Working with the IC 417 data

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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 Box Disk Contents. Includes instructions on how to force your computer to read any files with an extension you don't recognize (.tbl, .reg).

FOR CONTEXT: I know we have a wide range of ages and capabilities here. There are things tagged "BONUS" in here - this means "if you get to this point and need something to do while everyone else catches up, work on this." You can also do this later, at home, when you are reviewing what we did this summer. You need not do it here and now, or even necessarily at all. But it will give you a deeper understanding of what is going on.

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.

Why? (a) Because we are looking for YSOs. (b) Because Camargo says there are several clusters of young stars in this region, and so we ought to be able to find some.

Relevant links for reference: IC 417 Bigger Picture and Goals

Obtaining the imaging data

DONE (but be sure you have the files you need!)

Why? Need to figure out what data are in this region from which we might obtain photometry (=quantitative measures of brightness of objects) to use to look for IR excess sources.

We found imaging data for this region from WISE, 2MASS, Spitzer/IRAC (GLIMPSE), UKIDSS, IPHAS, and Jose et al. 2008, but we can only get our hands on imaging data from DSS, WISE, 2MASS, GLIMPSE, and IPHAS. We have already used FinderChart and other IRSA tools. We have already used Skyview at Goddard. We have already used ds9.

Big goal: Learn how to get images so that you can do this in the future without me.

Process: Either re-pull FITS images for yourself for our region in DSS, 2MASS, and WISE, or get them from the Box drive. You'll need this for the next step. BONUS: Spitzer/IRAC (GLIMPSE), and IPHAS. (I don't have images for UKIDSS.) NB: GLIMPSE and IPHAS on the Box disk do not cover the whole region -- you'll have to pull these images yourself if you need them over a different region.

Relevant links for reference:

Investigating the big mosaics


Why? There is astrophysics in understanding what is bright/faint in each band. Spatial resolution is going to play a role downstream.

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 goal: 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. (NB: this has come up during more than one telecon, which is why this task is here!) Understand (remind yourself) which survey has the lowest (worst) spatial resolution, and which has the best.

Relevant links for reference:

Process: Load the images into a viewer of your choice, ds9 or IRSA Viewer. Compare the images. Answer the questions below.

Hints and tips: You may find it helpful to make 3-color images to more directly compare images in exactly the same region. Zoom in/out. Play with color stretches to bring out detail in the images.

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: 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 -- check the proposal for the number. If it helps, there are 3.26 light years in a parsec.)

Obtaining the catalog data and bandmerging across catalogs


Why? We need photometry (=a quantitative measure of brightness) of our sources. Others have already done photometry for us so we don't have to. We need to make the matches across catalogs -- no one else has ever done this before, by which I mean identified which sources in this region are seen in each one of these surveys, and tied the measurements together. (Think about that for a bit -- no one else has ever done this before...)

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 Spitzer) catalogs.

Big goal: Learn how to get catalogs so that you can do this in the future without me. Understand what bandmerging is and why we need to do it.

Relevant links for reference:

The process of merging the bands across catalogs is called "bandmerging." I did this for you because it would be a GIGANTIC pain in Excel (especially for 30,000 sources), or (worse) by hand. I"ve heard TopCat can do it easily, but I've never used that.

Process (What I did):

  1. Download catalogs from these sources over our region.
  2. Using a computer, load in catalogA. Then, for each of the sources in catalogB, metaphorically sit on each source in catalogB and look for a match in catalogA. 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, so having each and every source in here is less important than it might be.)
  3. Now have catalogA+catalogB. Do same for each source in catalogC, such that I merge in catalogC with catalogA+B. Repeat for catalogC, 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.

After this process, I have about 29,000 objects in a master catalog, about 200 of which we care about based on other steps below. 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}.

Previously identified sources


Why? Others have gone before us, and it pays to learn from them rather than reinvent the wheel.

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

Relevant links for reference: 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 IRSA Viewer

Process (what we did before):

  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.

