7 citations · 18 across the 6 of their papers we have counts for
9 papers
A differentiable forward model for weakly perturbed stellar streams: substructure forecasts from density and kinematics spectra
Noemi Anau Montel, Fabian Schmidt
Stellar streams are a promising way to gravitationally detect low-mass substructure, since their low dynamical temperature makes them retain the imprint of weak gravitational pertu…
Dynamic SBI: Round-free Sequential Simulation-Based Inference with Adaptive Datasets
Huifang Lyu, James Alvey, Noemi Anau Montel +2
Simulation-based inference (SBI) is emerging as a new statistical paradigm for addressing complex scientific inference problems. By leveraging the representational power of deep ne…
A robust neural determination of the source-count distribution of the Fermi-LAT sky at high latitudes
Christopher Eckner, Noemi Anau Montel, Florian List +2
Over the past 16 years, the Fermi Large Area Telescope (LAT) has significantly advanced our view of the GeV gamma-ray sky, yet several key questions remain - such as the compositio…
Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation
Oleg Savchenko, Guillermo Franco Abellán, Florian List +2
Knowledge of the primordial matter density field from which the large-scale structure of the Universe emerged over cosmic time is of fundamental importance for cosmology. However,…
Tests for model misspecification in simulation-based inference: from local distortions to global model checks
Noemi Anau Montel, James Alvey, Christoph Weniger
Model misspecification analysis strategies, such as anomaly detection, model validation, and model comparison are a key component of scientific model development. Over the last few…
Mean-Field Simulation-Based Inference for Cosmological Initial Conditions
Oleg Savchenko, Florian List, Guillermo Franco Abellán +2
Reconstructing cosmological initial conditions (ICs) from late-time observations is a difficult task, which relies on the use of computationally expensive simulators alongside soph…