arXiv subjects

Andres F. Barrientos

Publications and source records attributed to Andres F. Barrientos.

3 recordsLinked to original sources

arXiv2025

The Mass of the Vela Pulsar Progenitor and the Age of the Vela-Puppis Complex

The association of the Vela Pulsar with the Vela Supernova Remnant has long supported the hypothesis that core-collapse supernovae yield neutron stars, but its surrounding stellar population now offers new insights into progenitor evolution. By age-dating stars within 150 pc of the Vela Pulsar, we infer properties of its progenitor. These stars belong to the Vela-Puppis complex, revealing the region's star formation history. While stellar population models with standard assumptions suggest a likely progenitor age and mass, these predictions are internally inconsistent with the observed population, indicating that something is missing in the standard modeling approach. With those assumptions, there is very weak support for a $\lesssim$10 Myr old population, moderate support for a 40 Myr old population, and strong support for an intermediate age population around 65-100 Myrs old. The $\lesssim$10 Myr signal hinges on two peculiar O stars, which are unlike any others in the Vela-Puppis complex and imply nearly three times more main sequence stars than are observed. The 40 Myr-old population is supported by 6 red supergiants (RSGs) and several Be stars; but this population is again marginally inconsistent with the observed distribution of main sequence stars. The red giant (RG) and MS distributions are consistent with a 65-100 Myr old population. We discuss several possible resolutions, emphasizing how binary evolution and/or very rapid rotation could resolve these discrepancies. Gaia parallaxes and {\it Stellar Ages} enable these results; {\it Stellar Ages} is a novel stellar population modeling algorithm that combines individual and population-level age inferences.

astro-ph.SR↗

arXiv2025

Stellar Ages: A Code to Infer Properties of Stellar Populations

We present a novel statistical algorithm, Stellar Ages, which currently infers the age, metallicity, and extinction posterior distributions of stellar populations from their magnitudes. While this paper focuses on these parameters, the framework is readily adaptable to include additional properties, such as rotation, in future work. Historical age-dating techniques either model individual stars or populations of stars, often sacrificing population context or precision for individual estimates. Stellar Ages does both, combining the strengths of these approaches to provide precise individual ages for stars while leveraging population-level constraints. We verify the algorithm's capabilities by determining the age of synthetic stellar populations and actual stellar populations surrounding a nearby supernova, SN 2004dj. In addition to inferring an age, we infer a progenitor mass consistent with direct observations of the precursor star. The median age inferred from the brightest nearby stars is $\log_{10}$(Age/yr) = $7.19^{+0.10}_{-0.13}$, and its corresponding progenitor mass is $13.95^{+3.33}_{-1.96}$ $\text{M}_{\odot}$.

astro-ph.SR↗

arXiv2022

Bayesian inferences on uncertain ranks and orderings: Application to ranking players and lineups

It is common to be interested in rankings or order relationships among entities. In complex settings where one does not directly measure a univariate statistic upon which to base ranks, such inferences typically rely on statistical models having entity-specific parameters. These can be treated as random effects in hierarchical models characterizing variation among the entities. In this paper, we are particularly interested in the problem of ranking basketball players in terms of their contribution to team performance. Using data from the United States National Basketball Association (NBA), we find that many players have similar latent ability levels, making any single estimated ranking highly misleading. The current literature fails to provide summaries of order relationships that adequately account for uncertainty. Motivated by this, we propose a Bayesian strategy for characterizing uncertainty in inferences on order relationships among players and lineups. Our approach adapts to scenarios in which uncertainty in ordering is high by producing more conservative results that improve interpretability. This is achieved through a reward function within a decision theoretic framework. We apply our approach to data from the 2009-10 NBA season.

stat.ME↗