CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

no code implementations • 28 Oct 2024 • Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-Gonzalez, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge.

Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction

no code implementations • 24 Sep 2024 • Nilotpal Kakati, Etienne Dreyer, Eilam Gross

Accurately reconstructing particles from detector data is a critical challenge in experimental particle physics, where the spatial resolution of calorimeters has a crucial impact.

Denoising Super-Resolution

PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction

1 code implementation • 18 Feb 2024 • Junjian Lu, Siwei Liu, Dmitrii Kobylianski, Etienne Dreyer, Eilam Gross, Shangsong Liang

In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors.

Contrastive Learning

Configurable calorimeter simulation for AI applications

no code implementations • 3 Mar 2023 • Francesco Armando Di Bello, Anton Charkin-Gorbulin, Kyle Cranmer, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Lukas Heinrich, Lorenzo Santi, Marumi Kado, Nilotpal Kakati, Patrick Rieck, Matteo Tusoni

A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator.

Clustering graph construction

Secondary Vertex Finding in Jets with Neural Networks

1 code implementation • 6 Aug 2020 • Jonathan Shlomi, Sanmay Ganguly, Eilam Gross, Kyle Cranmer, Yaron Lipman, Hadar Serviansky, Haggai Maron, Nimrod Segol

Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers.

High Energy Physics - Experiment High Energy Physics - Phenomenology

Towards a Computer Vision Particle Flow

no code implementations • 19 Mar 2020 • Francesco Armando Di Bello, Sanmay Ganguly, Eilam Gross, Marumi Kado, Michael Pitt, Lorenzo Santi, Jonathan Shlomi

At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the complementary measurements of charged particle tracking devices, to provide a superior measurement of the particle content and kinematics.

Super-Resolution

Set2Graph: Learning Graphs From Sets

1 code implementation • NeurIPS 2020 • Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman

Many problems in machine learning can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions.

BIG-bench Machine Learning Clustering

Asymptotic formulae for likelihood-based tests of new physics

9 code implementations • 10 Jul 2010 • Glen Cowan, Kyle Cranmer, Eilam Gross, Ofer Vitells

We describe likelihood-based statistical tests for use in high energy physics for the discovery of new phenomena and for construction of confidence intervals on model parameters.

Data Analysis, Statistics and Probability High Energy Physics - Experiment