J/ApJS/272/1 Periodic variable stars from LAMOST DR9 (Qiao+, 2024)
A classification catalog of periodic variable stars for LAMOST DR9 based on
machine learning.
Qiao P., Xu T., Wang F., Mei Y., Deng H., Tan L., Liu C.
<Astrophys. J. Suppl. Ser., 272, 1 (2024)>
=2024ApJS..272....1Q 2024ApJS..272....1Q
ADC_Keywords: Stars, variable; Colors; Surveys; Optical; Photometry, infrared;
Models
Keywords: Catalogs ; Variable stars ; Cross-validation ; Light curves
Abstract:
Identifying and classifying variable stars is essential to time-domain
astronomy. The Large Area Multi-Object Fiber Optic Spectroscopic
Telescope (LAMOST) acquired a large amount of spectral data. However,
there is no corresponding variable source-related information in the
data, constraining LAMOST data utilization for scientific research. In
this study, we systematically investigated variable source
classification methods for LAMOST data. We constructed a 10-class
classification model using three mainstream machine-learning methods.
Through performance comparison, we chose the LightGBM and XGBoost
models. We further identified variable source candidates in the r band
in LAMOST DR9 and obtained 281,514 variable source candidates with
probabilities greater than 95%. Subsequently, we filtered out the
sources of periodic variable sources using the generalized
Lomb-Scargle periodogram and classified these periodic variable
sources using the classification model. Finally, we propose a reliable
periodic variable star catalog containing 176,337 stars with specific
types.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table6.dat 327 176337 *Classification results of variable stars for
LAMOST DR9 based on LightGBM and XGBoost
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Note on table6.dat: LightGBM is a machine-learning algorithm based on
gradient-boosted decision trees proposed by Microsoft in 2016
(Ke G., Meng Q., Finley T.+ 2017 Advances in Neural Information
Processing Systems 30 ed I. Guyon et al. (NeurIPS)). See Section 3.1.
The XGBoost algorithm, proposed by Tianqi Chen at the University of
Washington, is a powerful tool for classification and regression tasks
(Chen & Guestrin 2016, in Proceedings of the 22nd ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining,
KDD'16 (New York, NY, USA: Association for Computing Machinery));
see Jia et al. 2023RAA....23j5012J 2023RAA....23j5012J
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See also:
II/246 : 2MASS All-Sky Catalog of Point Sources (Cutri+ 2003)
II/328 : AllWISE Data Release (Cutri+ 2013)
V/156 : LAMOST DR7 catalogs (Luo+, 2019)
I/350 : Gaia EDR3 (Gaia Collaboration, 2020)
II/366 : ASAS-SN catalog of variable stars (Jayasinghe+, 2018-2020)
J/AJ/134/973 : SDSS Stripe 82 star catalogs (Ivezic+, 2007)
J/MNRAS/414/2602 : Automated classification of HIP variables (Dubath+, 2011)
J/AJ/146/101 : LINEAR. III. Cat. of periodic variables (Palaversa+, 2013)
J/ApJS/213/9 : Catalina Surveys periodic variable stars (Drake+, 2014)
J/A+A/566/A43 : EPOCH Project. EROS-2 LMC periodic variables (Kim+, 2014)
J/A+A/580/A17 : α-element abund. of Cepheid stars (Genovali+, 2015)
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/ApJS/259/11 : LAMOST variable sources based on ZTF phot. (Xu+, 2022)
J/ApJS/267/7 : YSO candidates from LAMOST LRS DR9 & ZTF (Zhang+, 2023)
J/A+A/675/A195 : ZTF DR11 classif. in ZTF/4MOST sky (Sanchez-Saez+, 2023)
Byte-by-byte Description of file: table6.dat
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Bytes Format Units Label Explanations
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1- 9 I9 --- LAMOST [101092/914316131] LAMOST DR9 source
identifier
11- 26 I16 --- ZTF ZTF DR11 Source identifier
28- 37 F10.6 deg RAdeg Right Ascension (J2000)
39- 47 F9.6 deg DEdeg [-9.3/87] Declination (J2000)
49- 55 F7.4 mas plx [-9/52.1]? Gaia DR3 parallax
57- 67 F11.6 d Per [0.02/1000] Period, best derived period (1)
