scholarly journals ThickBrick: optimal event selection and categorization in high energy physics. Part I. Signal discovery

2021 ◽  
Vol 2021 (3) ◽  
Author(s):  
Konstantin T. Matchev ◽  
Prasanth Shyamsundar
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
High Energy Physics ◽  
Statistical Significance ◽  
High Energy ◽  
Event Selection ◽  
Systematic Uncertainties ◽  
Novel Approach ◽  
Signal Region ◽  
Energy Physics

Abstract We provide a prescription called ThickBrick to train optimal machine-learning-based event selectors and categorizers that maximize the statistical significance of a potential signal excess in high energy physics (HEP) experiments, as quantified by any of six different performance measures. For analyses where the signal search is performed in the distribution of some event variables, our prescription ensures that only the information complementary to those event variables is used in event selection and categorization. This eliminates a major misalignment with the physics goals of the analysis (maximizing the significance of an excess) that exists in the training of typical ML-based event selectors and categorizers. In addition, this decorrelation of event selectors from the relevant event variables prevents the background distribution from becoming peaked in the signal region as a result of event selection, thereby ameliorating the challenges imposed on signal searches by systematic uncertainties. Our event selectors (categorizers) use the output of machine-learning-based classifiers as input and apply optimal selection cutoffs (categorization thresholds) that are functions of the event variables being analyzed, as opposed to flat cutoffs (thresholds). These optimal cutoffs and thresholds are learned iteratively, using a novel approach with connections to Lloyd’s k-means clustering algorithm. We provide a public, Python implementation of our prescription, also called ThickBrick, along with usage examples.

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 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.

Neural network trigger algorithms for heavy quark event selection in a fixed target high energy physics experiment

1992 ◽  
Vol 25 (4) ◽  
pp. 413-421 ◽  
Author(s):  
Lalit Gupta ◽  
Anand M. Upadhye ◽  
Bruce Denby ◽  
Salvator R. Amendolia ◽  
Giovanni Grieco
Keyword(s):  
Neural Network ◽  
Heavy Quark ◽  
High Energy Physics ◽  
High Energy ◽  
Fixed Target ◽  
Event Selection ◽  
Physics Experiment ◽  
Energy Physics

scholarly journals Nanosecond machine learning event classification with boosted decision trees in FPGA for high energy physics

2021 ◽  
Vol 16 (08) ◽  
pp. P08016
Author(s):  
T.M. Hong ◽  
B.T. Carlson ◽  
B.R. Eubanks ◽  
S.T. Racz ◽  
S.T. Roche ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Trees ◽  
High Energy Physics ◽  
High Energy ◽  
Event Classification ◽  
Boosted Decision Trees ◽  
Learning Event ◽  
Energy Physics

scholarly journals CLUE: A Fast Parallel Clustering Algorithm for High Granularity Calorimeters in High-Energy Physics

2020 ◽  
Vol 3 ◽  
Author(s):  
Marco Rovere ◽  
Ziheng Chen ◽  
Antonio Di Pilato ◽  
Felice Pantaleo ◽  
Chris Seez
Keyword(s):  
Heterogeneous Computing ◽  
Clustering Algorithm ◽  
High Energy Physics ◽  
High Energy ◽  
Spatial Index ◽  
Phase 2 ◽  
Number Of Clusters ◽  
Density Based Clustering ◽  
Parallel Clustering ◽  
Energy Physics

One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitized energy deposits (hits) in the reconstruction stage. In this article, we propose a fast and fully parallelizable density-based clustering algorithm, optimized for high-occupancy scenarios, where the number of clusters is much larger than the average number of hits in a cluster. The algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. We also show a comparison of the performance on CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing in high-energy physics.

scholarly journals Uncertainty-aware machine learning for high energy physics

2021 ◽  
Vol 104 (5) ◽  
Author(s):  
Aishik Ghosh ◽  
Benjamin Nachman ◽  
Daniel Whiteson
Keyword(s):  
Machine Learning ◽  
High Energy Physics ◽  
High Energy ◽  
Energy Physics

scholarly journals Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics

2021 ◽  
Vol 81 (2) ◽  
Author(s):  
Laurits Tani ◽  
Diana Rand ◽  
Christian Veelken ◽  
Mario Kadastik
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithms ◽  
High Energy Physics ◽  
Simulated Data ◽  
High Energy ◽  
Alternative Methods ◽  
Hyperparameter Optimization ◽  
Swarm Optimization ◽  
Physics Experiments ◽  
Energy Physics

AbstractThe analysis of vast amounts of data constitutes a major challenge in modern high energy physics experiments. Machine learning (ML) methods, typically trained on simulated data, are often employed to facilitate this task. Several choices need to be made by the user when training the ML algorithm. In addition to deciding which ML algorithm to use and choosing suitable observables as inputs, users typically need to choose among a plethora of algorithm-specific parameters. We refer to parameters that need to be chosen by the user as hyperparameters. These are to be distinguished from parameters that the ML algorithm learns autonomously during the training, without intervention by the user. The choice of hyperparameters is conventionally done manually by the user and often has a significant impact on the performance of the ML algorithm. In this paper, we explore two evolutionary algorithms: particle swarm optimization and genetic algorithm, for the purposes of performing the choice of optimal hyperparameter values in an autonomous manner. Both of these algorithms will be tested on different datasets and compared to alternative methods.

scholarly journals Quantum machine learning and its supremacy in high energy physics

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Particle Physics ◽  
High Energy Physics ◽  
Recognition Task ◽  
High Energy ◽  
Particle Identification ◽  
Quantum Machine Learning ◽  
The Future ◽  
Energy Physics

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.

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