Difference between revisions of "Working with the IC 417 data"

From CoolWiki
Jump to navigationJump to search
m
Line 127: Line 127:
 
***ob = Meyer & Macak found it (and you corrected the positions for it) because it is an OB star (ob=OB)
 
***ob = Meyer & Macak found it (and you corrected the positions for it) because it is an OB star (ob=OB)
 
You will find some like 'xpxwha', which means Xavier found it in both of his processings (xp,xw), *and* it is an Halpha star (ha). There are many that have just one code.
 
You will find some like 'xpxwha', which means Xavier found it in both of his processings (xp,xw), *and* it is an Halpha star (ha). There are many that have just one code.
−
'''CAUTION 1:''' There are files from me with everything in the region (long) and files with just the things in which we are interested (short) -- meaning the literature-identified plus the new Xavier-identified.  
+
*'''CAUTION 1:''' There are files from me with everything in the region (long) and files with just the things in which we are interested (short) -- meaning the literature-identified plus the new Xavier-identified.  
−
'''CAUTION 2, AND THIS ONE'S A BIGGIE''': '''These catalog files generally have a mixture of detections and limits, measurements and errors, flux densities and magnitudes.''' You will need to be careful in importing this into Excel. The data are all Vega mags; some have errors and some have limits.  
+
*'''CAUTION 2, AND THIS ONE'S A BIGGIE''': '''These catalog files generally have a mixture of detections and limits, measurements and errors, flux densities and magnitudes.''' You will need to be careful in importing this into Excel. The data are all Vega mags; some have errors and some have limits.
  
 
=Making color-color and color-magnitude plots=
 
=Making color-color and color-magnitude plots=

Revision as of 20:05, 20 April 2015

This page is an updated version of the Working with the C-CWEL data page (and to some extent the Working with the HG-WELS data page), which was an update of the Working with the C-WAYS data page, which was an update 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 2015 IC 417 team visit.

Please note: NONE of these pages are meant to be used without applying your brain! They are NOT cookbooks! This is presented as a linear progression because of the nature of this page, but we have already done some things "out of order", and moreover, chances are excellent that you will go back and redo different pieces of this at different stages of your work.

FOR REFERENCE: IC 417 Bigger Picture and Goals

FOR REFERENCE: IC 417 DVD Contents. Includes instructions on how to force your computer to read any files with an extension you don't recognize (.tbl, .reg).

Useful Positions

(just for reference)

We are studying a square that is ~1 deg on a side, centered on 5:28:00 34:30:00.

Obtaining the data and bandmerging across catalogs

DONE

We found data for this region from WISE, 2MASS, Spitzer/IRAC (GLIMPSE), UKIDSS, IPHAS, and Jose et al. 2008.

We have already used FinderChart and other IRSA tools to retrieve 2MASS and WISE (and even Spitzer) data and catalogs.

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

More specific shorter term goals: Obtain the relevant catalogs.

Relevant links:

What I did:

  1. Download catalogs from these sources over our region.
  2. Using a computer, upload catalog1. Then, for each of the sources in catalog2, metaphorically sit on each source in catalog2 and look for a match in catalog1. If I find a match, associate those sources. If I do not find a match, sometimes I added the entire source to the catalog, and sometimes I just dropped it. (there are a LOT of sources here, many we do not care about.)
  3. Now have catalog1+catalog2. Do same for each source in catalog3, such that I merge in catalog3 with catalog1+2. Repeat for catalog4, etc.
  4. Given ensemble catalog, look for matches with Xavier's list of interesting sources. Tag them in the database.
  5. Repeat for Jose et al. 2008 Halpha stars, OB stars, and IPHAS' list of Halpha stars.

The process of merging the bands across catalogs is called "bandmerging."

After this process, I have about 29,000 objects in a master catalog, about 200 of which we care about. Remember that we are interested in the ENTIRE set of {things Xavier tagged as possibly young from the AllWISE catalog} PLUS {things Xavier tagged as possibly young from his own PhotVis reprocessing of the WISE data} PLUS {things Jose et al. tagged as Halpha stars} PLUS {things Meyer & Macak tagged as OB stars} PLUS {things IPHAS tagged as bright in Halpha}.

Questions for you:

  1. How do you download catalogs for any of these surveys?

Investigating the big mosaics

Should I decommission this?

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, and you should plan on investing some time doing this section. Some aspects of this were already discussed in the context of the Resolution worksheet.

