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

Mar 20, 2023, 10:00 AM → Mar 22, 2023, 5:00 PM Europe/Zurich

HIT E 51 (ETH Zurich)

Annapaola de Cosa (ETH Zurich), Saul Alonso-Monsalve (ETH Zurich), Davide Sgalaberna (ETH Zurich)

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.

Monday, March 20

10:00 AM → 12:00 PM Methods: Introduction

10:00 AM Welcome

Speaker: Prof. Davide Sgalaberna (ETH Zurich)

Welcome IPA-ML.pdf

10:30 AM Physics and machine learning: an overview

Speaker: Dr Saul Alonso Monsalve (ETH Zurich)

ipaml-sam-23-03-20.pdf

12:00 PM → 1:30 PM Lunch

1:30 PM → 2:45 PM Methods: Machine learning and statistics

1:30 PM Machine learning lecture

Speaker: Dr Mauro Donega (ETH Zurich)

20230320_IPAML.pdf

2:45 PM → 3:15 PM Break

3:15 PM → 5:00 PM Tutorials

3:15 PM Machine learning (BDT) tutorial

Speakers: Simone Pigazzini (ETH Zurich), Massimiliano Galli

full notebook

intro_slides

Notebook on Colab

4:15 PM Deep learning tutorial

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)

Tuesday, March 21

9:00 AM → 10:20 AM Applications: Neutrinos (I)

9:00 AM Introduction on neutrino experiments workflow with emphasis on challenges

Speaker: Prof. Davide Sgalaberna (ETH Zurich)

Neutrino Workflow.pdf

9:20 AM Image reconstruction in a 3D plastic scintillator detector using deep learning

Speaker: Dr Saul Alonso Monsalve (ETH Zurich)

ipaml-sam-23-03-21.pdf

9:40 AM Neutrino interaction classification and transfer learning

Speaker: Dr Leigh Whitehead (University of Cambridge)

lhw_zurich_machine_learning_200323.pdf

10:00 AM Event filtering and mitigation of simulation biases

Speaker: Dr Marta Babicz (University of Zurich)

Marta_slides.pdf

10:20 AM → 10:50 AM Break

10:50 AM → 11:30 AM Applications: Neutrinos (II)

10:50 AM Event reweighting and generative models in neutrino experiments

Speaker: Dr Cristovao Vilela (CERN)

cvilela_ipa-ml_20230321.pdf

11:10 AM Multi-task data reconstruction chain for imaging detectors in neutrino experiments

Speaker: Prof. Kazuhiro Terao (SLAC)

2023-03-21-ETH.pdf

2023_03_21_IPA_Kazu

11:30 AM → 12:10 PM Applications: Astrophysics (I)

11:30 AM Large Scale Structure Cosmology with Artificial Intelligence

Speaker: Dr Tomasz Kacprzak (ETH Zurich)

11:50 AM Cosmological constraints from combined probes of large scale structure with deep learning

Speaker: Arne Thomsen (ETH Zurich)

IPA_ml_arne_no_animations.pdf

12:10 PM → 1:20 PM Lunch

1:20 PM → 2:40 PM Applications: Astrophysics (II)

1:20 PM Background rejection in Cherenkov Telescopes

Speaker: Prof. Adrian Biland (ETH Zurich)

ML_IACT.pdf

1:40 PM New generative AI models

Speaker: Luca Biggio (ETH Zurich)

gen_models.pptx.pdf

2:00 PM Geometric Learning/ Physics-informed AI for cosmology

Speaker: Dr Tilman Troester (ETH Zurich)

geometric_deep_learning.key

geometric_deep_learning_tilman_troester.pdf

2:20 PM PointNets for galaxy redshift surveys

Speaker: Sotirios Anagnostidis (ETH Zurich)

PointNet for galaxy redshift surveys.pdf

2:40 PM → 3:10 PM Break

3:10 PM → 5:00 PM Applications: LHC experiments (I)

3:10 PM Introduction on LHC experiments workflow with emphasis on challenges

Speaker: Prof. Annapaola de Cosa (ETH Zurich)

IPAMLWorkshop_LHCPhyscis.pdf

3:30 PM Object identification and reconstruction

Speaker: Dr Alessandro Calandri (ETH Zurich)

ML_object_IPAworkshop_21032023.pdf

3:50 PM Ultrafast ML inference for triggering

Speaker: Dr Thea Aarrestad (ETH Zurich)

ipaml_triggering.pdf

4:10 PM Machine learning for data quality monitoring

Speaker: Roberto Seidita

ML4DQM.pdf

4:30 PM Probabilistic models in ML for HEP

Speaker: Davide Valsecchi (ETH Zurich)

23_03_21 - IPA ML workshop - Probabilistic models in ML for HEP.pdf

Wednesday, March 22

9:00 AM → 10:20 AM Applications: LHC experiments (II)

9:00 AM Data-MC matching

Speaker: Massimiliano Galli

230322_MLSeminars.pdf

9:20 AM Analysis techniques

Speaker: Florian Eble

analysis_techniques_HEP.pdf

10:00 AM Quantum machine learning

Speaker: Vasilis Belis (ETH Zurich)

qml_hep_belis.pdf

10:20 AM → 10:50 AM Break

10:50 AM → 12:30 PM Applications: Crossing boundaries in and beyond physics

10:50 AM Machine learning applications

Speaker: Dr Mauro Verzetti (Google)

[External] ML presentation @ ETH.pdf

11:30 AM Robotics, event-based imaging

Speaker: Prof. Davide Scaramuzza (ETH Zurich)

Scaramuzza_event_cameras.pdf

12:00 PM Knowledge distillation

Speaker: Patrick Odagiu (ETH Zurich)

Knowledge Distillation.pdf

12:30 PM → 1:30 PM Lunch

1:30 PM → 3:00 PM Methods: Round table and closing

1:30 PM Round table

2:45 PM Closing

Speaker: Prof. Annapaola de Cosa

IPAMLWorkshop_Closing.pdf