Working with the LLAMMa data - Revision history

Rebull at 20:26, 27 July 2016

2016-07-27T20:26:44Z

Rebull at 03:53, 27 July 2016

2016-07-27T03:53:00Z

Rebull at 03:49, 27 July 2016

2016-07-27T03:49:41Z

Rebull at 21:51, 25 July 2016

2016-07-25T21:51:58Z

Rebull at 19:27, 20 July 2016

2016-07-20T19:27:00Z

Rebull at 19:18, 20 July 2016

2016-07-20T19:18:19Z

Rebull: /* Data Tables (part 2): Moving into Excel */

2016-07-20T19:17:39Z

Data Tables (part 2): Moving into Excel

Rebull: /* Data Tables (part 2): Moving into Excel */

2016-07-20T19:17:10Z

Data Tables (part 2): Moving into Excel

Rebull: /* Data Tables (part 2): Moving into Excel */

2016-07-20T19:16:20Z

Data Tables (part 2): Moving into Excel

Rebull: /* Data Tables (part 2): Moving into Excel */

2016-07-20T19:15:22Z

Data Tables (part 2): Moving into Excel

@@ Line 152: / Line 152: @@
 '''Relevant links for reference''':
 *[http://irsa.ipac.caltech.edu/irsaviewer/app.html IRSA Viewer]
 *[[Color-Magnitude and Color-Color plots]]
 *[[Gutermuth color selection]] - mostly Gutermuth color selection; includes early version of analogy with M&Ms. Koenig color selection is similar in concept but uses different bands (WISE rather than Spitzer).

@@ Line 47: / Line 47: @@
 =Investigating the big mosaics =
-<strike><font color="green">DO THIS!</font></strike><font color="red">DONE</font>
+<strike><font color="green">DO THIS!</font></strike> <font color="red">DONE MONDAY</font>
 '''Why?''' There is astrophysics in understanding what is bright/faint in each band. Spatial resolution is going to play a role for us downstream.
@@ Line 144: / Line 144: @@
 =Data Tables (part 1) and Color-Color and Color-Magnitude Diagrams (part 1)=
-<font color="green">DO THIS!</font>
+<font color="green"><strike>DO THIS!</strike> skipped for now. consolidate with later tasks?</font>
 '''Why?''' Rob 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.) The lab this morning was a metaphor. Let's at least try a few similar plots.
@@ Line 198: / Line 198: @@
 =Data Tables (part 2): Moving into Excel=
-<font color="green">DO THIS!</font>
+<font color="green"><strike>DO THIS!</strike></font> <font color="red">DONE TUESDAY</font>
 '''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.
@@ Line 229: / Line 229: @@
 =Making SEDs =
-<font color="green">DO THIS!</font>
+<font color="green"><strike>DO THIS!</strike></font> <font color="red">DONE TUESDAY</font>
 '''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!

@@ Line 47: / Line 47: @@
 =Investigating the big mosaics =
-<font color="green">DO THIS!</font>
+<strike><font color="green">DO THIS!</font></strike><font color="red">DONE</font>
 '''Why?''' There is astrophysics in understanding what is bright/faint in each band. Spatial resolution is going to play a role for us downstream.

@@ Line 65: / Line 65: @@
 *[http://nitarp.ipac.caltech.edu/resource/8 ds9 NITARP Tutorial] (listed on that page, along with installation tips)
 *[http://hea-www.harvard.edu/RD/ds9/site/Download.html ds9 Download site]
-*[http://irsa.ipac.caltech.edu/fftools/app.html IRSA Viewer]
+*[http://irsa.ipac.caltech.edu/irsaviewer/app.html IRSA Viewer]
 '''Process''': Load images of our region over a range of wavelengths into a viewer of your choice, ds9 or IRSA tools, or a mixture of them. Ideally, you should pick at least one optical band (POSS, SDSS, or IPHAS), one NIR (2MASS), one MIR (IRAC, or WISE1/2/3), and one FIR band (MIPS, WISE4, PACS, or SPIRE). Compare the images. Answer the questions below.
@@ Line 129: / Line 129: @@
 '''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 [http://irsa.ipac.caltech.edu/fftools/app.html IRSA Viewer]
+'''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 [http://irsa.ipac.caltech.edu/irsaviewer/app.html IRSA Viewer]
@@ Line 151: / Line 151: @@
 '''Relevant links for reference''':
-*[http://irsa.ipac.caltech.edu/fftools/app.html IRSA Viewer]
+*[http://irsa.ipac.caltech.edu/irsaviewer/app.html IRSA Viewer]
 *[[Color-Magnitude and Color-Color plots]]
 *[[Gutermuth color selection]] - mostly Gutermuth color selection; includes early version of analogy with M&Ms. Koenig color selection is similar in concept but uses different bands (WISE rather than Spitzer).

