Learning to Discover – Advanced pattern Recognition

Organizers

Andreas Salzburger, contact: Andreas.Salzburger@cern.ch , David Rousseau, Cherifa Sabrina Amrouche, Cecile Germain Slava Voloshynovskiy, Marco Rovere, Maurice Garcia-Sciveres, Marc Schoenauer, Paolo Calafiura, Conor Fitzpatrick.

Date of start
Date of end
Learning to discover
Participants

How to register: Please read

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Context

Pattern recognition is the fundament of the event reconstruction in high energy physics, but has also become - alongside the detector simulation - one of the main computing intensive tasks.

For the Large Hadron Collider (LHC), the  luminosity is expected to increase by a factor five by 2025, leading to proton collision with very large complexity. The current pattern recognition algorithms, which role is to reconstruct the individual particles from the energy deposition on the detectors, are stretching to their limits. A general effort is needed by putting together experts from the different experiments, as well as Data Science experts in novel optimisation algorithms, to try out completely different approaches and foster from recent advances in machine learning: Graph Networks, Recurrent Neural Networks, Convolutional Neural Networks, Monte Carlo Tree Search, Probabilistic hashing are methods which have been proposed to solve this problem, on the one hand, and further optimisation of current pattern recognition algorithms, such as concurrent algorithmic execution or optimisation for accelerated hardware are pursued on the other hand. Several programs exploiting machine learning techniques have been based lately within the context of the tracking machine learning challenge, in the field of quantum computing and carried out by HEP experiments in order to meet the requirements for future data taking campaigns at the LHC and other HEP experiments.

 

Objective

The objective of this workshop is to review current pattern recognition techniques in high energy physics and examine them for optimisation and adaption for future challenges.

In addition, machine learning based or assisted pattern recognition and event reconstruction algorithms are discussed with contributions from within and from related fields, but also applications in industry and an outside perspective on pattern recognition in high energy physics is tried. The interplay of HEP pattern recognition software and computing hardware development and evolution will be discussed.

A coherent summary of the Tracking Machine learning challenge and its valuable lessons will be given and next steps discussed alongside.

Where appropriate, hands-on session can be organised.

 

Format

Contributions are allocated with 45 minutes + 45 minutes of discussion

For more details about the schedules please click here