Theory

Information Theory

• arXiv:1805.11965 (hep-th) A Mini-Introduction To Information Theory (Edward Witten)

Machine Learning

• e-book The Hundred-Page Machine Learning Book (Andriy Burkov)

Machine Learning in Particle Physics (Claire David )

• Machine Learning in Particle Physics
• Assignment 1: Classifier By Hand
• Assignment 2: Decision Trees
• Assignment 3: Neural Network By Hand!

Twiki/Tutorials

• US ATLAS Machine Learning Training Event, 27-29 Jul 2022 Good intro talks and tutorials.
• US ATLAS Machine Learning Training 2023, 25-28 Jul 2023

• ATLAS Machine Learning Forum Information and Resources

• git: HEPML-LivingReview
• arXiv:2102.02770 (hep-ph) A Living Review of Machine Learning for Particle Physics (Matthew Feickert, Benjamin Nachman)

Conference/Meetings

• 5th ML in HEP School, 1-10 July 2019
• 6th ML in HEP School, 16-30 Jul 2020 No contents?
• 7th ML in HEP School, 15-30 Jul 2021 No contents?
• 8th ML in HEP School, 11-18 Apr 2023

• INSIGHTS Workshop on Statistics, Machine Learning and Outreach, 17-21 Sep 2018
• ML4Jets, 15-17 Jan 2020
• 4th Inter-experiment Machine Learning Workshop, 19-23 Oct 2020
• Fast Machine Learning for Science Workshop 2022, 3-6 Oct 2022
• 6th ATLAS Machine Learning Workshop, 18-20 Jan 2023

• 51st SLAC Summer Institute (SSI 2023), 7-18 Aug 2023

• Artificial Intelligence and the Uncertainty challenge in Fundamental Physics, Nov 27 - Dec1, 2023

Presentations/Talks

• EP-IT Data science seminars on 29 Jan 2020 Application and development of advanced deep neural networks for high-granularity calorimeters (Jan Kieseler)
• ATL-PHYS-PUB-2020-018 Deep Learning for Pion Identification and Energy Calibration with the ATLAS Detector
• Jet/Etmiss Main Meeting on 12 Aug 2020 Machine Learning for Pion Identification and Energy Calibration
• TDAQ Phase II Performance, Physics Studies & Event Selection Meeting on 12 Nov 2020 3 talks on ML for tracking, tau and cluster calibration.
• Inverted CERN School of Computing on 07 Mar 2023 Graph Neural Networks, From fundamentals to physics application
• Machine Learning Working Group meeting on 18 Apr 2023 MLPF: Machine Learning for Particle Flow (Joosep Pata)
• Machine Learning Working Group meeting on 18 Apr 2023 HGPflow: Particle flow as a Hypergraph learning task (Nilotpal Kakati)

Papers

• arXiv:1312.6203 (cs.LG) Spectral Networks and Locally Connected Networks on Graphs
• arXiv:1603.04467 (cs.DC) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• arXiv:1609.04747 (cs.LG) An overview of gradient descent optimization algorithms
• arXiv:1610.04161 (cs.LG) Why Deep Neural Networks for Function Approximation?
• arXiv:1711.00165 (stat.ML) Deep Neural Networks as Gaussian Processes
• arXiv:2002.12220 (hep-ph) Les Houches 2019 Physics at TeV Colliders: New Physics Working Group Report (Contribution 22: Practical Machine Learning for regression and classification and applications in HEP phenomenology; Contribution 23: Model-Independent Signal Detection: A Challenge using Benchmark Monte Carlo Data and Machine Learning)
• arXiv:2002.01427 (physics.data-an) On the impact of modern deep-learning techniques to the performance and time-requirements of classification models in experimental high-energy physics
• arXiv:2008.12305 (hep-ph) Parton distribution functions
• arXiv:2010.11998 (hep-ph) Mapping Machine-Learned Physics into a Human-Readable Space
• arXiv:2012.00964 (hep-ph) Equivariant Energy Flow Networks for Jet Tagging
• arXiv:2012.01249 (hep-ph) Graph Neural Networks for Particle Tracking and Reconstruction
• arXiv:2012.02531 (hep-ex) Quark-Gluon Jet Discrimination Using Convolutional Neural Networks
• arXiv:2203.03669 (hep-ph) A simple guide from Machine Learning outputs to statistical criteria
• arXiv:2212.08759 (hep-ex) Decay-aware neural network for event classification in collider physics
• arXiv:2306.08106 (hep-ex) Applications of Deep Learning to physics workflows (Whitepaper resulting from Accelerating Physics with ML@MIT workshop in Jan/Feb 2023)
• arXiv:2309.12417 (hep-ex) Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC
• arXiv:2310.16121 (hep-ph) 19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
• arXiv:2311.17914 (hep-ex) Reconstruction of electromagnetic showers in calorimeters using Deep Learning

Uncertainties

• PHYSTAT seminar on 16 Nov 2022 On Relating Uncertainties in Machine Learning and High Energy Physics (Michael Kagan)
• PHYSTAT seminar on 25 Jan 2023 Learning Uncertainties the Frequentist Way (Jesse Thaler)

Object tagging

• Atlas Machine Learning Forum: Modern ML for Object ID on 18 Nov 2021
• ATL-COM-PHYS-2021-406 Identification of hadronically-decaying top quarks using UFO jets with ATLAS in Run 2

TensorFlow

• arXiv:1603.04467 (cs.DC) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• TensorFlow.org
• TensorFlow 2.0 tutorial, 27-28 Feb 2020

Quantum Computing

• ATLAS weekly on 3 Mar 2020 Quantum Computing Applications for ATLAS (Koji Terashi)

Examples

• gitLab: SUSY Machine Learning using Keras with TensorFlow. Submitting jobs to batch system.

-- KenjiHamano - 2020-01-29