Thursday Mornning Session
- Xabier Cid Vidal (CERN)
Tim Head (Ecole Polytechnique Federale de Lausanne (CH))
Machine learning is used at all stages of the LHCb experiment. It is routinely used: in the process of deciding which data to record and which to reject forever, as part of the reconstruction algorithms (feature engineering), and in the extraction of physics results from our data. This talk will highlight current use cases, as well as ideas for ambitious future applications, and how we can...
Andrey Ustyuzhanin (Yandex School of Data Analysis (RU))
In my talk I'm going to give an overview of the ML tools/services Yandex School of Data Analysis (YSDA) team has developed. In particular I will focus on approaches that our team has developed during collaboration with LHCb on HEP data analysis (uGB+FL, GB-reweighting). Each approach is implemented within hep_ml Python package. To get acquainted with this tool you can install it right away in...
Caterina Doglioni (Lund University (SE)), Dustin James Anderson (California Institute of Technology (US)), Vladimir Gligorov (CERN)
The LHC provides experiments with an unprecedented amount of data. Experimental collaborations need to meet storage and computing requirements for the analysis of this data: this is often a limiting factor in the physics program that would be achievable if the whole dataset could be analysed. In this talk, I will describe the strategies adopted by the LHCb, CMS and ATLAS collaborations to...
Giovanni Punzi (Universita di Pisa & INFN (IT)), Luciano Frances Ristori (Fermi National Accelerator Lab. (US))
Charge particle reconstruction is one of the most demanding computational tasks found in HEP, and it becomes increasingly important to perform it in real time. We envision that HEP would greatly benefit from achieving a long-term goal of making track reconstruction happen transparently as part of the detector readout ("detector-embedded tracking"). We describe here a track-reconstruction...
Ben Nachman (SLAC National Accelerator Laboratory (US)), Michael Aaron Kagan (SLAC National Accelerator Laboratory (US))
In this talk we present recent developments in the application of machine learning, computer vision, and probabilistic models to the analysis and interpretation of LHC events. First, we will introduce the concept of jet-images and computer vision techniques for jet tagging. Jet images enabled the connection between jet substructure and tagging with the fields of computer vision and image...