46 citations · 247 across the 27 of their papers we have counts for
28 papers
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning
Jeff Shen, Francois Lanusse, Liam Holden Parker +24
Sequential scientific data span many resolutions and domains, and unifying them into a common representation is a key step toward developing foundation models for the sciences. Ast…
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unst…
AION-1: Omnimodal Foundation Model for Astronomical Sciences
Liam Parker, Francois Lanusse, Jeff Shen +24
While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In th…
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…
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small…
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…
Listening to the Noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles C. Margossian, Ruben Ohana +1
In recent years, denoising problems have become intertwined with the development of deep generative models. In particular, diffusion models are trained like denoisers, and the dist…
: 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…
Simulation-based stacking
Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke
Simulation-based inference has been popular for amortized Bayesian computation. It is typical to have more than one posterior approximation, from different inference algorithms, di…
Removing Dust from CMB Observations with Diffusion Models
David Heurtel-Depeiges, Blakesley Burkhart, Ruben Ohana +1
In cosmology, the quest for primordial -modes in cosmic microwave background (CMB) observations has highlighted the critical need for a refined model of the Galactic dust foregr…
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…
Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering
Chirag Modi, Shivam Pandey, Matthew Ho +3
Simulation-based inference (SBI) is a promising approach to leverage high fidelity cosmological simulations and extract information from the non-Gaussian, non-linear scales that ca…
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…
Separation of dust emission from the Cosmic Infrared Background in Herschel observations with Wavelet Phase Harmonics
Constant Auclair, Erwan Allys, François Boulanger +7
The low brightness dust emission at high Galactic latitude is of interest to study the interplay between physical processes in shaping the structure of the interstellar medium (ISM…
: 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…
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…
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-…
Single frequency CMB B-mode inference with realistic foregrounds from a single training image
Niall Jeffrey, François Boulanger, Benjamin D. Wandelt +3
With a single training image and using wavelet phase harmonic augmentation, we present polarized Cosmic Microwave Background (CMB) foreground marginalization in a high-dimensional…