@@ Line 251: / Line 251: @@
 *''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.)
+*Ultimately (see next section), 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 ~300 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.
@@ Line 273: / Line 273: @@
 *[http://irsa.ipac.caltech.edu/applications/finderchart/ FinderChart] to check images on the fly
-'''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 recognize those points that are limits rather than detections, and assess the SED appropriately. 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".
+'''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 recognize those points that are limits rather than detections, and assess the SED appropriately. 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 "YSO that does not seem to have a disk", or "still surviving as new YSO candidates" or "maybe this is ok" or some other status.
 ''More mechanics of process'':
 Get the file with all the SEDs in it from the Box drive. We will do the first 12 (the first page) 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 300 sources, that's at absolute minimum ~60 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? Are there disagreements (e.g., WISE and Spitzer don't agree)? Look at the images at the same time if at all possible... Are there data missing that should be there (e.g., there are no 2MASS data in the SED but you can see a source at that location in the 2MASS images)?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! (If we are working in a communal document, be sure not to overwrite each other!
+'''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? Are there disagreements (e.g., WISE and Spitzer don't agree)? Look at the images at the same time if at all possible... Are there data missing that should be there (e.g., there are no 2MASS data in the SED but you can see a source at that location in the 2MASS images)? 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! (If we are working in a communal document, be sure not to overwrite each other!
 '''Advice:'''
 #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 are 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. I may have inverted the direction of the limits in places -- please tell me if you find these.
-#Accreting young stars, or stars that are rotating quickly (often because they are young) are bright in Halpha and ultraviolet (u band). 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. If the u band point does not fall in a line with the Wien side of the SED, that's OK, and in fact a GOOD THING, because that is more evidence that the star is young.
+#Accreting young stars, or stars that are rotating quickly (often because they are young) are bright in Halpha and ultraviolet (u band). If Halpha or the u-band points are 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. The Halpha point is red and the u band point is blue in my SEDs to help you remember this.
 #Remember that in the context of IR excesses and SED classes, 2 to 25 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 wackiness in the optical is less of a critical 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. Longer wavelengths than 25 um help us understand the nature of the object, but the formal SED classification is based on 2-25 um (only).
 #Black triangles = IPHAS ri; red triangle = Halpha (which means that bright is ok). Black + signs = SDSS griz; blue + sign = SDSS u (which means that bright is ok). Arrows anywhere are limits in the direction indicated. Black diamonds = 2MASS. Blue circles = IRAC; green circles = mean (average) IRAC from YSOVAR light curves. Black stars = WISE.  Black box (near the IRAC points) = MIPS. longer wavelength SEDs continue: asterisks out here mean SPIRE, and boxes/arrows out here mean SCUBA.

@@ Line 243: / Line 243: @@
 '''Process''': Program a spreadsheet to convert between mags and flux densities. Make at least one SED yourself.  Even if you run out of time so that you don't actually make your own SEDs right now, make sure you understand how to get the fluxes from the magnitudes (and then make your own SEDs later). ''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 from our shortlist: 230443.74+623406.8, 230444.46+623233.4, 230446.76+622907.8.  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.)
+We will ultimately need to make SEDs for everything, but for purposes of this example, let's work with these three objects from our shortlist: <span style="background:#ffff00">230443.74+623406.8, 230444.46+623233.4, 230446.76+622907.8</span>.  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.)
 AT MINIMUM, the goal here is to get at least an optical+2MASS+Spitzer 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 all optical through 850 um, or doing more SEDs than just these 3).

