VARTOOLS: A program for analyzing astronomical time-series data

被引:134
|
作者
Hartman, J. D. [1 ]
Bakos, G. A. [1 ]
机构
[1] Princeton Univ, Dept Astrophys Sci, 4 Ivy Lane, Princeton, NJ 08544 USA
关键词
Methods: data analysis; Methods: statistical; Time; Techniques: photometric; PERIOD ANALYSIS; SPECTRAL-ANALYSIS; LIGHT CURVES; ALGORITHM; SEARCH; TRANSITS; PLANET; PHOTOMETRY; NOISE; MODEL;
D O I
10.1016/j.ascom.2016.05.006
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
This paper describes the VARTOOLS program, which is an open-source command-line utility, written in C, for analyzing astronomical time-series data, especially light curves. The program provides a general-purpose set of tools for processing light curves including signal identification, filtering, light curve manipulation, time conversions, and modeling and simulating light curves. Some of the routines implemented include the Generalized Lomb-Scargle periodogram, the Box-Least Squares transit search routine, the Analysis of Variance periodogram, the Discrete Fourier Transform including the CLEAN algorithm, the Weighted Wavelet Z-Transform, light curve arithmetic, linear and non-linear optimization of analytic functions including support for Markov Chain Monte Carlo analyses with non-trivial covariances, characterizing and/or simulating time-correlated noise, and the TFA and SYSREIVI filtering algorithms, among others. A mechanism is also provided for incorporating a user's own compiled processing routines into the program. VARTOOLS is designed especially for batch processing of light curves, including built-in support for parallel processing, making it useful for large time-domain surveys such as searches for transiting planets. Several examples are provided to illustrate the use of the program. (C) 2016 The Author(s). Published by Elsevier B.V.
引用
收藏
页码:1 / 72
页数:72
相关论文
共 50 条
  • [1] PERIODOGRAMS FOR MULTIBAND ASTRONOMICAL TIME SERIES
    VanderPlas, Jacob T.
    Ivezic, Zeljko
    ASTROPHYSICAL JOURNAL, 2015, 812 (01)
  • [2] Wotan: Comprehensive Time-series Detrending in Python']Python
    Hippke, Michael
    David, Trevor J.
    Mulders, Gijs D.
    Heller, Rene
    ASTRONOMICAL JOURNAL, 2019, 158 (04)
  • [3] Detrending time series for astronomical variability surveys
    Kim, Dae-Won
    Protopapas, Pavlos
    Alcock, Charles
    Byun, Yong-Ik
    Bianco, Federica B.
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2009, 397 (01) : 558 - 568
  • [4] THE ANALYSIS OF INDEXED ASTRONOMICAL TIME-SERIES .1. BASIC METHODS
    KOEN, C
    LOMBARD, F
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 1993, 263 (02) : 287 - 308
  • [5] ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA
    Richards, Joseph W.
    Starr, Dan L.
    Butler, Nathaniel R.
    Bloom, Joshua S.
    Brewer, John M.
    Crellin-Quick, Arien
    Higgins, Justin
    Kennedy, Rachel
    Rischard, Maxime
    ASTROPHYSICAL JOURNAL, 2011, 733 (01)
  • [6] ATAT: Astronomical Transformer for time series and Tabular data
    Cabrera-Vives, G.
    Moreno-Cartagena, D.
    Astorga, N.
    Reyes-Jainaga, I.
    Foerster, F.
    Huijse, P.
    Arredondo, J.
    Munoz Arancibia, A. M.
    Bayo, A.
    Catelan, M.
    Estevez, P. A.
    Sanchez-Saez, P.
    Alvarez, A.
    Castellanos, P.
    Gallardo, P.
    Moya, A.
    Rodriguez-Mancini, D.
    ASTRONOMY & ASTROPHYSICS, 2024, 689
  • [7] The analysis of indexed astronomical time-series - VIII. Cross-correlating noisy autoregressive series
    Koen, C
    MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, 2003, 344 (03) : 798 - 808
  • [8] Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series
    Foreman-Mackey, Daniel
    Agol, Eric
    Ambikasaran, Sivaram
    Angus, Ruth
    ASTRONOMICAL JOURNAL, 2017, 154 (06)
  • [9] DETECTING VARIABILITY IN MASSIVE ASTRONOMICAL TIME-SERIES DATA. II. VARIABLE CANDIDATES IN THE NORTHERN SKY VARIABILITY SURVEY
    Shin, Min-Su
    Yi, Hahn
    Kim, Dae-Won
    Chang, Seo-Won
    Byun, Yong-Ik
    ASTRONOMICAL JOURNAL, 2012, 143 (03)
  • [10] On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
    Jamal, Sara
    Bloom, Joshua S.
    ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES, 2020, 250 (02)