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IPA workshop on Machine Learning for particle physics and astrophysics (IPA-ML)

→ Europe/Zurich
HIT E 51 (ETH Zurich)

HIT E 51

ETH Zurich

Hönggerberg campus 8093 Zurich Switzerland
Annapaola de Cosa (ETH Zurich), Saul Alonso-Monsalve (ETH Zurich), Davide Sgalaberna (ETH Zurich)
Description

The workshop, organised by the Institute for Particle Physics and Astrophysics (IPA) at ETH Zurich, aims to discuss diverse challenges in HEP and Astrophysics and how they are addressed using machine learning (ML). The event will consist of multiple talks from experts from academia and industry about successful applications of ML, together with some hands-on tutorials and discussions to provide the audience with sufficient knowledge on how to start profiting from these methods autonomously and making ML accessible to young researchers.

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Participants
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  • Mon, March 20
    • 10:00 AM → 12:00 PM
      Methods: Introduction
      • 10:00 AM
        Welcome 30m
        Speaker: Prof. Davide Sgalaberna (ETH Zurich)
        Welcome IPA-ML.pdf
      • 10:30 AM
        Physics and machine learning: an overview 1h 30m
        Speaker: Dr Saul Alonso Monsalve (ETH Zurich)
        ipaml-sam-23-03-20.pdf
    • 12:00 PM → 1:30 PM
      Lunch 1h 30m
    • 1:30 PM → 2:45 PM
      Methods: Machine learning and statistics
      • 1:30 PM
        Machine learning lecture 1h 15m
        Speaker: Dr Mauro Donega (ETH Zurich)
        20230320_IPAML.pdf
    • 2:45 PM → 3:15 PM
      Break 30m
    • 3:15 PM → 5:00 PM
      Tutorials
      • 3:15 PM
        Machine learning (BDT) tutorial 1h
        Speakers: Simone Pigazzini (ETH Zurich), Massimiliano Galli
        full notebook
        intro_slides
        Notebook on Colab
      • 4:15 PM
        Deep learning tutorial 45m

        In this tutorial, you'll learn how to use deep learning to perform particle identification. We'll cover the basics of neural networks and how to build them using Python and TensorFlow. Participants will train their own neural networks using a dataset of particle tracks, and learn how to evaluate their performance. To participate, you'll need a Google account for Google Colab.

        Prerequisites:

        No prior experience with deep learning is required, but some familiarity with Python programming is recommended.

        Materials needed:

        A laptop computer with internet access and a Google account. Participants should open the notebook deep_learning_tutorial.ipynb on Google Colab from the following Github project: https://github.com/saulam/IPA-ML.

        Speaker: Dr Saul Alonso Monsalve (ETH Zurich)
  • Tue, March 21
    • 9:00 AM → 10:20 AM
      Applications: Neutrinos (I)
      • 9:00 AM
        Introduction on neutrino experiments workflow with emphasis on challenges 20m
        Speaker: Prof. Davide Sgalaberna (ETH Zurich)
        Neutrino Workflow.pdf
      • 9:20 AM
        Image reconstruction in a 3D plastic scintillator detector using deep learning 20m
        Speaker: Dr Saul Alonso Monsalve (ETH Zurich)
        ipaml-sam-23-03-21.pdf
      • 9:40 AM
        Neutrino interaction classification and transfer learning 20m
        Speaker: Dr Leigh Whitehead (University of Cambridge)
        lhw_zurich_machine_learning_200323.pdf
      • 10:00 AM
        Event filtering and mitigation of simulation biases 20m
        Speaker: Dr Marta Babicz (University of Zurich)
        Marta_slides.pdf
    • 10:20 AM → 10:50 AM
      Break 30m
    • 10:50 AM → 11:30 AM
      Applications: Neutrinos (II)
    • 11:30 AM → 12:10 PM
      Applications: Astrophysics (I)
      • 11:30 AM
        Large Scale Structure Cosmology with Artificial Intelligence 20m
        Speaker: Dr Tomasz Kacprzak (ETH Zurich)
      • 11:50 AM
        Cosmological constraints from combined probes of large scale structure with deep learning 20m
        Speaker: Arne Thomsen (ETH Zurich)
        IPA_ml_arne_no_animations.pdf
    • 12:10 PM → 1:20 PM
      Lunch 1h 10m
    • 1:20 PM → 2:40 PM
      Applications: Astrophysics (II)
    • 2:40 PM → 3:10 PM
      Break 30m
    • 3:10 PM → 5:00 PM
      Applications: LHC experiments (I)
  • Wed, March 22
    • 9:00 AM → 10:20 AM
      Applications: LHC experiments (II)
    • 10:20 AM → 10:50 AM
      Break 30m
    • 10:50 AM → 12:30 PM
      Applications: Crossing boundaries in and beyond physics
    • 12:30 PM → 1:30 PM
      Lunch 1h
    • 1:30 PM → 3:00 PM
      Methods: Round table and closing
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