Big picture goal: Recognize 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). Recognize how the images differ among the various bands, and why.

Relevant links:

Data: Get from me (or download yourself) the fits files for DSS, WISE, 2MASS, Spitzer/IRAC (GLIMPSE), and IPHAS. (I don't have images for UKIDSS.)


Questions for you:

  1. MOST IMPORTANT of these questions: Compare the mosaics across the bands. What changes? What stays the same? Why? (This is a DEEP question! See also next questions.)
  2. 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?
  3. What is saturated? Are the same objects saturated in all bands? What are some other instrumental effects you can see?
  4. Notice the pixel scale. Which survey has the lowest resolution (biggest pixels)? (Bonus: is that the same as the native pixels for the survey? You will need to Google, or go back to your IC 417 Resolution Worksheet notes.
  5. Make a three-color image. Do the stars match up? Does the nebulosity?
  6. BONUS: 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.)

Previously identified sources

Essentially done.

You've already really done this prior to the visit, but this is where it kind of falls in the logical progression here. (I see the progression as : pick target, get data, get numbers for the sources in the images first from the literature and where others have done it for us, then do more ourselves, then make plots, etc.)

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

Relevant links:

Process:

  1. Search ADS, SIMBAD.
  2. Identify literature of relevance.
  3. Read literature.
  4. Extract from it the data we care about.
  5. For data tables of sources obtained via non-electronic detectors (even some electronic detectors), assess how good the positions are. Can we blindly match these sources to the ensemble catalog (which has positions better than an arcsec)?
  6. If not, use FinderChart to investigate each source by hand to 'correct' its position to be one that can be merged blindly with the rest of the catalog.
  7. Then, merge in the literature observations (in this case, optical multiwavelength catalog from Jose et al for everything they detected), and conclusions about objects (in this case, list of Halpha stars from two places, list of OB stars in essence from 2 places).
  8. Tag the interesting objects as interesting in the database so we can find them again.

IMPORTANT: when we are done with all of this, at the end of the process, for the sources we have settled on as 'interesting', we should get into SIMBAD and search for each of our sources, just to get another handle on whether or not anyone has done anything on them before -- because there just might be something else already identified in the literature.

Data Tables, part 1

You may have already have developed some of these skills via the of stuff I sent in April. If not, this is the time to learn!

Big picture goal: Learn how to manipulate data tables. Look at the data tables to identify what you need. Start to cope with source list proliferation.

More specific shorter term goals: Understand how to import plain text tables into Excel (or another spreadsheet of your choice). Really look at the files as retrieved from the WISE archive and look at files as sent by Xavier and determine the relevant information out of these catalogs. Keep track of which files are which.

Relevant links:

  • YouTube video on what tbl files are, how to access them, and specifically how to import tbl files into xls. (10min)

Note that many data tables come with many, many, many lines (like more than 100) at the top explaining what the contents of the file are. These are useful for keeping with the file (like a FITS header is useful to keep with the image), but when reading it into Excel, you may wish to delete all but a note to yourself about what the file is, and the headers of the data columns themselves.

Tasks and Questions for you:

  1. Practice for getting catalogs on your own: Get a WISE catalog, either from a download or the DVD. Find a way to read the file, perhaps by loading it into Excel. Cope with the long header - scan it, keep it elsewhere, but you may not want to read it in to your Excel copy. What information do we need? How do you figure out which ones have bad photometry?
  2. I have already merged the catalogs from WISE, 2MASS, Spitzer/IRAC (GLIMPSE), UKIDSS, IPHAS, and Jose et al. 2008. The entire catalog has ~29,000 sources. This pretty much brings Excel to its knees. There is some utility in having the entire data set in order to make plots of 'everything' in order to see how the YSOs stand out as different. I dumped the entire catalog into a text file for you if you want it.
  3. I have a short list of ~200 sources that are our focus for our work. This catalog has all the data, but also an indication of why the source is on the list of interesting sources. Values for the "whyhere" column are combinations of 2-letter codes:
      • xp = xavier found it from PhotVis processing (x=Xavier, p=PhotVis)
      • xw = xavier found it from the allWISE processing (x=xavier, w=wise)
      • ha = Jose et al found it (and you corrected the positions for it) because it is an Halpha star (ha=Halpha)
      • ih = IPHAS tagged it as bright in Halpha (=possibly young)
      • ob = Meyer & Macak found it (and you corrected the positions for it) because it is an OB star (ob=OB)

You will find some like 'xpxwha', which means Xavier found it in both of his processings (xp,xw), *and* it is an Halpha star (ha). There are many that have just one code.

