Isolated pulsar population synthesis with simulation-based inference

Abstract

We combine pulsar population synthesis with simulation-based inference (SBI) to constrain the magnetorotational properties of isolated Galactic radio pulsars. We first develop a framework to model neutron star birth properties and their dynamical and magnetorotational evolution. We specifically sample initial magnetic field strengths, $B$, and spin periods, $P$, from lognormal distributions and capture the late-time magnetic field decay with a power law. Each lognormal is described by a mean, ${\mu }_{\log B},{\mu }_{\log P}$, and standard deviation, ${\sigma }_{\log B},{\sigma }_{\log P}$, while the power law is characterized by the index, $a_{\mathrm{late}}$. We subsequently model the stars' radio emission and observational biases to mimic detections with three radio surveys, and we produce a large database of synthetic $P$--$\dot{P}$ diagrams by varying our five magnetorotational input parameters. We then follow an SBI approach that focuses on neural posterior estimation and train deep neural networks to infer the parameters' posterior distributions. After successfully validating these individual neural density estimators on simulated data, we use an ensemble of networks to infer the posterior distributions for the observed pulsar population. We obtain ${\mu }_{\log B}=13.10_{-0.10}^{+0.08}$, ${\sigma }_{\log B}=0.45_{-0.05}^{+0.05}$ and ${\mu }_{\log P}=-1.00_{-0.21}^{+0.26}$, ${\sigma }_{\log P}=0.38_{-0.18}^{+0.33}$ for the lognormal distributions and $a_{\mathrm{late}}=-1.80_{-0.61}^{+0.65}$ for the power law at the $95\%$ credible interval. We contrast our results with previous studies and highlight uncertainties of the inferred $a_{\mathrm{late}}$ value. Our approach represents a crucial step toward robust statistical inference for complex population synthesis frameworks and forms the basis for future multiwavelength analyses of Galactic pulsars.

31 pages, 16 figures, 5 tables, 2 appendices; published version

References in corpus (39)

• Array Programming with NumPy
• The NumPy array: a structure for efficient numerical computation
• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• galpy: A Python Library for Galactic Dynamics
• A New Electron Density Model for Estimation of Pulsar and FRB Distances
• An Ultraluminous X-ray Source Powered by An Accreting Neutron Star
• The Parkes multibeam pulsar survey: VI. Discovery and timing of 142 pulsars and a Galactic population analysis
• Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
• Real-time gravitational-wave science with neural posterior estimation
• A time-resolved picture of our Milky Way's early formation history
• Fast likelihood-free cosmology with neural density estimators and active learning
• The observed velocity distribution of young pulsars
• On the birthrates of Galactic neutron stars
• 2D Cooling of Magnetized Neutron Stars
• On the rate of core collapse supernovae in the Milky Way
• Tests of the nuclear equation of state and superfluid and superconducting gaps using the Cassiopeia A neutron star
• Magnetic, thermal and rotational evolution of isolated neutron stars
• Population synthesis of isolated Neutron Stars with magneto--rotational evolution
• Magnetic, thermal and rotational evolution of isolated neutron stars
• Supernova Fallback as Origin of Neutron Star Spins and Spin-kick Alignment
• The observed velocity distribution of young pulsars II: analysis of complete PSR$\pi$
• A guided map to the Spiral arms in the galactic disk of the Milky Way
• Kicks and Induced Spins of Neutron Stars at Birth
• On the pulse--width statistics in radio pulsars. I. Importance of the interpulse emission
• Neural posterior estimation for exoplanetary atmospheric retrieval
• The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population
• Magneto-thermal evolution of neutron stars with coupled Ohmic, Hall and ambipolar effects via accurate finite-volume simulations
• A neural simulation-based inference approach for characterizing the Galactic Center $\gamma$-ray excess
• The Galactic population and properties of young, highly-energetic pulsars
• Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
• X-ray Emission from Isolated Neutron Stars revisited: 3D magnetothermal simulations
• 3D Magneto-thermal Simulations of Tangled Crustal Magnetic Field in Central Compact Objects
• 3D code for MAgneto-Thermal evolution in Isolated Neutron Stars, MATINS: The Magnetic Field Formalism
• Population statistics study of radio and gamma-ray pulsars in the Galactic plane
• A simulation-based inference pipeline for cosmic shear with the Kilo-Degree Survey
• Accurate X-ray Timing in the Presence of Systematic Biases With Simulation-Based Inference
• Analyzing the Galactic pulsar distribution with machine learning
• Testing the robustness of simulation-based gravitational-wave population inference
• Back to the Starting Point: on the Simulation of Initial Magnetic Fields and Spin Periods of Non-accretion Pulsars

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