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Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural Networks
Keming Zhang, Joshua S. Bloom
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. Although prev…
Automating Inference of Binary Microlensing Events with Neural Density Estimation
Keming Zhang, Joshua S. Bloom, B. Scott Gaudi +3
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 pa…
deepSIP: Linking Type Ia Supernova Spectra to Photometric Quantities with Deep Learning
Benjamin E. Stahl, Jorge Martinez-Palomera, WeiKang Zheng +3
We present {\tt deepSIP} (deep learning of Supernova Ia Parameters), a software package for measuring the phase and -- for the first time using deep learning -- the light-curve sha…
Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters
Jorge Martínez-Palomera, Joshua S. Bloom, Ellianna S. Abrahams
The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time-domain survey cadences and the training of classification models…
On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
Sara Jamal, Joshua S. Bloom
Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse datasets has thus far hampe…