Working with L1688

There are a LOT of things on this Wiki, very few of which are arranged linearly. The Wiki is a web, after all, so there are links interwoven all over the place. However, we do realize that this can be frustrating for new users, so here is a single analysis chain, start to finish, with the products already done so that you can step into and out of the process as you want or need.

Download the tarball with all my reduced images and data tables here.

Downloading the data

Go here and follow the tutorial to investigate what observations exist and download the data. Search on L1688. Find the AORs, download the IRAC and MIPS mosaics. The data I'm using for this example are all from pid 177, but you should investigate the coverage of the other programs too.

It turns out, though, that L1688 is part of the c2d Legacy program (pid 177 is part of that), so you may also wish to consult the page on already-reduced Spitzer data.

Making the mosaics

Go here and follow the tutorial to make the mosaics from the post-BCD pieces you have downloaded.

Mosaics from all 4 IRAC channels and 2 of the 3 MIPS channels are included in the tarball above if you wish to short-cut this process.

Getting data from other wavelengths

Go here and follow the tutorial to find data (images and data tables) at other wavelengths. Think about the questions at the end of that page as they pertain to these data.

A POSS image is included in the tarball above if you wish to short-cut this process. Reduced photometry from 2MASS is included in the data tables as well.

Investigating the mosaics

Go here and follow the tutorial (or use your own software) to explore the Spitzer mosaics as well as the mosaics at other wavelengths. Think about the questions at the end of that page as they pertain to these data.

1. Look for image artifacts in the mosaics - they are certainly still there. What are real features of the sky and what is a feature of the instrument and/or reduction? IRAC artifact examples and MIPS artifact examples are online.
2. Which objects are saturated, in which bands?
3. How big are any of the features in the nebulosity? (What do I mean by big?) in pixels, arcseconds, parsecs, and/or light years? (Hint: you need to know how far away this thing is, and it's about 125 pc away. If it helps, there are 3.26 light years in a parsec.)

Previously identified sources

How many of these sources are 'famous' and how many are new that you have just discovered?

Go here and follow the tutorial to explore the literature. ( Move on to these examples and questions when you're ready.) Find some old and some recent papers on L1688. For how many decades have people been studying this region? Find some of the famous objects in your images. Note that this is a pretty famous region, so it's doubtful that you will easily be able to construct a list of ALL the famous objects in this region in a short time, even using SIMBAD. Keep in mind too that people work in different coordinate systems. A paper reporting on radio data may have a slightly different coordinate system than Spitzer (which is tied to 2MASS). A paper from anything earlier than the 90s might be working in 1950 coordinates rather than 2000 coordinates, and may or may not provide high enough precision coordinates for you to easily find the corresponding object in your Spitzer images; you may need to find some archival data (POSS or 2MASS) to 'rediscover' the old object. You will have to use your judgment to decide if any given object is really the same as the object in the Spitzer image, or if the Spitzer image didn't really detect the object.

Doing photometry

Using MOPEX to do many sources at once: Go here and get the doc file that is linked in partway down. Find the part on using Apex-1Frame. Follow that for each channel to get the photometry.

Using APT to do one source at a time: COMING SOON. If you do this, I recommend you start with a short list of famous objects from the above step.

Reduced (and bandmerged) photometry is included in the tarball above if you wish to short-cut this process, and I promise I won't think any less of you.

Questions, assuming you are using MOPEX:

1. Compare the source extractions from any one band with its corresponding mosaic. Is it getting fooled by detector artifacts?
2. Overlay the source extractions from, say, IRAC band 3 with the image from IRAC band 1, or the source extractions from MIPS-24 with image from IRAC band 1. Are there a lot of stars (or other objects) in common? How does the nebulosity compare?

Bandmerging the photometry

Now that you have a measurement of the brightness of each object in each frame, you need to match the same object across multiple bands, called "bandmerging." Maybe, if you used APT, you kept track of that manually. If you did the photometry automatically, you need to have the computer match the sources. Some of the sources you extracted might not even be real.

There is no GUI tool right now to bandmerge the photometry; there might be one eventually.

The code I use is written in IDL (expensive to have a site license, even for academics), and for each band, starting with 2MASS JHK, looks for matches within 1 arcsecond in IRAC-1, then IRAC-2, then IRAC-3, then IRAC-4, and then within 2 arcseconds in MIPS-1, and then within 10 arcseconds in MIPS-2. (Bonus question: why should this matching distance change?)

Bandmerged photometry (including 2MASS data!) is included in the tarball above if you wish to short-cut this process, and I promise I won't think any less of you.

Make sure that I've merged the right sources across several bands by spotchecking a few of them. (Find an object that the catalog says is detected in at least 3 bands and then overlay the images in a 3-color image or Spot to see if there is really a source there, at exactly that spot, in all bands, or if it's a cluster of objects, or different objects getting bright at different bands.

Working with the data tables

I've provided the data tables in something called "IPAC table format" in the tarball above. There is a long version with many columns, and a short version with many fewer columns. Spot, Leopard, and IPAC's Skyview all understand IPAC table format. You can also import the file into Excel to play with it there. NOTE THAT there are a LOT of sources in this relatively small mosaic. You will want the computer to do as much work for you as possible - you don't want to be counting sources manually.

