no code implementations • 8 Mar 2024 • Joschka Birk, Anna Hallin, Gregor Kasieczka
This is the first successful transfer between two different and actively studied classes of tasks and constitutes a major step in the building of foundation models for particle physics.
no code implementations • 2 Feb 2024 • Patrick Odagiu, Zhiqiang Que, Javier Duarte, Johannes Haller, Gregor Kasieczka, Artur Lobanov, Vladimir Loncar, Wayne Luk, Jennifer Ngadiuba, Maurizio Pierini, Philipp Rincke, Arpita Seksaria, Sioni Summers, Andre Sznajder, Alexander Tapper, Thea K. Aarrestad
We study various machine learning based algorithms for performing accurate jet flavor classification on field-programmable gate arrays and demonstrate how latency and resource consumption scale with the input size and choice of algorithm.
no code implementations • 21 Dec 2023 • Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs
Uncertainty estimation is a key issue when considering the application of deep neural network methods in science and engineering.
no code implementations • 18 Dec 2023 • Ranit Das, Gregor Kasieczka, David Shih
We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability.
1 code implementation • 30 Nov 2023 • Joschka Birk, Erik Buhmann, Cedric Ewen, Gregor Kasieczka, David Shih
We introduce the first generative model trained on the JetClass dataset.
1 code implementation • 13 Oct 2023 • Sebastian Bieringer, Gregor Kasieczka, Maximilian F. Steffen, Mathias Trabs
A Metropolis-Hastings step is widely used for gradient-based Markov chain Monte Carlo methods in uncertainty quantification.
no code implementations • 29 Sep 2023 • Erik Buhmann, Cedric Ewen, Darius A. Faroughy, Tobias Golling, Gregor Kasieczka, Matthew Leigh, Guillaume Quétant, John Andrew Raine, Debajyoti Sengupta, David Shih
In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation.
1 code implementation • 11 Sep 2023 • Erik Buhmann, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Krüger, Peter McKeown
We further distill the diffusion model into a consistency model allowing for accurate sampling in a single step and resulting in a $46\times$ ($37\times$ over CaloClouds) speed-up.
2 code implementations • 8 May 2023 • Erik Buhmann, Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, William Korcari, Katja Krüger, Peter McKeown
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics.
1 code implementation • 17 Jan 2023 • Erik Buhmann, Gregor Kasieczka, Jesse Thaler
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations.
no code implementations • 30 Nov 2022 • Ranit Das, Gregor Kasieczka, David Shih
Choosing which properties of the data to use as input to multivariate decision algorithms -- a. k. a.
no code implementations • 19 Jul 2022 • Gabriele Benelli, Thomas Y. Chen, Javier Duarte, Matthew Feickert, Matthew Graham, Lindsey Gray, Dan Hackett, Phil Harris, Shih-Chieh Hsu, Gregor Kasieczka, Elham E. Khoda, Matthias Komm, Mia Liu, Mark S. Neubauer, Scarlet Norberg, Alexx Perloff, Marcel Rieger, Claire Savard, Kazuhiro Terao, Savannah Thais, Avik Roy, Jean-Roch Vlimant, Grigorios Chachamis
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
no code implementations • 15 Mar 2022 • Andreas Adelmann, Walter Hopkins, Evangelos Kourlitis, Michael Kagan, Gregor Kasieczka, Claudius Krause, David Shih, Vinicius Mikuni, Benjamin Nachman, Kevin Pedro, Daniel Winklehner
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources.
no code implementations • 7 Dec 2021 • Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, David Shih
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics.
no code implementations • 6 Jul 2021 • Gregor Kasieczka, Benjamin Nachman, David Shih
The identification of anomalous overdensities in data - group or collective anomaly detection - is a rich problem with a large number of real world applications.
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.
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
2 code implementations • 3 Sep 2020 • Sascha Diefenbacher, Engin Eren, Gregor Kasieczka, Anatolii Korol, Benjamin Nachman, David Shih
We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (DCTR) protocol.
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.
2 code implementations • 11 May 2020 • Erik Buhmann, Sascha Diefenbacher, Engin Eren, Frank Gaede, Gregor Kasieczka, Anatolii Korol, Katja Krüger
Accurate simulation of physical processes is crucial for the success of modern particle physics.
Instrumentation and Detectors High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
1 code implementation • 13 Jan 2020 • Gregor Kasieczka, David Shih
While deep learning has proven to be extremely successful at supervised classification tasks at the LHC and beyond, for practical applications, raw classification accuracy is often not the only consideration.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
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