arXiv subjects

Amery Gration

Publications and source records attributed to Amery Gration.

7 recordsLinked to original sources

arXiv2026

Dyad: a binary-star dynamics and statistics library for Python

Dyad is a Python library for studying the dynamics of binary stars considered as gravitational two-body systems. The dynamics of a binary star are determined by its primary and secondary stars' masses along with the secondary star's six orbital elements as defined in a frame comoving with the primary star. In a population of binary stars these eight parameters vary from member to member and can each be treated as a random variable having some probability distribution. Dyad provides a class, dyad.TwoBody, and a module, dyad.stats, for dealing with such a population of binary stars. The dyad.TwoBody class represents a gravitational two-body system while the dyad.stats module provides a suite of classes representing the probability distributions of (1) stellar masses (including those proposed by Kroupa, 2001, and Salpeter, 1955) as well as (2) mass ratios and orbital elements (including those proposed by Duquennoy & Mayor, 1991, and Moe & Di Stefano, 2017). The software repository is at https://github.com/AmeryGration/dyad .

astro-ph.IM↗

arXiv2026

A method for constructing the joint mass function of binary stars

The initial mass function (IMF) describes the distribution of stellar masses in a population of newly born stars and is amongst the most fundamental concepts in astrophysics. It is not only the direct result of the star formation process but it also explains the evolution of galaxies' luminosities, metal yields, star-formation efficiencies, and supernova production rates. Because most stars exist in binary systems, however, a full statistical account of stellar mass requires not the IMF but rather the joint distribution of a binary population's primary- and secondary-star masses. This joint distribution must respect the IMF of the stars from which the population has been assembled as well as the distribution of mass ratios that results from the assembly mechanism. Despite its importance, this joint distribution is known only in the case of random pairing. Here we present a method for constructing it in the general case. We also illustrate the use of our method by recovering the known result for random pairing and by finding the previously unknown result for uniform pairing.

astro-ph.SR↗

arXiv2025

Stellar velocity distributions in binary-rich ultrafaint dwarf galaxies

Ultrafaint dwarf (UFD) galaxies are dominated by dark matter, the distribution of which may be inferred from the kinematics of that galaxy's stellar population. Star-by-star observations are available for the satellite UFD galaxies of the Milky Way, making them uniquely good laboratories in which to test cosmological predictions at the smallest scales. However, the kinematics of these galaxies are complicated by the presence of binary stars, which alter the stellar velocity distribution. In particular these binary stars increase the galaxy's stellar velocity dispersion, which is related to the total galactic mass by the virial theorem. Without correctly eliminating or accounting for binary stars we may therefore overestimate the masses of UFD galaxies or even confuse globular clusters for UFD galaxies. Here we write down the probability density function for the observed line-of-sight (LOS) velocity of a stellar population containing both visual and spectroscopic binary stars, which we then use to determine the effect of those binary stars on the observed LOS velocity dispersion. For the coldest UFD galaxies the fractional increase in LOS velocity dispersion is of order one and for the coldest globular clusters is of order 100. However, if the stellar initial mass function is bottom light, as it may be for UFD galaxies and globular clusters, then both of these values increase by half a dex.

astro-ph.GA↗

arXiv2024

Mapping dust in the giant molecular cloud Orion A

The Sun is located close to the Galactic mid-plane, meaning that we observe the Galaxy through significant quantities of dust. Moreover, the vast majority of the Galaxy's stars also lie in the disc, meaning that dust has an enormous impact on the massive astrometric, photometric and spectroscopic surveys of the Galaxy that are currently underway. To exploit the data from these surveys we require good three-dimensional maps of the Galaxy's dust. We present a new method for making such maps in which we form the best linear unbiased predictor of the extinction at an arbitrary point based on the extinctions for a set of observed stars. This method allows us to avoid the artificial inhomogeneities (so-called 'fingers of God') and resolution limits that are characteristic of many published dust maps. Moreover, it requires minimal assumptions about the statistical properties of the interstellar medium. In fact, we require only a model of the first and second moments of the dust density field. The method is suitable for use with directly measured extinctions, such as those provided by the Rayleigh-Jeans colour excess method, and inferred extinctions, such as those provided by hierarchical Bayesian models like StarHorse. We test our method by mapping dust in the region of the giant molecular cloud Orion A. Our results indicate a foreground dust cloud at a distance of 350 pc, which has been identified in work by another author.

astro-ph.GA↗

arXiv2020

Completeness of the Gaia-verse III: using hidden states to infer gaps, detection efficiencies and the scanning law from the DR2 light curves

The completeness of the Gaia catalogues heavily depends on the status of that space telescope through time. Stars are only published with each of the astrometric, photometric and spectroscopic data products if they are detected a minimum number of times. If there is a gap in scientific operations, a drop in the detection efficiency or Gaia deviates from the commanded scanning law, then stars will miss out on potential detections and thus be less likely to make it into the Gaia catalogues. We lay the groundwork to retrospectively ascertain the status of Gaia throughout the mission from the tens of individual measurements of the billions of stars, by developing novel methodologies to infer both the orientation and angular velocity of Gaia through time and gaps and efficiency drops in the detections. We have applied these methodologies to the Gaia DR2 variable star epoch photometry -- which are the only publicly available Gaia time-series at the present time -- and make the results publicly available. We accompany these results with a new Python package scanninglaw (https://github.com/gaiaverse/scanninglaw) that you can use to easily predict Gaia observation times and detection probabilities for arbitrary locations on the sky.

astro-ph.GA↗

arXiv2019

The fate of planetesimal discs in young open clusters: implications for 1I/'Oumuamua, the Kuiper belt, the Oort cloud and more

We perform $N$-body simulations of the early phases of open cluster evolution including a large population of planetesimals, initially arranged in Kuiper-belt like discs around each star. Using a new, 4th-order and time-reversible $N$-body code on Graphics Processing Units (GPUs), we evolve the whole system under the stellar gravity, i.e. treating planetesimals as test particles, and consider two types of initial cluster models, similar to IC348 and the Hyades, respectively. In both cases, planetesimals can be dynamically excited, transferred between stars or liberated to become free-floating (such as A/2017 U1 or 'Oumuamua) during the early cluster evolution. We find that planetesimals captured from another star are not necessarily dynamically distinct from those native to a star. After an encounter both native and captured planetesimals can exhibit aligned periastrons, qualitatively similar to that seen in the Solar system and commonly thought to be the signature of Planet 9. We discuss the implications of our results for both our Solar system and exoplanetary systems.

astro-ph.EP↗

arXiv2018

Dynamical modelling of dwarf-spheroidal galaxies using Gaussian-process emulation

We present a novel and efficient method for fitting dynamical models of stellar kinematic data in dwarf spheroidal galaxies (dSph). Our approach is based on Gaussian-process emulation (GPE), which is a sophisticated form of curve fitting that requires fewer training data than alternative methods. We use a set of validation tests and diagnostic criteria to assess the performance of the emulation procedure. We have implemented an algorithm in which both the GPE procedure and its validation are fully automated. Applying this method to synthetic data, with fewer than 100 model evaluations we are able to recover a robust confidence region for the three-dimensional parameter vector of a toy model of the phase-space distribution function of a dSph. Although the dynamical model presented in this paper is low-dimensional and static, we emphasize that the algorithm is applicable to any scheme that involves the evaluation of computationally expensive models. It therefore has the potential to render tractable previously intractable problems, for example, the modelling of individual dSphs using high-dimensional, time-dependent N-body simulations.

astro-ph.GA↗