  • CAUTION 1: There are files from me with everything in the region (long) and files with just the things in which we are interested (short) -- meaning the literature-identified plus the new Xavier-identified.
  • CAUTION 2, AND THIS ONE'S A BIGGIE: These catalog files generally have a mixture of detections and limits, measurements and errors, flux densities and magnitudes. You will need to be careful in importing this into Excel. The data are all Vega mags; some have errors and some have limits.

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 complete WISE catalog imported into Excel in the last step. At the very least, make a plot of the entire distribution and highlight the YSO candidates from Xavier.

Relevant links:

Tasks and Questions for you:

  1. To do these next steps, you need a catalog that merges the WISE catalog, the 2MASS catalog, and the catalog from Xavier. If you did all the steps in the prior task, you have this now; I also have a copy of this for you on the DVD.
  2. Start from the complete WISE catalog. Pick at least one color-color or color-magnitude plot to make (Suggestion: W1-W2 vs W3-W4, K vs. W1-W4 plot, or a W1 vs. W1-W4 plot). Figure out a way to ignore the "no data" flags (exactly what they are depends on which file you are starting from - they could be 'null' or they could be '-9'). Does the photometry seem ok? Identify the regions where you expect to find the plain stars and the IR excess objects.
  3. Which objects are selected by Xavier's method as YSO candidates? Overplot them on the same plot as above with a different color and/or shape symbol. You may wish to try drawing the line segments on the plot too.
  4. Bonus - read in one of my catalogs from the DVD that has literature sources indicated (e.g, the 'interesting' file loaded into Excel in the last step). Overplot these on the same plot, with a different color and/or shape symbol. Are the literature sources tagged as YSO candidates by Xavier or not?
  5. EXTRA BONUS, especially when circling back to this step - are there any sources you can identify in this diagram that might be worth a second look in SED or images?

Notes: For a W1-W2 vs W3-W4 plot. W1-W2 is (should be) centered on zero, because most of the objects seen in W1 and W2 are plain stars, so the color is zero. W3-W4 is notably NOT zero, because the only objects seen at W4 are the ones notably bright at W4, so they all are brighter than plain stars at W4. This is going to be a different morphology than a I1-I2 vs I3-I4 plot, where a much larger number of sources are seen, and a large fraction of those are plain stars. This gave me heart failure during the 2012 summer visit until I realized this. For a K vs. W1-W4 plot, or a W1 vs. W1-W4 plot... The YSO candidates are bright and red, generally. There are other CMDs you can try. See any of the literature we read in the Spring for ideas. After we include some optical data, there will be even more CMDs we can try. We will come back to this step.

Data Tables, part 2

OK, now we need to do some more advanced things with data tables, not necessarily limited just to those in Excel.

Big picture goal: More on manipulating data tables. Cope with source list proliferation. ds9 regions files.

More specific shorter term goals: Look at sources in WISE image. Learn how to use ds9 regions files. Possibly identify sources that might have Spitzer data in prep for photometry.

Tasks and Questions for you:

  1. For each of the *known* objects, you have the RA/Dec - find some of the objects in the images you obtained above. (Hint: you want a ds9 regions file: File:Brc38knownysos.reg.txt is the file for just the known ysos. The file off the DVD that has "withnames" in the filename also prints the row number on top of the region.) Take notes on any that look like the photometry may be corrupted or that you can easily dismiss as galaxies or other contaminants now that you are looking at better and/or different data than they had before. TEST SAMPLE: the first 4 previously known objects in the 'interesting' list (row numbers 1127, 1144, 1352, 1682) (hint to find them in the image: they are sorted by, and therefore numbered by, RA). Which objects have a WISE match? Which might we worry about?
  2. BONUS ONLY IF YOU ARE INTERESTED IN BANDMERGING ... For each of the known YSOs (identified in the 'interesting' file), you have the RA/Dec - find the same objects in the big WISE catalog. Which objects are the matches? What constitutes a 'match'? Are there any with no matches? Spot check a few of my matches between strictly literature catalogs. Did I match them correctly? Which ones are the same between papers and which are new to just one paper?
  3. Now, start to pull together stuff from this step plus those above. You can plot RA/Dec of everything. Here, I've done this both in an x/y plot and overplotted on the image (click to get a big one). The first one has several types of objects (defined in plot itself) plotted just in a graph; the second one just has the "sources of interest" (==previously identified YSOs+new candidate YSOs), overplotted on the WISE-1 image (the green circle is the 20 arcmin radius circle that defines our region of interest.
  • Brc38where1jul2013.png
  • Brc38where2jul2013.png

Why are there some regions where WISE sources are missing? Are they really missing, or just missing from the catalog? Why are the Spitzer sources where they are? Why is there a patch of black in the center of the plot on the left (hint: Beltran et al.)?

