Neural Posterior Estimation for White Dwarf Spectroscopic Characterization

Abstract

White dwarf spectroscopic characterization is entering a big data era, with the number of spectroscopically characterized white dwarfs expected to grow from $\sim$100,000 to over 300,000 in upcoming years. Traditional methods like least-squares fitting and Markov Chain Monte Carlo have become computationally prohibitive for large-scale analysis, requiring minutes to days per star. Furthermore, these methods impose fundamental limitations on model complexity by requiring explicit likelihood functions, typically restricting them to Gaussian assumptions. We present neural posterior estimation (NPE), a simulation-based inference technique that directly approximates posterior distributions through neural networks trained on simulated spectra. Our approach provides accurate parameter inference in milliseconds per star after upfront training costs, enabling statistical tests of the procedure's reliability. We demonstrate NPE's effectiveness on DA, DB, and carbon-atmosphere white dwarfs, validating its calibration with simulation-based calibration and tests of accuracy with random points. Application to SDSS data shows excellent agreement with previous studies, recovering parameters from previous work within 6.8% for effective temperature and 2.1% for surface gravity, on average. We also apply our technique on WD 1153+012, a hot DQ star with a carbon-oxygen-hydrogen atmosphere, using high-resolution spectroscopy. This methodology combines computational efficiency with the flexibility to model complex atmospheres, making it ideal for upcoming surveys. Our approach also integrates spectroscopic and photometric constraints through an iterative procedure, providing comprehensive characterization of white dwarfs.