Scientific Computing Seminar

Date and Place: Thursdays and hybrid (live in 32-349/online via Zoom). For detailed dates see below!

Content

In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everybody interested in the topics is welcome.

List of Talks

Event Information:

  • Tue
    02
    Dec
    2025

    SC Seminar: Jan Kieseler

    15:15Hybrid (Room 32-349 and via Zoom)

    Prof. Dr. Jan Kieseler, Institute of Experimental Particle Physics (ETP), Karlsruhe Institute of Technology (KIT)

    Title: From Detector Signals to Physics with Machine Learning

    Abstract:

    Large-scale particle detectors at CERN’s Large Hadron Collider (LHC) and its upcoming high-luminosity upgrade (HL-LHC) record millions of collisions per second, producing sparse, irregular, high-dimensional sensor data from conceptually very different sub-detector systems, such as tracking and calorimetry. Future collider concepts push granularity requirements even further: they aim at unprecedented measurement precision and, despite operating at lower particle densities, place new demands on particle detection (reconstruction) algorithms. Meeting the physics goals of these experiments—precision measurements and searches for extremely rare processes—requires algorithms that can reliably extract thousands of overlapping particles under tight performance and computing constraints, and translate robustly to ultimate-precision detectors.

    This motivates a shift away from classical, hand-crafted reconstruction methods—still ubiquitous in modern detectors—toward machine-learning approaches that respect detector geometry, enforce locality, and adapt to varying particle densities. Such models must learn what is physically resolvable by the detector, avoid global operations that hinder robustness, and perform early information compression to keep inference scalable on heterogeneous hardware. At the same time, improved truth definitions and geometry-aware target formulations are essential to achieve stable generalisation across detector configurations and physics conditions.

    This talk will outline these requirements from a physics perspective and discuss corresponding machine-learning strategies—locality-preserving architectures, generic simulation benchmarks, and scalable inference schemes—that can sustainably meet the demands of next-generation detectors while also improving reconstruction quality in existing ones.

    How to join online

    You can join online via Zoom, using the following link:
    https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1