  1. Can you identify which objects are likely to be in the region of Spitzer coverage? (Hint: I would do this using ds9 regions files and the Spitzer mosaics.) You will need this in order to complete the next section.

Things to note:

  • We did a cone search on the wise catalog, and Xavier made a square mosaic. So we 'lose' the corners. which is fine; we have a ton of other things to worry about with the objects we have.
  • There are far more sources seen at w1 that w4 (and you should know why by now!) :) most of the WISE sources are nicely on sources seen in w1; there are far fewer sources in the w4 image (as per large-scale image inspection above!).

For completeness: The WISE catalog provides some matches to 2MASS, but it is unclear to me exactly where those matches come from, like which sources out of the 2MASS catalog did they use (which error flags did they keep, or did they just look for any matches in the 2MASS catalog?). When I take the WISE catalog's 2MASS matches and compare it to the 2MASS catalog I pull out of Gator myself, I find about 3% fewer matches than the WISE catalog does. I use a matching tolerance of 1 arcsec to match up sources. These 'extra' 3% must be either objects matched to the WISE source that are more than 1 arcsec away, or with a different set of error flags than I would use coming out of 2MASS. What this means in practice is that: (a) we can keep and use the WISE catalog's matches to the 2MASS sources; (b) we should remember this caveat, and if we notice any 2MASS data downstream from here that is weird (like a mismatch in the SED), then we should go back and check those 2MASS matches by hand with the original 2MASS catalog.

Doing Spitzer photometry

DONE for the test set. (Ultimately need to do for all sources of interest with Spitzer data - look at batch processing ("source list") photometry in APT.)

OK, this step is going to take the longest, be the most complex, and involve the most steps out of everything (so far, anyway!).

Never just trust that the computer has done it right. It probably did what you asked it to do correctly, but maybe 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. Ultimately, do Spitzer photometry on a source-by-source basis for the 'interesting' sources. Understand the units of Spitzer images. Understand how to assess if your photometry makes sense.

More specific shorter term goals: For now, just do Spitzer photometry in all bands for a short list of test guys. Assess whether your photometry seems right.

Relevant links:

To start, we should decide as a group which small set of sources to measure, and have everyone measure the same sources. We will then compare all of our measurements among the whole group. Ultimately, we will need to measure photometry for everything we care about that falls in the Spitzer maps. NEARLY ARBITRARILY, I've picked 5384, 3704, 4009, 3896 as some good test particles.

The measurements you get from the Spitzer images are in flux density, e.g., they will come in Jy (microJy or milliJy). It is easier to compare measurements if you convert these to magnitudes first. Ultimately, you need magnitudes for use in color-color and color-mag diagrams anyway. You will also need to convert these into the right units for addition to the SEDs. For now, just convert them to magnitudes. See Units#Units_of_Spitzer_Photometry and Central wavelengths and zero points for the numbers and formulae you need.

Steps:

  1. obtain a ds9 regions file for the YSO candidates surviving to this point. (should have from tasks above)
  2. obtain the Spitzer mosaics (should have from tasks above)
  3. For each source that we care about in the image, identify x, y in each of the 4 IRAC channels and at least the first MIPS channel (24 microns). CAUTION: it is NOT going to be the same x, y in each image, and the sky coverage will NOT be the same for each channel (meaning that if the source is not in I1 it may be in I2, I4, and M1 but not I3. Sources WILL have the same RA and Dec, of course. Note that the image, when loaded into APT, is NOT north-up. There are three ways past users came up with to find the object in the image:
    1. load the image into ds9. put north up. find the object. now find the object in the image in APT.
    2. load the image into ds9. put north up. find the object. note the x and y. type the x and y into APT.
    3. under APT's tools, look for an option like "object locator" and type in the coordinates. It should be ##h##m##s and -##d##m##s format. One person in 2012 could not make this work. Most of the rest seemed to be able to make this work.
  4. While you are doing this, make note of the properties in the image of each source; you will need this downstream. Does it look clean or corrupted by a nearby object, an instrumental artifact, bright nebulosity, etc.?
  5. Go load the image into APT. Explore the parameters so that you understand what is going on. When you are ready to get to work, Measure photometry for all the sources in this image. Make sure the units work out such that you get the right numbers. Repeat for each channel, each Spitzer pointing.
  6. Add the flux density measurements for the Spitzer bands to your copy (mini copy?) of the master catalog for the corresponding source. ('bandmerging' by hand!)
  7. For making CMDs/CCDs, you need magnitudes. For making SEDs, you need flux densities. Sometimes you get magnitudes from archives, and sometimes you get flux densities. You need to be able to convert fluently between these quantities. So, convert the flux densities to magnitudes for later use. M=2.5 log (Fvega/F)... see Units
  8. When comparing multiple teams' measurements of the brightness of an object, it is easier to do if converted to magnitudes first.

