chart. To use these images to also examine the original Spitzer images, load them (and the Spitzer images) into ds9, pick one of the small finder chart images, and then pick 'Frame/Match/Frame/WCS'. All will snap to alignment with North up, on the same scale, with the object in the center.

Questions for you:

  1. Which objects are still point sources at all available bands?
  2. Which are instrumental artifacts? Or MOPEX hiccups?

Making SEDs

WARNING: lots of math and programming spreadsheets here too.. you WILL do this more than once to get the units right!

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

More specific shorter term goals: Make at least one SED yourself. Examine the SEDs for all of our candidate objects. Use them to reassess our photometry if necessary, and to drop the bad objects off the YSO candidate list.

Relevant links: Units and SED plots and Studying Young Stars

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?
  3. Make some SEDs of things you know are not young stars. What do they look like?
  4. Which objects look like they have 1 or 2 bad photometry points? Go back and check the photometry for them.
  5. Which objects look like clear YSO SEDs? Which objects do not?

Literature again

This step is important for this particular project, because of the nature of the existing literature for the objects we are studying.

Big picture goal: Understand at least the basics of how what we did is different than what Chauhan et al. did with the IRAC data.

More specific shorter term goals: Knowing what you do now, go back and reread Chauhan et al. Do a detailed comparison of our method for finding young stars and that from Chauhan et al.

Relevant links: How can I find out what scientists already know about a particular astronomy topic or object? and I'm ready to go on to the "Advanced" Literature Searching section and BRC Spring work.

Questions for you:

  1. What are the steps (cookbook-style) that Chauhan et al. used to find YSOs?
  2. What were our steps?
  3. How are they different?
  4. Did we find the same sources as they did? Why or why not?
  5. Which method do you think works better?

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?

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!

We need to write an AAS abstract and then the poster, and if we're lucky, a paper!

We need 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.