292 citations · 1.7k across the 26 of their papers we have counts for
16 papers · 1 filter
flowMC: Normalizing-flow enhanced sampling package for probabilistic inference in Jax
Kaze W. K. Wong, Marylou Gabrié, Daniel Foreman-Mackey
flowMC is a Python library for accelerated Markov Chain Monte Carlo (MCMC) leveraging deep generative modeling. It is built on top of the machine learning libraries JAX and Flax. A…
The Thresher: Lucky Imaging without the Waste
James A. Hitchcock, D. M. Bramich, Daniel Foreman-Mackey +2
In traditional lucky imaging (TLI), many consecutive images of the same scene are taken with a high frame-rate camera, and all but the sharpest images are discarded before construc…
exoplanet: Gradient-based probabilistic inference for exoplanet data & other astronomical time series
Daniel Foreman-Mackey, Rodrigo Luger, Eric Agol +13
"exoplanet" is a toolkit for probabilistic modeling of astronomical time series data, with a focus on observations of exoplanets, using PyMC3 (Salvatier et al., 2016). PyMC3 is a f…
PyTorchDIA: A flexible, GPU-accelerated numerical approach to Difference Image Analysis
James A. Hitchcock, Markus Hundertmark, Daniel Foreman-Mackey +4
We present a GPU-accelerated numerical approach for fast kernel and differential background solutions. The model image proposed in the Bramich (2008) difference image analysis algo…
Multi-Wavelength Photometry Derived from Monochromatic Kepler Data
Christina Hedges, Rodrigo Luger, Jessie Dotson +2
The Kepler mission has provided a wealth of data, revealing new insights in time-domain astronomy. However, Kepler's single band-pass has limited studies to a single wavelength. In…
A Fast, 2D Gaussian Process Method Based on Celerite: Applications to Transiting Exoplanet Discovery and Characterization
Tyler Gordon, Eric Agol, Daniel Foreman-Mackey
Gaussian processes (GPs) are commonly used as a model of stochastic variability in astrophysical time series. In particular, GPs are frequently employed to account for correlated s…