69 citations · 388 across the 23 of their papers we have counts for
23 papers
Mitigating Model Misspecification in Simulation-Based Inference for Galaxy Clustering
Sébastien Pierre, Bruno Régaldo-Saint Blancard, ChangHoon Hahn +1
Simulation-based inference (SBI) has become an important tool in cosmology for extracting additional information from observational data using simulations. However, all cosmologica…
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task
Siavash Golkar, Alberto Bietti, Mariel Pettee +12
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications. This paper int…
{\sc SimBIG}: Cosmological Constraints using Simulation-Based Inference of Galaxy Clustering with Marked Power Spectra
Elena Massara, ChangHoon Hahn, Michael Eickenberg +7
We present the first CDM cosmological analysis performed on a galaxy survey using marked power spectra. The marked power spectrum is the two-point function of a marked field, wh…
: Cosmological Constraints from the Redshift-Space Galaxy Skew Spectra
Jiamin Hou, Azadeh Moradinezhad Dizgah, ChangHoon Hahn +7
Extracting the non-Gaussian information of the cosmic large-scale structure (LSS) is vital in unlocking the full potential of the rich datasets from the upcoming stage-IV galaxy su…
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…
: A Forward Modeling Approach To Analyzing Galaxy Clustering
ChangHoon Hahn, Michael Eickenberg, Shirley Ho +7
We present the first-ever cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the new forward modeling…
: Mock Challenge for a Forward Modeling Approach to Galaxy Clustering
ChangHoon Hahn, Michael Eickenberg, Shirley Ho +7
Simulation-Based Inference of Galaxies () is a forward modeling framework for analyzing galaxy clustering using simulation-based inference. In this work…
Towards a non-Gaussian Generative Model of large-scale Reionization Maps
Yu-Heng Lin, Sultan Hassan, Bruno Régaldo-Saint Blancard +2
High-dimensional data sets are expected from the next generation of large-scale surveys. These data sets will carry a wealth of information about the early stages of galaxy formati…
Generative Models of Multi-channel Data from a Single Example -- Application to Dust Emission
Bruno Régaldo-Saint Blancard, Erwan Allys, Constant Auclair +5
The quest for primordial -modes in the cosmic microwave background has emphasized the need for refined models of the Galactic dust foreground. Here, we aim at building a realist…
TNT: Vision Transformer for Turbulence Simulations
Yuchen Dang, Zheyuan Hu, Miles Cranmer +2
Turbulence is notoriously difficult to model due to its multi-scale nature and sensitivity to small perturbations. Classical solvers of turbulence simulation generally operate on f…
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Pablo Lemos, Miles Cranmer, Muntazir Abidi +5
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements…
Cosmological Information in the Marked Power Spectrum of the Galaxy Field
Elena Massara, Francisco Villaescusa-Navarro, ChangHoon Hahn +6
Marked power spectra are two-point statistics of a marked field obtained by weighting each location with a function that depends on the local density around that point. We consider…
Wavelet Moments for Cosmological Parameter Estimation
Michael Eickenberg, Erwan Allys, Azadeh Moradinezhad Dizgah +10
Extracting non-Gaussian information from the non-linear regime of structure formation is key to fully exploiting the rich data from upcoming cosmological surveys probing the large-…
The CAMELS project: public data release
Francisco Villaescusa-Navarro, Shy Genel, Daniel Anglés-Alcázar +44
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynami…
HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows
Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt +11
A wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop to…
The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence
Francisco Villaescusa-Navarro, Shy Genel, Daniel Angles-Alcazar +25
We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containi…