scholarly journals The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

2021 ◽  
Author(s):  
Gregor Kasieczka ◽  
Benjamin Nachman ◽  
David Shih ◽  
Oz Amram ◽  
Anders Andreassen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Supervised Learning ◽  
High Energy Physics ◽  
High Energy ◽  
New Physics ◽  
Lessons Learned ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
New Paradigm

Abstract 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. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

scholarly journals CP VIOLATION AND BEAUTY DECAYS — A CASE STUDY OF HIGH IMPACT, HIGH SENSITIVITY AND EVEN HIGH PRECISION PHYSICS

2000 ◽  
Vol 15 (08) ◽  
pp. 1079-1156
Author(s):  
I. I. BIGI
Keyword(s):  
Cp Violation ◽  
Degrees Of Freedom ◽  
High Energy Physics ◽  
Dipole Moments ◽  
High Sensitivity ◽  
High Energy ◽  
New Physics ◽  
B Decays ◽  
New Paradigm ◽  
Parametric Reliability

The narrative of these lectures contains three main threads: (i) CP violation despite having so far been observed only in the decays of neutral kaons has been recognized as a phenomenon of truly fundamental importance. The KM ansatz constitutes the minimal implementation of CP violation: without requiring unknown degrees of freedom it can reproduce the known CP phenomenology in a nontrivial way. (ii) The physics of beauty hadrons — in particular their weak decays — opens a novel window onto fundamental dynamics: they usher in a new quark family (presumably the last one); they allow us to determine fundamental quantities of the Standard Model like the b quark mass and the CKM parameters V(cb), V(ub), V(ts) and V(td); they exhibit speedy or even rapid [Formula: see text] oscillations. (iii) Heavy Quark Expansions allow us to treat B decays with an accuracy that would not have been thought possible a mere decade ago. These three threads are joined together in the following manner: (a) Huge CP asymmetries are predicted in B decays, which represents a decisive test of the KM paradigm for CP violation. (b) Some of these predictions are made with high parametric reliability, which (c) can be translated into numerical precision through the judicious employment of novel theoretical technologies. (d) Beauty decays thus provide us with a rich and promising field to search for New Physics and even study some of its salient features. At the end of it there might quite possibly be a New Paradigm for High Energy Physics. There will be some other threads woven into this tapestry: electric dipole moments, and CP violation in other strange and in charm decays.

Alternate methods for anomaly detection in high-energy physics via semi-supervised learning

2020 ◽  
Vol 35 (23) ◽  
pp. 2050131
Author(s):  
Mohd Adli Md Ali ◽  
Nu’man Badrud’din ◽  
Hafidzul Abdullah ◽  
Faiz Kemi
Keyword(s):  
Anomaly Detection ◽  
Supervised Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Classification Model ◽  
Training Dataset ◽  
Weakly Supervised Learning ◽  
Physics Community ◽  
Weakly Supervised ◽  
Energy Physics

Recently, the concept of weakly supervised learning has gained popularity in the high-energy physics community due to its ability to learn even with a noisy and impure dataset. This method is valuable in the quest to discover the elusive beyond Standard Model (BSM) particle. Nevertheless, the weakly supervised learning method still requires a learning sample that describes the features of the BSM particle truthfully to the classification model. Even with the various theoretical framework such as supersymmetry and the quantum black hole, creating a BSM sample is not a trivial task since the exact feature of the particle is unknown. Due to these difficulties, we propose an alternative classifier type called the one-class classification (OCC). OCC algorithms require only background or noise samples in its training dataset, which is already abundant in the high-energy physics community. The algorithm will flag any sample that does not fit the background feature as an abnormality. In this paper, we introduce two new algorithms called EHRA and C-EHRA, which use machine learning regression and clustering to detect anomalies in samples. We tested the algorithms’ capability to create distinct anomalous patterns in the presence of BSM samples and also compare their classification output metrics to the Isolation Forest (ISF), a well-known anomaly detection algorithm. Five Monte Carlo supersymmetry datasets with the signal to noise ratio equal to 1, 0.1, 0.01, 0.001, and 0.0001 were used to test EHRA, C-EHRA and ISF algorithm. In our study, we found that the EHRA with an artificial neural network regression has the highest ROC-AUC score at 0.7882 for the balanced dataset, while the C-EHRA has the highest precision-sensitivity score for the majority of the imbalanced datasets. These findings highlight the potential use of the EHRA, C-EHRA, and other OCC algorithms in the quest to discover BSM particles.

scholarly journals Autoencoders for unsupervised anomaly detection in high energy physics

2021 ◽  
Vol 2021 (6) ◽  
Author(s):  
Thorben Finke ◽  
Michael Krämer ◽  
Alessandro Morandini ◽  
Alexander Mück ◽  
Ivan Oleksiyuk
Keyword(s):  
Anomaly Detection ◽  
Particle Physics ◽  
High Energy Physics ◽  
High Energy ◽  
New Physics ◽  
Promising Tool ◽  
Unsupervised Anomaly Detection ◽  
Model Independent ◽  
Jet Images ◽  
Energy Physics

