arXiv subjects

Ben Pennell

Publications and source records attributed to Ben Pennell.

3 recordsLinked to original sources

arXiv2026

Mass-dependent multiplicity fraction in low mass stars revealed by Gaia astrometry

Stellar multiplicity at different orbital periods is probed by different techniques: radial velocities at the shortest periods, direct imaging at the longest, and astrometry in between. Gaia DR3 provides unprecedented astrometric information to constrain binary populations with periods of tens to thousands of days. Beyond the small fraction of direct orbit solutions, Gaia publishes coarser astrometric diagnostics for all objects, such as on-sky accelerations, jerks, or the single-star goodness-of-fit measure, RUWE. We show that these diagnostics together are sensitive to a far wider range of orbital periods than orbit solutions alone. Built on the Gaiamock emulator, we develop a forward-modelling framework to constrain rates of multiplicity from Gaia data, using all astrometric solution types. We apply this framework to $366~027$ primaries of $0.2$--$0.75\,M_\odot$ between $50$ and $200$ pc, including vastly more M-dwarfs than previous multiplicity studies. For fiducial models of the period and mass-ratio distributions, the inferred multiplicity fraction is approximately constant at ${\sim}45\%$ from the solar-type regime down to $0.4\,M_\odot$, only then dropping to ${\sim}15\%$ by $0.2\,M_\odot$. This mass-multiplicity relationship revises the conventional picture of a monotonically rising mass--multiplicity relation, and aligns the upper M-dwarf regime with solar-type stars rather than placing it on a continuous decline.

astro-ph.SR↗

arXiv2025

Dormant black hole candidates from Gaia DR3 summary diagnostics

We present a rigorous identification of candidates for dormant black holes (BHs) and neutron stars (NSs) in binaries using summary statistics from Gaia DR3, rather than full orbital solutions. Although Gaia astrometric orbits have already revealed a small sample of compact object binaries, many systems remain undetected due to stringent quality cuts imposed on the published orbits. Using a forward-modelling framework that simulates Gaia observables, in particular the re-normalised unit weight error (ruwe) and radial velocity (RV) scatter, we infer posterior distributions for companion mass and orbital period via MCMC sampling, marginalising over nuisance orbital parameters. We validate our approach by comparing the predicted masses and periods against full orbit solutions from DR3, and by successfully recovering known compact object binaries as promising candidates. The method is best suited for systems with red giant primaries, which have more reliable Gaia RV scatter and a light centroid more likely dominated by one component, compared to main-sequence stars, and they are less likely to be triples with short-period inner binaries, which produce confounding signatures. We applied the method to three million giants and identify 389 systems with best-fit companion masses $\gtrsim 3\,M_\odot$. Recovery simulations suggest our selection method is substantially more sensitive than the DR3 non-single-star catalogue, particularly for binaries with periods below 1 year and above $\sim 6$ years. These candidates represent promising targets for spectroscopic follow-up and Gaia DR4 analysis to confirm the presence of compact objects. Candidate main-sequence stars with massive companions face a larger set of confounding effects. Therefore, we present an analogous catalogue of 279 additional main-sequence candidates only as an appendix.

astro-ph.SR↗

arXiv2024

Emulating Recombination with Neural Networks using Universal Differential Equations

With an aim towards modeling cosmologies beyond the $\Lambda$CDM paradigm, we demonstrate the automatic construction of recombination history emulators while enforcing a prior of causal dynamics. These methods are particularly useful in the current era of precision cosmology, where extremely constraining datasets provide insights into a cosmological model dominated by unknown contents. Cosmic Microwave Background (CMB) data in particular provide a clean glimpse into the interaction of dark matter, baryons, and radiation in the early Universe, but interpretation of this data requires knowledge of the Universe's ionization history. The exploration of new physics with new CMB data will require fast and flexible calculation of this ionization history. We develop a differentiable machine learning model for recombination physics using a neural network ordinary differential equation architecture (Universal Differential Equations, UDEs), building towards automatic dimensionality reduction and the avoidance of manual tuning based on cosmological model.

astro-ph.CO↗