J/MNRAS/503/3975 Var., period. and contact binaries in WISE (Petrosky+, 2021)
Variability, periodicity, and contact binaries in WISE.
Petrosky E., Hwang H.-C., Zakamska N.L., Chandra V., Hill M.J.
<Mon. Not. R. Astron. Soc., 503, 3975-3991 (2021)>
=2021MNRAS.503.3975P 2021MNRAS.503.3975P (SIMBAD/NED BibCode)
ADC_Keywords: Binaries, eclipsing ; Binaries, orbits ;
Photometry ; Infrared sources ;
Keywords: methods: statistical - catalogues; binaries: eclipsing -
binaries: general - stars: variables: general - binaries: close
Abstract:
The time-series component of Wide-field Infrared Survey Explorer
(WISE) is a valuable resource for the study of variable objects. We
present an analysis of an all-sky sample of ∼450 000
AllWISE+NEOWISE infrared light curves of likely variables identified
in AllWISE. By computing periodograms of all these sources, we
identify ∼56 000 periodic variables. Of these, ∼42 000 are
short-period (P < 1 d), near-contact, or contact eclipsing binaries,
many of which are on the main sequence. We use the periodic and
aperiodic variables to test computationally inexpensive methods of
periodic variable classification and identification, utilizing various
measures of the probability distribution function of fluxes and of
time-scales of variability. The combination of variability measures
from our periodogram and non-parametric analyses with infrared colours
from WISE and absolute magnitudes, colours, and variability amplitude
from Gaia is useful for the identification and classification of
periodic variables. Furthermore, we show that the effectiveness of
non-parametric methods for the identification of periodic variables is
comparable to that of the periodogram but at a much lower
computational cost. Future surveys can utilize these methods to
accelerate more traditional time-series analyses and to identify
evolving sources missed by periodogram-based selections.
Description:
We present an analysis of an all-sky sample of ∼450 000 AllWISE and
NEOWISE infrared light curves of likely variables identified in AllWISE.
We use the periodic and aperiodic variables to test computationally
inexpensive methods of periodic variable classification and
identification, utilizing various measures of the probability distribution
function of fluxes and of time-scales of variability. The combination of
variability measures from our periodogram and non-parametric analyses and
variability amplitude from Gaia is useful for the identification and
classification of periodic variables. We employ a different period-search
algorithm, use data from a more recent release, and cross-match our results
with Gaia DR2.
We present a catalogue of 454103 WISE variables objects with a variety of
non-parametric measures and periodogram measurements. We explicitly flag
periodic variables and eclipsing binaries. The non-parametric measures and
the identification of eclipsing binaries are the subjects of this section 4.
We identify an all-sky sample of ∼56 000 periodic variable candidates,
of which ∼51 000 are binaries. We also present the calculation of various
non-parametric variability measures for the remaining 394 000 aperiodic
variables.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
catalog.dat 353 454103 Catalogue of WISE variables objects with a
variety of non-parametric measures and
periodogram measurements
--------------------------------------------------------------------------------
See also:
II/311 : WISE All-Sky Data Release (Cutri+ 2012)
II/328 : AllWISE Data Release (Cutri+ 2013)
I/345 : Gaia DR2 (Gaia Collaboration, 2018)
Byte-by-byte Description of file: catalog.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 19 A19 --- WISE AllWISE designation, JHHMMSS.ss+DDMMSS.s
(wise_id)
21- 31 F11.7 deg RAdeg Right ascension (J2000) (ra)
33- 43 F11.7 deg DEdeg Declination (J2000) (dec)
