Table information for 'bgds2.ssa_time_series'

General

Table Description: This table contains metadata about the photometric time series from BGDS in IVOA SSA format. The actual data is available through a datalink service or in the lc_allband table.

This table is not available for ADQL queries and through the TAP endpoint.

Resource Description:

From the Bochum Galactic Disk Survey, time series have been obtained in the r and i bands on an (up to) nightly basis. Depending on the field, the time series contain up to more than 300 nights over 9 years. Each measurement in r and i represents the averaged flux over 10 minutes of observation (from 9 averaged 10s images). Additionally, intermittent measurements in Johnson UVB, Sloan z and the narrowbands OIII, NB, Halpha and SII have been recorded as well.

The Bochum Galactic Disk Survey is a project to monitor the stellar content of the Galactic disk in a 6 degree wide stripe centered on the Galactic plane. The data has been recorded from September 2010 to September 2019 in Sloan r and i simultaneously with the Robotic Bochum Twin Telescope (RoBoTT) at the Universitaetssternwarte Bochum near Cerro Armazones in the Chilean Atacama desert. It contains measurements of about 2x10^7 stars over nine years.

The source images are available from ivo://org.gavo.dc/bgds/q/sia.

For a list of all services and tables belonging to this table's resource, see Information on resource 'BGDS DR2 time series'

Citing this table

This table has an associated publication. If you use data from it, it may be appropriate to reference 2015AN....336..590H (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:bgds2_ssa_time_s,
  year=2024,
  title={{BGDS} DR2 time series},
  author={Hackstein, M. and Haas, M. and Fein, C. and Chini, R.},
  url={http://dc.g-vo.org/tableinfo/bgds2.ssa_time_series},
  howpublished={{VO} resource provided by the {GAVO} Data Center}
}

If you use GDS data, please cite 2015AN....336..590H.

Resource Documentation

The time series from BGDS DR2 by default come as IVOA Spectral Data Model compliant VOTables. If you what plain text instead, add &format=txt to the dlget URIs. The columns you get back then are epoch in MJD, magnitude and magnitude error in mag, and a link to a cutout showing where the measurement came from.

Accessing and Plotting Light Curves From pyVO

Let us briefly illustrate how to access and plot the GDS data with pyVO. First, some basic definitions that we will need in the code below; there is no need to understand this code in detail:

import pyvo as vo
import matplotlib.pyplot as plt
import numpy as np

service = vo.dal.TAPService("http://dc.g-vo.org/tap")

def get_lc(ra, dec, rad, filter):
    """Returns a light curve given by mjds and mags
    around the coordinates ra and dec.
    """
    # Search for the object coordinates in the metadata table:
    id_set = service.search("SELECT obs_id"
            " FROM bgds2.ssa_time_series"
                    f" WHERE DISTANCE({ra}, {dec}, ra, dec)<{rad}/3600."
                    " AND ssa_bandpass like '% " + filter + "%'")
    if len(id_set) >= 1:
      # Use the ID from the first result in the time series metadata table
      # (there is only one result for the examples here, but sources in
      # overlapping observation fields can have up to four light curves
      # per filter) to receive the corresponding light curve from the
      # light curve table:
      lc_set = service.search(
            "SELECT mjds, mags FROM bgds2.lc_allbands"
        "  WHERE obs_id = '" + id_set.getcolumn("obs_id")[0] + "'")
      mjds = lc_set.getcolumn("mjds")
      mags = lc_set.getcolumn("mags")
      return mjds[0], mags[0]

    else:
      print(f"There is no GDS light curve for the filter {filter}"
         f" in a radius of {rad}'' around the coordinates {ra} {dec}.")

def plot_ts(r_mjds, r_mags, i_mjds, i_mags):
    """Plot r' and i' light curves given by r_mjds, i_mjds and r_mags, i_mags.
    """
    plt.plot(r_mjds, r_mags, 'ro', markersize=3.0)
    plt.plot(i_mjds, i_mags, 'ko', markersize=3.0)
    plt.gca().invert_yaxis()
    plt.xlabel('MJD (d)')
    plt.ylabel('Magnitude')
    plt.show()
    return

def fold_lc(mjds, mags, p):
    """Fold a light curve given by mjds and mags with a period p.
    """
    # Fold the dates mjds by the period p:
    mjds_fold = mjds % p / p