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. Does the addition of the Spitzer points change your opinion on which YSO candidates are going to survive to the end of this process?
  4. Look at the new photometry with a critical eye. Which points look like they should be double-checked?

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 and get started actually doing it and weeding. Also look at any previously identified objects that were not selected by the color cuts.

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

You will need to obtain images of the 'interesting' objects in WISE, POSS, and 2MASS. You can do this using the IRSA FinderChart, or the big images from before coupled with ds9 regions files. Either way, it takes time; I think it goes much faster with FinderChart. You can submit a batch list of targets. For the objects that have Spitzer data, you will also need to examine these images, but presumably in the APT step above, you have already done some if not all of this image assessment. For each of these image sets, it will take a while to calibrate your eyeball, so you will probably need to do the whole list, and then go back and redo the first several or so to be sure that you have it right.

Test set: This list represents a set of things not in BRC 38 that I deliberately picked so as to show you the range of things you might find in BRC 38, but are of a wider variety than we are likely to find in BRC 38. Some are deliberately tricky. After you do these, feel free to start on the BRC 38 set. File:Imagetestsetblank.xlsx also see File:Imagestestsetblank.tbl.txt, which is a tbl file of this list, suitable for bulk uploading to FinderChart or other IRSA tools with this look-and-feel.

Relevant links:

Questions for you (NB: these are questions that you should consider for each source when examining each BRC 38 object, not just the test set here):

  1. Which objects are still point sources at all available bands? Keep good notes on this!
  2. Which are instrumental artifacts? Or instrumental hiccups?
  3. Which might have corrupted photometry?

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 how to convert magnitudes back and forth to flux densities. Understand what an SED is and why it matters.

More specific shorter term goals: Program a spreadsheet to convert between mags and flux densities. Make at least one SED yourself.

Make sure you understand how to get the fluxes from the magnitudes. This is not easy to do right the first time, so you will get the wrong answer the first few times you try.

Relevant links:

We will ultimately need to make SEDs for everything, but for purposes of this example, let's work with the same test set of four as we worked with for Spitzer photometry above: 5384, 3704, 4009, and 3896. Start with just one. You will ultimately plot log (lambda*F(lambda)) vs log (lambda) -- see the Units page. 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. Make sure to keep careful track of those things that are limits rather than detections.

Another try at explaining:

  • What do you have? UBVRIriHalpha, JHK, WISE data in Vega mags. IRAC, MIPS data in microJanskys.
  • What do you need to get? everything into Jy, which are units of Fnu. Then convert your Fnu in Jy into Fnu in cgs units, ergs/s/cm2/Hz, so multiply by 10^-23. Then convert your Fnu into Flambda in cgs units, so multiply by c/lambda^2, with c=2.99d10 cm/s and lambda in cm (not microns!). Then get lambda*Flambda by multiplying by lambda in cm. Plot log (lambda*Flambda) vs. log (lambda).
  • Once you make your first SED correctly, the rest are easy. But that first one is hard.
  • Then you need to look through each of the SEDs and decide which look like you expect, which need photometry to be checked, and which seem unlikely to be legitimate YSOs. This is a judgement call, and your judgement will improve with time as you gain some experience.

Questions for you:

  1. What do the IR excesses look like in your plots? Do they look like you expected? Like objects in Monday's ppt or elsewhere?
  2. Find some SEDs of things you know are not young stars for comparison - pick some with zero IR color. (You may be able to find some sources with zero color among the previously identified YSOs). What do they look like?

