Physics - Data Analysis

Classification of periodic variable stars with novel cyclic-permutation invariant neural networks

We present Cyclic-Permutation Invariant Neural Networks, a novel class of neural networks (NNs) designed to be invariant to phase shifts of period-folded periodic sequences by means of 'symmetry padding'. In the context of periodic variable star …

Real-time Likelihood-free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation

Fast and automated inference of binary-lens, single-source (2L1S) microlensing events with sampling-based Bayesian algorithms (e.g., Markov Chain Monte Carlo, MCMC) is challenged on two fronts: the high computational cost of likelihood evaluations …

Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks

Automating Inference of Binary Microlensing Events with Neural Density Estimation

A recurrent neural network for classification of unevenly sampled variable stars

Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time (`light curves'). Unlike in many other physical domains, however, large (and source- specific) temporal gaps in data arise naturally due to …