26 citations · 93 across the 8 of their papers we have counts for
9 papers
Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Nilotpal Kakati, Daniel Murnane, Baran Hashemi +5
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one model can serve many of those step…
Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Jeffrey Krupa, Yiyang Zhao, Mihaly Novak +9
We present an application of automatic differentiation for particle transport through matter using a Geant4-like radiation transport simulation with a full electromagnetic physics…
Product Manifold Machine Learning for Physics
Nathaniel S. Woodward, Sang Eon Park, Gaia Grosso +2
Physical data are representations of the fundamental laws governing the Universe, hiding complex compositional structures often well captured by hierarchical graphs. Hyperbolic spa…
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
Philip Harris, Michael Kagan, Jeffrey Krupa +2
Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a varie…
Hardware-accelerated Inference for Real-Time Gravitational-Wave Astronomy
Alec Gunny, Dylan Rankin, Jeffrey Krupa +7
The field of transient astronomy has seen a revolution with the first gravitational-wave detections and the arrival of multi-messenger observations they enabled. Transformed by the…
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig +27
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.…
FPGAs-as-a-Service Toolkit (FaaST)
Dylan Sheldon Rankin, Jeffrey Krupa, Philip Harris +13
Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically a…
GPU-accelerated machine learning inference as a service for computing in neutrino experiments
Michael Wang, Tingjun Yang, Maria Acosta Flechas +7
Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algori…
GPU coprocessors as a service for deep learning inference in high energy physics
Jeffrey Krupa, Kelvin Lin, Maria Acosta Flechas +13
In the next decade, the demands for computing in large scientific experiments are expected to grow tremendously. During the same time period, CPU performance increases will be limi…