2 code implementations • 1 May 2024 • Chang Sun, Thea K. Årrestad, Vladimir Loncar, Jennifer Ngadiuba, Maria Spiropulu

Model size and inference speed at deployment time, are major challenges in many deep learning applications.

1 code implementation • 28 Nov 2023 • Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba, Maria Spiropulu, Jean-Roch Vlimant

Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics.

1 code implementation • 23 Nov 2023 • Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba, Maria Spiropulu, Jean-Roch Vlimant

The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed.

no code implementations • 27 Jul 2021 • Eric A. Moreno, Jean-Roch Vlimant, Maria Spiropulu, Bartlomiej Borzyszkowski, Maurizio Pierini

The recurrent autoencoder outperforms other autoencoders based on different architectures.

2 code implementations • 11 Mar 2021 • Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steve Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alex Ballow, and Alina Lazar

The Exa. TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking.

no code implementations • 10 Mar 2021 • Jeremy Hewes, Adam Aurisano, Giuseppe Cerati, Jim Kowalkowski, Claire Lee, Wei-keng Liao, Alexandra Day, Angrit Agrawal, Maria Spiropulu, Jean-Roch Vlimant, Lindsey Gray, Thomas Klijnsma, Paolo Calafiura, Sean Conlon, Steve Farrell, Xiangyang Ju, Daniel Murnane

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC).

Object Reconstruction High Energy Physics - Experiment

1 code implementation • 21 Jan 2021 • Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria Spiropulu

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum.

no code implementations • 30 Jun 2020 • Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura, Sean Conlon, Steven Farrell, Prabhat, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Panagiotis Spentzouris, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables.

no code implementations • 25 Mar 2020 • Xiangyang Ju, Steven Farrell, Paolo Calafiura, Daniel Murnane, Prabhat, Lindsey Gray, Thomas Klijnsma, Kevin Pedro, Giuseppe Cerati, Jim Kowalkowski, Gabriel Perdue, Panagiotis Spentzouris, Nhan Tran, Jean-Roch Vlimant, Alexander Zlokapa, Joosep Pata, Maria Spiropulu, Sitong An, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision.

Instrumentation and Detectors High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability

no code implementations • 14 Dec 2019 • Dawit Belayneh, Federico Carminati, Amir Farbin, Benjamin Hooberman, Gulrukh Khattak, Miaoyuan Liu, Junze Liu, Dominick Olivito, Vitória Barin Pacela, Maurizio Pierini, Alexander Schwing, Maria Spiropulu, Sofia Vallecorsa, Jean-Roch Vlimant, Wei Wei, Matt Zhang

These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.

3 code implementations • 26 Sep 2019 • Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B. Newman, Avikar Periwal, Maria Spiropulu, Javier M. Duarte, Maurizio Pierini

We develop a jet identification algorithm based on an interaction network, designed to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs, distinguish them from ordinary jets originating from the hadronization of quarks and gluons.

High Energy Physics - Experiment High Energy Physics - Phenomenology

2 code implementations • 14 Aug 2019 • Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, Jean-Roch Vlimant

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons.

High Energy Physics - Experiment High Energy Physics - Phenomenology

1 code implementation • 13 Aug 2019 • Alexander Zlokapa, Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar, Maria Spiropulu

The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

no code implementations • 13 Aug 2019 • Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant, Javier M. Duarte, Joshua Job, Daniel Lidar, Maria Spiropulu

At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling with track density.

1 code implementation • 14 Jun 2019 • Joosep Pata, Maria Spiropulu

At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task.

Data Analysis, Statistics and Probability Distributed, Parallel, and Cluster Computing Computational Physics

1 code implementation • 26 Nov 2018 • Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events.

no code implementations • 18 Oct 2018 • Jesus Arjona Martinez, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup.

3 code implementations • 14 Oct 2018 • Steven Farrell, Paolo Calafiura, Mayur Mudigonda, Prabhat, Dustin Anderson, Jean-Roch Vlimant, Stephan Zheng, Josh Bendavid, Maria Spiropulu, Giuseppe Cerati, Lindsey Gray, Jim Kowalkowski, Panagiotis Spentzouris, Aristeidis Tsaris

The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification.

High Energy Physics - Experiment Data Analysis, Statistics and Probability

3 code implementations • 29 Jun 2018 • Thong Q. Nguyen, Daniel Weitekamp III, Dustin Anderson, Roberto Castello, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider.

1 code implementation • 16 Dec 2017 • Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs.

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