J/ApJS/259/11 LAMOST variable sources based on ZTF phot. (Xu+, 2022)
A catalog of LAMOST variable sources based on time-domain photometry of ZTF.
Xu T., Liu C., Wang F., Huang W., Deng H., Mei Y., Cao Z.
<Astrophys. J. Suppl. Ser., 259, 11 (2022)>
=2022ApJS..259...11X 2022ApJS..259...11X
ADC_Keywords: Stars, variable; Photometry; Optical; Models; Surveys
Keywords: Variable stars ; Light curves ; Cross-validation
Abstract:
The identification and analysis of different variable sources is a hot
topic in astrophysical research. The Large Sky Area Multi-Object Fiber
Spectroscopic Telescope (LAMOST) spectroscopic survey has accumulated
a mass of spectral data but contains no information about variable
sources. Although a few related studies present variable source
catalogs for the LAMOST, the studies still have a few deficiencies
regarding the type and number of variable sources identified. In this
study, we present a statistical modeling approach to identify variable
source candidates. We first cross-match the Kepler, Sloan Digital Sky
Survey, and Zwicky Transient Facility catalogs to obtain light-curve
data of variable and nonvariable sources. The data are then modeled
statistically using commonly used variability parameters. Then, an
optimal variable source identification model is determined using the
Receiver Operating Characteristic curve and four credible evaluation
indices such as precision, accuracy, recall, and F1-score. Based on
this identification model, a catalog of LAMOST variable sources
(including 631,769 variable source candidates with a probability
greater than 95%, and so on) is obtained. To validate the correctness
of the catalog, we perform a two-by-two cross-comparison with the Gaia
catalog and other published variable source catalogs. We achieve the
correct rate ranging from 50% to 100%. Among the 123,756 sources
cross-matched, our variable source catalog identifies 85,669 with a
correct rate of 69%, which indicates that the variable source catalog
presented in this study is credible.
Description:
In this study, we tried a statistical modeling approach to the
identification of LAMOST variable sources. The specific implementation
is described in Section 2 in detail. We further apply these models to
the data sets of LAMOST DR6 and ZTF DR2 and get the final catalog of
LAMOST variable source candidates in Section 3.
The ZTF DR2 contains light-curve data acquired between 2018 March and
2019 June, covering a time span of around 470 days.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table4.dat 536 631769 LAMOST variable source candidates catalog
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See also:
II/351 : VISTA Magellanic Survey (VMC) catalog (Cioni+, 2011)
V/156 : LAMOST DR7 catalogs (Luo+, 2019)
J/A+A/405/231 : Algol-type EBs differential photometry (Kim+, 2003)
J/AJ/134/973 : SDSS Stripe 82 star catalogs (Ivezic+, 2007)
J/A+A/557/L10 : Rotation periods of 12000 Kepler stars (Nielsen+, 2013)
J/A+A/560/A4 : Rotation periods of active Kepler stars (Reinhold+, 2013)
J/ApJS/213/9 : Catalina Surveys periodic variable stars (Drake+, 2014)
J/AJ/151/101 : Kepler Mission. VIII. False positives (Abdul-Masih+, 2016)
J/MNRAS/460/1970 : Kepler δ Sct stars amp. modulation (Bowman+, 2016)
J/AJ/151/68 : Kepler Mission. VII. Eclipsing binaries in DR3 (Kirk+, 2016)
J/MNRAS/469/3688 : CSS Periodic Variable Star Catalogue (Drake+, 2017)
J/ApJS/237/28 : WISE catalog of periodic variable stars (Chen+, 2018)
J/A+A/616/A10 : Ppen clusters GaiaDR2 HR diagrams (Gaia Collaboration, 2018)
J/A+A/622/A60 : Gaia DR2 misclassified RR Lyrae list (Clementini+, 2019)
J/ApJS/249/18 : The ZTF catalog of periodic variable stars (Chen+, 2020)
J/ApJS/249/22 : Radial velocity variable stars from LAMOST DR4 (Tian+, 2020)
J/A+A/645/A34 : LAMOST DR4 New mercury-manganese stars (Paunzen+, 2021)
http://www.ztf.caltech.edu/ : Zwicky Transient Facility home page
Byte-by-byte Description of file: table4.dat
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Bytes Format Units Label Explanations
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1- 12 F12.8 deg RAdeg Right Ascension (J2000)
14- 25 F12.9 deg DEdeg [-6.8/80.3] Declination (J2000)
27- 42 I16 --- gID ? ZTF g band identifier (oid)
44- 59 I16 --- rID ? ZTF r band identifier (oid)
61- 73 F13.9 --- Q(g) [1.89/335.2]? Q value in g band (1)
75- 87 F13.9 --- Q(r) [3.25/317.6]? Q value in r band (1)
89- 99 F11.9 --- P(Q-g) [0.17/0.99]? Variability probability of Q in
g band
101-111 F11.9 --- P(Q-r) [0.2/0.99]? Variability probability of Q in
r band
113-125 F13.9 --- Q1(g) [1.9/330.43]? Q1 value in g band (2)
127-139 F13.9 --- Q1(r) [2.86/298.2]? Q1 value in r band (2)
141-151 F11.9 --- P(Q1-g) [0.024/1]? Variability probability of Q1 in
g band
153-163 F11.9 --- P(Q1-r) [0.19/1]? Variability probability of Q1 in r
band
165-175 E11.5 --- Q2(g) [4.5/316.2]? Q2 value in g band (3)
