Kilonova Light Curve Parameter Estimation Using Likelihood-Free Inference

Abstract

Rapid parameter estimation is critical when dealing with short lived signals such as kilonovae. We present a parameter estimation algorithm that combines likelihood-free inference with a pre-trained embedding network, optimized to efficiently process kilonova light curves. Our method is capable of retrieving the mass, velocity, and lanthanide fraction of the neutron star ejecta with an accuracy and precision on par with nested sampling methods while taking significantly less computational time. Our inference uniquely utilizes a pre-trained embedding network that marginalizes the time of arrival and the luminosity distance of the signal, allowing inference of signals at distances up to 200 Mpc. We find that including a pre-trained embedding outperforms the use of likelihood-free inference alone, reducing training time, model size, and offering the capability to marginalize over certain nuisance parameters. This framework has been integrated into the publicly available Nuclear Multi-Messenger Astronomy codebase, enabling the broader scientific community to deploy the model for their inference purposes. Our algorithm is broadly applicable to parameterized or simulated light curves of other transient objects, and can be adapted for quick sky localization.

References in corpus (33)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Deep Residual Learning for Image Recognition
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics
• The Zwicky Transient Facility: System Overview, Performance, and First Results
• X-ray spectral modelling of the AGN obscuring region in the CDFS: Bayesian model selection and catalogue
• Origin of the heavy elements in binary neutron-star mergers from a gravitational wave event
• The Emergence of a Lanthanide-Rich Kilonova Following the Merger of Two Neutron Stars
• GW170817: Joint Constraint on the Neutron Star Equation of State from Multimessenger Observations
• Kilonovae
• VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning
• Neutrino-driven winds in the aftermath of a neutron star merger: nucleosynthesis and electromagnetic transients
• POSSIS: predicting spectra, light curves and polarization for multi-dimensional models of supernovae and kilonovae
• Augmented Language Models: a Survey
• Optical follow-up of the neutron star-black hole mergers S200105ae and S200115j
• GW170817$-$the first observed neutron star merger and its kilonova: implications for the astrophysical site of the r-process
• Modeling Kilonova Light Curves: Dependence on Nuclear Inputs
• Fast-transient Searches in Real Time with ZTFReST: Identification of Three Optically-discovered Gamma-ray Burst Afterglows and New Constraints on the Kilonova Rate
• An updated nuclear-physics and multi-messenger astrophysics framework for binary neutron star mergers
• The critical role of nuclear heating rates, thermalization efficiencies and opacities for kilonova modelling and parameter inference
• LSST Target-of-Opportunity Observations of Gravitational Wave Events: Essential and Efficient
• Detectability of kilonovae in optical surveys: $post$-$mortem$ examination of the LVC O3 run follow-up
• A strategy for LSST to unveil a population of kilonovae without gravitational-wave triggers
• Neutron star-black hole mergers in next generation gravitational-wave observatories
• An Infrared Search for Kilonovae with the WINTER Telescope. I. Binary Neutron Star Mergers
• Optimizing Serendipitous Detections of Kilonovae: Cadence and Filter Selection
• Optimizing Cadences with Realistic Light Curve Filtering for Serendipitous Kilonova Discovery with Vera Rubin Observatory
• Data Compression and Inference in Cosmology with Self-Supervised Machine Learning
• Surrogate light curve models for kilonovae with comprehensive wind ejecta outflows and parameter estimation for AT2017gfo
• Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows
• Chemical Distribution of the Dynamical Ejecta in the Neutron Star Merger GW170817
• Superphot+: Realtime Fitting and Classification of Supernova Light Curves
• A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences