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. =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: -------------------------------------------------------------------------------- FileName Lrecl Records Explanations -------------------------------------------------------------------------------- ReadMe 80 . This file table4.dat 536 631769 LAMOST variable source candidates catalog -------------------------------------------------------------------------------- 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 {delta} 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 -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 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 -------------------------------------------------------------------------------- Note (1): Q value following Equation (1) is: Q= |m_max_-m_min_|/({sigma}_max_^2^+{sigma}_min_^2^)^0.5^ where m_max_ and m_min_ are the maximum and minimum magnitude in the light curves, respectively. Terms {sigma}_max_ and {sigma}_min_ 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: {sigma}=(1/(N-1){Sigma}_i_(m_i_-))^0.5^ where N is the number of detection times of light curves from the ZTF catalog, m_i_ 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 (C_{nu}_) following Equation (3) is: C_{nu}_={sigma}/ The C_{nu}_ 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 C_{nu}_ 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(|m_i_-^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. -------------------------------------------------------------------------------- History: From electronic version of the journal ================================================================================ (End) Prepared by [AAS], Emmanuelle Perret [CDS] 04-Aug-2022