176 A1 --- n_Q2(g) [i] i=inf
178-190 F13.9 --- Q2(r) [4.6/296.7]? Q2 value in r band (3)
192-202 F11.9 --- P(Q2-g) [0.95/1]? Variability probability of Q2 in g
band
204-214 F11.9 --- P(Q2-r) [0.95/1]? Variability probability of Q2 in r
band
216-226 F11.9 --- Std(g) [0.0096/2.73]? Standard Deviation in
g-band (4)
228-238 F11.9 --- Std(r) [0.0079/1.95]? Standard Deviation in
r-band (4)
240-250 F11.9 --- P(Std-g) [0.016/0.98]? Variability probability of
standard Deviation in g band
252-262 F11.9 --- P(Std-r) [0.016/0.98]? Variability probability of
standard Deviation in r band
264-274 F11.9 --- iStd(g) [0.004/3.02]? Iterative standard deviation
in g band (5)
276-286 F11.9 --- iStd(r) [0.004/2.18]? Iterative standard deviation
in r band (5)
288-298 F11.9 --- P(iStd-g) [0.008/1]? Variability probability of
iterative standard deviation in g band
300-310 F11.9 --- P(iStd-r) [0.008/1]? Variability probability of
iterative standard deviation in r band
312-322 F11.9 --- Var(g) [0.0006/0.16]? Coefficient of variation (Cv)
in g band (6)
324-334 F11.9 --- Var(r) [0.00058/0.14]? Coefficient of variation (Cv)
in r band (6)
336-346 F11.9 --- P(Var-g) [0.04/0.91]? Variability probability of
coefficient of variation in g band
348-358 F11.9 --- P(Var-r) [0.04/0.93]? Variability probability of
coefficient of variation in r band
360-371 E12.6 --- sKu(g) [-1.94/514.1]? Small kurtosis in g band (7)
373-384 E12.6 --- sKu(r) [-1.97/671.5]? Small kurtosis in r band (7)
386-396 F11.9 --- P(sKu-g) [0.27/0.9]? Variability probability of small
kurtosis in g band
398-408 F11.9 --- P(sKu-r) [0.27/0.9]? Variability probability of small
kurtosis in r band
410-421 E12.6 --- Skew(g) [-22.42/21.5]? Skewness in g band (7)
423-434 E12.6 --- Skew(r) [-25.4/25]? Skewness in r band (7)
436-446 F11.9 --- P(Skew-g) [0.16/0.86]? Variability probability of
skewness in g band
448-458 F11.9 --- P(Skew-r) [0.16/0.86]? Variability probability of
skewness in r band
460-465 F6.4 --- MAD(g) [0.004/2.8]? Median absolute deviation in g
band (8)
467-472 F6.4 --- MAD(r) [0.003/1.6]? Median absolute deviation in r
band (8)
474-484 F11.9 --- P(MAD-g) [0.008/0.97]? Variability probability of
Median absolute deviation in g band
486-496 F11.9 --- P(MAD-r) [0.008/0.97]? Variability probability of
Median absolute deviation in r band
498-504 F7.5 --- Amp(g) [0.017/3.6]? Amplitude in g band (9)
506-512 F7.5 --- Amp(r) [0.014/3.1]? Amplitude in r band (9)
514-524 F11.9 --- P(gAmp) [0/1]? Variability probability of amplitude
in g band
526-536 F11.9 --- P(rAmp) [0/1]? Variability probability of amplitude
in r band
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Note (1): Q value following Equation (1) is:
Q= |mmax-mmin|/(σmax2+σmin2)0.5
where mmax and mmin are the maximum and minimum magnitude in the
light curves, respectively. Terms σmax and σmin are
their magnitude measurement errors.
Note (2): Q1 is a variant form of Q. After the maximum and minimum magnitude
of the light curves are removed, we recalculated this parameter by the
same calculation method as Q.
Note (3): Q2 is also a variant form of Q. After removing the maximum, minimum,
submaximum, and subminimum magnitudes of the light curves,
recalculated this parameter by the same calculation method as Q.
Note (4): Standard deviation (Std) following Equation (2) is:
σ=(1/(N-1)Σi(mi-))0.5
where N is the number of detection times of light curves from the ZTF
catalog, mi is the magnitude of each observation in the light
curves, and is the mean of magnitude.
Note (5): After calculating the standard deviation of the light curve,
the data other than the median plus or minus twice the standard
deviation are removed, and the standard deviation is recalculated.
This process is repeated until the resulting standard deviation
converges to a stable value.
Note (6): Coefficient of variation (Cν) following Equation (3) is:
Cν=σ/
The Cν is a simple variability index and is defined as the ratio
of the standard deviation to the mean magnitude. If a light curve has
substantial variability, the Cν of this light curve is generally
significant.
Note (7): See Equations (4) and (5) in Section 2.1. For a normal distribution,
the small Kurtosis and the skewness should be equal to zero.
Note (8): The median absolute deviation (MAD) is described as the median
discrepancy of the data from the median data. Following Equation (6):
MAD=m(|mi_-^m|)
where ^m is the median of the magnitude. A normal distribution should
have a value of about 0.675. The interquartile ranges of a normal
distribution can be used to illustrate this.
Note (9): The amplitude is half of the difference between the median of the
maximum and minimum 5% magnitudes. The amplitude of a set of numbers
from 0 to 1000 should be 475.5.
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History:
From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 04-Aug-2022