High Energy Physics has a long record of using Machine Learning algorithms, in particular during the latest steps of data analysis (e.g. signal/background classification, linear regression, etc.. ).
The interest in Deep Learning techniques in the past years has been boosted with intense R&D and prototyping activities that are exploring the use of such techniques across multiple steps in the HEP experiments life cycle and data processing steps, ranging from data quality monitoring, trigger, etc. to reconstruction and simulation.
The availability of reliable, user-friendly and scalable resources, including full software and hardware stacks, is critical to fully support activities in the ML/DL domain. In this context, the CERN IT department is launching an initiative to gather information about the status of its ML/DL dedicated infrastructure, the state-of-the-art and the needs of the HEP ML/DL community at CERN.
This workshop constitutes a first step to understand the requirements in terms of software and hardware and consolidate a vision that can enable further advancement of the field.
Invitation: Applications Session - Open Session
While this first workshop focuses on infrastructure and services, we have added a slot for short presentations from applications in the area of ML/DL. Contact us if you're interested in giving a 10 minute overview of your own application.
