The VISTA Variables in the Vía Láctea infrared variability catalogue (VIVA-I)

Thanks to the VISTA Variables in the Via Lactea (VVV) ESO Public Survey it is now possible to explore a large number of objects in those regions. This paper addresses the variability analysis of all VVV point sources having more than 10 observations in VVVDR4 using a novel approach. In total, the near-IR light curves of 288,378,769 sources were analysed using methods developed in the New Insight Into Time Series Analysis project. As a result, we present a complete sample having 44, 998, 752 variable star candidates (VVV-CVSC), which include accurate individual coordinates, near-IR magnitudes (ZYJHKs), extinctions A(Ks), variability indices, periods, amplitudes, among other parameters to assess the science. Unfortunately, a side effect of having a highly complete sample, is also having a high level of contamination by non-variable (contamination ratio of non-variables to variables is slightly over 10:1). To deal with this, we also provide some flags and parameters that can be used by the community to de-crease the number of variable candidates without heavily decreasing the completeness of the sample. In particular, we cross-identified 339,601 of our sources with Simbad and AAVSO databases, which provide us with information for these objects at other wavelegths. This sub-sample constitutes a unique resource to study the corresponding near-IR variability of known sources as well as to assess the IR variability related with X-ray and Gamma-Ray sources. On the other hand, the other 99.5% sources in our sample constitutes a number of potentially new objects with variability information for the heavily crowded and reddened regions of the Galactic Plane and Bulge. The present results also provide an important queryable resource to perform variability analysis and to characterize ongoing and future surveys like TESS and LSST.

[1]  James P. Emerson,et al.  VISTA data flow system: pipeline processing for WFCAM and VISTA , 2004, SPIE Astronomical Telescopes + Instrumentation.

[2]  L. M. Sarro,et al.  Automated supervised classification of variable stars - I. Methodology , 2007, 0711.0703.

[3]  R. D'iaz,et al.  Millimagnitude Photometry for Transiting Extrasolar Planetary Candidates. III. Accurate Radius and Period for OGLE-TR-111-b , 2007, astro-ph/0701356.

[4]  E. L. Wright,et al.  PRELIMINARY RESULTS FROM NEOWISE: AN ENHANCEMENT TO THE WIDE-FIELD INFRARED SURVEY EXPLORER FOR SOLAR SYSTEM SCIENCE , 2011, 1102.1996.

[5]  Pavlos Protopapas,et al.  Automatic Survey-invariant Classification of Variable Stars , 2017, 1801.09737.

[6]  Keith T. Noddle,et al.  The VISTA Science Archive , 2012, 1210.2980.

[7]  N. Cross,et al.  New Insights into Time Series Analysis II - No Correlated Observations , 2015 .

[8]  P. Dubath,et al.  Random forest automated supervised classification of Hipparcos periodic variable stars , 2011, 1101.2406.

[9]  R. de Grijs,et al.  THE VVV TEMPLATES PROJECT TOWARDS AUTOMATED CLASSIFICATION OF VVV LIGHT CURVES , 2011, 1405.4517.

[10]  M. Catelán,et al.  The WFCAM multiwavelength Variable Star Catalog , 2015 .

[11]  D. Minniti,et al.  VVV Survey Microlensing: The Galactic Longitude Dependence , 2018, The Astrophysical Journal.

[12]  D. Minniti,et al.  VVV Survey Microlensing: The Galactic Latitude Dependence , 2019, The Astrophysical Journal.

[13]  A. Gimenez,et al.  Accurate masses and radii of normal stars: modern results and applications , 2009, 0908.2624.

[14]  The Optical Gravitational Lensing Experiment. The OGLE-III Catalog of Variable Stars. III. RR Lyrae Stars in the Large Magellanic Cloud , 2009 .

[15]  G. Bruce Berriman,et al.  Astrophysics Source Code Library , 2012, ArXiv.

[16]  Gravitational Waves and Pulsating Stars: What Can We Learn from Future Observations? , 1996, Physical review letters.

[17]  H. Ford,et al.  VARIABLE POINT SOURCES IN SLOAN DIGITAL SKY SURVEY STRIPE 82. I. PROJECT DESCRIPTION AND INITIAL CATALOG (0 hr ⩽α⩽ 4 hr) , 2009, 0912.0976.

