scholarly journals The ASAS-SN catalogue of variable stars – IV. Periodic variables in the APOGEE survey

2019 ◽  
Vol 487 (4) ◽  
pp. 5932-5945 ◽  
Author(s):  
Michał Pawlak ◽  
O Pejcha ◽  
P Jakubčík ◽  
T Jayasinghe ◽  
C S Kochanek ◽  
...  
Keyword(s):  
Visual Inspection ◽  
Variable Stars ◽  
Eclipsing Binaries ◽  
Spectral Lines ◽  
Light Curves ◽  
Machine Learning Techniques ◽  
Red Giants ◽  
Rr Lyrae ◽  
Learning Techniques ◽  
Evolution Experiment

ABSTRACT We explore the synergy between photometric and spectroscopic surveys by searching for periodic variable stars among the targets observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) using photometry from the All-Sky Automated Survey for Supernovae (ASAS-SN). We identified 1924 periodic variables among more than $258\, 000$ APOGEE targets; 465 are new discoveries. We homogeneously classified 430 eclipsing and ellipsoidal binaries, 139 classical pulsators (Cepheids, RR Lyrae, and δ Scuti), 719 long-period variables (pulsating red giants), and 636 rotational variables. The search was performed using both visual inspection and machine learning techniques. The light curves were also modelled with the damped random walk stochastic process. We find that the median [Fe/H] of variable objects is lower by 0.3 dex than that of the overall APOGEE sample. Eclipsing binaries and ellipsoidal variables are shifted to a lower median [Fe/H] by 0.2 dex. Eclipsing binaries and rotational variables exhibit significantly broader spectral lines than the rest of the sample. We make ASAS-SN light curves for all the APOGEE stars publicly available and provide parameters for the variable objects.

scholarly journals Identifying Variable Stars from Kepler Data Using Machine Learning

2021 ◽  
Vol 3 (4) ◽  
pp. 32-37
Author(s):  
J. Adassuriya ◽  
J. A. N. S. S. Jayasinghe ◽  
K. P. S. C. Jayaratne
Keyword(s):  
Machine Learning ◽  
Variable Stars ◽  
Modern Technology ◽  
Machine Learning Algorithms ◽  
Light Curves ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Red Giants ◽  
Kepler Mission

Machine learning algorithms play an impressive role in modern technology and address automation problems in many fields as these techniques can be used to identify features with high sensitivity, which humans or other programming techniques aren’t capable of detecting. In addition, the growth of the availability of the data demands the need of faster, accurate, and more reliable automating methods of extracting information, reforming, and preprocessing, and analyzing them in the world of science. The development of machine learning techniques to automate complex manual programs is a time relevant research in astrophysics as it’s a field where, experts are dealing with large sets of data every day. In this study, an automated classification was built for 6 types of star classes Beta Cephei, Delta Scuti, Gamma Doradus, Red Giants, RR Lyrae and RV Tarui with widely varying properties, features extracted from training dataset of stellar light curves obtained from Kepler mission. The Random Forest classification model was used as the Machine Learning model and both periodic and non-periodic features extracted from light curves were used as the inputs to the model. Our implementation achieved an accuracy of 86.5%, an average precision level of 0.86, an average recall value of 0.87, and average F1-Score of 0.86 for the testing dataset obtained from the Kepler mission.

scholarly journals SuperWASP variable stars: classifying light curves using citizen science

2021 ◽  
Vol 502 (1) ◽  
pp. 1299-1311
Author(s):  
Heidi B Thiemann ◽  
Andrew J Norton ◽  
Hugh J Dickinson ◽  
Adam McMaster ◽  
Ulrich C Kolb
Keyword(s):  
Variable Stars ◽  
Eclipsing Binaries ◽  
Light Curves ◽  
Data Set ◽  
Resultant Data ◽  
Sky Survey ◽  
Short Period ◽  
Contact Binaries ◽  
Analysis Of Results

