Mini Review

Published on 19 Oct 2023

How big is Big Data? A comprehensive survey of data production, storage, and streaming in science and industry

in Big Data and AI in High Energy Physics

Luca Clissa
Mario Lassnig
Lorenzo Rinaldi

Frontiers in Big Data

doi 10.3389/fdata.2023.1271639

748 views

Correction

Published on 16 Oct 2023

Corrigendum: Applications and techniques for fast machine learning in science

in Big Data and AI in High Energy Physics

Allison McCarn Deiana
Nhan Tran
Joshua Agar
Michaela Blott
Giuseppe Di Guglielmo
Javier Duarte
Philip Harris
Scott Hauck
Mia Liu
Mark S. Neubauer

Frontiers in Big Data

doi 10.3389/fdata.2023.1301942

370 views

Original Research

Published on 21 Mar 2023

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals

in Big Data and AI in High Energy Physics

John Andrew Raine
Samuel Klein
Debajyoti Sengupta
Tobias Golling

Frontiers in Big Data

doi 10.3389/fdata.2023.899345

1,239 views
9 citations

Original Research

Published on 07 Nov 2022

Effect of the high-level trigger for detecting long-lived particles at LHCb

in Big Data and AI in High Energy Physics

Lukas Calefice
Arthur Hennequin
Louis Henry
Brij Jashal
Diego Mendoza
Arantza Oyanguren
Izaac Sanderswood
Carlos Vázquez Sierra
Jiahui Zhuo
Part of LHCb-RTA Collaboration

Frontiers in Big Data

doi 10.3389/fdata.2022.1008737

1,904 views
1 citation

Methods

Published on 30 Sep 2022

Using machine learning to improve neutron identification in water Cherenkov detectors

in Big Data and AI in High Energy Physics

Blair Jamieson
Matt Stubbs
Sheela Ramanna
John Walker
Nick Prouse
Ryosuke Akutsu
Patrick de Perio
Wojciech Fedorko

Frontiers in Big Data

doi 10.3389/fdata.2022.978857

1,946 views
2 citations

Original Research

Published on 17 Jun 2022

Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks

in Big Data and AI in High Energy Physics

Mo Jia
Karan Kumar
Liam S. Mackey
Alexander Putra
Cristovao Vilela
Michael J. Wilking
Junjie Xia
Chiaki Yanagisawa
Karan Yang

Frontiers in Big Data

doi 10.3389/fdata.2022.868333

2,046 views

Review

Published on 12 Apr 2022

Applications and Techniques for Fast Machine Learning in Science

in Big Data and AI in High Energy Physics

Allison McCarn Deiana
Nhan Tran
Joshua Agar
Michaela Blott
Giuseppe Di Guglielmo
Javier Duarte
Philip Harris
Scott Hauck
Mia Liu
Mark S. Neubauer

Frontiers in Big Data

doi 10.3389/fdata.2022.787421

16,865 views
28 citations

Original Research

Published on 23 Mar 2022

Graph Neural Networks for Charged Particle Tracking on FPGAs

in Big Data and AI in High Energy Physics

Abdelrahman Elabd
Vesal Razavimaleki
Shi-Yu Huang
Javier Duarte
Markus Atkinson
Gage DeZoort
Peter Elmer
Scott Hauck
Jin-Xuan Hu
Shih-Chieh Hsu

Frontiers in Big Data

doi 10.3389/fdata.2022.828666

4,508 views
13 citations

Original Research

Published on 28 Feb 2022

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

in Big Data and AI in High Energy Physics

Pratik Jawahar
Thea Aarrestad
Nadezda Chernyavskaya
Maurizio Pierini
Kinga A. Wozniak
Jennifer Ngadiuba
Javier Duarte
Steven Tsan

Frontiers in Big Data

doi 10.3389/fdata.2022.803685

2,540 views
10 citations

Community Case Study

Published on 07 Jan 2022

Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence

in Big Data and AI in High Energy Physics

Alessandro Di Girolamo
Federica Legger
Panos Paparrigopoulos
Jaroslava Schovancová
Thomas Beermann
Michael Boehler
Daniele Bonacorsi
Luca Clissa
Leticia Decker de Sousa
Tommaso Diotalevi

Frontiers in Big Data

doi 10.3389/fdata.2021.753409

1,909 views
1 citation

Original Research

Published on 16 Nov 2021

Anomaly Detection for the Centralised Elasticsearch Service at CERN

in Big Data and AI in High Energy Physics

Jennifer R. Andersson
Jose Alonso Moya
Ulrich Schwickerath

Frontiers in Big Data

doi 10.3389/fdata.2021.718879

3,259 views
3 citations

Technology and Code

Published on 16 Nov 2021

Teeport: Break the Wall Between the Optimization Algorithms and Problems

in Big Data and AI in High Energy Physics

Zhe Zhang
Xiaobiao Huang
Minghao Song

Frontiers in Big Data

doi 10.3389/fdata.2021.734650

1,465 views
1 citation

Editorial

Published on 05 Aug 2021

Editorial: Innovative Analysis Ecosystems for HEP Data

in Big Data and AI in High Energy Physics

Sezen Sekmen
Gian Michele Innocenti
Bo Jayatilaka

Frontiers in Big Data

doi 10.3389/fdata.2021.736105

3,321 views

Editorial

Published on 01 Jul 2021

Editorial: Heterogeneous Computing for AI and Big Data in High Energy Physics

in Big Data and AI in High Energy Physics

Daniele D’Agostino
Daniele Cesini

Frontiers in Big Data

doi 10.3389/fdata.2021.652881

1,757 views
2 citations

Original Research

Published on 25 Jun 2021

Porting HEP Parameterized Calorimeter Simulation Code to GPUs

in Big Data and AI in High Energy Physics

Zhihua Dong
Heather Gray
Charles Leggett
Meifeng Lin
Vincent R. Pascuzzi
Kwangmin Yu

Frontiers in Big Data

doi 10.3389/fdata.2021.665783

1,708 views
3 citations

Original Research

Published on 24 Jun 2021

Interactive Web-Based Visualization of Multidimensional Physical and Astronomical Data

in Big Data and AI in High Energy Physics

Faruk Diblen
Luc Hendriks
Bob Stienen
Sascha Caron
Rena Bakhshi
Jisk Attema

Frontiers in Big Data

doi 10.3389/fdata.2021.626998

2,480 views
2 citations

Navigation group

Articles

How big is Big Data? A comprehensive survey of data production, storage, and streaming in science and industry

Corrigendum: Applications and techniques for fast machine learning in science

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals

Effect of the high-level trigger for detecting long-lived particles at LHCb

Using machine learning to improve neutron identification in water Cherenkov detectors

Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks

Applications and Techniques for Fast Machine Learning in Science

Graph Neural Networks for Charged Particle Tracking on FPGAs

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

Preparing Distributed Computing Operations for the HL-LHC Era With Operational Intelligence

Anomaly Detection for the Centralised Elasticsearch Service at CERN

Teeport: Break the Wall Between the Optimization Algorithms and Problems

Editorial: Innovative Analysis Ecosystems for HEP Data

Editorial: Heterogeneous Computing for AI and Big Data in High Energy Physics

Porting HEP Parameterized Calorimeter Simulation Code to GPUs

Interactive Web-Based Visualization of Multidimensional Physical and Astronomical Data