[18]  R. Smart,et al.  VIRAC: The VVV Infrared Astrometric Catalogue , 2017, 1710.08919.

[19]  J. C. Beamin,et al.  A near-infrared catalogue of the Galactic novae in the VVV survey area , 2013, 1304.2673.

[20]  M. Catelán,et al.  THE ROTATIONAL BEHAVIOR OF KEPLER STARS WITH PLANETS , 2015, 1502.05051.

[21]  Jan Skowron,et al.  OGLE-III MICROLENSING EVENTS AND THE STRUCTURE OF THE GALACTIC BULGE , 2014, 1405.3134.

[22]  J. Scargle.  Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data , 1982 .

[23]  Discovery of Fast, Large-amplitude Optical Variability of V648 Car (=SS73-17) , 2012, 1207.5112.

[24]  C. S. Fernandes,et al.  Seven temperate terrestrial planets around the nearby ultracool dwarf star TRAPPIST-1 , 2017, Nature.

[25]  Pavlos Protopapas,et al.  QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE , 2011 .

[26]  D. Minniti,et al.  VVV Survey Microlensing Events in the Galactic Center Region , 2017, 1712.07667.

[27]  Pavlos Protopapas,et al.  SUPERVISED DETECTION OF ANOMALOUS LIGHT CURVES IN MASSIVE ASTRONOMICAL CATALOGS , 2014, ArXiv.

[28]  M. Catelán,et al.  Characterization of the VVV Survey RR Lyrae Population across the Southern Galactic Plane , 2017, 1703.01711.

[29]  R. Rebolo,et al.  Magnetic cycles and rotation periods of late-type stars from photometric time series , 2016, 1607.03049.

[30]  T. Mazeh,et al.  ROTATION PERIODS OF 34,030 KEPLER MAIN-SEQUENCE STARS: THE FULL AUTOCORRELATION SAMPLE , 2014, 1402.5694.

[31]  Alain Klotz,et al.  The TAROT Suspected Variable Star Catalog , 2007 .

[32]  N. Cross,et al.  New insights into time series analysis - II - Non-correlated observations , 2016, 1611.07838.

[33]  R. Stellingwerf.  Period determination using phase dispersion minimization , 1978 .

[34]  P. Lucas,et al.  Short- and long-term near-infrared spectroscopic variability of eruptive protostars from VVV , 2019, Monthly Notices of the Royal Astronomical Society.

[35]  M. Catelán,et al.  Pulsating Stars: Smith/Pulsating Stars , 2015 .

[36]  J. S. Hall.  Photo-Electric Photometry in the Infra-Red with the Loomis Telescope , 1934 .

[37]  M. Pinsonneault,et al.  Rotation and magnetism of Kepler pulsating solar-like stars : Towards asteroseismically calibrated age-rotation relations , 2014, 1403.7155.

[38]  P. Estévez,et al.  Proper motions in the VVV Survey: Results for more than 15 million stars across NGC 6544 , 2017, 1709.07919.

[39]  N. Cross,et al.  New Insights into Time Series Analysis III: Setting constraints on period analysis , 2018, Monthly Notices of the Royal Astronomical Society.

[40]  B. Stalder,et al.  ATLAS: A High-cadence All-sky Survey System , 2018, 1802.00879.

[41]  L. Valenzuela,et al.  Unsupervised classification of variable stars , 2018, 1801.09723.

[42]  K. Hełminiak,et al.  Tracing the structure of the Milky Way with detached eclipsing binaries from the VVV survey – I. The method and initial results , 2013, 1304.5255.

[43]  F. Paz-Chinch'on,et al.  The variability behaviour of CoRoT M-giant stars , 2015, 1508.05358.

[44]  S. C. Maciel,et al.  Stellar parameters for stars of the CoRoT exoplanet field , 2015, 1506.02956.

[45]  M. Catelán,et al.  Pulsating Stars , 1942, Science.

[46]  J. Richards,et al.  ON MACHINE-LEARNED CLASSIFICATION OF VARIABLE STARS WITH SPARSE AND NOISY TIME-SERIES DATA , 2011, 1101.1959.

[47]  O. Gonzalez,et al.  The Milky Way Bulge: Observed properties and a comparison to external galaxies , 2015, 1503.07252.

[48]  M. Catelán,et al.  Bulge RR Lyrae stars in the VVV tile b201 , 2015, 1501.00947.