    # Sort mjds and mags by the phase:
    idx_fold = np.argsort(mjds_fold)
    mjds_fold = mjds_fold[idx_fold]
    mags_fold = mags[idx_fold]
    return mjds_fold, mags_fold

def find_p(mjds, mags, min_p, max_p, p_step):
    """Find the period p in a given range from min_p to max_p in steps of p_step
    with minimal theta.
    """
    theta_list = []
    p_candidates = np.arange(min_p, max_p, p_step)

    for j in range(len(p_candidates)):
      mjds_fold, mags_fold = fold_lc(mjds, mags, p_candidates[j])
      # Shift the magnitudes of the folded light curve mags_fold by one step to
      # compute differences:
      mags_shift = [mags_fold[1:len(mags_fold)]]
      mags_shift = np.append(mags_shift,mags_fold[0])
      theta = np.sum((mags_fold - mags_shift)**2)
      theta_list.append(theta)

    theta_list = np.asarray(theta_list)
    min_ind = np.argmin(theta_list)
    p = p_candidates[min_ind]
    return p, p_candidates, theta_list

def plot_theta(p_candidates, theta_list):
    """Plot theta (given by a list/array theta_list) in dependence of the period
    given by a an array p_candidates.
    """
    plt.plot(p_candidates, theta_list, 'ko', markersize=2.5)
    plt.xlim([np.min(p_candidates), np.max(p_candidates)])
    plt.xlabel('$P$ (d)')
    plt.ylabel('$\theta$')
    plt.show()
    return

def plot_phase(r_mjds, r_mags, i_mjds, i_mags, p):
    """Fold r' and i' light curves given by r_mjds, i_mjds and r_mags, i_mags
    with a period p and plot two phases.
    """
    r_mjds_fold, r_mags_fold = fold_lc(r_mjds, r_mags, p)
    i_mjds_fold, i_mags_fold = fold_lc(i_mjds, i_mags, p)
    plt.plot([r_mjds_fold, [x+1 for x in r_mjds_fold]],
            [r_mags_fold, r_mags_fold], 'ro', markersize=3.0)
    plt.plot([i_mjds_fold, [x+1 for x in i_mjds_fold]],
            [i_mags_fold, i_mags_fold], 'ko', markersize=3.0)
    plt.axvline(1, color = 'gray', linestyle='-', markersize=0.3)
    plt.xlim([0.,2.])
    plt.gca().invert_yaxis()
    plt.xlabel('Phase')
    plt.ylabel('Magnitude')
    plt.title('$P = %.4f\,\mathrm{d}$' % p)
    plt.show()
    return

def get_multi_wl_set(ra, dec, rad):
    """Returns a data set from the multi-wavelength photometry catalogue
    (matched Gaia DR3 ID gaia_id, arrays for magnitudes multi_wl_mags and
    spectral flux density in mJy multi_wl_fluxes in UBVr'i'z', and an estimated
    spectral type spt from UBV photometry) around the coordinates ra and dec.
    """
    wl_cols = ["u", "b", "v", "r", "i", "z"]
    multi_wl_set = service.search(
            "SELECT gaia_id, u_med_mag, b_med_mag, v_med_mag,"
            " r_med_mag, i_med_mag, z_med_mag, u_flux, b_flux, v_flux,
                    " r_flux, i_flux, z_flux, spt FROM bgds2.multi_wl_phot"
                    f" WHERE DISTANCE({ra}, {dec}, ra, dec)<{rad}/3600.")

    if len(multi_wl_set) >= 1.:
      multi_wl_mags = np.zeros(6)
      multi_wl_fluxes = np.zeros(6)

      for n in range(len(wl_cols)):
          multi_wl_mags[n] = multi_wl_set.getcolumn(wl_cols[n] + "_med_mag")[0]
          multi_wl_fluxes[n] = multi_wl_set.getcolumn(wl_cols[n] + "_flux")[0]

      gaia_id = multi_wl_set.getcolumn("gaia_id")
      spt = multi_wl_set.getcolumn("spt")
      return gaia_id, multi_wl_mags, multi_wl_fluxes, spt

    else:
      print(f"There is no GDS photometry in a radius of {rad}"
                            f" around the coordinates {ra} {dec}.")

def plot_sed(multi_wl_fluxes):
    """Plot the spectral flux density in UBVr'i'z' in dependence of the
    filter wavelength.
    """
    wls = np.array([365, 433, 550, 623, 764, 906])
    filters = ["U", "B", "V", "r'", "i'", "z'"]
    plt.plot(wls, multi_wl_fluxes, 'ko', markersize=5.0)

    for n in range(len(multi_wl_fluxes)):
      plt.text(wls[n]+7, multi_wl_fluxes[n]-8, filters[n])

    plt.xlabel('Wavelength (nm)')
    plt.ylabel('Flux (mJy)')
    plt.show()
    return

def calc_abs_mag(mag, plx):
    """Calculate the absolute magnitude abs_mag from the relative magnitude mag
    and the parallax plx in milli-second of arc.
    """
    # Approximation of the distance dist in parsec:
    dist = 1000./plx

    abs_mag = np.round(mag - 5.*(np.log10(dist) - 1.), 3)
    return abs_mag

Light curves

The GDS contains light curves in up to 10 filters per observation field, among which the r’ and i’ light curves contain the largest numbers of data points. We can take a look at the r’ and i’ light curves of a star, e.g., the highly variable V352 Nor:

r_mjds, r_mags = get_lc(241.71551, -52.07636, 1., "r")
i_mjds, i_mags = get_lc(241.71551, -52.07636, 1., "i")
plot_ts(r_mjds, r_mags, i_mjds, i_mags)

The result is:

A scatterplot of two fairly parallel sets of reddish dots over more than 2000 days, showing steep maxima up to 7 mag, reaching down to 16 mag.  Prominent gaps in the coverage are visible.

