1 code implementation • 16 Dec 2024 • Anja Butter, François Charton, Javier Mariño Villadamigo, Ayodele Ore, Tilman Plehn, Jonas Spinner
Usually, they are used to generate configurations with a fixed number of particles.
no code implementations • 4 Nov 2024 • Anja Butter, Sascha Diefenbacher, Nathan Huetsch, Vinicius Mikuni, Benjamin Nachman, Sofia Palacios Schweitzer, Tilman Plehn
Machine learning enables unbinned, highly-differential cross section measurements.
1 code implementation • 1 Nov 2024 • Johann Brehmer, Víctor Bresó, Pim de Haan, Tilman Plehn, Huilin Qu, Jonas Spinner, Jesse Thaler
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider.
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.
1 code implementation • 24 May 2024 • Radha Mastandrea, Benjamin Nachman, Tilman Plehn
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics.
1 code implementation • 23 May 2024 • Jonas Spinner, Victor Bresó, Pim de Haan, Tilman Plehn, Jesse Thaler, Johann Brehmer
We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architecture for high-energy physics.
no code implementations • 29 Apr 2024 • Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions.
no code implementations • 11 Jan 2023 • Barry M. Dillon, Luigi Favaro, Friedrich Feiden, Tanmoy Modak, Tilman Plehn
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021.
no code implementations • 22 Oct 2021 • Anja Butter, Theo Heimel, Sander Hummerich, Tobias Krebs, Tilman Plehn, Armand Rousselot, Sophia Vent
Generative networks are opening new avenues in fast event generation for the LHC.
1 code implementation • 1 Jul 2021 • Lisa Benato, Erik Buhmann, Martin Erdmann, Peter Fackeldey, Jonas Glombitza, Nikolai Hartmann, Gregor Kasieczka, William Korcari, Thomas Kuhr, Jan Steinheimer, Horst Stöcker, Tilman Plehn, Kai Zhou
We introduce a Python package that provides simply and unified access to a collection of datasets from fundamental physics research - including particle physics, astroparticle physics, and hadron- and nuclear physics - for supervised machine learning studies.
1 code implementation • 16 Apr 2021 • Barry M. Dillon, Tilman Plehn, Christof Sauer, Peter Sorrenson
In particular, the Dirichlet setup solves the problem and improves both the performance and the interpretability of the networks.
no code implementations • 22 Dec 2020 • Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka, Daniel Whiteson
QCD-jets at the LHC are described by simple physics principles.
Super-Resolution
High Energy Physics - Phenomenology
no code implementations • 14 Aug 2020 • Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample.
no code implementations • 27 May 2020 • Martin Bauer, Patrick Foldenauer, Peter Reimitz, Tilman Plehn
We systematically study models with light scalar and pseudoscalar dark matter candidates and their potential signals at the LHC.
High Energy Physics - Phenomenology High Energy Physics - Experiment
1 code implementation • 30 Jan 2017 • Gregor Kasieczka, Tilman Plehn, Michael Russell, Torben Schell
Machine learning based on convolutional neural networks can be used to study jet images from the LHC.
High Energy Physics - Phenomenology
no code implementations • 19 Mar 2015 • Gregor Kasieczka, Tilman Plehn, Torben Schell, Thomas Strebler, Gavin P. Salam
The performance of top taggers, for example in resonance searches, can be significantly enhanced through an increased set of variables, with a special focus on final-state radiation.
High Energy Physics - Phenomenology
3 code implementations • 18 Jul 2014 • Christoph Borschensky, Michael Krämer, Anna Kulesza, Michelangelo Mangano, Sanjay Padhi, Tilman Plehn, Xavier Portell
We present state-of-the-art cross section predictions for the production of supersymmetric squarks and gluinos at the upcoming LHC run with a centre-of-mass energy of $\sqrt{s} = 13$ and $14$ TeV, and at potential future $pp$ colliders operating at $\sqrt{s} = 33$ and $100$ TeV.
High Energy Physics - Phenomenology
3 code implementations • 13 Jun 2012 • Michael Krämer, Anna Kulesza, Robin van der Leeuw, Michelangelo Mangano, Sanjay Padhi, Tilman Plehn, Xavier Portell
This document emerged from work that started in January 2012 as a joint effort by the ATLAS, CMS and LPCC supersymmetry (SUSY) working groups to compile state-of-the-art cross section predictions for SUSY particle production at the LHC.
High Energy Physics - Phenomenology
no code implementations • 14 Jun 2010 • Tilman Plehn, Michael Spannowsky, Michihisa Takeuchi, Dirk Zerwas
Using a Standard-Model top tagger on fully hadronic top decays we can not only extract the stop signal but also measure the top momentum.
High Energy Physics - Phenomenology High Energy Physics - Experiment