Working with the C-CWEL data

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This page is an updated version 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 2013 C-CWEL 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: C-CWEL Bigger Picture and Goals

FOR REFERENCE: C-CWEL DVD Contents. Includes instructions on how to force your computer to read any files with an extension you don't recognize (.tbl, .reg). still need to assemble src lists: all srcs, all bands; ysoc from xk, all bands; prev kn, all bands; "interesting", all bands"; ysoc regions file; known regions file; 'interesting' regions file; catalog of just my spitzer phot; regions file of just things with spitzer data; catalog of just sources with spitzer phot but without my numbers; HAVE PICKED test particles for some sections, still need for other sections; need to drop those into files too (include a few BRC38 test particle SEDs).


Useful Positions

(just for reference)

BRC 38: 21:40:42 +58:16:12.8

And, we care about stuff within a ~20 arcmin radius of these positions. (which means 40 arcmin on a side if you are pulling a square rather than a cone.)

Obtaining the data

We have just one region we care about, so just one places to search on the sky. We need to get the relevant data from both 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 BRC 38.

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 only be Spitzer data centered on BRC 38. You will need to find the data, and download it.

Big picture goal: Get the data you need for this project. 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:

Questions for you:

  1. In WISE, in addition to position, the most important search parameter is the size of the image. What do you need to do to get the data we need?
  2. In Spitzer, because it was not an all-sky survey, there will be only one small observation. In the general case, you might pull up several different observations in your region of interest. For just the BRC 38 observations, compare the various observations you get as search results. How are they the same/different? You'll need just the Level 2 (post-BCD) mosaics.
  3. How do you download source lists from WISE, as opposed to images? The catalog will come with 2MASS matches already done, but you can also get catalogs from 2MASS this way; you will need this below.

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, 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.

For the WISE data, we can use the mosaics as provided by the WISE archive. BRC 38 happens to fall on a boundary between tiles as provided. If you want in the future to make a big mosaic (or a "pretty picture"), we can either use Goddard's Skyview to make mosaics, or MONTAGE. We have mosaics that Xavier made us that are centered on BRC 38 (and I honestly don't know what software he used to generate them). Look on the DVD I gave the educators.

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 probably get by just fine with 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). Recognize how the images differ among the various bands, and why.

Relevant links:

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? Example Spitzer/MIPS image with substantial artifacts here if you want to train your eye, or see an extreme case. (Bonus: which way was the scan map 'painting' the sky?)
  4. 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?)
  5. 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?
  6. How much more detail do you see with Spitzer that was missed by WISE? or IRAS? How does the resolution and sensitivity vary?
  7. 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.)

Getting data from other wavelengths

You have really already done most of this in your literature search and resolution worksheet this Spring. However, if you don't still have the images you found before on your laptop, you might as well go grab them again. You want at the very least POSS and IRAS images.

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

More specific shorter term goals: Go get data for BRC 27 from POSS (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. (Bonus: 2MASS images too.) 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. Between POSS and WISE, what is bright, and what is faint? (Bonus: compare 2MASS, POSS, and WISE.) (Still intrigued? Another Bonus: Get IRAS too. Between IRAS and WISE, what breaks into pieces or dissolves into nebulosity when viewed at higher spatial resolution?)
  2. Even though I've alredy gotten these and matched them for you, it is worth spending a few minutes figuring out how to get catalogs from 2MASS (not images per se!) so hat you don't have to depend heavily on me in the future. 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.
  3. BONUS: use the catalog trick to get Akari data in our region of interest.

Previously identified sources

You've already really done the majority of 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 images, get multiwavelength images, 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.) In the context specifically of bandmerging (later step), it's probably good to spot-check my matching! But this spot checking is probably done more easily as part of a later 'data tables' step, once you have facility with ds9 and its regions files.

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

Relevant links:

IMPORTANT: when we are done weeding source lists as per the below, we will have to 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 some of our sources are beyond where the prior papers were working, and they just might be something else already identified in the literature.

Bandmerging the photometry

This section could logically go elsewhere in this logical progression, but I think of bandmerging as having two parts -- combining the data from the literature, but also (later) combining data we have measured. Since I've basically done it for you, let's just keep this step here. This step is an explicit step in the journal articles we read.

Big picture goal: Create a master catalog where there is one line per unique object that contains the measurements at all wavelengths and other relevant information all collected on same line. Understand what this process is, since I've already done most of it for you. You need this catalog in order to make color-color and color-magnitude diagrams, and make SEDs.

More specific shorter term goals: Pick a literature object above and see if you can find it in the WISE catalog.

Relevant links:

For future reference: The WISE data comes out of the WISE archive already matched to 2MASS, so generally you don't have to do that. If you pull already reduced data from Spitzer for any given target, you will also most likely have quite a few objects bandmerged already.

