Variational Autoencoder with Normalizing flow for X-ray spectral fitting
By: Fiona Redmen , Ethan Tregidga , James F. Steiner and more
Potential Business Impact:
Speeds up understanding black holes by 1000x.
Black hole X-ray binaries (BHBs) can be studied with spectral fitting to provide physical constraints on accretion in extreme gravitational environments. Traditional methods of spectral fitting such as Markov Chain Monte Carlo (MCMC) face limitations due to computational times. We introduce a probabilistic model, utilizing a variational autoencoder with a normalizing flow, trained to adopt a physical latent space. This neural network produces predictions for spectral-model parameters as well as their full probability distributions. Our implementations result in a significant improvement in spectral reconstructions over a previous deterministic model while performing three orders of magnitude faster than traditional methods.
Similar Papers
Physically Interpretable Representation Learning with Gaussian Mixture Variational AutoEncoder (GM-VAE)
Machine Learning (CS)
Finds hidden patterns in messy science data.
Variational Autoencoder for Generating Broader-Spectrum prior Proposals in Markov chain Monte Carlo Methods
Machine Learning (CS)
Find underground water faster and easier.
Hyperspectral Variational Autoencoders for Joint Data Compression and Component Extraction
Machine Learning (CS)
Shrinks huge satellite pictures to share them faster.