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 • 22 Apr 2024 • Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions.
no code implementations • 5 Sep 2023 • Michael James Fenton, Alexander Shmakov, Hideki Okawa, Yuji Li, Ko-Yang Hsiao, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
We explore the performance of the extended capability of SPA-NET in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson.
no code implementations • 19 May 2023 • Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson
Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive.
no code implementations • 16 Dec 2022 • Junze Liu, Aishik Ghosh, Dylan Smith, Pierre Baldi, Daniel Whiteson
Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors.
no code implementations • 20 Oct 2022 • Jack H. Collins, Yifeng Huang, Simon Knapen, Benjamin Nachman, Daniel Whiteson
In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.
no code implementations • 15 Sep 2022 • Phiala Shanahan, Kazuhiro Terao, Daniel Whiteson
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community.
1 code implementation • 7 Jun 2021 • Alexander Shmakov, Michael James Fenton, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics.
no code implementations • 6 Feb 2021 • Eric Albin, Daniel Whiteson
We explore the sensitivity offered by a global network of cosmic ray detectors to a novel, unobserved phenomena: widely separated simultaneous extended air showers.
High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics
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
1 code implementation • 3 Nov 2020 • Julian Collado, Jessica N. Howard, Taylor Faucett, Tony Tong, Pierre Baldi, Daniel Whiteson
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information.
Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology
1 code implementation • 19 Oct 2020 • Michael James Fenton, Alexander Shmakov, Ta-Wei Ho, Shih-Chieh Hsu, Daniel Whiteson, Pierre Baldi
Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques.
no code implementations • 17 Sep 2017 • Meghan Frate, Kyle Cranmer, Saarik Kalia, Alexander Vandenberg-Rodes, Daniel Whiteson
We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals.
Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology
no code implementations • 10 Mar 2017 • Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, Andreas Søgaard
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.
2 code implementations • 28 Jan 2016 • Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson
We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters.
BIG-bench Machine Learning Vocal Bursts Intensity Prediction
no code implementations • NeurIPS 2014 • Peter J. Sadowski, Daniel Whiteson, Pierre Baldi
Particle colliders enable us to probe the fundamental nature of matter by observing exotic particles produced by high-energy collisions.
no code implementations • 13 Oct 2014 • Pierre Baldi, Peter Sadowski, Daniel Whiteson
The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5$\sigma$ significance barrier without more data.
2 code implementations • 19 Feb 2014 • Pierre Baldi, Peter Sadowski, Daniel Whiteson
Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features.
High Energy Physics - Phenomenology High Energy Physics - Experiment