[49]  N. Lomb.  Least-squares frequency analysis of unequally spaced data , 1976 .

[50]  C. Rodrigues,et al.  Orbital Period Variation of KIC 10544976: Applegate Mechanism versus Light Travel Time Effect , 2019, The Astronomical Journal.

[51]  Akshay Pai,et al.  Deep-learnt classification of light curves , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[52]  Robert Jedicke,et al.  Pan-STARRS: A Large Synoptic Survey Telescope Array , 2002, SPIE Astronomical Telescopes + Instrumentation.

[53]  P. Lucas,et al.  A population of eruptive variable protostars in VVV , 2016, 1602.06267.

[54]  Pavlos Protopapas,et al.  QSO Selection Algorithm Using Time Variability and Machine Learning: Selection of 1,620 QSO Candidates from MACHO LMC Database , 2011, 1101.3316.

[55]  M. Catelán,et al.  VVV SURVEY OBSERVATIONS OF A MICROLENSING STELLAR MASS BLACK HOLE CANDIDATE IN THE FIELD OF THE GLOBULAR CLUSTER NGC 6553 , 2015, 1508.06957.

[56]  D. York,et al.  Variable Stars Observed in the Galactic Disk by AST3-1 from Dome A, Antarctica , 2017, 1701.00484.

[57]  J. Mathis,et al.  The relationship between infrared, optical, and ultraviolet extinction , 1989 .

[58]  Pavlos Protopapas,et al.  AUTOMATIC CLASSIFICATION OF VARIABLE STARS IN CATALOGS WITH MISSING DATA , 2013, ArXiv.

[59]  Robert Mann,et al.  Astronomical Data Analysis Software and Systems XXI , 2012 .

[60]  R. de Grijs,et al.  VISTA Variables in the Via Lactea (VVV): The public ESO near-IR variability survey of the Milky Way , 2009, 0912.1056.

[61]  Noureddine El Karoui,et al.  Optimizing Automated Classification of Variable Stars in New Synoptic Surveys , 2012, 1201.4863.

[62]  S. Villanova,et al.  Search for exoplanetary transits in the Galactic bulge , 2018, Monthly Notices of the Royal Astronomical Society.

[63]  Peredur M. Williams,et al.  Archiving multi-epoch data and the discovery of variables in the near-infrared , 2009, 0905.3073.

[64]  M. Dworetsky.  A period-finding method for sparse randomly spaced observations or “How long is a piece of string?” , 1983 .

[65]  M. Catelán,et al.  Symbiotic stars in OGLE data - I. Large Magellanic Cloud systems , 2013, 1309.7345.

[66]  S. C. Maciel,et al.  Overview of semi-sinusoidal stellar variability with the CoRoT satellite , 2013, 1305.0811.

[67]  W. Gieren,et al.  Millimagnitude optical photometry for the transiting planetary candidate OGLE-TR-109 , 2006, astro-ph/0604284.

[68]  J. Wren,et al.  ROTSE All-Sky Surveys for Variable Stars. I. Test Fields , 2000 .

[69]  P. Lucas,et al.  Extreme infrared variables from UKIDSS – II. An end-of-survey catalogue of eruptive YSOs and unusual stars , 2017, 1708.02680.

[70]  M. Catelán,et al.  New type II Cepheids from VVV data towards the Galactic center , 2019, Astronomy & Astrophysics.

[71]  M. Catelán,et al.  Stellar Cycles from Photometric Data: CoRoT Stars , 2015, 1508.06194.

[72]  Mitaka,et al.  A near infrared variable star survey in the Magellanic Clouds: The Small Magellanic Cloud data , 2018, Monthly Notices of the Royal Astronomical Society.

[73]  J. Fernández,et al.  Millimagnitude photometry for transiting extrasolar planetary candidates - V. Follow-up of 30 OGLE transits. New candidates , 2009, 0910.4892.

[74]  E. O. Ofek,et al.  Automating Discovery and Classification of Transients and Variable Stars in the Synoptic Survey Era , 2011, 1106.5491.

[75]  B. Reipurth,et al.  NEAR-INFRARED VARIABILITY IN THE ORION NEBULA CLUSTER , 2015, 1505.01495.

[76]  P. Lucas,et al.  Infrared spectroscopy of eruptive variable protostars from VVV , 2016, 1602.06269.