Important notes: In this specific case, I've done the merging for you, BUT...:

  • There are four objects that appear in the Choudhury data tables without coordinates, and I cannot locate coordinates for them. I wrote the author, but never heard back. Turns out that the T2 flux densities are also .. off. I can't tell what units they are in, except that they are definitely NOT the units as indicated in the table. More on this flux thing below. But for now I've been forced to just drop the objects without coordinates from our master catalog.
  • Akari data really does just have one source in this region that we might care about (18 over whole region but only 1 tagged before or now as YSO candidate), and Nakano already identified it. It's merged into my db but would mean 4 more columns in your Excel catalog for very little gain, so haven't given it to you.
  • IPHAS data release 2 still not out; the data that were mailed to us by Barentsen do not cover the entire area. Unfortunately, it's missing coverage in the south, the region where we are most likely to have YSOs that are optically detectable. We will work with what we can and watch for the IPHAS data when fully released.
  • Nakano's coordinates are .. not as good as I would have hoped. However, they list the 2MASS match, so I took the 2MASS name and decoded coordinates from that, then went forward with the merging process.
  • Xavier's new WISE color selection routines are far more restrictive than they were a year ago, and as such they omit a lot of the 'garbage' that last year's group had to still cope with. We therefore have a much shorter list than we might have had otherwise! Xavier's old classification scheme was more lenient, but it is possible that the new version discards real YSOs along with all the junk. I've retained the old classification scheme in case we whiz through the new ones and want more to chew on.
  • I get that there are between 20 and 50 objects that potentially have Spitzer data, including those already listed in the literature with one or more Spitzer bands. I did some quick-and-dirty automatic photometry. (This was how I discovered that the Choudhury T2 flux densities were just ..wrong. I match their T1 flux densities pretty well, but not their T2.) There is absolutely no guarantee that what I did is right, especially since I did it 'blind', e.g., just trusting that the computer did it right, not checking to see if the answers were sensible (except via a bulk comparison to literature photometry values).

Data Tables, part 1

Hopefully, you already have developed some of these skills via C-CWEL Excel Practice. 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. You should have a WISE catalog, either from your earlier download or the DVD. Load that puppy 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. You should have a copy of Xavier's catalog with classifications on the DVD. Load that into another Excel worksheet or file. This one has a lot less documentation within the catalog; its documentation is a separate file, and you will need to consult this to decrypt which sources are YSO candidates and which are not. Figure out how to do this. There may be some utility in keeping the codes (rather than global search and replace).
  3. Are these two files in the same order? (Did you change the order during the prior 'decryption' step? I did!) How could you find out, and fix this if necessary? Do they contain the same number of sources? Can you just copy the classification column out of Xavier's catalog into the WISE catalog? You need to tie Xavier's class to the WISE object, because in the next step, you will need a way of identifying the YSO candidates in plots.

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. 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.
  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 my catalog of literature sources. 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?

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: Spot check source matching. Identify sources that have Spitzer data in prep for photometry using ds9 regions files as well as catalog information.

Tasks and Questions for you:

  1. We already have good coordinates from each paper for BRC 38. I matched them all; copies of these files are on the DVD and they should import cleanly into xls, but be careful about the limits (see below). Spot check a few. Did I match them correctly? Which ones are the same between papers and which are new to just one paper?
    1. CAUTION 1: Some of the literature reported whole data tables, and some just reported the YSOs. So there are files from me with everything in the literature (long) and files with just the things someone in the literature tagged as YSOs (short). The files you want (for now) are the ones that are just the ones someone in the literature tagged as YSOs.
    2. CAUTION 2, AND THIS ONE'S A BIGGIE: This file has 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 UBVRIriHalphaJHK are all Vega mags, some have errors and some have limits. The IRAC and MIPS data are all in FLUX DENSITIES (uJy), and often have limits.
  2. 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) 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.
  3. For each of the known YSOs, you have the RA/Dec - find the same objects in the WISE catalog. Which objects are the matches? What constitutes a 'match'? Are there any with no matches?
  4. Now, start to pull together stuff from this step plus those above. Here are plots of the spatial distribution of sources in all three regions we care about (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.)?

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.

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

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. 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: Do photometry on all the Spitzer mosaics for the sources that have survived so far as YSO candidates (or YSOs from the literature). Assess whether your photometry seems right. Add it to the SEDs.

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. NEED TO PICK SUBSET OF ~5 TO MEASURE AS GROUP

The measurements you get from the Spitzer images will come in Jy (microJy or milliJy). You will need to convert these into the right units for addition to the SEDs. And, probably, you will need to convert these flux densities back into magnitudes for use in color-color and color-mag diagrams, though we may or may not get to this point in July.

Steps:

  1. obtain a ds9 regions file for the YSO candidates surviving to this point. (should have from tasks above)
  2. obtain the big Spitzer mosaics (should have from tasks above)
  3. For each source in the image, identify x, y in each of the 4 IRAC channels and at least the first MIPS channel (24 microns). You should look at the MIPS-2 (70 microns) images, but few sources are expected in this channel. 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 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, 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 photometry for the Spitzer bands to your 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.
  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?



update or do soething useful with this More visual view of source list profusion and how they map into these following questions.



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:

need 5 test guys and 5 brc38 guys, may or may not be same 5 guys from prior step(s) Pick some BRC 38 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. 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. 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. 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 in 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

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

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 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

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!

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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