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 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.

Comparative Analysis of Machine Learning Techniques Using Predictive Modeling

2020 ◽  
Vol 13 ◽  
Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Data Science ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Future Trends ◽  
Data Set ◽  
Learning Stage ◽  
Learning Techniques ◽  
Different Types

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.

Machine Learning Techniques for Internet of Things

2019 ◽  
pp. 160-180
Author(s):  
P. Priakanth ◽  
S. Gopikrishnan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Independent Learning ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Guided Learning ◽  
Learning Techniques ◽  
Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

Machine Learning Techniques for Internet of Things

2021 ◽  
pp. 1490-1506
Author(s):  
P. Priakanth ◽  
S. Gopikrishnan
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Independent Learning ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Guided Learning ◽  
Learning Techniques ◽  
Learning Machine

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

scholarly journals CP VIOLATIONS (AND MORE) AFTER THE FIRST TWO YEARS OF LHCB

2013 ◽  
Vol 53 (A) ◽  
pp. 528-533
Author(s):  
Giulio Auriemma
Keyword(s):  
Dark Matter ◽  
Dark Energy ◽  
Particle Physics ◽  
Hadron Collider ◽  
Higgs Sector ◽  
New Physics ◽  
Baryon Asymmetry ◽  
Open Problems ◽  
Cp Violations ◽  
The Standard Model

The most interesting cosmological open problems, baryon asymmetry, dark matter, inflation and dark energy, are not explained by the standard model of particle physics (SM). The final<br />goal of the Large Hadron Collider an experimental verification of the SM in the Higgs sector, and also a search for evidence of new physics beyond it. In this paper we will report some of the results obtained in 2010 and 2011, from the LHCb experiment dedicated to the study of CP violations and rare decays of heavy quarks.

scholarly journals Multiple solutions in supersymmetry and the Higgs

2015 ◽  
Vol 373 (2032) ◽  
pp. 20140035 ◽  
Author(s):  
Ben C. Allanach
Keyword(s):  
Supersymmetry Breaking ◽  
Particle Physics ◽  
Multiple Solutions ◽  
Higgs Mass ◽  
Hadron Collider ◽  
Quantum Corrections ◽  
Beyond The Standard Model ◽  
Universal Model ◽  
The Standard Model ◽  
Supersymmetric Particles

Weak-scale supersymmetry is a well-motivated, if speculative, theory beyond the Standard Model of particle physics. It solves the thorny issue of the Higgs mass, namely: how can it be stable to quantum corrections, when they are expected to be 10 15 times bigger than its mass? The experimental signal of the theory is the production and measurement of supersymmetric particles in the Large Hadron Collider (LHC) experiments. No such particles have been seen to date, but hopes are high for the impending run in 2015. Searches for supersymmetric particles can be difficult to interpret. Here, we shall discuss the fact that, even given a well-defined model of supersymmetry breaking with few parameters, there can be multiple solutions. These multiple solutions are physically different and could potentially mean that points in parameter space have been ruled out by interpretations of LHC data when they should not have been. We shall review the multiple solutions and illustrate their existence in a universal model of supersymmetry breaking.

scholarly journals Searches for extra spatial dimensions with the CMS detector at the LHC

2015 ◽  
Vol 30 (15) ◽  
pp. 1540017 ◽  
Author(s):  
Greg Landsberg
Keyword(s):  
Hadron Collider ◽  
Center Of Mass ◽  
New Physics ◽  
Current Status ◽  
Beyond The Standard Model ◽  
Cern Large Hadron Collider ◽  
The Standard Model ◽  
Cms Detector ◽  
Spatial Dimensions ◽  
8 Tev

The success of the first three years of operations of the CERN Large Hadron Collider (LHC) at center-of-mass energies of 7 TeV and 8 TeV radically changed the landscape of searches for new physics beyond the Standard Model (BSM) and our very way of thinking about its possible origin and its hiding place. Among the paradigms of new physics that have been probed quite extensively at the LHC, are various models that predict the existence of extra spatial dimensions. In this review, the current status of searches for extra dimensions with the Compact Muon Solenoid (CMS) detector is presented, along with prospects for future searches at the full energy of the LHC, expected to be reached in the next few years.

scholarly journals Search for single production of vectorlike top partners through th channel at the HE-LHC and FCC-hh

2021 ◽  
Vol 81 (7) ◽  
Author(s):  
Xi-Yan Tian ◽  
Liu-Feng Du ◽  
Yao-Bei Liu
Keyword(s):  
Standard Model ◽  
Production Cross ◽  
Branching Fraction ◽  
Hadron Collider ◽  
High Energy ◽  
New Physics ◽  
Beyond The Standard Model ◽  
The Standard Model ◽  
Single Production ◽  
Integrated Luminosity

AbstractThe vectorlike top partners are potential signature of some new physics beyond the Standard Model at the TeV scale. In this paper, we propose to search for the vectorlike T quark with charge 2/3 in the framework of a simplified model where the top partners only couples with the third generation of Standard Model quarks. We investigate the observability for electroweak production of a vectorlike T quark in association with a standard model bottom quark through the process $$pp \rightarrow T\bar{b}j$$ p p → T b ¯ j with the subsequent decay mode of $$T\rightarrow t(\rightarrow b W^+\rightarrow b \ell ^{+} \nu _{\ell })h( \rightarrow \gamma \gamma )$$ T → t ( → b W + → b ℓ + ν ℓ ) h ( → γ γ ) , at the proposed High Energy Large Hadron Collider (HE-LHC) and Future Circular Collider in hadron-hadron mode (FCC-hh) including the realistic detector effects. The 95% confidence level excluded regions and the $$5\sigma $$ 5 σ discovery reach in the parameter plane of $$\kappa _{T}-m_T$$ κ T - m T , are respectively obtained at the HE-LHC with the integrated luminosity of 15 ab$$^{-1}$$ - 1 and the FCC-hh with the integrated luminosity of 30 ab$$^{-1}$$ - 1 . We also analyze the projected sensitivity in terms of the production cross section times branching fraction at the HE-LHC and FCC-hh.

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.

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