Abstract Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches. We scrutinize the usage of autoencoders for unsupervised anomaly detection based on reconstruction loss to show their capabilities, but also their limitations. As a particle physics benchmark scenario, we study the tagging of top jet images in a background of QCD jet images. Although we reproduce the positive results from the literature, we show that the standard autoencoder setup cannot be considered as a model-independent anomaly tagger by inverting the task: due to the sparsity and the specific structure of the jet images, the autoencoder fails to tag QCD jets if it is trained on top jets even in a semi-supervised setup. Since the same autoencoder architecture can be a good tagger for a specific example of an anomaly and a bad tagger for a different example, we suggest improved performance measures for the task of model-independent anomaly detection. We also improve the capability of the autoencoder to learn non-trivial features of the jet images, such that it is able to achieve both top jet tagging and the inverse task of QCD jet tagging with the same setup. However, we want to stress that a truly model-independent and powerful autoencoder-based unsupervised jet tagger still needs to be developed.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

scholarly journals Anomaly Detection for Resonant New Physics with Machine Learning

2018 ◽  
Vol 121 (24) ◽  
Author(s):  
Jack Collins ◽  
Kiel Howe ◽  
Benjamin Nachman
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
New Physics

scholarly journals Deep Learning and Its Application to LHC Physics

2018 ◽  
Vol 68 (1) ◽  
pp. 161-181 ◽  
Author(s):  
Dan Guest ◽  
Kyle Cranmer ◽  
Daniel Whiteson
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Learning Tools ◽  
Future Prospects ◽  
Higher Dimensional ◽  
Lhc Physics ◽  
Core Concepts ◽  
Energy Physics

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

scholarly journals Fast simulation of the electromagnetic calorimeter response using Self-Attention Generative Adversarial Networks

2021 ◽  
Vol 251 ◽  
pp. 03043
Author(s):  
Fedor Ratnikov ◽  
Alexander Rogachev
Keyword(s):  
Network Architecture ◽  
High Energy Physics ◽  
Hadron Collider ◽  
High Energy ◽  
Electromagnetic Calorimeter ◽  
Generative Adversarial Networks ◽  
Fast Simulation ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider need, so the experiment is in urgent need of new fast simulation techniques. The application of Generative Adversarial Networks is a promising solution to speed up the simulation while providing the necessary physics performance. In this paper we propose the Self-Attention Generative Adversarial Network as a possible improvement of the network architecture. The application is demonstrated on the performance of generating responses of the LHCb type of the electromagnetic calorimeter.

scholarly journals Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

2021 ◽  
Vol 251 ◽  
pp. 03055
Author(s):  
John Blue ◽  
Braden Kronheim ◽  
Michelle Kuchera ◽  
Raghuram Ramanujan
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Event Simulation ◽  
Simulation Process ◽  
Adversarial Networks ◽  
Wide Range ◽  
Detector Simulation

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.

scholarly journals tt̄H Coupling Measurement with the ATLAS Detector at the LHC

2018 ◽  
Vol 182 ◽  
pp. 02052
Author(s):  
Asma Hadef
Keyword(s):  
Higgs Boson ◽  
Cross Sections ◽  
Top Quark ◽  
High Energy Physics ◽  
Center Of Mass ◽  
High Energy ◽  
New Physics ◽  
Decay Modes ◽  
Center Of Mass Energy ◽  
Higgs Decay

The Higgs boson was discovered on the 4th of July 2012 with a mass around 125 GeV by ATLAS and CMS experiments at LHC. Determining the Higgs properties (production and decay modes, couplings,...) is an important part of the high-energy physics programme in this decade. A search for the Higgs boson production in association with a top quark pair (tt̄H) at ATLAS [1] is summarized in this paper at an unexplored center-of-mass energy of 13 TeV, which could allow a first direct measurement of the top quark Yukawa coupling and could reveal new physics. The tt̄H analysis in ATLAS is divided into 3 channels according to the Higgs decay modes: H → Hadrons, H → Leptons and H → Photons. The best-fit value of the ratio of observed and Standard Model cross sections of tt̄H production process, using 2015-2016 data and combining all tt̄H final states, is 1:8±0:7, corresponds to 2:8σ (1:8σ) observed (expected) significance.

scholarly journals The TrackML high-energy physics tracking challenge on Kaggle

2019 ◽  
Vol 214 ◽  
pp. 06037
Author(s):  
Moritz Kiehn ◽  
Sabrina Amrouche ◽  
Paolo Calafiura ◽  
Victor Estrade ◽  
Steven Farrell ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Training Dataset ◽  
High Luminosity ◽  
Test Dataset ◽  
Event Reconstruction ◽  
Computer Scientists ◽  
Set Up ◽  
Energy Physics

The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms.

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