45- 50 F6.4 arcsec e_RAdeg Right ascension error (sigra)
52- 57 F6.4 arcsec e_DEdeg Declination error (sigdec)
59- 64 F6.3 mag W1mag W1 magnitude (w1mpro)
66- 70 F5.3 mag e_W1mag W1 magnitude error (w1sigmpro)
72- 75 F4.1 --- W1snr W1 signal-to-noise ratio (w1snr)
77- 82 F6.3 mag W2mag W2 magnitude (w2mpro)
84- 88 F5.3 mag e_W2mag ?=9.999 W2 magnitude error (w2sigmpro)
90 I1 --- f_W2mag [0,1] W2 magnitude boolean mask
(w2sigmpromask)
92- 97 F6.3 mag W3mag ?=99.999 W3 magnitude (w3mpro)
99-103 F5.3 mag e_W3mag ?=9.999 W3 magnitude error (w3sigmpro)
105 I1 --- f_W3mag [0,1] W3 magnitude boolean mask
(w3sigmpromask)
107-112 F6.3 mag W4mag ?=99.999 W4 magnitude (w4mpro)
114-118 F5.3 mag e_W4mag ?=9.999 W4 magnitude error (w4sigmpro)
120 I1 --- f_W4mag [0,1] W4 magnitude boolean mask
(w4sigmpromask)
122-125 A4 --- varFlag Variability flags for all four bands Mean
magnitude (var_flg)
127-131 I5 --- Npts Number of observations in light curve
after quality cuts (num_pts)
133-140 F8.5 mag magMean Mean magnitude (mean_mag)
142-149 F8.6 mag s_magMean Standard deviation of magnitude (std_mag)
151-157 F7.5 mag Amp Amplitude Fainter Fraction (amp)
159-166 F8.6 --- FF Fainter Fraction Relative Asymmetry
(Zakamska & Greene 2014MNRAS.442..784Z 2014MNRAS.442..784Z) (FF)
168-172 F5.2 --- PhaseCov Phase coverage Magnitude Ratio/M-test value
(Kinemuchi et al. 2006AJ....132.1202K 2006AJ....132.1202K,
Cat. J/AJ/132/1202) (phase_cov)
174 I1 --- f_PhaseCov [0,1] Phase coverage boolean mask
(phase_covmask)
176-193 F18.15 --- R Ratio of variability amplitude on short
time-scales to that on long time-scales (R)
195 I1 --- f_R [0,1] Ratio of variability amplitude
on short time-scales to that on long
time-scales boolean mask (Rmask)
197-208 E12.6 --- MaxStat ?=-999 Maximum statistic (max_stat)
210 I1 --- f_MaxStat [0,1] Maximum statistic boolean mask
(max_statmask)
212-218 F7.2 d Baseline Time-series duration (baseline)
220-227 F8.4 --- Skew Skewness (skew)
229-236 F8.2 --- Kur Kurtosis (kur)
238-246 F9.4 --- CutoffStat ?=-999 Cut-off maximum statistic value to
reject null hypothesis (cutoff_stat)
248 I1 --- f_CutoffStat [0,1] Cut-off maximum statistic value to
reject null hypothesis boolean mask
(cutoff_statmask)
250 I1 --- Periodic [0,1] Periodic sources receive a value of
True (periodic)
252-261 F10.5 d Pmhaov ?=-999 MHAOV period (P_mhaov)
263 I1 --- f_Pmhaov [0,1] MHAOV period boolean mask
(P_mhaovmask)
265-275 F11.6 d e_Pmhaov ?=-999 MHAOV period error (sigP_mhaov)
277 I1 --- fePmhaov [0,1] MHAOV period error boolean mask
(sigP_mhaovmask)
279-287 F9.4 mag magMed ?=-999 Median magnitude (median_mag)
289 I1 --- f_magMed [0,1] Median magnitude boolean mask
(median_magmask)
291-299 F9.4 mag e_magMed ?=-999 Median magnitude error (median_err)
301 I1 --- femagMed [0,1] Median magnitude error boolean mask
(median_errmask)
303-313 F11.6 --- RelAsym ?=-999 Relative Asymmetry
(Zakamska & Greene 2014MNRAS.442..784Z 2014MNRAS.442..784Z)
(rel_asym)
315 I1 --- f_RelAsym [0,1] Relative Asymmetry
(Zakamska & Greene 2014MNRAS.442..784Z 2014MNRAS.442..784Z)
boolean mask (rel_asymmask)
317-327 F11.6 --- M ?=-999 Magnitude Ratio/M-test value
(Kinemuchi et al. 2006AJ....132.1202K 2006AJ....132.1202K) (M)
329 I1 --- f_M [0,1] Magnitude Ratio/M-test value
(Kinemuchi et al. 2006AJ....132.1202K 2006AJ....132.1202K)
boolean mask (Mmask)
331-338 F8.3 mag magMax ?=-999 Maximum magnitude (max_mag)
340 I1 --- f_magMax [0,1] Maximum magnitude boolean mask
(max_magmask)
342-349 F8.3 mag magMin ?=-999 Minimum magnitude (min_mag)
351 I1 --- f_magMin [0,1] Minimum magnitude boolean mask
(min_magmask)
353 I1 --- EB [0,1] Eclipsing Binaries (EB)
--------------------------------------------------------------------------------
History:
From electronic version of the journal
(End) Luc Trabelsi [CDS] 10-Apr-2024