Process (what to do now) and questions:

  1. Load in one of the big images of your choice into ds9 or IRSA Viewer.
  2. Get the regions files that have all the literature sources and overlay them. (You probably want to do them one at a time and delete each one before loading the next.) Where are they in the image?
  3. Delete any regions you have loaded, and get the regions file that has the cluster locations from Camargo et al. and overlay them.
    1. One file is the FSR clusters as reported in Camargo et al., with the radii as reported in Camargo et al. Table 1. You should overplot this, compare it to the figure (or the file with the Camargo F19 clusters) and think, "Holy crap these are a lot smaller than they show in Fig 19!" Yep. I don't know what is going on either. BONUS: go reread Camargo and see if you can figure out why.
    2. Load the file with the F19 clusters marked. Can you see the clusters their computer found by you yourself looking at the distribution of point sources in the image by eye? They used 2MASS to find these clusters, so you may wish to look first in JHK to see if you can find them by eye. Looking in WISE is also useful -- are they apparent there? They might not be obvious to you. Either way, are you more or less confidence that Camargo et al. (and references therein) have actually identified clusters? Just because their computer said it and they said it in their paper does not mean it is right. You need to decide if you believe them. (Their confidence is high enough that they published it, so that should tell you that they believe it. To rigorously decide if you believe them, you need to read their description of what they did in their paper and decide if you believe that. Yes, that's a double bonus task.)
  4. Delete any regions files you have loaded, and get the regions file that has all the "sources in which we are interested" and overlay them. Where are they in the images? (Heads up Garrison! :) This is what you wanted to do!)

Hints and Advice: The files on the box drive are:

  • CamargoF19Clusters.reg = the clusters with positions as reported in Camargo et al., with sizes corresponding to what I can see in their Fig 19 (to the accuracy I can read it off the figure).
  • FSRtable1.reg = the FSR clusters as reported in Camargo et al., with the radii as reported in Camargo et al. Table 1.
  • carbonstars.reg = the one carbon star we know about
  • interestingthings20150604.reg = all the sources in which we are interested (nothing dropped yet) -- DON'T LOAD THIS UNTIL YOU HAVE LOADED AND ABSORBED ALL THE REST OF THESE HERE because there are a lot of these sources and they in essence drown out the rest of the things here, in no small part because they overlay many of the other symbols.
  • iphashalphastars.reg = all the objects reported by IPHAS as being bright in Halpha
  • josehalphastars.reg = all the objects Jose et al. report as being bright in Halpha
  • obstars.reg = all the OB stars from the literature we could find. BONUS: Kronberger1 is shown in Camargo et al. F 19 as having an OB star in it. We don't have this star in our list. Can you go figure out what this object should be and where it comes from? We can add it to our list of objects in which we are interested.
  • ourbigregion.reg = just for reference, in case you are using images other than mine -- this is a region file that defines the region that we are studying, e.g., the region covered in Camargo et al. F 19.

Data Tables (part 1) and Color-Color and Color-Magnitude Diagrams (part 1)


Why? Xavier found sources with YSO-like colors by making a bunch of CMDs and CCDs and selecting objects from these diagrams. It behooves us to get a sense of what he did. (And, we will need to make more CMDs/CCDs downstream.)

Big goal: Learn how to manipulate data tables using IRSA Viewer for now (because it's easier for a quick plot and because it handles 29,000 sources more elegantly than Excel does). Make some of the plots Xavier made when he selected 'interesting' sources. Do our plots look like his from his paper?

Relevant links for reference:

Process: Go get a WISE catalog for our region from IRSA, not me. Look at the data tables and Xavier's paper (available on the Box drive) to identify what you should plot. Make some plots that he made and see how our region compares to the regions he used in his most recent paper.

Advice and Hints: Remember that a plain star should have zero infrared color for basically any combination. (At least, it is 0 as long as the color is (shorter wavelength) minus (longer wavelength) !) You may find that W3-W4 is notably NOT zero for rather a lot of objects, because the only objects seen at W3 or W4 at the distances we are talking about here 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, IRAC color-color diagram (I1-I2 vs I3-I4 plot from somewhere else in the galactic plane), where a much larger number of sources are seen, they are closer on average, and a large fraction of those are plain stars. This gave me heart failure during the 2012 summer visit until I realized this. YSO candidates are bright and red, generally. There are other CMDs you can try. After we include some optical data, there will be even more CMDs we can try.

