295 citations · 953 across the 17 of their papers we have counts for
9 papers · 1 filter
The Cross-Survey Decade: A Call to Action
Gioia Rau, Robert Benjamin, Federica Bianco +24
By 2027, three flagship wide-field surveys will be operating simultaneously from ground and space, observing overlapping sky and representing more than $6 billion in US and Europea…
Rubin-Euclid Derived Data Products: Initial Recommendations
Leanne P. Guy, Jean-Charles Cuillandre, Etienne Bachelet +117
This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an…
The LSST-DESC 3x2pt Tomography Optimization Challenge
Joe Zuntz, François Lanusse, Alex I. Malz +25
This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tom…
Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation
Keming Zhang, Joshua S. Bloom, B. Scott Gaudi +3
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 o…
Deep Generative Models for Galaxy Image Simulations
Francois Lanusse, Rachel Mandelbaum, Siamak Ravanbakhsh +3
Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for t…
Hybrid Physical-Deep Learning Model for Astronomical Inverse Problems
Francois Lanusse, Peter Melchior, Fred Moolekamp
We present a Bayesian machine learning architecture that combines a physically motivated parametrization and an analytic error model for the likelihood with a deep generative model…
The Role of Machine Learning in the Next Decade of Cosmology
Michelle Ntampaka, Camille Avestruz, Steven Boada +27
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmologic…
CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
Francois Lanusse, Quanbin Ma, Nan Li +5
Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints,…
Enabling Dark Energy Science with Deep Generative Models of Galaxy Images
Siamak Ravanbakhsh, Francois Lanusse, Rachel Mandelbaum +2
Understanding the nature of dark energy, the mysterious force driving the accelerated expansion of the Universe, is a major challenge of modern cosmology. The next generation of co…