@@ Line 211: / Line 211: @@
 It's useful to keep track of why the sources are in the list. Values for the "whyhere" column are combinations of letter codes:
-*<span style="background:#00FF00">I = selected based on IR colors (Rob's approach; I=*I*RAC)</span>
+*<span style="background:#ffff00">I = selected based on IR colors (Rob's approach; I=*I*RAC)</span>
-*<span style="background:#00FF00">X = selected based on X-ray detection+ star-like SED (X=*X*-ray)</span>
+*<span style="background:#ffff00">X = selected based on X-ray detection+ star-like SED (X=*X*-ray)</span>
-*<span style="background:#00FF00">V = selected based on it being variable over the YSOVAR campaign (V=*V*ariable)</span>
+*<span style="background:#ffff00">V = selected based on it being variable over the YSOVAR campaign (V=*V*ariable)</span>
-*<span style="background:#00FF00">H = selected based on it being bright in Halpha (only two of these; H = *H*alpha)</span>
+*<span style="background:#ffff00">H = selected based on it being bright in Halpha (only two of these; H = *H*alpha)</span>
-*<span style="background:#00FF00">L = selected because it matches a SCUBA source (L = "*L*ong wavelength" because I kept getting "S" confused with Spitzer rather than SCUBA)</span>
+*<span style="background:#ffff00">L = selected because it matches a SCUBA source (L = "*L*ong wavelength" because I kept getting "S" confused with Spitzer rather than SCUBA)</span>
-*<span style="background:#00FF00">L? = selected because it MAY match a SCUBA source (source confusion issues we discussed before)</span>
+*<span style="background:#ffff00">L? = selected because it MAY match a SCUBA source (source confusion issues we discussed before)</span>
-*<span style="background:#00FF00">C = selected because it seems to have changed in brightness significantly between the cryo and YSOVAR data (C= *c*ryo to ysovar variable)</span>
+*<span style="background:#ffff00">C = selected because it seems to have changed in brightness significantly between the cryo and YSOVAR data (C= *c*ryo to ysovar variable)</span>
 Many have just one code, but many have multiple codes. IVC means the source is in the list because it has IRAC colors consistent with a YSO (I), it's variable over the YSOVAR data (V), and variable between that and the cryo data (C).

@@ Line 209: / Line 209: @@
 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.
-<span style="background:#00FF00">It's useful to keep track of why the sources are in the list. Values for the "whyhere" column are combinations of letter codes:
+It's useful to keep track of why the sources are in the list. Values for the "whyhere" column are combinations of letter codes:
-Many have just one code, but many have multiple codes. IVC means the source is in the list because it has IRAC colors consistent with a YSO (I), it's variable over the YSOVAR data (V), and variable between that and the cryo data (C).</span>
+*<span style="background:#00FF00">I = selected based on IR colors (Rob's approach; I=*I*RAC)</span>

@@ Line 209: / Line 209: @@
 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.
-<font color="orange">It's useful to keep track of why the sources are in the list. Values for the "whyhere" column are combinations of letter codes:
+It's useful to keep track of why the sources are in the list. Values for the "whyhere" column are combinations of letter codes:
 *I = selected based on IR colors (Rob's approach; I=*I*RAC)
 *X = selected based on X-ray detection+ star-like SED (X=*X*-ray)
@@ Line 218: / Line 218: @@
 *C = selected because it seems to have changed in brightness significantly between the cryo and YSOVAR data (C= *c*ryo to ysovar variable)
-Many have just one code, but many have multiple codes. IVC means the source is in the list because it has IRAC colors consistent with a YSO (I), it's variable over the YSOVAR data (V), and variable between that and the cryo data (C).</font>
+Many have just one code, but many have multiple codes. IVC means the source is in the list because it has IRAC colors consistent with a YSO (I), it's variable over the YSOVAR data (V), and variable between that and the cryo data (C).