69 citations · 388 across the 23 of their papers we have counts for
8 papers · 1 filter
SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering
Pablo Lemos, Liam Parker, ChangHoon Hahn +8
We present the first simulation-based inference (SBI) of cosmological parameters from field-level analysis of galaxy clustering. Standard galaxy clustering analyses rely on analyzi…
: The First Cosmological Constraints from Non-Gaussian and Non-Linear Galaxy Clustering
ChangHoon Hahn, Pablo Lemos, Liam Parker +8
The 3D distribution of galaxies encodes detailed cosmological information on the expansion and growth history of the Universe. We present the first cosmological constraints that ex…
: The First Cosmological Constraints from the Non-Linear Galaxy Bispectrum
ChangHoon Hahn, Michael Eickenberg, Shirley Ho +7
We present the first cosmological constraints from analyzing higher-order galaxy clustering on non-linear scales. We use , a forward modeling framework…
Galaxy Clustering Analysis with SimBIG and the Wavelet Scattering Transform
Bruno Régaldo-Saint Blancard, ChangHoon Hahn, Shirley Ho +8
The non-Gaussisan spatial distribution of galaxies traces the large-scale structure of the Universe and therefore constitutes a prime observable to constrain cosmological parameter…
Multiple Physics Pretraining for Physical Surrogate Models
Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker +11
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers. I…
xVal: A Continuous Numerical Tokenization for Scientific Language Models
Siavash Golkar, Mariel Pettee, Michael Eickenberg +11
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific…
Learnable wavelet neural networks for cosmological inference
Christian Pedersen, Michael Eickenberg, Shirley Ho
Convolutional neural networks (CNNs) have been shown to both extract more information than the traditional two-point statistics from cosmological fields, and marginalise over astro…
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
Bruno Régaldo-Saint Blancard, Michael Eickenberg
Separating signals from an additive mixture may be an unnecessarily hard problem when one is only interested in specific properties of a given signal. In this work, we tackle simpl…