[77]  L. Galbany,et al.  Unraveling the Infrared Transient VVV-WIT-06: The Case for the Origin as a Classical Nova , 2018, The Astrophysical Journal.

[78]  Z. T. Spetsieri,et al.  Comparative performance of selected variability detection techniques in photometric time series data , 2016, 1609.01716.

[79]  Donald W. Sweeney,et al.  Large Synoptic Survey Telescope: From Science Drivers to Reference Design , 2008 .

[80]  N. Mowlavi,et al.  Gaia Data Release 2 , 2018, Astronomy & Astrophysics.

[81]  Eduardo Serrano,et al.  LSST: From Science Drivers to Reference Design and Anticipated Data Products , 2008, The Astrophysical Journal.

[82]  M. Rejkuba,et al.  Mapping the stellar age of the Milky Way bulge with the VVV , 2019, Astronomy & Astrophysics.

[83]  G. Wade,et al.  Rotational modulation in TESS B stars , 2019, Monthly Notices of the Royal Astronomical Society.

[84]  M. Catelán,et al.  Mapping the outer bulge with RRab stars from the VVV Survey , 2016, 1604.01336.

[85]  M. Zechmeister,et al.  The generalised Lomb-Scargle periodogram. A new formalism for the floating-mean and Keplerian periodograms , 2009, 0901.2573.

[86]  M. Schultheis,et al.  Reddening and metallicity maps of the Milky Way bulge from VVV and 2MASS II. The complete high resolution extinction map and implications for Galactic bulge studies , 2012, 1204.4004.

[87]  On the nature of bulges in general and of box/peanut bulges in particular: input from N-body simulations , 2005 .

[88]  M. Catelán,et al.  Milky Way demographics with the VVV survey , 2018, Astronomy & Astrophysics.

[89]  M. Catelán,et al.  An Automated Tool to Detect Variable Sources in the Vista Variables in the Vía Láctea Survey: The VVV Variables (V4) Catalog of Tiles d001 and d002 , 2018, The Astrophysical Journal.

[90]  Mark Clampin,et al.  Transiting Exoplanet Survey Satellite , 2014, 1406.0151.

[91]  M. S. Roberts.  Galactic astronomy. , 1981, Science.

[92]  Stefano Casertano,et al.  A Near-infrared Period–Luminosity Relation for Miras in NGC 4258, an Anchor for a New Distance Ladder , 2018, 1801.02711.

[93]  Sergey E. Koposov,et al.  THE CATALINA SURVEYS PERIODIC VARIABLE STAR CATALOG , 2014, 1405.4290.

[94]  Pavlos Protopapas,et al.  The EPOCH Project - I. Periodic variable stars in the EROS-2 LMC database , 2014, 1403.6131.

[95]  M. Irwin,et al.  A new near-IR window of low extinction in the Galactic plane , 2018, Astronomy & Astrophysics.

[96]  Pablo A. Estévez,et al.  Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection , 2017, ArXiv.

[97]  P. Giommi,et al.  The PLATO 2.0 mission , 2013, 1310.0696.

[98]  V. Debattista,et al.  The structure behind the Galactic bar traced by red clump stars in the VVV survey★ , 2018, Monthly Notices of the Royal Astronomical Society: Letters.

[99]  Umaa Rebbapragada,et al.  The Zwicky Transient Facility: System Overview, Performance, and First Results , 2018, Publications of the Astronomical Society of the Pacific.

[100]  Pavlos Protopapas,et al.  META-CLASSIFICATION FOR VARIABLE STARS , 2016, 1601.03013.

[101]  Ciro Donalek,et al.  Challenges in the automated classification of variable stars in large databases , 2017 .

[102]  Pavlos Protopapas,et al.  CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS , 2016, ArXiv.

[103]  T. Mazeh,et al.  Measuring the rotation period distribution of field M dwarfs with Kepler , 2013, 1303.6787.

[104]  T. Guillot,et al.  Planets, candidates, and binaries from the CoRoT/Exoplanet programme , 2018, Astronomy & Astrophysics.

[105]  K. Z. Stanek,et al.  A NEW CEPHEID DISTANCE TO THE GIANT SPIRAL M101 BASED ON IMAGE SUBTRACTION OF HUBBLE SPACE TELESCOPE/ADVANCED CAMERA FOR SURVEYS OBSERVATIONS , 2011 .