Assessing SEDs

Now that you've made some SEDs, we need to next look at them with a critical eye.

Big picture goal: Understand what to expect in a YSO SED and how to discard objects for having questionable SEDs (or put them on the list for checking source matching, photometry, etc).

More specific goals: Examine the SEDs for all of our candidate objects. Use them to further evaluate the quality of the YSO candidates from the YSO candidate list.

Relevant links:

Test set: This list represents a set of things not in BRC 38 (they actually are all BRC27) that I deliberately picked so as to show you the range of things you might find in BRC 38, but are of a wider variety than we are likely to find in BRC 38. Some are deliberately tricky. After you do these, feel free to start on the BRC 38 set using the SEDs you made in the prior step. File:Sedtestsetblank.xlsx and File:Brc27trainingjustSEDs.pptx

For the BRC 38 set, after you make all the SEDs, you will need to spend some time really looking at the SEDs -- all of them! -- but for now, return to the set of four test SEDs you made above. Look at their similarities and differences. Identify the bad points, 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. Make sure to keep careful track of those things that are limits rather than detections. Compare your notes on SEDs with notes on images (e.g., if it's tagged "iffy" in images and "iffy" in SEDs, chances are excellent that it is not a good candidate).

Questions for you:

  1. Which objects look like clear YSO SEDs? Which objects do not? Keep good notes on this throughout BRC 38!
  2. What's the deal with this one (why does it look like this)? (In my SED, the y-axis units are cgs units [sorry], *=FTN data, +=optical literature data, diamonds=2mass, circles=irac, stars=WISE, arrows=limits, and boxes=MIPS if they exist, which they don't here.)

Chauhan109sed.png


Answer: This source is near a bright nebulous patch in the WISE images that either is being inappropriately tagged as a point source (with its flux densities attached to this source) or whose brightness is contaminating the photometry beyond recovery. The Spitzer data are critical for sorting out what is going on here. And I still don't know what is going on with the optical data - it's apparently wrong for this source, but this is the best possible match given the information we have in the literature, so maybe the people who wrote the paper with the optical data screwed something up either in bandmerging or in their photometry.

Assembling the Keepers and Duds

Goal: Make progress towards assembling final list of things we think are real YSO candidates and those we think are junk.

By this point, you have (will have) a set of objects identified from the literature or from Xavier, and you should have (will have) notes on each of the objects, obtained from the literature check, the image check, and the SED check. Collate all of these such that you can tag objects as "unlikely to be real YSOs", or "literature YSOs (that may or may not have a disk)", or "still surviving as new YSO candidates".

Revisiting CMDs and CCDs

Goal: We have more data, so we should use it to refine our list of true YSO candidates vs. junk.

By this point you should have (will have) a list of things that have survived tests. We have optical data for a lot of these objects. You can make optical CMDs and see if the "literature YSOs (that may or may not have a disk)" and "still surviving as new YSO candidates" fall in the 'right place' in these diagrams. For context, take all the optical data we have for all the objects in the region and plot r vs r-i from the IPHAS data. Overplot the survivors on this diagram. Are they where they should be (above the ZAMS)? Do this again, but for r-Halpha vs. r-i. Are the survivors where they should be (substantially above the unreddened main sequence locus)? Tag any of the survivors as less likely if they don't meet these criteria.

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 at all?)

  • 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.

Putting this in context a little: Methodology

This step is important for this particular project, because of the nature of the existing literature for the objects we are studying. Again, we may or may not get to this before you leave California.

Big picture goal: Understand how what we did is different than what others (Chauhan, Choudhury, (Rebull/Johnson)) did with the IRAC (IRAC+MIPS) data to find young stars.

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

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 C-CWEL Journal Club.

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 recover all of the young stars identified in the literature? Some will not have IR excesses, so those will not be recovered by an IR-based color selection.

Putting this in context a little: Science

Goal: put our work in context with other literature on BRC38 and other star-forming regions from the literature.

How many YSOs with IR excess did we see? How many literature YSOs did not have IR excesses? Do we have any evidence that the YSOs from the literature are not actually YSOs? How many new YSOs did we see? What is the fraction of Class I/flat/II/III? How do those fractions compare to what was found by C-WAYS in BRC 27?

Writing it up!

Goal: 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.

Retrieved from "https://vmcoolwiki.ipac.caltech.edu/index.php?title=Working_with_the_IC_417_data&oldid=12794"