Working with the C-WAYS data

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READ THIS PAGE WITH CAUTION -- IT IS ACTIVELY BEING UPDATED IN PREPARATION FOR YOUR VISIT IN JULY 2012. AS LONG AS THIS MESSAGE IS STILL HERE, REGARD NOTHING HERE AS 'DONE' --Rebull 16:47, 26 March 2012 (PDT)


This page is an updated version of the Working with the BRCs page, which was an update of the Working with CG4+SA101 page, which was an update of the Working with L1688 page. This page was developed and updated specifically for the 2012 CWAYS team visit. Please note: NONE of these pages are meant to be used without applying your brain! They are NOT cookbooks!

FOR REFERENCE: C-WAYS Bigger Picture and Goals

FOR REFERENCE: File:Cwaysdvdreadme.txt from the DVD, in case yours is formatted so badly you can't read it. Includes instructions on how to force your computer to read any files with an extension you don't recognize (.tbl, .reg).

Useful Positions

  • BRC 27 07:03:59 -11:23:09
  • BRC 34 21:33:32 +58:04:33
  • BRC 38 21:40:42 +58:16:12.8

Obtaining the data

We have 3 regions we care about, so three places to search on the sky. We need to look at least for data from WISE and Spitzer.

WISE -- How do I download data from WISE? has a simple walkthrough, with links to more tutorials. And, Access the WISE archive directly here. You will need to get images and catalogs, both, for a 20 arcmin radius from the center position of the three BRCs we are studying.

Spitzer -- 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 a 2010 NITARP project. I haven't developed one customized to your project. However, as you know, there will be Spitzer data centered on the BRC we are focusing on, and then other data from other programs within 20 arcmin (radius) of that position. You will need to decide which data actually overlap our region, and download it.

Big picture goal: Get you comfortable enough to search for your own favorite target in WISE and Spitzer, 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 that are returned. Pick specific observations out of the archive and understand why we picked them. Obtain the relevant catalogs.

Relevant links: How do I download data from WISE? Access the WISE archive directly. How do I download data from Spitzer? Access the SHA directly.

Questions for you:

  1. Compare the various observations you get as your search results when you search by position. How are they the same/different? Which do we want to download?
  2. How do you get images from Spitzer?
  3. How do you download source lists from WISE, as opposed to images? The catalog will come with 2MASS matches, but you can also get catalogs from 2MASS this way; you will need this below.

Getting data from other wavelengths

You have already done at least part of this in your literature search and resolution worksheet this Spring.

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 from POSS (images) and IRAS (images) and 2MASS (images and catalogs) for comparison to our data.

Relevant links: How can I get data from other wavelengths to compare with infrared data from Spitzer? and Resolution

Questions for you:

  1. Figure out how to get images from at least POSS and IRAS. Get images covering about 20 arcminutes in radius from the center position so that they are easy to compare, but larger scale images might be useful to give a sense of context too. Watch your pixel scale! Start to look at these images in context of the WISE images. What is bright, and what is faint?
  2. Figure out how to get catalogs from 2MASS. You can do this via the WISE archive. The WISE catalogs come with some 2MASS matches, but the details of the matching process are sometimes a little obscure, so original 2MASS catalogs are also useful. Get catalogs covering about 20 arcminutes in radius from the center position. This will be a big file.

Making and 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.

For the WISE data, we can use the mosaics as provided by the WISE archive. Two of our three objects happen to fall on boundaries between tiles as provided, and one of those (BRC 27) is actually on a corner! For when we want to make a big mosaic (or a "pretty picture"), we can either use Goddard's Skyview to make mosaics, or MONTAGE.

For Spitzer data, 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 "enhanced product search" in the SHA). In this case, we can most likely get by just fine withe the online mosaics.

Big picture goal: Recognize at a glance what is an instrumental artifact and what is real. Understand what is seen at each WISE and Spitzer band and all the other archival bands.

More specific shorter term goals: Understand what is part of the sky and what is an artifact (e.g., not part of the sky). Understand what might need reprocessing. Recognize how the images differ among the BRCs, and among the various bands.

Relevant links:

Questions for you:

  1. What is saturated? Are the same objects saturated in all bands? What are some other instrumental effects you can see?
  2. Notice the pixel scale. What is the real pixel scale of the original instrument (WISE, IRAC, MIPS)? What are the pixel scales of the images? Does that actually change the resolution? (for advanced folks - why did we do this?)
  3. Compare the mosaics across the bands. What changes? What stays the same? Why? (This is a DEEP question! See also next questions.)
  4. 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?
  5. Do the star counts differ among the three BRCs? Why?
  6. 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.)
  7. Make a three-color image. What happens when you include a MIPS-24 or WISE-22 mosaic in as one of the three colors with shorter bands as the other two colors? (NB: this might be easiest within the same telescope, e.g., use just WISE bands or just Spitzer bands to make a 3-color image.) 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 22 or 24 microns?
  8. How much more detail do you see with Spitzer that was missed by WISE? or IRAS? How does the resolution and sensitivity vary?

Previously identified sources

You've already started to think about this as part of our spring work.

Big picture goal: Understand what has already been studied and what hasn't in the 3 regions we care about.

More specific shorter term goals: Find the previously-known objects in the images (and in the data tables- see next step).

Relevant links:

Questions for you:

  1. Obtain updated coordinates from each paper, for each BRC. Which ones are the same between papers and which are new?
  2. 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?
  3. For each of the known objects, you have the RA/Dec - find the objects in the WISE catalog. Which objects are the matches? What constitutes a 'match'? Are there any with no matches?

