13 citations · 41 across the 10 of their papers we have counts for
10 papers
Field-level multi-tracers simulation-based inference of cosmological parameters from 3D maps
Giulio Scelfo, Satvik Mishra, Mauro Rigo +2
Extracting maximum cosmological information from current and upcoming large-scale structure data requires going beyond summary statistics as currently used in likelihood-based infe…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
CIGaRS I: Combined simulation-based inference from type Ia supernovae and host photometry
Konstantin Karchev, Roberto Trotta, Raul Jimenez
Using type Ia supernovae as cosmological probes requires empirical corrections that are correlated with their host environment. Here we present a unified Bayesian hierarchical mode…
Simulation-based population inference of LISA's Galactic binaries: Bypassing the global fit
Rahul Srinivasan, Enrico Barausse, Natalia Korsakova +1
The Laser Interferometer Space Antenna (LISA) is expected to detect thousands of individually resolved gravitational wave sources, overlapping in time and frequency, on top of unre…
Near-instantaneous Atmospheric Retrievals and Model Comparison with FASTER
Anna Lueber, Konstantin Karchev, Chloe Fisher +3
In the era of the James Webb Space Telescope (JWST), the dramatic improvement in the spectra of exoplanetary atmospheres demands a corresponding leap forward in our ability to anal…
STAR NRE: Solving supernova selection effects with set-based truncated auto-regressive neural ratio estimation
Konstantin Karchev, Roberto Trotta
Accounting for selection effects in supernova type Ia (SN Ia) cosmology is crucial for unbiased cosmological parameter inference -- even more so for the next generation of large, m…
Dark Matter Halo Parameters from Overheated Exoplanets via Bayesian Hierarchical Inference
María Benito, Konstantin Karchev, Rebecca K. Leane +3
Dark Matter (DM) can become captured, deposit annihilation energy, and hence increase the heat flow in exoplanets and brown dwarfs. Detecting such a DM-induced heating in a populat…
Bayesian evidence estimation from posterior samples with normalizing flows
Rahul Srinivasan, Marco Crisostomi, Roberto Trotta +2
We propose a novel method (), based on normalizing flows, to estimate the Bayesian evidence (and its numerical uncertainty) from a pre-existing set of samples drawn from the…
SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data
Konstantin Karchev, Matthew Grayling, Benjamin M. Boyd +3
We present the first fully simulation-based hierarchical analysis of the light curves of a population of low-redshift type Ia supernovae (SNae Ia). Our hardware-accelerated forward…
SimSIMS: Simulation-based Supernova Ia Model Selection with thousands of latent variables
Konstantin Karchev, Roberto Trotta, Christoph Weniger
We present principled Bayesian model comparison through simulation-based neural classification applied to SN Ia analysis. We validate our approach on realistically simulated SN Ia…