Specific questions/tasks for you: In his paper, Xavier was not using our region. His plots WILL look different than ours. But can you find points from IC417 that are in the same region as the YSOs in Xavier's plots?

  1. His fig 2 has w1-w2 on the y axis and w2-w3 on the x axis. Make this plot in IRSA Viewer. Do we have objects in the same place as the YSOs?
  2. His fig 4,left is the same, but it doesn't look much like ours. Why? (Hint: what region is plotted?)
  3. His fig 4,right has w1 on the y axis and w1-w3 on the x axis. Make this plot in IRSA Viewer. Do we have objects in the same place as the YSOs?
  4. BONUS: Keep going. Do our versions of his plots look like his? Why or why not?
  5. BONUS #2: Read in our massive, full (all 29,000 sources) bandmerged catalog and make more plots. You will need the tbl file with all the -9s in it -- the tbl file requires there to be an actual value in each "cell" of the tbl file in order to be valid.

Image Inspection


Why? OK, we've picked sources based on color (or, rather, Xavier did). For each of the sources in which we are interested (= Xavier's sources plus the literature YSOs), are they really point sources in the images? (AKA, Do you believe what the computer is telling you?) Will you believe the computer if it says that there is a detection there, especially at 22 um?

Relevant links for reference: FinderChart

Process (what we already did):

  1. Assemble list of sources in which we are interested from work above.
  2. Feed list to FinderChart and load POSS, 2MASS, and WISE images. Watch the size of the images you retrieve because it matters for context and automatic stretching that FinderChart does.
  3. Inspect each image. Is it really there at all bands? Is it a point source? Remember the reason that the source is on the list in the first place. (This is encoded in the stuff I gave you.) I expect a source that Xavier selected to have some WISE data, because he started from WISE data. Stars that are Halpha-selected may in fact NOT be detected in WISE. Resolution matters.
  4. Since we are looking for IR excesses, what the image looks like in 2MASS and WISE is the most important. It may well not be there in POSS, but that won't affect our SEDs because (a) we aren't using photometry from POSS, and (b) the optical images we have (or more precisely, the catalogs) go deeper than POSS.
  5. For each source, check and see if we all agree. Ideally, reconcile differences, but this may be best done in concert with SED assessment in a few steps.

Data Tables (part 2)


Why? For each of the sources we care about, we need to make SEDs so that we can decide if these sources have IR excesses. We also need to make CMDs/CCDs too. Getting data tables into Excel is the first step in that process.

Relevant links for reference: YouTube video on what tbl files are, how to access them, and specifically how to import tbl files into xls. (10min)

Process: Get "workingcatalog-interesting" and "workingcatalog-all" into Excel, with all columns divided appropriately.

Hints and Advice: 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. Personally, I recommend generally keeping the original file and naming subsequent files similar names. For example, iphas.original.txt, iphas.xlsx, etc.

I made a catalog which has all the photometry for just the ~200 sources in which we are interested. It's useful (as before) to keep track of why the sources are in the list. 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)
  • cs = Carbon star.

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 multiple 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. Look at the filename and contents, and ask questions until you are sure you are using the right file.
  • 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.

BONUS: Try making some color-color or color-magnitude diagrams. Example. Make a new column for W1-W4 and program Excel to do the math for you. Plot W1 vs. W1-W4. Make sure the axes go in the correct direction such that brighter objects are at the top. How does this look different than the plot of everything in the field that you made a few steps above using the full WISE catalog? Why is this?

Making SEDs


Why? For each of the sources we care about, we need to make SEDs so that we can decide if these sources have IR excesses. Let's do this!

BRACE YOURSELF: lots of math and programming spreadsheets (You may have already have developed some of these skills via the shortlist of stuff I sent in April. If not, this is the time to learn!) here... you WILL do this more than once to get the units right!

Relevant links for reference:

Process: Program a spreadsheet to convert between mags and flux densities. Make at least one SED yourself. Even if you don't get to the point of making SEDs, 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.

We will ultimately need to make SEDs for everything, but for purposes of this example, let's work with these three objects off the first few on our shortlist: 052532.00+345835.7, 052532.08+345815.2, 052532.62+344000.0. 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 these SEDs. Look at their similarities and differences. Make sure to keep careful track of those things that are limits rather than detections. (Build skills for next step.)

