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Open AccessJournal ArticleDOI

Binary Companions of Evolved Stars in APOGEE DR14: Search Method and Catalog of ∼5000 Companions

TLDR
In this paper, a Monte Carlo sampler (The Joker) is used to perform a search for companions to 96,231 red-giant stars observed in the APOGEE survey (DR14) with $ ≥ 3$ spectroscopic epochs.
Abstract
Multi-epoch radial velocity measurements of stars can be used to identify stellar, sub-stellar, and planetary-mass companions. Even a small number of observation epochs can be informative about companions, though there can be multiple qualitatively different orbital solutions that fit the data. We have custom-built a Monte Carlo sampler (The Joker) that delivers reliable (and often highly multi-modal) posterior samplings for companion orbital parameters given sparse radial-velocity data. Here we use The Joker to perform a search for companions to 96,231 red-giant stars observed in the APOGEE survey (DR14) with $\\geq 3$ spectroscopic epochs. We select stars with probable companions by making a cut on our posterior belief about the amplitude of the stellar radial-velocity variation induced by the orbit. We provide (1) a catalog of 320 companions for which the stellar companion properties can be confidently determined, (2) a catalog of 4,898 stars that likely have companions, but would require more observations to uniquely determine the orbital properties, and (3) posterior samplings for the full orbital parameters for all stars in the parent sample. We show the characteristics of systems with confidently determined companion properties and highlight interesting systems with candidate compact object companions.

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Gaia Data Release 2. Summary of the contents and survey properties

Anthony G. A. Brown, +452 more
TL;DR: The second Gaia data release, Gaia DR2 as mentioned in this paper, is a major advance with respect to Gaia DR1 in terms of completeness, performance, and richness of the data products.
Journal ArticleDOI

GW190425: Observation of a Compact Binary Coalescence with Total Mass ∼ 3.4 M O

B. P. Abbott, +1274 more
TL;DR: In 2019, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9 and the Virgo detector was also taking data that did not contribute to detection due to a low SINR but were used for subsequent parameter estimation as discussed by the authors.
Journal ArticleDOI

GW190814: Gravitational Waves from the Coalescence of a 23 Solar Mass Black Hole with a 2.6 Solar Mass Compact Object

Richard J. Abbott, +1337 more
TL;DR: In this paper, the authors reported the observation of a compact binary coalescence involving a 222 −243 M ⊙ black hole and a compact object with a mass of 250 −267 M ⋆ (all measurements quoted at the 90% credible level) The gravitational-wave signal, GW190814, was observed during LIGO's and Virgo's third observing run on 2019 August 14 at 21:10:39 UTC and has a signal-to-noise ratio of 25 in the three-detector network.
References
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Scikit-learn: Machine Learning in Python

TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
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Matplotlib: A 2D Graphics Environment

TL;DR: Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.
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Inference from Iterative Simulation Using Multiple Sequences

TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
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Least squares quantization in PCM

TL;DR: In this article, the authors derived necessary conditions for any finite number of quanta and associated quantization intervals of an optimum finite quantization scheme to achieve minimum average quantization noise power.
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Astropy: A community Python package for astronomy

TL;DR: Astropy as discussed by the authors is a Python package for astronomy-related functionality, including support for domain-specific file formats such as flexible image transport system (FITS) files, Virtual Observatory (VO) tables, common ASCII table formats, unit and physical quantity conversions, physical constants specific to astronomy, celestial coordinate and time transformations, world coordinate system (WCS) support, generalized containers for representing gridded as well as tabular data, and a framework for cosmological transformations and conversions.
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