Bayesian Model Comparison and Analysis of the Galactic Disk Population of Gamma-Ray Millisecond Pulsars

Pulsed emission from almost one hundred millisecond pulsars (MSPs) has been detected in $\gamma$-rays by the Fermi Large-Area Telescope. The global properties of this population remain relatively unconstrained despite many attempts to model their spatial and luminosity distributions. We perform here a self-consistent Bayesian analysis of both the spatial distribution and luminosity function simultaneously. Distance uncertainties, arising from errors in the parallax measurement or Galactic electron-density model, are marginalized over. We provide a public Python package for calculating distance uncertainties to pulsars derived using the dispersion measure by accounting for the uncertainties in Galactic electron-density model YMW16. Finally, we use multiple parameterizations for the MSP population and perform Bayesian model comparison, finding that a broken power law luminosity function with Lorimer spatial profile are preferred over multiple other parameterizations used in the past. The best-fit spatial distribution and number of $\gamma$-ray MSPs is consistent with results for the radio population of MSPs.Comment: 13 pages, 8 figures, 3 tables + Appendix. Public code and source list available from http://github.com/tedwards2412/MSPDis

Albatross:a scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way

Stellar streams are potentially a very sensitive observational probe of galactic astrophysics, as well as the dark matter population in the Milky Way. On the other hand, performing a detailed, high-fidelity statistical analysis of these objects is challenging for a number of key reasons. First, the modelling of streams across their (potentially billions of years old) dynamical age is complex and computationally costly. Secondly, their detection and classification in large surveys such as Gaia renders a robust statistical description regarding e.g. the stellar membership probabilities, challenging. As a result, the majority of current analyses must resort to simplified models that use only subsets or summaries of the high quality data. In this work, we develop a new analysis framework that takes advantage of advances in simulation-based inference techniques to perform complete analysis on complex stream models. To facilitate this, we develop a new, modular dynamical modelling code sstrax for stellar streams that is highly accelerated using jax. We test our analysis pipeline on a mock observation that resembles the GD1 stream, and demonstrate that we can perform robust inference on all relevant parts of the stream model simultaneously. Finally, we present some outlook as to how this approach can be developed further to perform more complete and accurate statistical analyses of current and future data