Disentangling cataclysmic variables in <i>Gaia</i>’s HR diagram

Abril, Javier; Schmidtobreick, L.; Ederoclite, A.; López-Sanjuan, C.

doi:10.1093/mnrasl/slz181

Cited by 53 publications

(49 citation statements)

References 31 publications

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“…The catalogs we use to extract labels are, in order of priority, 1. the cataclysmic variables catalog, compiled by Abril et al (2020), including Ritter & Kolb (2003); 2. ROMABZCAT, the multifrequency catalog of blazars from Massaro et al (2015); 3. the catalog of type I AGNs from Oh et al (2015); 4. the Million Quasars Catalogue from Flesch (2019); 5. the spectroscopically classified SNe in the TNS; 28 6. the objects classified as YSOs in Simbad (Wenger et al 2000); 7. the CRTS catalog of northern periodic sources (Drake et al 2014); 8. the CRTS catalog of southern periodic sources (Drake et al 2017); 9. the LINEAR catalog of periodic variables (Palaversa et al 2013); 10. the Gaia Data Release 2 (DR2) catalog of variable stars (Mowlavi et al 2018); and 11. the All-Sky Automated Survey for Supernova (ASAS-SN) catalog of variable stars (Jayasinghe et al 2019).…”

Section: Training Setsmentioning

confidence: 99%

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

et al. 2021

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We introduce the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker, an astronomical alert broker designed to provide a rapid and self-consistent classification of large etendue telescope alert streams, such as that provided by the Zwicky Transient Facility (ZTF) and, in the future, the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). ALeRCE is a Chilean-led broker run by an interdisciplinary team of astronomers and engineers working to become intermediaries between survey and follow-up facilities. ALeRCE uses a pipeline that includes the real-time ingestion, aggregation, cross-matching, machine-learning (ML) classification, and visualization of the ZTF alert stream. We use two classifiers: a stamp-based classifier, designed for rapid classification, and a light curve-based classifier, which uses the multiband flux evolution to achieve a more refined classification. We describe in detail our pipeline, data products, tools, and services, which are made public for the community (see https://alerce.science). Since we began operating our real-time ML classification of the ZTF alert stream in early 2019, we have grown a large community of active users around the globe. We describe our results to date, including the real-time processing of 1.5 × 10 8 alerts, the stamp classification of 3.4 × 10 7 objects, the light-curve classification of 1.1 × 10 6 objects, the report of 6162 supernova candidates, and different experiments using LSST-like alert streams. Finally, we discuss the challenges ahead in going from a single stream of alerts such as ZTF to a multistream ecosystem dominated by LSST.

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Section: Training Setsmentioning

confidence: 99%

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

et al. 2021

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“…The white dwarf luminosity function was also reexplored with unprecedented level of detail (Torres et al 2021); and Torres et al (2019) further investigated the memberships of white dwarfs into the thin disc, thick disc, and halo Galactic populations. Significant progress was also made for largescale identification and characterization of white dwarfs in binaries with main-sequence stars, either in common proper motion pairs (El-Badry, Rix & Weisz 2018), non-interacting unresolved systems (Inight et al 2021), or cataclysmic variables (Abril et al 2020;Pala et al 2020).…”

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confidence: 99%

A catalogue of white dwarfs in Gaia EDR3

Fusillo

Tremblay

Cukanovaite

et al. 2021

Monthly Notices of the Royal Astronomical Society

182

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We present a catalogue of white dwarf candidates selected from Gaia early data release three (EDR3). We applied several selection criteria in absolute magnitude, colour, and Gaia quality flags to remove objects with unreliable measurements while preserving most stars compatible with the white dwarf locus in the Hertzsprung-Russell diagram. We then used a sample of over 30 000 spectroscopically confirmed white dwarfs and contaminants from the Sloan Digital Sky Survey (SDSS) to map the distribution of these objects in the Gaia absolute magnitude-colour space. Finally, we adopt the same method presented in our previous work on Gaia DR2 to calculate a probability of being a white dwarf (PWD) for ≃ 1.3 million sources which passed our quality selection. The PWD values can be used to select a sample of ≃ 359 000 high-confidence white dwarf candidates. We calculated stellar parameters (effective temperature, surface gravity, and mass) for all these stars by fitting Gaia astrometry and photometry with synthetic pure-H, pure-He and mixed H-He atmospheric models. We estimate an upper limit of 93 per cent for the overall completeness of our catalogue for white dwarfs with G ≤ 20 mag and effective temperature (Teff) >7000 K, at high Galactic latitudes (|b| > 20○). Alongside the main catalogue we include a reduced-proper-motion extension containing ≃ 10 200 white dwarf candidates with unreliable parallax measurements which could, however be identified on the basis of their proper motion. We also performed a cross-match of our catalogues with SDSS DR16 spectroscopy and provide spectral classification based on visual inspection for all resulting matches.

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“…Most MSPs companions are located between the main sequence and the WD cooling branch. There appears to be no obvious correlation between HR position and orbital period (for such a correlation in cataclysmic variables that occupy the same region see Abril et al 2020).…”

Section: Resultsmentioning

confidence: 99%

Gaia pulsars and where to find them

Antoniadis

2020

Monthly Notices of the Royal Astronomical Society

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While the majority of massive stars have a stellar companion, most pulsars appear to be isolated. Taken at face value, this suggests that most massive binaries break apart due to strong natal kicks received in supernova explosions. However, the observed binary fraction can still be subject to strong selection effects, as monitoring of newly-discovered pulsars is rarely carried out for long enough to conclusively rule out multiplicity. Here, we use the second Gaia Data Release (DR2) to search for companions to 1534 rotation-powered pulsars with positions known to better than 0${_{.}^{\prime\prime}}$5. We find 22 matches to known pulsars, including 1 not reported elsewhere, and 8 new possible companions to young pulsars. We examine the photometric and kinematic properties of these systems and provide empirical relations for identifying Gaia sources with potential millisecond pulsar companions. Our results confirm that the observed multiplicity fraction is small. However, we show that the number of binaries below the sensitivity of Gaia and radio timing in our sample could still be significantly higher. We constrain the binary fraction of young pulsars to be $f_{\rm young}^{\rm true}\le 5.3(8.3)\%$ under realistic(conservative) assumptions for the binary properties and current sensitivity thresholds. For massive stars (≥10 M⊙) in particular, we find $f_{\rm OB}^{\rm true}\le 3.7\%$ which sets a firm independent upper limit on the galactic neutron-star merger rate, ≤7.2 × 10−4 yr−1. Ongoing and future projects such as the CHIME/pulsar program, MeerTime, HIRAX and ultimately the SKA, will significantly improve these constraints in the future.

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Disentangling cataclysmic variables in Gaia’s HR diagram

Cited by 53 publications

References 31 publications

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker

A catalogue of white dwarfs in Gaia EDR3

Gaia pulsars and where to find them

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