2020-02-19T10:03:34Z 100 PPAKM31 – Optical Integral Field Spectroscopy of Star-Forming Regions in M31 These observations cover five star-forming regions in the Andromeda galaxy (M31) with optical integral field spectroscopy. Each has a field of view of roughly 1 kpc across, at 10pc physical resolution. In addition to the calibrated data cubes, we provide flux maps of the Hβ, [OIII]5007, Hα, [NII]6583, [SII]6716 and [SII]6730 line emission. Line fluxes have not been corrected for dust extinction. All data products have associated error maps. andromeda-galaxy interstellar-medium h-ii-regions Kreckel, K.; Tomičić, N.; Sandstrom, K.; Groves, B. Potsdam Multi-aperture Spectrophotometer (PMAS) with the PPaK fiber-bundle Calar Alto 3.5m telescope 2017ApJ...844..155T Research Archive Optical hdus for image_meta as returned by get_image_maps. """ res = {} for row in image_meta: if row["bandpassid"]=='[NII]6583': # we don't need that one later, so skip it continue res[row["bandpassid"]] = pyfits.open(row["accref"]) return res if __name__=="__main__": line_maps = get_line_maps(get_image_metadata(FIELD)) # Select pixels with a reasonable SNR; the errors are in extension # 1, and we use [OIII]5007, since the other lines ought to be stronger snr_cutoff = 5. snr_mask = (line_maps["[OIII]5007"][0].data > line_maps["[OIII]5007"][1].data*snr_cutoff) # The line maps all have identical WCS: use any to turn pixels to pos. # This is a somewhat tricky way to get the positions of all valid # pixels: w = wcs.WCS(header=line_maps["Halpha"][0].header, naxis=2) ras, decs = w.wcs_pix2world( snr_mask.nonzero()[1], snr_mask.nonzero()[0], 1) # Create table with ra & dec positions and our flux measurements. t = table.Table([ ras, decs, line_maps["[OIII]5007"][0].data[snr_mask], line_maps["Hbeta"][0].data[snr_mask], line_maps["Halpha"][0].data[snr_mask], line_maps["[SII]6716"][0].data[snr_mask], line_maps["[SII]6730"][0].data[snr_mask]], names=('ra', 'dec', 'OIII_5007','Hbeta','Halpha','SII_6716','SII_6730')) # Send the table to TOPCAT via SAMP with samp.connection() as conn: samp.send_table_to(conn, t, "topcat") You can change FIELD (or change the script so your flux table has all fields). *Make a plot of the line ratios:* To visualise where the data likes with respect to the Kewley+2001 criterion, once you have the table in TOPCAT, configure the plot rougly likes this: * Graphics → Plane Plot * In X, type the denominator of the Kewley criterion; in case you forget how the columns are called, check Views → Column Info. Anyway, it's ``(SII_6716+SII_6730)/Halpha`` * Similarly, for Y, use ``OIII_5007/Hbeta``. * In the ``Form`` tab, set the shading mode to aux, and set the Aux axis to Halpha * Under ``Axes``, set both axes to log. * Now add the Kewley criterion with Layers → Add Function Control and adding using ``exp10(0.72/(log10(x)-0.32)+1.30)`` as the function to plot; to see where this comes from, read :bibcode:`2001ApJ...556..121K`. The result would be something like this (see below for the black squares): .. image:: /ppakm31/q/cdl/static/shot1.png *Sanity check:* To see how the various points on your plot look in reality, start Aladin and tell it to either some common survey or PPAKM31 images themselves; you can find the latter in the discovery tree left in Aladin's window. Then, configure TOPCAT can make Aladin follow its focus by clicking Views → Activation Action and then checking “Send Sky Coordinates”. See if you can correlate the position in the plot with the visual (or infrared, or whatever) appearance. *Compare to known clusters:* To see what known star clusters fare in our plot, get a catalogue of them. In TOPCAT, use VO → Cone Search, and in keywords, enter something like “M31 cluster”; to find what we've plotted above, J/ApJ/827/33, you'll have to check “description” in the match fields before sending off the query (they don't mention M31 in their title or subject); but, really, other catalogues of clusters or