Today, High Energy Physics (HEP) research is at a crossroads. While the Large Hadron Collider (LHC) keeps accumulating data to establish the Standard Model on a solid footing, compelling theoretical underpinnings point to the existence of new physics at higher energy scales. In the future, the High Luminosity LHC will precisely measure the properties of the Higgs boson, using a few thousand petabytes of data. Many other precision experiments in HEP are under construction or going to start soon. Hence, the future course of the field will be largely data-driven.
Machine Learning techniques will be heavily employed in analyzing this humongous data for possible hints of new physics. Already, remarkable progress has been achieved in developing different classification, identification, characterization, and estimation strategies for use in the searches performed at LHC.
The primary purpose of this meeting is human resources development and capacity building in frameworks related to deep machine learning and artificial intelligence for HEP. The programme will begin with a set of pedagogical lectures, tutorials and hands-on coding sessions to bring the introductory group of participants (mostly students and post-docs) up to speed. The second part will be a working-group style workshop, with well-spaced brainstorming sessions seeding possible collaborative activities. We foresee this workshop to be the first of a series, bringing together different experts on a common platform to exchange ideas, start joint projects, and develop a cooperative training program for young professionals in this field.
Eligibility: All Ph.D. and postdoctoral researchers working in related field are welcome to apply for the preschool.
This program is focused on application of Deep Machine Learning (DML) in High Energy Physics and the eligibility requirements for the participants include being a student or postdoc, working in theoretical or experimental particle physics or astro-particle physics. Additionally, participants should have experience with programming languages, especially Python and C++, and some knowledge of simulated event generation and data analysis frameworks such as Madgraph, Pythia, Delphes, ROOT etc.. Students having basic knowledge of machine learning tools and working on problems that can be benefitted from the applications of machine learning will be preferred.
Preschool Date: 12 June 2023 to 23 June 2023
Accommodation will be provided for outstation participants at our on campus guest house.
ICTS is committed to building an environment that is inclusive, non-discriminatory and welcoming of diverse individuals. We especially encourage the participation of women and other under-represented groups.