scholarly journals Using Machine Learning for Precision Measurements

2019 ◽  
Vol 214 ◽  
pp. 06022
Author(s):  
Dimitri Bourilkov
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Hadron Collider ◽  
New Physics ◽  
Precise Determination ◽  
Machine Learning Techniques ◽  
Test Case ◽  
Mixing Angle ◽  
Learning Techniques ◽  
Electroweak Mixing Angle

The use of machine learning techniques for classification is well established. They are applied widely to improve the signal-to-noise ratio and the sensitivity of searches for new physics at colliders. In this study I explore the use of machine learning for optimizing the output of high precision experiments by selecting the most sensitive variables to the quantity being measured. The precise determination of the electroweak mixing angle at the Large Hadron Collider using linear or deep neural network regressors is developed as a test case.

scholarly journals Machine learning techniques for detecting topological avatars of new physics

2019 ◽  
Vol 377 (2161) ◽  
pp. 20190392 ◽  
Author(s):  
A. J. Bevan
Keyword(s):  
Machine Learning ◽  
Particle Physics ◽  
Data Science ◽  
Hadron Collider ◽  
Nuclear Research ◽  
New Physics ◽  
Machine Learning Techniques ◽  
Beyond The Standard Model ◽  
Learning Techniques ◽  
The Standard Model

The search for highly ionizing particles in nuclear track detectors (NTDs) traditionally requires experts to manually search through samples in order to identify regions of interest that could be a hint of physics beyond the standard model of particle physics. The advent of automated image acquisition and modern data science, including machine learning-based processing of data presents an opportunity to accelerate the process of searching for anomalies in NTDs that could be a hint of a new physics avatar. The potential for modern data science applied to this topic in the context of the MoEDAL experiment at the large Hadron collider at the European Centre for Nuclear Research, CERN, is discussed. This article is part of a discussion meeting issue ‘Topological avatars of new physics’.

scholarly journals Detecting an axion-like particle with machine learning at the LHC

2021 ◽  
Vol 2021 (11) ◽  
Author(s):  
Jie Ren ◽  
Daohan Wang ◽  
Lei Wu ◽  
Jin Min Yang ◽  
Mengchao Zhang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mass Range ◽  
New Physics ◽  
Machine Learning Techniques ◽  
Global Symmetry ◽  
Sequential Decay ◽  
Production Processes ◽  
Learning Techniques ◽  
Photon Coupling

Abstract Axion-like particles (ALPs) appear in various new physics models with spon- taneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astrophysics bounds are so far quite weak. In this work, we investi- gate such light ALPs through the ALP-strahlung production processes pp → W±a, Za with the sequential decay a → γγ at the 14 TeV LHC with an integrated luminosity of 3000 fb−1 (HL-LHC). Building on the concept of jet image which uses calorimeter towers as the pixels of the image and measures a jet as an image, we investigate the potential of machine learning techniques based on convolutional neural network (CNN) to identify the highly boosted ALPs which decay to a pair of highly collimated photons. With the CNN tagging algorithm, we demonstrate that our approach can extend current LHC sensitivity and probe the ALP mass range from 0.3 GeV to 5 GeV. The obtained bounds are stronger than the existing limits on the ALP-photon coupling.

scholarly journals The Application Of Machine Learning In Test Case Prioritization - A Review

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Elinda Kajo Mece ◽  
Kleona Binjaku ◽  
Hakik Paci
Keyword(s):  
Machine Learning ◽  
Regression Testing ◽  
Machine Learning Techniques ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Testing Time ◽  
Engineering Software ◽  
Learning Techniques ◽  
Learning Machine

Regression testing is very important but also a very costly and time-consuming activity that ensures the developers that changes in the application will not bring new errors. Retest all, selection of test cases and prioritization of test cases (TCP)  approaches are used to enhance the efficiency and effectiveness in regression testing. While test case selection techniques decrease testing time and cost, it can exclude some critical test cases that can detect the faults. On the other hand, test case prioritization considers all test cases and execute them until resources are exhausted or all test cases are executed, while always focusing on the most important ones. Over the years, machine learning has found wide usage in solving different problems in software engineering. Software development and maintenance problems can be defined as learning problems and machine learning techniques have shown to be very effective in solving these problems. In the range of application of machine learning, machine learning techniques have also found usage in solving the test case prioritization problem. In this paper, we investigate the application of machine learning techniques in test case prioritization. We survey some of the most recent studies made in this field and provide information like techniques of machine learning used in TCP process, metrics used to measure the effectiveness of the proposed methods, data used to define the priority of test cases and some advantages or limitations of application of machine learning in TCP.

