A special seminar hosted by The Data Science Institute and the Department of Electrical Engineering, Columbia University.

Guest Speaker

Daniel Murnane, Postdoctoral Researcher at Berkeley Lab

Hosted By: Savannah Thais, Associate Research Scientist in the Data Science Institute

Details & Recording

Thursday, March 23, 2023 (11:00 AM – 12:00 PM ET) – HYBRID

  • In-Person Location: Northwest Corner Building, 14th Floor (DSI Suite) – 550 W 120th St, New York, NY 10027
  • Virtual: Zoom link to be sent upon registration

Talk Information

Multi-Tasking ML for Point Clouds at the LHC

Abstract: The Large Hadron Collider is one of the world’s most data-intensive experiments. Every second, millions of collisions are processed, each one resembling a jigsaw puzzle with thousands of pieces. With the upcoming upgrade to the High Luminosity LHC, this problem will only become more complex. To make sense of this data, deep learning techniques are increasingly being used. For example, graph neural networks and transformers have proven effective at handling point cloud tasks such as track reconstruction and jet tagging. In this talk, I will review the point cloud problems in collider physics and recent deep learning solutions investigated by the Exatrkx project – an initiative to implement innovative algorithms for HEP at exascale. These architectures can accurately perform tracking and tagging with low latency, even in the high luminosity regime. Additionally, I will explore how multi-tasking and multi-modal networks can combine several of these different tasks.