6 papers
Machine Can Automatically Discover Parametric Functions to Model HEP Data
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +5
In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat unti…
MITRA: An AI Assistant for Knowledge Retrieval in Physics Collaborations
Abhishikth Mallampalli, Sridhara Dasu
Large-scale scientific collaborations, such as the Compact Muon Solenoid (CMS) at CERN, produce a vast and ever-growing corpus of internal documentation. Navigating this complex in…
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…
MuCol Milestone Report No. 7: Consolidated Parameters
Rebecca Taylor, Antoine Chancé, Dario Augusto Giove +459
This document is comprised of a collection of consolidated parameters for the key parts of the muon collider. These consolidated parameters follow on from the October 2024 Prelimin…
The Muon Collider
Carlotta Accettura, Simon Adrian, Rohit Agarwal +450
Muons offer a unique opportunity to build a compact high-energy electroweak collider at the 10 TeV scale. A Muon Collider enables direct access to the underlying simplicity of the…
ESPPU INPUT: C within the "Linear Collider Vision"
Matthew B. Andorf, Mei Bai, Pushpalatha Bhat +34
The Linear Collider Vision calls for a Linear Collider Facility with a physics reach from a Higgs Factory to the TeV-scale with collisions. One of the technologies under…