H II regions should do just as well. To quickly get a position to search for, click into your plot; this will fill out RA and Dec in the dialog, and all that's left is to enter, perhaps, 0.1 into the radius field (because that's about the FoV of our images) and fire off the request. Whatever catalogue you used, the entries will probably not have the fluxes we need to put them into our flux ratio plot. So: let's just add them from our data by a positional crossmatch. To do that, in TOPCAT's main window say Joins → Pair Match. Our pixel size is about 1 arcsec, so the default match radius is ok. Select our fluxes as table 1 and the cluster catalogue as table 2, hit “Go”, and then return to the flux plot. In there, do Layers → Add position control. Configure this to plot the match table, and again the flux expressions from above. If you then tell TOPCAT to plot the points as large black squares, you will have reproduced (more or less) the plot above. .. _pyVO: https://github.com/astropy/pyvo .. _Aladin: https://aladin.u-strasbg.fr/aladin.gml .. _TOPCAT: http://www.star.bris.ac.uk/~mbt/topcat/ ]]> { "Ha6563": "Halpha", "Hb4861": "Hbeta", "NII6583": "[NII]6583", "OIII5007": "[OIII]5007", "SII6716": "[SII]6716", "SII6730": "[SII]6730",} cdlcube_id Extracted narrow-band images. //obscore#publishSIAP application/x-votable+xml;content=datalink Datalink Original Cube 6/2623,2666-2667,2709,2752 55819.5 55819.5 2.95119e-19 4.08734e-19 preview "\schema.data" "image/jpeg" \standardPreviewPath "ari" "2020-05-01" if "_cube" in \srcstem: raise SkipThis("Cubes are extra") yield row mat = re.search("Date observed: ([0-9-]+) to ([0-9-]+)", str(@header_["comment"])) @meanDate = (dateTimeToMJD(parseDate(mat.group(1)) )+dateTimeToMJD(parseDate(mat.group(2))))/2 \srcstem.split(".")[0].split("_")[-1] \bandLabels "line" @cubename = re.sub(r"_[A-Za-z0-9]+$", "_cube", \srcstem) @specwcs = getWCSAxis(@header_, 3) @meanDate @line @specwcs.pixToPhys(1)/1e10 @specwcs.pixToPhys(0)/1e10 @specwcs.pixToPhys(2)/1e10 'F' \srcstem Spectral cubes of the fields. Look for these with full metadata in the ivoa.obscore table, obs_collection 'ppakm31 cubes'. //obscore#publishObscoreLike preview "ppmakm31.cubes" "image/jpeg" \standardPreviewPath vars["s_region"] = result["coverage"] vars["s_ra"] = result["centerAlpha"] vars["s_dec"] = result["centerDelta"] mat = re.search("Date observed: ([0-9-]+) to ([0-9-]+)", str(@header_["comment"])) @t_min = dateTimeToMJD(parseDate(mat.group(1))) @t_max = dateTimeToMJD(parseDate(mat.group(2))) ax = getWCSAxis(@header_, 3) @em_min = ax.pixToPhys(1) @em_max = ax.pixToPhys(@NAXIS3) PPAKM31 cube datalink service data '\schema/data' FITSProductDescriptor "#cube" \bandLabels template = re.sub("_cube.fits", "_%s.fits", descriptor.accref ).split("/", 1)[-1] for band, bandLabel in bands.items(): yield descriptor.makeLinkFromFile( template%band, description=f"A map in {bandLabel} derived from this cube.", localSemantics=f"band{bandLabel}", contentQualifier="#image") ppakm31 web i/fixed PPAK_M31_F4_OIII5007', '11.24364', '[OIII]5007', 'getproduct/ppakm31/data/PPAK_M31_F4_OIII5007.fits', 'ppakm31/q/cdl/dlmeta?ID=ivo://org.gavo.dc/~?ppakm31' '/data/PPAK_M31_F4_cube.fits">Original Cube') ]]> /getproduct/ppakm31/data/PPAK_M31_F4_OIII5007.fits?preview=True self.assertHasStrings("JFIF", "*456789:CDEFGHI") /getproduct/ppakm31/data/PPAK_M31_F4_cube.fits?preview=True self.assertHasStrings("JFIF", "*456789:CDEFGHI") cdl/dlmeta rows = self.getVOTableRows() for r in rows: if r["semantics"]=="#derivation" and "OIII5007" in r["access_url"]: self.assertEqual(r["description"], "A map in [OIII]5007 derived from this cube.") self.assertEqual(r["local_semantics"], "band[OIII]5007") break else: self.fail("OIII line map missing as #derivation") self.assertHasStrings( 'MIN value="3.68516589077e-07"', # of the BAND param 'MAX value="11.2436402393 41.9189435754 0.0436407329"', # CIRCLE 'ucd="pos.cartesian.z;instr.pixel"' # auto pixel 3 )