General catalogue of variable stars: Version GCVS 5.1

Samus, N. N.; Kazarovets, E. V.; Durlevich, O. V.; Kireeva, N. N.; Pastukhova, E. N.

doi:10.1134/s1063772917010085

Cited by 602 publications

(470 citation statements)

References 9 publications

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“…The object was not well observed after the termination of the superoutburst to detect possible rebrightenings. The maximum V -magnitude is taken from ASAS-3 prediscovery observation, which is not reflected on GCVS (Samus et al 2011) and other catalogs. 17.…”

Section: Acknowledgementsmentioning

confidence: 99%

WZ Sge-type dwarf novae

Katô

2015

Publications of the Astronomical Society of Japan

161

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We have summarized the current understanding and recently obtained findings about WZ Sge-type dwarf novae. We also reviewed the historical development of the understanding of these objects, provided the modern criteria, and reviewed the past research in relation to superhumps, early superhumps and the outburst mechanism. We regard that the presence of early superhumps (reflecting the 2:1 resonance) and long or multiple rebrightenings are the best distinguishing properties of WZ Sge-type dwarf novae. We provided the updated list of nearly 100 WZ Sge-type dwarf novae mainly based on the data obtained by the VSNET Collaboration up to Kato et al. (2015) and discussed the statistics. We could detect early superhumps with amplitude larger than 0.02 mag in 63% of the studied WZ Sge-type dwarf novae, which makes early superhumps a useful distinguishing feature for WZ Sge-type dwarf novae. Theoretical light curves of early superhumps generally appear to reproduce the existence of many low-amplitude objects, supporting the geometrical origin of early superhumps. Using the recently developed method of measuring mass ratios using developing phase of superhumps (stage A superhumps), we showed that there is a linear relation between the period variation of superhumps and the mass ratio in WZ Sge-type objects. By using this relation, we were able to draw an evolutionary picture of a large number of WZ Sge-type and identified the type of outburst to be an evolutionary sequence: type C → D → A → B → E, with some outliers for type-B objects. The duration of stage A (evolutionary phase) of superhumps is also well correlated with the estimated mass ratios. By using mass ratios from stage A superhumps and durarion of stage A, we have been able to identify best candidates for period bouncers.

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Section: Acknowledgementsmentioning

confidence: 99%

WZ Sge-type dwarf novae

Katô

2015

Publications of the Astronomical Society of Japan

161

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“…These sources however suffer from either small numbers, only being applicable to a few variable types, or in the Armstrong et al (2015) case using variability classes derived from the light curves rather than externally recognized types. We cross-matched the observed K2 targets in fields 0-3 (4 was not available at that time) with catalogues of known variable stars, including those from AAVSO, 3 GCVS (Samus et al 2009) and ASAS (Richards et al 2012). This led to a small number of targets (a few tens of each class at best), not enough for a full training set.…”

Section: Training Setmentioning

confidence: 99%

K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4

Armstrong¹,

Kirk²,

Lam³

et al. 2015

Mon. Not. R. Astron. Soc.

101

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We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximize the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star classification, through the combination of Kohonen Self-Organizing Maps (SOMs, an unsupervised machine learning algorithm) and the more common Random Forest (RF) supervised machine learning technique. We apply this method to data from the K2 mission fields 0-4, finding 154 ab-type RR Lyraes (10 newly discovered), 377 δ Scuti pulsators, 133 γ Doradus pulsators, 183 detached eclipsing binaries, 290 semidetached or contact eclipsing binaries and 9399 other periodic (mostly spotmodulated) sources, once class significance cuts are taken into account. We present light-curve features for all K2 stellar targets, including their three strongest detected frequencies, which can be used to study stellar rotation periods where the observed variability arises from spot modulation. The resulting catalogue of variable stars, classes, and associated data features are made available online. We publish our SOM code in PYTHON as part of the open source PYMVPA package, which in combination with already available RF modules can be easily used to recreate the method.

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“…Table 4 presents the list of newly identified variables, their colors from Udalski et al (2000) and the suggested classification according to the GCVS scheme (Samus' et al 2017). Table 4 also lists one new variable, LMC SC19 184609, which was not selected as a candidate variable by the final run of the NN classifier.…”

Section: Blind Test On the New Data Setmentioning

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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General catalogue of variable stars: Version GCVS 5.1

Cited by 602 publications

References 9 publications

WZ Sge-type dwarf novae

WZ Sge-type dwarf novae

K2 variable catalogue – II. Machine learning classification of variable stars and eclipsing binaries in K2 fields 0–4

Machine learning search for variable stars

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