Tracking at Hadron Colliders with Machine Learning

About

The reconstruction of charged particle trajectories is one of the main requirement for being able to achieve the research goals in collider physics. The resolution on kinematics obtained at low transverse momentum is crucial to many analysis, in particular in the calculation of transverse missing energy and identification of primary vertex. The canonical algorithm implemented in the experiment is based on a seeded combinatorial trajectory following using Kalman filter formalism to update trajectory parameters with sequence of measurements. The algorithm suffers, by construction, from combinatorial explosion and run-time is scaling worse than quadratically with the number concurrent collisions, tracks and hits. With the ever increasing performance of the LHC, and stagnation of computing funding, we are facing a tension between the computation needs and computing budget. While other ways of speeding up the algorithms are pursued ; part of the community is turning to machine learning and other pattern recognition technique to provide faster algorithms for track reconstruction with a view to reconciling costs and budgets. In this seminar I will review utilization of machine learning and advanced technique in tracking-like algorithms, underlying the challenges, promising solutions and possible future research on the topic.

.

0:00:00 Slide 1

0:01:12 Slide 2

0:02:08 Slide 3

0:02:45 Slide 4

0:03:36 Slide 5

0:04:25 Slide 6

0:04:35 Slide 7

0:05:17 Slide 8

0:05:34 Slide 9

0:06:06 Slide 10