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Found 6 papers, 2 papers with code
Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs).
Inferring astrophysical X-ray polarization with deep learning
We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021.
Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming
The careful analysis of strongly gravitationally lensed radio and optical images of distant galaxies can in principle reveal DM (sub-)structures with masses several orders of magnitude below the mass of dwarf spheroidal galaxies.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology
Constraining the Parameters of High-Dimensional Models with Active Learning
Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy.
Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer
We present a study for the generation of events from a physical process with deep generative models.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
