6 papers
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Sparse Methods for Vector Embeddings of TPC Data
Tyler Wheeler, Michelle P. Kuchera, Raghuram Ramanujan +7
Time Projection Chambers (TPCs) are versatile detectors that reconstruct charged-particle tracks in an ionizing medium, enabling sensitive measurements across a wide range of nucle…
Classifying metal-poor stars with machine learning using nucleosynthesis calculations
Nicole Vassh, Yilin Wang, Richard M. Woloshyn +4
We apply the capabilities of machine learning (ML) to discern patterns in order to classify metal-poor stars. To do so, we train an ML model on a bank of nucleosynthesis calculatio…
Unpaired Translation of Point Clouds for Modeling Detector Response
Mingyang Li, Michelle Kuchera, Raghuram Ramanujan +3
Modeling detector response is a key challenge in time projection chambers. We cast this problem as an unpaired point cloud translation task, between data collected from simulations…
Implicit Quantile Networks For Emulation in Jet Physics
B. Kronheim, A. Al Kadhim, M. P. Kuchera +2
The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task i…