Suchergebnisse - Modern Machine Learning and Particle Physics: An In-Depth Review

Modern machine learning and particle physics: an in-depth review von Bhattacherjee, Biplob, Mukherjee, Swagata

“… Modern machine learning (ML) techniques are ubiquitous in the field of particle physics …”
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Modern Machine Learning and Particle Physics von Matthew D. Schwartz

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Modern Machine Learning and Particle Physics von Schwartz, Matthew D

“… This article will review some aspects of the natural synergy between modern machine learning and particle physics, focusing on applications at the Large Hadron Collider …”
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A Living Review of Machine Learning for Particle Physics von Feickert, Matthew, Nachman, Benjamin

“… Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics …”
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Searches for the BSM scenarios at the LHC using decision tree-based machine learning algorithms: a comparative study and review of random forest, AdaBoost, XGBoost and LightGBM frameworks von Choudhury, Arghya, Mondal, Arpita, Sarkar, Subhadeep

“… Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia …”
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Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC von Mondal, Spandan, Mastrolorenzo, Luca

“… The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC …”
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Unsupervised and lightly supervised learning in particle physics von Bardhan, Jai, Mandal, Tanumoy, Mitra, Subhadip, Neeraj, Cyrin, Patra, Monalisa

“… We review the main applications of machine learning models that are not fully supervised in particle physics, i.e …”
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Probing intractable beyond-standard-model parameter spaces armed with machine learning von Baruah, Rajneil, Mondal, Subhadeep, Patra, Sunando Kumar, Roy, Satyajit

“… This article attempts to summarize the effort by the particle physics community in addressing the tedious work of determining the parameter spaces of beyond-the-standard-model (BSM …”
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Top-philic machine learning von Barman, Rahool Kumar, Biswas, Sumit

“… In this article, we review the application of modern machine learning (ML) techniques to boost the search for processes involving the top quarks at the LHC …”
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Machine learning in experimental neutrino physics von Poonthottathil, N.

“… This article describes how neutrino experiments, will utilize machine learning techniques to identify and reconstruct different neutrino event topology in detectors …”
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Interplay of traditional methods and machine learning algorithms for tagging boosted objects von Bose, Camellia, Chakraborty, Amit, Chowdhury, Shreecheta, Dutta, Saunak

“… Interest in deep learning in collider physics has been growing in recent years, specifically in applying these methods in jet classification, anomaly detection, particle identification etc …”
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Unveiling the secrets of new physics through top quark tagging von Sahu, Rameswar, Ashanujjaman, Saiyad, Ghosh, Kirtiman

“… as early as 2008, recent years have witnessed a surge in the utilization of machine learning-based approaches for the classification of top-jets from QCD jets …”
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How deep learning is complementing deep thinking in ATLAS von Kar, Deepak

“… ATLAS collaboration uses machine learning (ML) algorithms in many different ways in its physics programme, starting from object reconstruction, simulation …”
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Foundations of automatic feature extraction at LHC–point clouds and graphs von Bhardwaj, Akanksha, Konar, Partha, Ngairangbam, Vishal

“… Deep learning algorithms will play a key role in the upcoming runs of the Large Hadron Collider (LHC …”
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Nanosensors Based on Breathomics for Human Disease Diagnosis: a New Frontier in Personalized Healthcare von Taha, Bakr Ahmed, Addie, Ali J., Haider, Adawiya J., Arsad, Norhana

“… The rapid rise of the world’s population has increased the need for advances in early illness detection, including point-of-care and minimally invasive …”
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