ABSTRACT We present the first analysis of results from the SuperWASP variable stars Zooniverse project, which is aiming to classify 1.6 million phase-folded light curves of candidate stellar variables observed by the SuperWASP all sky survey with periods detected in the SuperWASP periodicity catalogue. The resultant data set currently contains >1 million classifications corresponding to >500 000 object–period combinations, provided by citizen–scientist volunteers. Volunteer-classified light curves have ∼89 per cent accuracy for detached and semidetached eclipsing binaries, but only ∼9 per cent accuracy for rotationally modulated variables, based on known objects. We demonstrate that this Zooniverse project will be valuable for both population studies of individual variable types and the identification of stellar variables for follow-up. We present preliminary findings on various unique and extreme variables in this analysis, including long-period contact binaries and binaries near the short-period cut-off, and we identify 301 previously unknown binaries and pulsators. We are now in the process of developing a web portal to enable other researchers to access the outputs of the SuperWASP variable stars project.

scholarly journals Studying bright variable stars with the Multi-site All-Sky CAmeRA (MASCARA)

2018 ◽  
Vol 617 ◽  
pp. A32 ◽  
Author(s):  
O. Burggraaff ◽  
G. J. J. Talens ◽  
J. Spronck ◽  
A.-L. Lesage ◽  
R. Stuik ◽  
...  
Keyword(s):  
Variable Stars ◽  
Eclipsing Binaries ◽  
Light Curves ◽  
Previous Record ◽  
La Palma ◽  
Powerful Means ◽  
New Variables ◽  
Candidate Variable ◽  
Order Variability

Context. The Multi-site All-Sky CAmeRA (MASCARA) aims to find the brightest transiting planet systems by monitoring the full sky at magnitudes 4 < V < 8.4, taking data every 6.4 s. The northern station has been operational on La Palma since February 2015. These data can also be used for other scientific purposes, such as the study of variable stars. Aims. In this paper we aim to assess the value of MASCARA data for studying variable stars by determining to what extent known variable stars can be recovered and characterised, and how well new, unknown variables can be discovered. Methods. We used the first 14 months of MASCARA data, consisting of the light curves of 53 401 stars with up to one million flux points per object. All stars were cross-matched with the VSX catalogue to identify known variables. The MASCARA light curves were searched for periodic flux variability using generalised Lomb–Scargle periodograms. If significant variability of a known variable was detected, the found period and amplitude were compared with those listed in the VSX database. If no previous record of variability was found, the data were phase folded to attempt a classification. Results. Of the 1919 known variable stars in the MASCARA sample with periods 0.1 < P < 10 days, amplitudes >2%, and that have more than 80 h of data, 93.5% are recovered. In addition, the periods of 210 stars without a previous VSX record were determined, and 282 candidate variable stars were newly identified. We also investigated whether second order variability effects could be identified. The O’Connell effect is seen in seven eclipsing binaries, of which two have no previous record of this effect. Conclusions. MASCARA data are very well suited to study known variable stars. They also serve as a powerful means to find new variables among the brightest stars in the sky. Follow-up is required to ensure that the observed variability does not originate from faint background objects.

scholarly journals Professional/Amateur Cooperation in the Study of Variable Stars as Illustrated by the Fourth Edition of the GCVS

1988 ◽  
Vol 98 ◽  
pp. 188-189
Author(s):  
M.S. Frolov
Keyword(s):  
Classical Group ◽  
Cataclysmic Variables ◽  
Variable Stars ◽  
Eclipsing Binaries ◽  
Fourth Edition ◽  
Rr Lyrae Stars ◽  
Rr Lyrae ◽  
Long Time ◽  
Hand Involvement ◽  
Lyrae Stars

Let us divide variable stars into two main groups: the first “classical” group, includes objects known for a long time, such as Cepheids, RR-Lyrae stars, Miras, cataclysmic variables, eclipsing binaries, etc. The second group includes micropulsating variables of δ Scuti and β Cephei types, magnetic variables, rotating variables of BY Draconis type, etc.Historically, the contribution of amateurs in investigating the first group was very significant, and it continues to increase. On the other hand, involvement in studying the second group of stars was practically equal to zero some years ago, but today one can see the beginnings of an expansion of amateur work on this second group of variables – among brighter objects, of course. One reason is the beginning of cooperation between amateurs and professional astronomers having powerful instruments.