69- 72 A4 --- Type Variable source type (2)
74- 81 F8.6 --- IterStd [0.0049/2.6] Recalculated standard
deviation (3)
83- 86 I4 --- Nobs [50/1311] Number of observations
88- 95 F8.6 --- Std [0.007/2] Magnitude standard deviation
97- 106 A10 --- Skew Skewness of light curves
108- 117 A10 --- Kurt Kurtosis of the magnitude distribution
119- 126 F8.6 --- Amp [0.015/3.1] Amplitude (4)
128- 139 F12.6 --- chi2 [0.4/29701] χ2 statistic
141- 148 F8.6 --- Cusum [0.027/2.22] Range of magnitude cumulative
sums (5)
150- 157 F8.6 --- MAD [0.003/1.7] Median Absolute Deviation
159- 175 F17.6 --- sKurt [-2.4/1341195100] Small sample kurtosis of
the magnitudes
177- 184 F8.6 --- Eta [0.01/2.7] Von Neumann statistic,
Kim+ (2014, J/A+A/566/A43)
186- 193 F8.6 --- SWTest [0.03/1] Shapiro-Wilk normality test
statistics (Shapiro & Wilk 1965,
doi:10.1093/biomet/52.3-4.591)
195- 202 F8.6 --- KSVar [0.2/1] Stetson K statistic
(Stetson 1994PASP..106..250S 1994PASP..106..250S)
204- 212 F9.6 --- HMAR [0.03/24.7] Half magnitude amplitude ratio
(6)
214- 221 F8.6 --- fu1std [0.006/0.7] Fraction of observations
beyond 1 standard deviation
(D'Isanto+ 2016MNRAS.457.3119D 2016MNRAS.457.3119D)
223- 231 A9 --- gskew Median-of-magnitudes based measure of the
skew
233- 240 F8.6 --- MedBRP [0.006/1] Fraction, photometric points
within amplitude/10 of median (7)
242- 249 F8.6 --- f1power [0/1] f1 power (8)
251- 258 F8.6 --- PerAmp [0.001/0.4] Largest difference, min or max
magnitude and median (9)
260- 267 F8.5 mag BP-RP [-0.6/7.6]? Gaia DR3 color, BP-RP
269- 276 A8 mag G-BP Gaia DR3 color, G-BP
278- 285 F8.5 mag G-RP [-0.5/4.2]? Gaia DR3 color, G-RP
287- 292 F6.3 mag W1-W2 [-2.3/2.5] ALLWISE color, W1-W2
294- 299 F6.3 mag W2-W3 [-2.8/8.5]? ALLWISE color, W2-W3
301- 306 F6.3 mag W1-W3 [-2.9/8.5]? ALLWISE color, W1-W3
308- 313 F6.3 mag J-Ks [-1.3/3.6]? 2MASS color, J-Ks
315- 320 F6.3 mag J-H [-1.3/2.1]? 2MASS color, J-H
322- 327 F6.3 mag H-Ks [-1.9/2.6]? 2MASS color, H-Ks
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Note (1): Best period derived from the LAMOST observations following
Lomb (1976Ap&SS..39..447L 1976Ap&SS..39..447L); Scargle (1982ApJ...263..835S 1982ApJ...263..835S);
Kovacs+ (2002A&A...391..369K 2002A&A...391..369K); Schwarzenberg-Czerny (1996ApJ...460L.107S 1996ApJ...460L.107S);
Stellingwerf (1978ApJ...224..953S 1978ApJ...224..953S).
Note (2): Variable source types as follows:
CEP = Cepheid-type variable (56 occurrences)
DSCT = δ-Scuti variables (10973 occurrences)
EA = Algol-type binaries (55764 occurrences)
EB = β-Lyrae type binaries (4671 occurrences)
EW = W Ursae Majoris type binaries (13774 occurrences)
Mira = Mira type variables (87 occurrences)
ROT = Rotational variables (79170 occurrences)
RRAB = RR Lyra, ab-type (2839 occurrences)
RRC = RR Lyra, c-type (1493 occurrences)
SR = Semi-regular variables (7510 occurrences)
Note (3): The data other than the median plus or minus twice the
standard deviation are removed, and the standard deviation is
recalculated (Xu+ 2022, J/ApJS/259/11).
Note (4): Half of the difference between the median of the maximum and
minimum 5% magnitudes.
Note (5): The range of magnitude cumulative sums (Kim+ 2011, J/ApJS/259/11).
Note (6): Half magnitude amplitude ratio, ratio of magnitudes fainter or
brighter than the average (Kim & Bailer-Jones 2016A&A...587A..18K 2016A&A...587A..18K).
Note (7): Fraction (≤1) of photometric points within amplitude/10 of
the median magnitude (Richards+ 2011ApJ...733...10R 2011ApJ...733...10R).
Note (8): (ξ20-ξ2)/ξ20-ξ2 of the fit
(van Roestel+ 2021AJ....161..267V 2021AJ....161..267V)
Note (9): Largest percentage difference between either the max or min
magnitude and the median (Richards+ 2011ApJ...733...10R 2011ApJ...733...10R).
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History:
From electronic version of the journal
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 26-Jul-2024