Search Results for author:
Found 9 papers, 6 papers with code
Stellar Spectra Fitting with Amortized Neural Posterior Estimation and nbi
Given the association of spectral data properties with the observing instrument, we discuss the utility of an ANPE "model zoo," where models are trained for specific instruments and distributed under the nbi framework to facilitate real-time stellar parameter inference.
nbi: the Astronomer's Package for Neural Posterior Estimation
We identify three critical issues: the need for custom featurizer networks tailored to the observed data, the inference inexactness, and the under-specification of physical forward models.
A Ubiquitous Unifying Degeneracy in Two-Body Microlensing Systems
While gravitational microlensing by planetary systems provides unique vistas on the properties of exoplanets, observations of a given 2-body microlensing event can often be interpreted with multiple distinct physical configurations.
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: high computational cost of likelihood evaluations with microlensing simulation codes, and a pathological parameter space where the negative-log-likelihood surface can contain a multitude of local minima that are narrow and deep.
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks
Neural networks (NNs) have been shown to be competitive against state-of-the-art feature engineering and random forest (RF) classification of periodic variable stars.
Automating Inference of Binary Microlensing Events with Neural Density Estimation
Automated inference of binary microlensing events with traditional sampling-based algorithms such as MCMC has been hampered by the slowness of the physical forward model and the pathological likelihood surface.
On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse datasets has thus far hampered a direct intercomparison of different approaches.
Instrumentation and Methods for Astrophysics
deepCR: Cosmic Ray Rejection with Deep Learning
We demonstrate that at a false positive rate of 0. 5%, deepCR achieves close to 100% detection rates in both extragalactic and globular cluster fields, and 91% in resolved galaxy fields, which is a significant improvement over the current state-of-the-art method LACosmic.
A recurrent neural network for classification of unevenly sampled variable stars
These networks can continue to learn from new unlabeled observations and may be used in other unsupervised tasks such as forecasting and anomaly detection.
Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Data Analysis, Statistics and Probability