Period determination

The periods of many periodic variables in the GDS are still unknown or uncertain. Short periods are not obvious when looking at the (unfolded) light curve, as is the case for the eclipsing binary EH Mon:

r_mjds, r_mags = get_lc(103.03654, -7.06476, 1., "r")
i_mjds, i_mags = get_lc(103.03654, -7.06476, 1., "i")
plot_ts(r_mjds, r_mags, i_mjds, i_mags)
A scatterplot, again in two bands; most dots are about 13, but there are five steep drops down to 15.

Among the numerous options for an automated period classification, here we will use the Lafler-Kinman algorithm.

The light curve is folded by trial periods and the sum of squares θ of the differences between two magnitude measurements of adjacent phase is calculated. Basically, θ measures how smooth a folded light is when folded by a trial period. Usually, θ is minimized when a light curve is folded by a multiple of the correct period, therefore, test values with local minima in θ are the best candidates for the period, in order from low to high.

r_p, r_p_candidates, r_theta_list = find_p(r_mjds, r_mags, 1., 15., 1e-4)
i_p, i_p_candidates, i_theta_list = find_p(i_mjds, i_mags, 1., 15., 1e-4)

# Exemplarily plot theta for the filter i':
plot_theta(i_p_candidates, i_theta_list)
A plot of θ versus the period, ranging from about 2 to about 14 days. there is a broad, dense black field across the plot, with sharp dips at about 3.6 days and its harmonics.
# The final period p is the average between period results from the
# r' and i' light curves:
p = (r_p + i_p) / 2.
plot_phase(r_mjds, r_mags, i_mjds, i_mags, p)
a scatter plot over the phase in two bands, showing fairly parallel primary and secondary dips.

Multi-wavelength catalogue

Median magnitudes and light curves are not only available separately for filters and fields, but there is also a multi-wavelength photometry catalogue that is matched with Gaia DR3 sources. The filter magnitudes can be noticeably effected by reddening; in this case, the roughly estimated spectral types from the GDS UBV photometry might be more useful. For Gaia DR3 3052220093755546112, median magnitudes in UBVr’i’z’ are available:

gaia_id, multi_wl_mags, multi_wl_fluxes, spt = get_multi_wl_set(
    175.11595, -62.02609, 1.)
print("Gaia DR3 ID: " + gaia_id + ", Spectral type from UBV photometry: " + spt)
print(f"U = {multi_wl_mags[0]}, B = {multi_wl_mags[1]},"
    f"V = {multi_wl_mags[2]}, r'; = {multi_wl_mags[3]},"
    f"i'; = {multi_wl_mags[4]}, z' = {multi_wl_mags[5]}")
plot_sed(multi_wl_fluxes)

The result is:

Gaia DR3 ID: 3052220093755546112, Spectral type from UBV photometry: K5
U = 14.15, B = 12.996, V = 11.781, r' = 11.366, i' = 10.853, z' = 10.551
A sparse scatterplot of calibrated flux over wavelength, showing an almost linear growth of dots labelled with the filter designations between 380 and 900 nm.

The match with Gaia DR3 allows to directly get information from the Gaia DR 3 catalogue like the Gaia DR parallaxes to compute absolute magnitudes:

gaia_set = service.search(
    "SELECT parallax FROM gaia.dr3lite WHERE source_id=" + gaia_id)
plx = gaia_set.getcolumn("parallax")[0]
multi_wl_abs_mags = np.zeros(6)

for n in range(len(multi_wl_abs_mags)):
    multi_wl_abs_mags[n] = calc_abs_mag(multi_wl_mags[n], plx)

print(f"M_U = {multi_wl_abs_mags[0]}, M_B = {multi_wl_abs_mags[1]},"
    f"M_V = {multi_wl_abs_mags[2]}, M_r' = {multi_wl_abs_mags[3]},"
    f"M_i' = {multi_wl_abs_mags[4]}, M_z' = {multi_wl_abs_mags[5]}")

This gives:

M_U = 10.193, M_B = 9.039, M_V = 7.824, M_r' = 7.409, M_i' = 6.896, M_z' = 6.594

Columns

Sorted by DB column index. [Sort alphabetically]