scholarly journals A Comparison Between Binary Star Light Curves and Those of Possible Binary Asteroids

1984 ◽  
Vol 80 ◽  
pp. 387-392
Author(s):  
H. J. Schober
Keyword(s):  
Light Curve ◽  
Binary Star ◽  
Variable Stars ◽  
Eclipsing Binaries ◽  
Light Curves ◽  
Binary Asteroids ◽  
Binary Nature ◽  
Rotation Rates ◽  
Photoelectric Observations

AbstractSince about ten years coordinated programs of photoelectric observations of asteroids are carried out to derive rotation rates and light curves. Quite a number of those asteroids exhibit features in their light curves, with similar characteristics as variable stars and especially eclipsing binaries. This would allow also an interpretation that there might be an evidence for the binary nature of some asteroids, based on observational hints. A few examples are given and a list of indications for the possible binary nature of asteroids, based on their light curve features, is presented.

scholarly journals First CCD observations of Magellanic Cloud variable stars from the Mt John University Observatory, New Zealand

1991 ◽  
Vol 148 ◽  
pp. 381-381
Author(s):  
William Tobin ◽  
A. C. Gilmore ◽  
Alan Wadsworth ◽  
S.R.D. West
Keyword(s):  
New Zealand ◽  
Evaluation Process ◽  
Variable Stars ◽  
Magellanic Cloud ◽  
Magellanic Clouds ◽  
Eclipsing Binaries ◽  
Light Curves ◽  
Ccd Observations ◽  
Differential Photometry

Late in 1988 the Mt John University Observatory acquired a cryogenic CCD system from Photometrics Ltd (Tucson). The chip is a Thomson CSF TH7882 CDA comprising 384 × 576 pixels. As part of the evaluation process, we have begun two differential photometry programs of the Magellanic Clouds using the Mt John 0.6m Boller & Chivens telescope. On this telescope each CCD pixel corresponds to 0.6 arcsec. Mt John's southerly latitude (44°S) permits year-round observations of the Clouds.The first program concerns B, V and I photometry of five blue eclipsing binaries selected, on the basis of Gaposchkin's (1970, 1977) photographic light curves, to have roughly equal components with minimal interaction. HV 12634 has also been observed for comparison with the CCD light curves published by Jensen et al. (1988). Fig. 1 shows the B observations so far obtained for HV 1761, but the reduction is preliminary, being based on aperture-integrated magnitudes. The field is populous, and a final reduction will require use of a crowded-field reduction package such as ROMAFOT.

scholarly journals Starspot migration in close binaries: A fast parameters evaluation from large sky surveys

2016 ◽  
Vol 12 (S325) ◽  
pp. 274-277
Author(s):  
B. Debski ◽  
S. Zola
Keyword(s):  
Visual Inspection ◽  
Magnetic Activity ◽  
Eclipsing Binaries ◽  
Light Curves ◽  
Close Binaries ◽  
Intrinsic Variability ◽  
Sky Surveys ◽  
Orbital Periods

AbstractWe developed a method that allows to classify the light curves of eclipsing binaries of the W UMa type (EW) with respect to their intrinsic variability. The algorithm measures several features of light curves, such as the amplitude of the O’Connell effect, the separation and location of maxima brightness as well as depths of the minima in subsequent orbital periods. This method is capable of distinguishing systems with presumed magnetic activity present from these without it, as well as recognizing systems with starspots migration and those with other types of intrinsic variability manifestation. The classification is done in an automatic way without a time consuming, visual inspection of light curves.

scholarly journals Morphology and Interaction of Galaxies using Deep Learning

2016 ◽  
Vol 12 (S325) ◽  
pp. 205-208
Author(s):  
Fernando Caro ◽  
Marc Huertas-Company ◽  
Guillermo Cabrera
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Visual Inspection ◽  
Interaction Analysis ◽  
Machine Learning Techniques ◽  
Huge Amount ◽  
New Approaches ◽  
Learning Techniques ◽  
First Galaxies ◽  
The Way

