Enhancing Sensitivity to Physics beyond the Standard Model and Detector Performance Monitoring in HEP Experiments with Machine Learning

Dear colleagues,

Enhancing sensitivity to physics beyond the Standard Model and detector performance monitoring in HEP experiments with Machine Learning

A major aim of experimental high-energy physics (HEP) is to find rare signals of new particles produced in large numbers of collisions or to look for deviations from Standard Model predictions.  

In such experiments, a challenging but essential aspect of data processing is to construct a complete physical model starting from measurements from different subdetectors. Moreover, the activities connected to detector operation and monitoring of its parameters can be a long and tedious task that is subject to human errors.

Neural networks have been used in HEP for a long time; however, recent developments in computer science have given rise to new sets of machine learning algorithms that, in many circumstances, out-perform more conventional algorithms. Recent research in high-energy physics indicates that deep neural networks can extract more information from low-level features in comparison to features created by experienced human analysts. Recently, attention has focused on deep learning to ensure a powerful and fully automatized discrimination of the backgrounds as well as to tackle the increase in detector resolutions and data rates in HEP experiments, also using an approach to process data inherited from computer vision, such as semantic segmentation and image captioning.

This Special Issue will collect contributions on the use of deep learning algorithms for complex physics analyses or enhance the sensitivity of searches for physics beyond the Standard Model. Contributions on machine learning approaches to detector operation and monitoring are also welcome.

Prof. Dr. Mario Merola
Prof. Dr. Sabino Meola
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Universe is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• HEP experiments
• Physics beyond SM
• Machine learning
• Computer vision
• Fast simulation
• Big data analysis
• Detector physics