Table Description:
This table is 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'
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_phot_nb, 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.phot_nb}, howpublished={{VO} resource provided by the {GAVO} Data Center} }
If you use GDS data, please cite 2015AN....336..590H.
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.
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
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:
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)
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)
# 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)
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
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
Sorted by DB column index. [Sort alphabetically]
Name | Table Head | Description | Unit | UCD |
---|---|---|---|---|
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 |
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 |
med_mag_nb | 〈mag〉 | Mean magnitude in Astrodon NB. | mag | phot.mag;em.line;stat.median |
err_mag_nb | Err〈mag〉 | Error in median magnitude in Astrodon NB | mag | stat.error;phot.mag;em.line |
amp | Amplitude | Difference between brightest and weakest observation. | mag | phot.mag;em.line;arith.diff |
flux | 〈flux〉 | Median flux in the Astrodon NB band; this is computed from the median magnitude based on Landolt standard stars. | mJy | phot.flux;em.line;stat.median |
err_flux | Err〈flux〉 | Error in median flux in the Astrodon NB band | mJy | stat.error;phot.flux;em.line |
nobs | #Obs | Number of observations in this light curve | N/A | meta.number;obs |
field | Field | Survey field observed. | N/A | meta.id;obs.field |
Columns that are parts of indices are marked like this.
The following services may use the data contained in this table:
VO nerds may sometimes need VOResource XML for this table.