Working with the data tables

OK, fair warning, some math involved, and the start of working with spreadsheets!

Big picture goal: Learn how to manipulate data tables.

More specific shorter term goals: Understand how to import plain text tables into Excel (or another spreadsheet of your choice). Look at files as retrieved from the WISE archive and look at files as sent by Xavier. How are they the same/different?

Relevant links:

  • 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. Which stars in the catalog are the stars identified in the literature?

Making color-color and color-magnitude plots

OK, fair warning, some math involved, and the start of programming spreadsheets!

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 color-color and color-mag plots using the data imported into Excel in the last step. Understand at least the first few steps of the Koenig et al method in detail. Program the spreadsheet to make the color cut and keep track of which objects pass or fail. Make a plot of the entire distribution and highlight the YSO candidates from Xavier.

Relevant links:

Questions for you:

  1. Pick at least one color-color or color-magnitude plot to make. Figure out a way to ignore the "no data" flags (exactly what they are depends on which file you are starting from). Does the photometry seem ok? Where are the plain stars? Where are the IR excess objects?
  2. Which objects are selected by Xavier's method as YSO candidates? You may wish to try drawing the line segments on the plot too.
  3. Optional next step (may help with larger concepts, or doing this yourself from scratch) - Taurus catalog has a catalog of legitimate young stars. Where do these objects fall with respect to either the Gutermuth or Koenig colors? Which ones would be retrieved or lost by these color selections? Do the YSO candidates that Xavier sent us have colors like (most of) these YSOs? (Hint: Make a color-color or color-magnitude plot of the entire distribution in the catalog, highlight the YSO candidates with one color symbols, and add the Taurus YSOs to the plots using a different color symbol.)


STOPPED UPDATING HERE


data tables -

  • full original wise catalog - in mags - is 2mass match sufficient?
  • full wise catalog from xavier - in mags - how many YSOs, contaminants?
  • CCD/CMD with these YSOc highlighted
  • (taurus YSOs on top? that's another data file)
  • then look at images - start weeding?
  • then SEDs? or this next:
  • find those within spitzer region (reg files, ds9 skills, finder chart)
  • do phot for spitzer?
  • then do SEDs - mag/flux conversion here.

Big picture goal: 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. See also Central wavelengths and zero points. 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 SED steps after that.


Questions for you:

  1. Of the objects I have that fit the Gutermuth criteria, are any of them false or otherwise bad sources? How can you tell?
  2. 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?


  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?
  1. For each of the known objects, you have the RA/Dec - find the objects in the WISE catalog. Which objects are the matches? What constitutes a 'match'? Are there any with no matches?

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?
  3. Which might have corrupted photometry?
  4. Which are correctly matched to literature values (or correctly identified as duplicates)? You'll need to go back to the literature above to check this.


Doing photometry

NEEDS UPDATING

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 (small) set of 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.)

NEW 7/7/11 -- region files for just i1, just i2, just i3, just i4, and 'final best catalog of everything with a valid detection somewhere':

AND, File:Fred.xls, the file in which we were collecting everyone's measurements.

UPDATE SEP 2011 Identification of Previously Known Objects on Candidate List tracks a lot of conversation about which objects are which, which then feeds into Matching to Spitzer and Weeding the SEDs which talks about photometry for a smaller set of objects.

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 have the tbl files, as opposed to region files, from me for this. you can use SHA to load tbl files over images, or another standalone software package called skyview. 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 have the tbl files, as opposed to region files, from me for this. you can use SHA to load tbl files over images, or another standalone software package called skyview. 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)

Cg424.png

Brc34i3.png

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!!


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 and for that matter the detailed object-by-object discussion in the appendix of the cg4 paper. See also Central wavelengths and zero points

Pick some objects to plot up, maybe some of the previously-identified ones from above would be a good place to start, or the ones you flagged above as having an IR excess. Start with just one. It will take time to get the units right, but once you do it right the first time, all the rest come along for free (if you're working in a spreadsheet). Spend some time looking at the SEDs. Look at their similarities and differences. Identify the bad ones, and discuss with the others why/whether to drop them off the list of YSO candidates. See also stuff above about data at other wavelengths, and include literature/archival data from other sources where appropriate and possible.

Questions for you:

  1. What do the IR excesses look like in your plots? Do they look like you expected? Like objects in CG4 or elsewhere?
  2. Make some SEDs of things you know are not young stars. What do they look like?
  3. Which objects look like they have 1 or 2 bad photometry points? Go back and check the photometry for them.
  4. Which objects look like clear YSO SEDs? Which objects do not?
  5. Any photometry look bad? Go back and check it!
  6. Any objects within the maps but undetected? Go back and get limits and add those too!

TIPS ON CREATING SED PLOTS USING EXCEL: File:SED PLOT EXAMPLE.XLSX --Legassie 15:20, 8 July 2011 (PDT)

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. Does our IRAC photometry agree within errors? (That "within errors" is very important...)
  5. Did we find the same specific sources as they did? Did we find more or fewer? or exactly the same? Did we recover all of theirs? Why or why not?
  6. Which method do you think works better?
  7. NON-CHAUHAN: Did we recover all of the young stars identified by Ogura or Gregorio-Hetem or any of the other papers? Why or why not?
  8. NON-CHAUHAN: Are any of our surviving YSO candidates listed in SIMBAD for any reason? Are they still likely YSOs, or have they shown up as galaxies there?

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.

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