Table information for 'ppakm31.cubes'

General

Table Description: Spectral cubes of the fields. Look for these with full metadata in the ivoa.obscore table, obs_collection 'ppakm31 cubes'.

This table is available for ADQL queries and through the TAP endpoint.

Resource Description: 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.

For a list of all services and tables belonging to this table's resource, see Information on resource 'PPAKM31 – Optical Integral Field Spectroscopy of Star-Forming Regions in M31'

Citing this table

This table has an associated publication. If you use data from it, it may be appropriate to reference 2017ApJ...844..155T (ADS BibTeX entry for the publication) either in addition to or instead of the service reference.

To cite the table as such, we suggest the following BibTeX entry:

@MISC{vo:ppakm31_cubes,
  year=2020,
  title={{PPAKM31} – Optical Integral Field Spectroscopy of Star-Forming Regions
in M31},
  author={Kreckel, K. and Tomičić, N. and Sandstrom, K. and Groves, B.},
  url={http://dc.zah.uni-heidelberg.de/tableinfo/ppakm31.cubes},
  howpublished={{VO} resource provided by the {GAVO} Data Center}
}

Resource Documentation

A Use Case

For a quick idea of what you can do with this data, consider Kewley et al's starburst criterion (2001ApJ...556..121K), which compares [OIII]/Hb with ([SII]6716+[SII]6730)/Ha. In this example, we will investigate where in the this plane our pixels lie.

We will use pyVO, TOPCAT, and Aladin.

Since we need to do somewhat complex operations on the image pixels, we'll use pyVO to get the source images and convert them into a table of fluxes. This table will then be investigated using TOPCAT (and a bit of Aladin).

Produce a flux table: You could use Aladin for going from the images to a table for fluxes, but this is a bit of drudgery here. Instead, here is a short python script using pyVO and numpy; it uses TAP to retrieve the image metadata and then get the images themselves by plain HTTP.

It then filters out all pixels with an SNR below 5 (with a bit of handwaving, assuming what's ok in the generally weakest band will be ok in the others) puts the remaining pixels into a nice relational table. It finally sends this table to TOPCAT, so start TOPCAT before running this:

from astropy.io import fits as pyfits
from astropy import table, wcs
from pyvo import samp
import numpy
import pyvo


# change the following to use other ppakm31 fields
FIELD = "F3"


def stringify(s):
    """returns s utf-8-decoded if it's bytes, s otherwise.

    (that's working around old astropy votable breakage)
    """
    if isinstance(s, bytes):
        return s.decode("utf-8")
    return s


def get_image_metadata(field):
    """returns metadata rows from the ppakm31 service for field.

    Access URL and schema are taken from a TOPCAT exploration of the service.
    """
    svc = pyvo.dal.TAPService("http://dc.g-vo.org/tap")

    res = []
    for row in svc.run_sync(
            "select accref, field, imagetitle, bandpassid, cube_link"
            "  from ppakm31.maps"
            "  where field={}".format(repr(field))):
        res.append({
            "bandpassid": stringify(row["bandpassid"]),
            "accref": stringify(row["accref"]),})

    return res


def get_line_maps(image_meta):
    """returns a dict band->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:

The result would be something like this (see below for the black squares):

/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.

Fields

Sorted by DB column index. [Sort alphabetically]

NameTable Head DescriptionUnitUCD
accref Product key Access key for the data N/A N/A
owner Owner Owner of the data N/A N/A
embargo Embargo ends Date the data will become/became public a N/A
mime Type MIME type of the file served N/A meta.code.mime
accsize File size Size of the data in bytes byte VOX:Image_FileSize
obs_id Obs_id Unique identifier for an observation N/A meta.id
obs_publisher_did Obs_publisher_did Dataset identifier assigned by the publisher. N/A meta.ref.ivoid
access_estsize Access_estsize Estimated size of data product kbyte phys.size;meta.file
obs_title Obs_title Free-from title of the data set N/A meta.title;obs
s_ra S_ra RA of (center of) observation, ICRS deg pos.eq.ra
s_dec S_dec Dec of (center of) observation, ICRS deg pos.eq.dec
t_min T_min Lower bound of times represented in the data set, as MJD d time.start;obs.exposure
t_max T_max Upper bound of times represented in the data set, as MJD d time.end;obs.exposure
s_region S_region Region covered by the observation, as a polygon N/A pos.outline;obs.field
em_min Em_min Minimal wavelength represented within the data set m em.wl;stat.min
em_max Em_max Maximal wavelength represented within the data set m em.wl;stat.max
s_xel1 S_xel1 Number of elements (typically pixels) along the first spatial axis. N/A meta.number
s_xel2 S_xel2 Number of elements (typically pixels) along the second spatial axis. N/A meta.number

Columns that are parts of indices are marked like this.

Other

The following services may use the data contained in this table:

VOResource

VO nerds may sometimes need VOResource XML for this table.