You can use Spot, Leopard, or IPAC's Skyview to overplot the sources onto an image of your choice. NOTE AGAIN THAT there are a LOT of sources in this relatively small mosaic. Skyview (and maybe Spot/Leopard) don't understand the full table, with all those columns; that's why I provided the "catshort" version with just positions.

Import the data table into Excel, as that's probably the easiest way for you to work with it. (DO I NEED TO WRITE A TUTORIAL ON THIS?) There should even be a (contrived) way to get Excel to cough back up a file that is close enough to IPAC table format that you can manually edit it to be compatible - you might wish to do this for example, if you want to take just the IRAC-4 detections and overlay them on an IRAC-1 image. (see questions in 'photometry' above.)

A few words on working with IPAC's skyview

(skyview is also free and available for a variety of platforms. There is a manual too.) Get into Skyview and "paint" ("pa") the image into a window. Use the left mouse button to pull up a "zoom window" in the upper left. Click the right button to zoom in and the left button to zoom out. Click the middle button to reposition the view. Read in the table and mark every source with a red circle of diameter 6 px. Use the "gpi" ("graphics pick") feature to click on individual sources and have the parameters from the tbl file printed in the terminal window. (Left button to select source, right button to quit out of it.) Use "er" to erase window.

      unix% skyview
      skyview version  02/28/00  (R3.4)
        Copyright (C) 1992, California Institute of Technology.
        U.S. Government Sponsorship under NASA Contract NAS7-918 is acknowledged.
      > pa mosaic3.6long.fits
       reading file . . done
       scanning for max min . . done
       building histogram . . done
       redoing histogram . . done
       stretching from -2.190281e+00 to 2.686462e+00
      > zoom_pan
     Centered on RA = 5h07m35.69s  Dec = -6d20m44.4s
      > table extract3.6long.tbl mark $RA $Dec red circle 6
      > gpi
      mark came from line 145 of file [...]
      > er

Questions and things to try

The final catalog should include only real detections, dropping false detections that are due to cosmic rays or other instrumental effects. (If you are constructing your own catalog, you will probably want to drop objects seen in just one band.)

1. How many stars are detected in each band? Is this about what you expected based on your answer to the questions in the mosaic section above? HINT: you can do this using Excel, you don't need to count these manually!! Ask if you need a further hint on exactly how to do this.
2. Which stars in the catalog are the stars identified in the literature?
3. Read about Spitzer units and then make sure that you understand the difference between flux and magnitudes. Check my unit conversion - or do the unit conversion yourself if you are working on your own. Make sure you understand how I got the magnitudes from the fluxes (or the fluxes from the magnitudes). You will need magnitudes for the next step, and fluxes for the step after that.

Making color-color and color-magnitude plots

Read about color-magnitude and color-color plots in general here first. Get Excel to cough up the magnitudes and learn how to make it generate a plot. (DO I NEED TO HAVE SOMEONE WRITE A TUTORIAL ON THIS?) The best way to pick which colors to plot, to be brutally honest, is to do a literature search, look at what other people did and how they interpreted it, and then make the same plot for your own data. Try some of the plots from the intro presentation, or papers mentioned in the IR section of Finding cluster members, but you should feel encouraged to branch out and try other papers that you find in the literature, since there are LOTS of color-color combinations, and they all tell you different things. Color-color plot ideas (Don't peek until you're stumped!)

1. Pick at least one color-color or color-magnitude plot to make. Figure out a way to make it ignore the -9 (no data) flags. Where are the plain stars? Where are the IR excess objects?
2. Where are the famous objects in the plot? Where are the new YSO candidates?
3. Make a new column in your Excel spreadsheet with some colors. Is there a way you can get Excel to tell you automatically which objects have an IR excess?

Making SEDs

Read about SEDs in general here first. Get Russ Laher's spreadsheet from me. Make sure you understand the difference between plotting up a flux density as a function of wavelength versus an energy density as a function of wavelength. (You may need to read about Spitzer units first.)

1. Pick some objects to plot up, maybe some of the famous ones from above would be a good place to start, or the ones you flagged above as having an IR excess.
2. What do the IR excesses look like in your plots? Do they look like the SEDs on this page or in the intro presentation?
3. Which ones of these sources have optical data as well, from your web search above, or from the literature? Add these to your bandmerged catalog (not sure if Russ' spreadsheet can handle this...)
4. Fit some SEDs with some blackbodies. How many blackbodies does it take to make a good fit to some of the sources with the most detections (like all 3 2MASS, all 4 IRAC, and MIPS-24)?
5. Bonus but very important question: How do you know that some of these sources aren't galaxies? Can you find something that is obviously a galaxy on the images? Can you think of a way using public data that already exist to check on the "galaxy-ness" of some of these objects?

Analyzing SEDs

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.

• 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 in this set of extractions? Read about finding cluster members and then decide how many of the objects in this set of extractions are really cluster members and which aren't.
2. Does the ratio of Class I, flat, II, and III objects change when considering just the objects you think are members? Assuming that the classes are really representative of ages, do you see how cluster membership can affect the age you estimate for a cluster?
3. Where are the objects with infrared excesses located on the images? Are all the Class Is in similar sorts of locations, but different from the Class IIIs?

For very advanced folks: suite of online models from D'Alessio et al. and suite of online models from Robitaille et al.. Compare these to the SEDs we have observed.

Writing it up!

Write up your results. Be sure 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.

Take inspiration for other things to include from other Spitzer papers on star-forming regions in the literature.