744 citations · 6.5k across the 102 of their papers we have counts for
22 papers · 1 filter
Global analysis of electroweak data in the Standard Model
J. de Blas, M. Ciuchini, E. Franco +5
We perform a global fit of electroweak data within the Standard Model, using state-of-the art experimental and theoretical results, including a determination of the electromagnetic…
Learning New Physics from an Imperfect Machine
Raffaele Tito d'Agnolo, Gaia Grosso, Maurizio Pierini +2
We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds di…
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Steven Tsan, Raghav Kansal, Anthony Aportela +6
Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at t…
Explaining machine-learned particle-flow reconstruction
Farouk Mokhtar, Raghav Kansal, Daniel Diaz +4
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from dif…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
Detecting long-lived particles trapped in detector material at the LHC
Jan Kieseler, Juliette Alimena, Jasmine Simms +3
We propose to implement a two-stage detection strategy for exotic long-lived particles that could be produced at the CERN LHC, become trapped in detector material, and decay later.…
Improving Variational Autoencoders for New Physics Detection at the LHC with Normalizing Flows
Pratik Jawahar, Thea Aarrestad, Nadezda Chernyavskaya +5
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a wo…
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Breno Orzari, Thiago Tomei, Maurizio Pierini +5
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset use…
Publishing statistical models: Getting the most out of particle physics experiments
Kyle Cranmer, Sabine Kraml, Harrison B. Prosper +30
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In…
Autoencoders on FPGAs for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
Ekaterina Govorkova, Ema Puljak, Thea Aarrestad +11
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely ch…
LHC physics dataset for unsupervised New Physics detection at 40 MHz
Ekaterina Govorkova, Ema Puljak, Thea Aarrestad +3
In particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data…
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
Eric A. Moreno, Jean-Roch Vlimant, Maria Spiropulu +2
We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trai…
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
Zhiqiang Que, Erwei Wang, Umar Marikar +10
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational i…
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks
Shah Rukh Qasim, Kenneth Long, Jan Kieseler +2
The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events…
Particle Cloud Generation with Message Passing Generative Adversarial Networks
Raghav Kansal, Javier Duarte, Hao Su +6
In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machi…
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Giuseppe Di Guglielmo, Farah Fahim, Christian Herwig +15
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off…
Jet Single Shot Detection
Adrian Alan Pol, Thea Aarrestad, Katya Govorkova +8
We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on C…
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
Farah Fahim, Benjamin Hawks, Christian Herwig +27
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains.…
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
Raghav Kansal, Javier Duarte, Breno Orzari +5
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
Joosep Pata, Javier Duarte, Jean-Roch Vlimant +2
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 cal…
Fast convolutional neural networks on FPGAs with hls4ml
Thea Aarrestad, Vladimir Loncar, Nicolò Ghielmetti +17
We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate a…