AbstractIn order to understand how galaxies form and evolve, the measurement of the parameters related to their morphologies and also to the way they interact is one of the most relevant requirements. Due to the huge amount of data that is generated by surveys, the morphological and interaction analysis of galaxies can no longer rely on visual inspection. For dealing with such issue, new approaches based on machine learning techniques have been proposed in the last years with the aim of automating the classification process. We tested Deep Learning using images of galaxies obtained from CANDELS to study the accuracy achieved by this tool considering two different frameworks. In the first, galaxies were classified in terms of their shapes considering five morphological categories, while in the second, the way in which galaxies interact was employed for defining other five categories. The results achieved in both cases are compared and discussed.

scholarly journals Systematic search for stellar pulsators in the eclipsing binaries observed by Kepler

2019 ◽  
Vol 630 ◽  
pp. A106 ◽  
Author(s):  
Patrick Gaulme ◽  
Joyce A. Guzik
Keyword(s):  
Stellar Evolution ◽  
Percent Level ◽  
Eclipsing Binaries ◽  
Light Curves ◽  
Systematic Search ◽  
Red Giants ◽  
Low Mass Stars ◽  
Orbital Periods ◽  
Red Giant ◽  
Stellar Properties

Eclipsing binaries (EBs) are unique targets for measuring precise stellar properties and can be used to constrain stellar evolution models. In particular, it is possible to measure masses and radii of both components of a double-lined spectroscopic EB at the percent level. Since the advent of high-precision photometric space missions (MOST, CoRoT, Kepler, BRITE, TESS), the use of stellar pulsation properties to infer stellar interiors and dynamics constitutes a revolution for studies of low-mass stars. The Kepler mission has led to the discovery of thousands of classical pulsators such as δ Scuti and solar-like oscillators (main sequence and evolved), but also almost 3000 EBs with orbital periods shorter than 1100 days. We report the first systematic search for stellar pulsators in the entire Kepler EB catalog. The focus is mainly aimed at discovering δ Scuti, γ Doradus, red giant, and tidally excited pulsators. We developed a data inspection tool (DIT) that automatically produces a series of plots from the Kepler light curves that allows us to visually identify whether stellar oscillations are present in a given time series. We applied the DIT to the whole Kepler EB database and identified 303 systems whose light curves display oscillations, including 163 new discoveries. A total of 149 stars are flagged as δ Scuti (100 from this paper), 115 as γ Doradus (69 new), 85 as red giants (27 new), and 59 as tidally excited oscillators (29 new). There is some overlap among these groups, as some display several types of oscillations. Despite the likelihood that many of these systems are false positives, for example, when an EB light curve is blended with a pulsator, this catalog gathers a vast sample of systems that are valuable for a better understanding of stellar evolution.

scholarly journals Theoretical Study of Cepheid Light Curves

1985 ◽  
Vol 82 ◽  
pp. 153-156
Author(s):  
C. G. Davis
Keyword(s):  
Theoretical Study ◽  
Heat Engine ◽  
Variable Stars ◽  
Light Curves ◽  
Diffusion Equations ◽  
Linear Features ◽  
Radiation Diffusion ◽  
Rr Lyrae ◽  
Non Linear ◽  
Linear Effects

Starting with the initial understanding that pulsation in variable stars is caused by the heat engine of Hydrogen and Helium ionization in their atmospheres (A.S. Eddington in Cox 1980) it was soon realized that non-linear effects were responsible for the detailed features on their light and velocity curves. With the advent of the computer we were able to solve the coupled set of hydrodynamics and radiation diffusion equations to model these non-linear features (Christy 1968, Cox et. al. 1966). Calculations including the effects of multi-frequency radiative transfer (Davis 1975) showed that grey diffusion was adequate for modeling Cepheids but not for the RR Lyrae or W Virginis type variables.

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