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 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 Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 53
Author(s):  
Massimo Giovannozzi ◽  
Ewen Maclean ◽  
Carlo Emilio Montanari ◽  
Gianluca Valentino ◽  
Frederik F. Van der Veken
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Performance Optimization ◽  
Hadron Collider ◽  
High Energy ◽  
Beam Dynamics ◽  
Machine Learning Techniques ◽  
Nonlinear Beam ◽  
Dynamic Aperture ◽  
Advanced Analysis

A Machine Learning approach to scientific problems has been in use in Science and Engineering for decades. High-energy physics provided a natural domain of application of Machine Learning, profiting from these powerful tools for the advanced analysis of data from particle colliders. However, Machine Learning has been applied to Accelerator Physics only recently, with several laboratories worldwide deploying intense efforts in this domain. At CERN, Machine Learning techniques have been applied to beam dynamics studies related to the Large Hadron Collider and its luminosity upgrade, in domains including beam measurements and machine performance optimization. In this paper, the recent applications of Machine Learning to the analyses of numerical simulations of nonlinear beam dynamics are presented and discussed in detail. The key concept of dynamic aperture provides a number of topics that have been selected to probe Machine Learning. Indeed, the research presented here aims to devise efficient algorithms to identify outliers and to improve the quality of the fitted models expressing the time evolution of the dynamic aperture.

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 Beam Measurements and Machine Learning at the CERN Large Hadron Collider

2021 ◽  
Vol 24 (9) ◽  
pp. 47-58
Author(s):  
Pasquale Arpaia ◽  
Gabriella Azzopardi ◽  
Frederic Blanc ◽  
Xavier Buffat ◽  
Loic Coyle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Hadron Collider ◽  
Hadron Collider ◽  
Cern Large Hadron Collider

scholarly journals Machine learning in accelerator physics: applications 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 ◽  
Accelerator Physics ◽  
Cern Large Hadron Collider

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

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Sign in / Sign up

Export Citation Format

Share Document