scholarly journals Fast and accurate primary user detection with machine learning techniques for cognitive radio networks

2021 ◽  
Vol 21 (1) ◽  
pp. 472
Author(s):  
G. A. Pethunachiyar ◽  
B. Sankaragomathi
Keyword(s):  
Machine Learning ◽  
Spectrum Sensing ◽  
Orthogonal Frequency Division Multiplexing ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Primary User ◽  
Detection Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques

<p class="IJASEITAbtract"><span>Spectrum decision is an important and crucial task for the secondary user to avail the unlicensed spectrum for transmission. Managing the spectrum is an efficient one for spectrum sensing. Determining the primary user presence in the spectrum is an essential work for using the licensed spectrum of primary user. The information which lacks in managing the spectrum are the information about the primary user presence, accuracy in determining the existence of user in the spectrum, the cost for computation and difficult in finding the user in low signal-to noise ratio (SNR) values. The proposed system overcomes the above limitations. In the proposed system, the various techniques of machine learning like decision tree, support vector machines, naive bayes, ensemble based trees, nearest neighbour’s and logistic regression are used for testing the algorithm. As a first step, the spectrum sensing is done in two stages with Orthogonal Frequency Division Multiplexing and Energy Detection algorithm at the various values of SNR. The results generated from the above algorithm is used for database generation. Next, the different machine learning techniques are trained and compared for the results produced by different algorithms with the characteristics like speed, time taken for training and accuracy in prediction. The accuracy and finding the presence of the user in the spectrum at low SNR values are achieved by all the algorithms. The computation cost of the algorithm differs from each other. Among the tested techniques, k-nearest neighbour (KNN) algorithm produces the better performance in a minimized time.</span></p>

scholarly journals Application of machine learning techniques at the CERN Large Hadron Collider

2020 ◽  
Author(s):  
Frederik Van Der Veken ◽  
Gabriella Azzopardi ◽  
Fred Blanc ◽  
Loic Coyle ◽  
Elena Fol ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
Machine Learning Techniques ◽  
Cern Large Hadron Collider ◽  
Learning Techniques

scholarly journals Optimal River Interlinking using Data Driven Machine Learning Techniques

2019 ◽  
Vol 8 (4) ◽  
pp. 760-765
Keyword(s):  
Machine Learning ◽  
Computational Models ◽  
Machine Learning Techniques ◽  
Test Case ◽  
Rapid Urbanization ◽  
Water Demand And Supply ◽  
Learning Techniques ◽  
Cost Efficient ◽  
Using Data ◽  
Water Scarce

India's increasing population, rapid urbanization and indiscriminate destruction of water bodies creating a grave threat on its existing water demand and supply balance. Primary fresh water sources such as rivers and wells are gradually getting dry. As a consequence, it is estimated that India would become a water scarce nation by 2050. To address the issue, massive survey work was conducted and various inter basin water transfer schemes were chalked out. However, these schemes became subject of controversy owing to its technical risk and huge cost. To make this effort cost efficient, in this investigation, computational approaches have been undertaken to assist in the decision making process. Current research endeavour suggests that these efforts are quite accurate, less costly and can be carried out in much less time. The inputs to these computational models are landscape elevation, land use data, soil information, precipitation level etc.. The estimated optimal river interlinking routes will be the output of the proposed model. Several efforts have been undertaken earlier in this direction with various limitations. In this paper, we address the same issue using machine learning approach. For experimental purpose Jogigopa-Tista link is considered as the test case. Optimal routing path is been estimated using the developed technique. Thereafter, the results are compared with the National Water Development Agency (NWDA) proposed routes. It is found that the proposed model's outcome exhibits a considerable amount of similarity with the NWDA proposed route.

scholarly journals Machine Learning Using Rapidity-Mass Matrices for Event Classification Problems in HEP

Universe ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 19
Author(s):  
Sergei V. Chekanov
Keyword(s):  
Machine Learning ◽  
Standard Model ◽  
Mass Matrix ◽  
Hadron Collider ◽  
Feature Space ◽  
New Physics ◽  
Machine Learning Techniques ◽  
Monte Carlo Event ◽  
Classification Problems ◽  
Event Classification

In this work, supervised artificial neural networks (ANN) with rapidity–mass matrix (RMM) inputs are studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event classification problems. The proposed standardization of the ANN feature space can simplify searches for signatures of new physics at the Large Hadron Collider (LHC) when using machine learning techniques. In particular, we illustrate how to improve signal-over-background ratios in the search for new physics, how to filter out Standard Model events for model-agnostic searches, and how to separate gluon and quark jets for Standard Model measurements.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques
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