295 citations · 953 across the 17 of their papers we have counts for
19 papers
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…
Benchmarking field-level cosmological inference from galaxy redshift surveys
Hugo Simon, François Lanusse, Arnaud de Mattia
Field-level inference has emerged as a promising framework to fully harness the cosmological information encoded in next-generation galaxy surveys. It involves performing Bayesian…
Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments
Yesukhei Jagvaral, Francois Lanusse, Rachel Mandelbaum
Forthcoming cosmological imaging surveys, such as the Rubin Observatory LSST, require large-scale simulations encompassing realistic galaxy populations for a variety of scientific…
Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics
Yesukhei Jagvaral, Francois Lanusse, Rachel Mandelbaum
Diffusion-based generative models represent the current state-of-the-art for image generation. However, standard diffusion models are based on Euclidean geometry and do not transla…
An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations
Nicholas Van Alfen, Duncan Campbell, Jonathan Blazek +5
We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies.…
Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models
Yesukhei Jagvaral, Francois Lanusse, Sukhdeep Singh +3
The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surv…
Modeling halo and central galaxy orientations on the SO(3) manifold with score-based generative models
Yesukhei Jagvaral, Rachel Mandelbaum, Francois Lanusse
Upcoming cosmological weak lensing surveys are expected to constrain cosmological parameters with unprecedented precision. In preparation for these surveys, large simulations with…
The N5K Challenge: Non-Limber Integration for LSST Cosmology
C. D. Leonard, T. Ferreira, X. Fang +9
The rapidly increasing statistical power of cosmological imaging surveys requires us to reassess the regime of validity for various approximations that accelerate the calculation o…
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…
CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST
Danila Korytov, Andrew Hearin, Eve Kovacs +27
This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the…
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…
Core Cosmology Library: Precision Cosmological Predictions for LSST
Nora Elisa Chisari, David Alonso, Elisabeth Krause +27
The Core Cosmology Library (CCL) provides routines to compute basic cosmological observables to a high degree of accuracy, which have been verified with an extensive suite of valid…
Weak lensing shear calibration with simulations of the HSC survey
Rachel Mandelbaum, François Lanusse, Alexie Leauthaud +8
We present results from a set of simulations designed to constrain the weak lensing shear calibration for the Hyper Suprime-Cam (HSC) survey. These simulations include HSC observin…
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…