Search Results for author: Maria Spiropulu

Found 20 papers, 13 papers with code

Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder

1 code implementation28 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.

Anomaly Detection

Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation

1 code implementation23 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.

Inductive Bias Jet Tagging +1

MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks

1 code implementation21 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.

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

no code implementations25 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

Interaction networks for the identification of boosted $H\to b\overline{b}$ decays

3 code implementations26 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

JEDI-net: a jet identification algorithm based on interaction networks

2 code implementations14 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

Quantum adiabatic machine learning with zooming

1 code implementation13 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.

BIG-bench Machine Learning Quantum Machine Learning

Charged particle tracking with quantum annealing-inspired optimization

no code implementations13 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.

Combinatorial Optimization

Processing Columnar Collider Data with GPU-Accelerated Kernels

1 code implementation14 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

Variational Autoencoders for New Physics Mining at the Large Hadron Collider

1 code implementation26 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.

Two-sample testing

Pileup mitigation at the Large Hadron Collider with Graph Neural Networks

no code implementations18 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.

Novel deep learning methods for track reconstruction

3 code implementations14 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

Topology classification with deep learning to improve real-time event selection at the LHC

3 code implementations29 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.

General Classification

An MPI-Based Python Framework for Distributed Training with Keras

1 code implementation16 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.

BIG-bench Machine Learning

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