Another try at explaining:

  • What do you have? UBVIriHalpha, JHK, WISE, Spitzer data, all in Vega mags.
  • What do you need to get? everything into Jy, which are units of Fnu -- look up how to convert between mags and flux density (Units page and Central wavelengths and zero points). 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!
  • Ultimately, 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. (This is also the next step.)

You can do all of this in one massive spreadsheet such that you do the calculations for all ~200 SEDs at once. This is the power of Excel. Or, you can make one at a time. (You will probably need to plot one at a time anyway, because stupid Excel.) You can start from the Excel you yourself created in the prior task, or from mine. Your call.

AT MINIMUM, the goal here is to get at least a 2MASS+WISE SED just for the three sources I'm asking about here. Therefore, you may wish to start from the most pared-down version of the Excel spreadsheets I've provided. You can always do more if you are feeling ambitious (e.g., doing optical through 22 um, or doing more SEDs than just these 3).

Questions for you:

  1. What do the IR excesses look like in your plots for these three example sources? Do they look like you expected? Like objects in Monday's ppt or elsewhere?
  2. BONUS, If you made more SEDs: For comparison, find these objects and look at their SEDs: 052743.28+343156.5 052839.40+344008.5. Do you expect them to have a large excess based on [3.4]-[22]? What do they look like? Why are we considering these objects?

Assessing SEDs

DO THIS! FIRST PASS DONE! Needs a second pass...

Why? Now that you've made at least one SED, you know how to do this. We will wave our magic wand and assume you can do it, given enough time, for all 200 sources of interest. With some help from me, then, to jump that barrier in the limited time we have here at Caltech, we need to next look at the SEDs from all 200 sources of interest with a critical eye. They will not all be clean and neat. We will need to fold in information learned from the image assessments.

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

Relevant links for reference:

Process Overview: 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. Combine with notes from the image assessment (or redo image assessment on the fly) to decide if each is a good candidate. Identify the bad ones, and discuss with the others why/whether to drop them off the list of YSO candidates. Look at their similarities and differences. Make sure to keep careful track of those things that are limits rather than detections. After you get through the SED assessment, we will reconvene and compare all our notes. Then, we will have a set of objects in which we are interested, and we should have (will have) notes on each of the objects, obtained from the image check and the SED check. We need to next collate all of these such that we 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".

More mechanics of process: Get the file with all the SEDs in it from the Box drive. We will do the first 9 as a group. Then you should work as a 2 or 3-person team and go through each of the objects you're assigned and make notes about what you see. There is a blank Excel file in the Box drive for you to use, or maybe we should use a Google doc to collect responses. (Ideally would have at least 5 teams of 2 or 3, and have at least 2 teams do each source. For 200 sources, that's at absolute minimum ~30 per team to have just one team per object.)

Questions to think about for each source: Does it look like a YSO SED? Does it look like the data weren't tied to the correct source or there were spatial resolution problems? Discuss with your partner about whether or not you believe each source, and why. Do you believe each point in the SED? If not, why not? Don't forget to compare your notes on SEDs with notes on images (e.g., if you decide it is "iffy" in images AND "iffy" in SEDs, chances are excellent that it is not a good candidate). Keep good notes on this!


  1. Limits in SEDs. Sometimes the source is too bright or too faint for these catalogs. In either case, the catalogs will often report, in essence, "I don't know how bright this thing really is, but I can tell that it must be brighter or fainter than this." That is what a limit means. The limits can be important in the SEDs -- because we are combining catalogs with different sensitivities, there may very well be objects that are undetected. Limits can also help us determine if the source is correctly matched across bands -- detected at a particular brightness but also having a limit at a nearby band that is much below that detection suggests a source mismatch.
  2. Accreting young stars, or stars that are rotating quickly (often because they are young) are bright in Halpha. If Halpha is much above the rest of the SED, that's OK, and in fact a GOOD THING, because that is more evidence that the star is young. That is why the Halpha point is colored red in my SEDs.
  3. Remember that in the context of IR excesses, 2 to 22 microns is the most important. There may be wackiness in the optical, and it may matter (especially if there is a mismatch between sources) but if UKIDSS J doesn't match with 2MASS J, that's less of an issue than if, say, IRAC 3.6 microns doesn't match with WISE 3.4 microns, because we care more about the IR side of the SED.
  4. Black triangles = IPHAS ri. Black plus signs = Jose et al. UBVI Red triangle = Halpha (which means that bright is ok). Black diamonds = 2MASS. Green squares = UKIDSS. black stars = WISE. blue circles = IRAC.

