VaST: A variability search toolkit

Sokolovsky, K. V.; Lebedev, A. A.

doi:10.1016/j.ascom.2017.12.001

Cited by 19 publications

(33 citation statements)

References 115 publications

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“…The variability detection threshold for these tests often has to be determined empirically for a given data set. Sokolovsky et al (2017) investigated 24 "variability indices" (also referred to as "light curve features") -statistical characteristics quantifying scatter and correlation between points in a light curve. The ability of these indices used individually or in a linear combination to discriminate variable objects from nonvariable ones was compared using multiple real and simulated data sets.…”

Section: Introductionmentioning

confidence: 99%

Machine learning search for variable stars

Pashchenko

Sokolovsky

Gavras

2017

Monthly Notices of the Royal Astronomical Society

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Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SV M), k Nearest Neighbors (kNN) Neural Nets (NN), Random Forests (RF) and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of OGLE-II Large Magellanic Cloud (LMC) photometry (30265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SV M and RF show a higher efficiency compared to LR and kNN.

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Section: Introductionmentioning

confidence: 99%

Machine learning search for variable stars

Pashchenko

Sokolovsky

Gavras

2017

Monthly Notices of the Royal Astronomical Society

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“…To perform aperture photometry and magnitude calibration, we used VaST 4 software (Sokolovsky & Lebedev, 2018). We derived magnitudes of an ensemble of comparison stars within the field of view from the APASS (B, V , R c , I c ) and the PanSTARRS1 (g, r, i) survey (Chambers et al, 2016).…”

Section: Observationsmentioning

confidence: 99%

New SU UMa-type star ZTF18abdlzhd in the Zwicky Transient Facility data

Antipin,

Zubareva,

Belinski

et al. 2021

Preprint

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We carried out a search for unknown dwarf novae in a public data release of the Zwicky Transient Facility survey and suspected that the object ZTF18abdlzhd is a SU UMa-type star. Performed multicolor CCD observations permit us to follow its fading from an outburst in August and an entire superoutburst in October 2020. The duration of the superoutburst is 13 days. We detected superhumps with period P = 0. d 06918(3) that are characteristic of UGSU type stars.

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“…The definitions of these indices may be found in Sokolovsky et al (2017b) while Pashchenko et al (2018) discuss correlations between the indices (the degree of correlation depends on the data). The VaST code (Sokolovsky & Lebedev 2018) was used to perform the simulations.…”

Section: Appendix A: Comparison Of Variability Indices With Hsc-based...mentioning

confidence: 99%

The Hubble Catalog of Variables (HCV)

Bonanos

Yang

Sokolovsky

et al. 2019

A&A

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Aims. Over its lifetime and despite not being a survey telescope, the Hubble Space Telescope (HST) has obtained multi-epoch observations by multiple, diverse observing programs, providing the opportunity for a comprehensive variability search aiming to uncover new variables. We have therefore undertaken the task of creating a catalog of variable sources based on archival HST photometry. In particular, we have used version 3 of the Hubble Source Catalog (HSC), which relies on publicly available images obtained with the WFPC2, ACS, and WFC3 instruments on board the HST. Methods. We adopted magnitude-dependent thresholding in median absolute deviation (a robust measure of light curve scatter) combined with sophisticated preprocessing techniques and visual quality control to identify and validate variable sources observed by Hubble with the same instrument and filter combination five or more times. Results. The Hubble Catalog of Variables (HCV) includes 84,428 candidate variable sources (out of 3.7 million HSC sources that were searched for variability) with V ≤ 27 mag; for 11,115 of them the variability is detected in more than one filter. The data points in the light curves of the variables in the HCV catalog range from five to 120 points (typically having less than ten points); the time baseline ranges from under a day to over 15 years; while ∼8% of all variables have amplitudes in excess of 1 mag. Visual inspection performed on a subset of the candidate variables suggests that at least 80 % of the candidate variables that passed our automated quality control are true variable sources rather than spurious detections resulting from blending, residual cosmic rays, and calibration errors.Conclusions. The HCV is the first, homogeneous catalog of variable sources created from the highly diverse, archival HST data and currently is the deepest catalog of variables available. The catalog includes variable stars in our Galaxy and nearby galaxies, as well as transients and variable active galactic nuclei. We expect that the catalog will be a valuable resource for the community. Possible uses include searches for new variable objects of a particular type for population analysis, detection of unique objects worthy of follow-up studies, identification of sources observed at other wavelengths, and photometric characterization of candidate progenitors of supernovae and other transients in nearby galaxies. The catalog is available to the community from the ESA Hubble Science Archive (eHST) at the European Space Astronomy Centre (ESAC) and the Mikulski Archive for Space Telescopes (MAST) at Space Telescope Science Institute (STScI).

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VaST: A variability search toolkit

Cited by 19 publications

References 115 publications

Machine learning search for variable stars

Machine learning search for variable stars

New SU UMa-type star ZTF18abdlzhd in the Zwicky Transient Facility data

The Hubble Catalog of Variables (HCV)

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