NameTable Head DescriptionUnitUCD
accref Product key Access key for the data N/A meta.ref.url;meta.dataset
owner Owner Owner of the data N/A N/A
embargo Embargo ends Date the data will become/became public yr 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 N/A
ssa_dstitle Title A compact and descriptive designation of the dataset. N/A meta.title;meta.dataset
ssa_creatorDID C. DID Dataset identifier assigned by the creator N/A meta.id
ssa_pubDID P. DID Dataset identifier assigned by the publisher N/A meta.ref.ivoid
ssa_cdate Proc. Date Processing/Creation date N/A time;meta.dataset
ssa_pdate Pub. Date Date last published. N/A N/A
ssa_bandpass Bandpass Bandpass (i.e., rough spectral location) of this dataset; this should be the most appropriate term from the vocabulary http://www.ivoa.net/rdf/messenger. N/A instr.bandpass
ssa_cversion C. Version Creator assigned version for this dataset (will be incremented when this particular item is changed). N/A meta.version;meta.dataset
ssa_targname Object Common name of object observed. N/A meta.id;src
ssa_targclass Ob. cls Object class (star, QSO,...; use Simbad object classification http://simbad.u-strasbg.fr/simbad/sim-display?data=otypes if at all possible) N/A src.class
ssa_redshift z Redshift of target object N/A src.redshift
ssa_targetpos Obj. pos Equatorial (ICRS) position of the target object. N/A pos.eq;src
ssa_snr SNR Signal-to-noise ratio estimated for this dataset N/A stat.snr
ssa_location Location ICRS location of aperture center deg pos.eq
ssa_aperture Aperture Angular diameter of aperture deg phys.angSize;instr.fov
ssa_dateObs Date Obs. Midpoint of exposure (MJD) d time.epoch
ssa_timeExt Exp. Time Exposure duration s time.duration
ssa_specmid Mid. Band Midpoint of region covered in this dataset m instr.bandpass
ssa_specext Bandwidth Width of the spectrum m instr.bandwidth
ssa_specstart Band start Lower value of spectral coordinate m em.wl;stat.min
ssa_specend Band end Upper value of spectral coordinate m em.wl;stat.max
ssa_length Length Number of points in the spectrum N/A N/A
ssa_dstype Data type Type of data (spectrum, time series, etc) N/A N/A
ssa_publisher Publisher Publisher of the datasets included here. N/A meta.curation
ssa_creator Creator Creator of the datasets included here. N/A N/A
ssa_collection Collection A short handle naming the collection this spectrum belongs to. N/A N/A
ssa_instrument Instrument Instrument or code used to produce these datasets N/A meta.id;instr
ssa_datasource Src Method of generation for the data (one of survey, pointed, theory, custom, artificial). N/A N/A
ssa_creationtype Using Process used to produce the data (archival, cutout, filtered, mosaic, projection, spectralExtraction, or catalogExtraction) N/A N/A
ssa_reference Ref. URL or bibcode of a publication describing this data. N/A meta.bib.bibcode
ssa_fluxStatError Err. flux Statistical error in flux s**-1 stat.error;phot.flux.density;em
ssa_fluxSysError Sys. Err flux Systematic error in flux s**-1 stat.error.sys;phot.flux.density;em
ssa_fluxcalib Calib Flux Type of flux calibration (ABSOLUTE, CALIBRATED, RELATIVE, NORMALIZED, or UNCALIBRATED). N/A N/A
ssa_binSize Spect. Bin Bin size in wavelength m em.wl;spect.binSize
ssa_spectStatError Err. Spect Statistical error in wavelength m stat.error;em
ssa_spectSysError Sys. Err. Spect Systematic error in wavelength m stat.error.sys;em
ssa_speccalib Calib. Spect. Type of wavelength calibration N/A meta.code.qual
ssa_specres Spec. Res. Resolution (in meters of wavelength) on the spectral axis m spect.resolution;em.wl
obs_id Obs_id Main identifier of this observation (a single object may have multiple observations in different bands and fields) N/A meta.id;meta.main
time_min 1st Obs First timestamp in time series (MJD Topocentric UTC) d time.epoch;stat.min
time_max Last Obs Last timestamp in time series (MJD Topocentric UTC) d time.epoch;stat.max
amp Amplitude Difference between brightest and weakest observation. mag phot.mag;em.opt.U;arith.diff
med_mag 〈mag〉 Median magnitude in the band given by filter mag phot.mag
field Field Survey field observed. N/A meta.id;obs.field
ra Ra ICRS right ascension for this object. N/A pos.eq.ra;meta.main
dec Dec ICRS declination for this object. N/A pos.eq.dec;meta.main
mjds T[] An array containing the time values of the time series d time.epoch
mags mag[] An array containing the magnitude for the times in mjd mag phot.mag

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.