CMDs and CCDs, part 2

DO THIS! DONE, at least with the results of the first pass above.

Why? We have now weeded the objects in which we are interested to get rid of the things that really have problems. We now have a shortlist of things that we are starting to believe may be YSOs. We have a lot of ancillary data, and we can do more checking using these data to see how confident we are in these objects, so that we can refine our list of true YSO candidates vs. junk.

We will reassess on the fly, but most likely, we will have everyone make one plot (like we did for the SEDs) and then we will wave our magic wand and assume you can make these plots, given enough time, for our entire catalog, and highlight the sources of interest. With some help from me, again, I will make several CMDs and CCDs and we will talk about them as a group.

Process: By this point you should have a list of things that have survived tests. You can make a wide variety of CMDs and CCDs with these objects highlighted. We will then go through each of them and decide what to believe.

Make a WISE color-mag diagram such as one of the ones you made earlier, but this time do it in Excel. Overplot the survivors of the image and SED tests on this diagram. Where do they fall? Are they where you expect them to be? You may wish to plot the WISE data for *everything* and then just overplot the interesting ones. Or you may wish to just plot the interesting ones, and look at WISE plots for comparison. Tag any of the survivors as less likely if they aren't where they should be.

Repeat for 2MASS color-color and color-mag diagrams. Don't forget about reddening. Are the sources where you expect them to be?

We have optical data for a lot of these objects. You can make several different possible 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, you could 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)?

Analyzing SEDs


Why? There are empirically defined groupings of SED shapes -- Class 0s and Class Is are the most embedded (presumably youngest); Class IIIs are the least embedded (presumably oldest). How do our new YSOs compare to this? Have we mostly found Class IIs?

Process: Add a new column in Excel to calculate the slope between 2 and 22 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 WISE-4 detections. (For very advanced folks: fit the slope to all available points between K and WISE-4. 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? What are any implications for apparent ages?
  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?
  3. How do those locations compare to the Camargo et al Fig 19 cluster positions and ages?

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.

Going back to check the literature

DO THIS (if we can).

Why? We did a pretty thorough literature check a few months ago, but (a) it's not infallible, and (b) astronomers have kept on publishing since then. For each of the objects we are asserting are new YSOs, we should go back and check the literature to be sure, e.g., that someone else didn't just publish a spectrum that says its a carbon star (meaning, not a young star).

Process: Go back into SIMBAD and search for each of our sources. Has anyone has done anything on them before? Are we the first ever on our planet to care about this source? Keep careful notes!

Putting this in context a little: Methodology

DO THIS (if we can).

Why? Other people have looked for YSOs here before. Need to understand how what we are doing is different, and why we are finding different objects.

Process: Knowing what you do now, go back and reread Jose et al. (2008). Make a detailed comparison of our method for finding young stars and that from the Jose et al. paper.

Relevant links for reference: 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.

Questions for you:

  1. What are the steps (cookbook-style) that Jose 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


Why? We've been doing a lot of nitty gritty work with the data. But now it's time to back up and look at the big picture again.

Goal: put our work in context with the literature.


  1. Go back and look at Camargo's figure. Overlay the surviving YSOs and Camargo's clusters on an image. Did we find YSOs in their clusters? Why or why not? Can you think of reasons why we would/wouldn't find them?
  2. 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?

Writing it up!


Why? Now that we have completed a lot of work, we need to tell other people what we did, and what we found out about the Universe that no one else knows yet.

Goal: We need to write an AAS abstract and then the poster.

We need to include:

  1. How the data were taken.
  2. How the data were reduced.
  3. What the IR properties are of the previously identified YSOs here, in context with other observations from the literature.
  4. What the IR properties are of the new sources we have found, including objects you think are new YSOs (or objects you think are not), and why you think that.
  5. Whether we found YSOs in the clusters from Camargo.