Machine Learning for Physics

12 Mar 2025, 09:00 → 14 Mar 2025, 18:50 Europe/Lisbon

LIP-Lisboa/3-311 - Sala de Seminários (LIP Lisboa)

This is a small workshop aimed at interested students to introduce Machine Learning techniques and algorithms applied to HEP. A series of introductory classes using dedicated tools are paired with hands-on exercises to familiarize with the needs and applications adopted for the HEP experiments at the LHC.

The workshop will take place for two full days, Thursday and Friday.

Lectures will be in presence.

Please register!

 

This workshop focuses on a specific aspect of the activities at the LHC and is part of the "Course on Physics at the LHC - 2025".

 

Contact: 

lisbon-ml-workshop@cern.ch

 

The instructors: 

	Pietro Vischia   Ramón y Cajal senior researcher at the Universidad de Oviedo and ICTEA (Spain), Adjunct Professor at IITM. Graduated in 2016 from IST. He is the coordinator of the MODE (Machine-Learning-Optimized Design of Experiments) Collaboration, and the Machine Learning Coordinador of the CMS Experiment at CERN. Specialist in Machine Learning applied to High Energy Physics. Researcher in high-dimensional spaces via gradient descent, eventually powered by quantum algorithms, and on the extension of machine learning methods to realistic neurons with spiking networks, to be then implemented in neuromorphic hardware devices. Within CMS, focussing on plugging inductive bias in machine learning algorithms for standard model Higgs physics (including the 2018 observation of the ttH process) and beyond-the-standard-model new physics searches in the Top, Higgs, and vector boson sectors. More info at https://vischia.github.io/.
	Inês Ochoa   Researcher at the Laboratory of Instrumentation and Experimental Particle Physics in Portugal. Graduated in 2015 from UCL. Coordinator of the HEP Software Foundation Reconstruction and Software Trigger group. Particle physicist in the ATLAS Collaboration, focussing on developing new machine learning algorithms for identifying Higgs boson decays pairs of b-quarks and searching for new phenomena with unsupervised learning techniques. More info at https://inesochoa.github.io/.
	Cristóvão Beirão da Cruz e Silva   Researcher at the Laboratory of Instrumentation and Experimental Particle Physics in Portugal. Graduated in 2016 from IST. Currently a particle physicist in the CMS collaboration. His research interests focus on detector R&D and the development of precision timing detectors, particularly for the PPS2 upgrade for the HL-LHC. He has additional expertise in data analysis using machine learning techniques, having contributed to the search for the Higgs boson decaying to two photons and SUSY searches with LHC data, particularly the search for the supersymmetric partner of the tau lepton and the search for the supersymmetric partner of the top quark in the compressed mass scenario.

 

 

 

 

 

 

 

Thursday 13 March

09:00 → 10:00 Introduction to Machine Learning for Physics (lecture)

This lecture will introduce the broad concept of Machine Learning, its connection to Artificial Intelligence, and will broadly review the use of ML in High Energy Physics.

Speaker: Prof. Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

10:00 → 10:30 Coffee Break

10:30 → 12:00 Automatic Differentiation and Supervised Learning (lecture)

This lecture will focus on supervised learning, a setting where the training data set is “labelled”, that is the target quantity of learning is known. Automatic Differentiation, the technique that powers up modern machine learning frameworks will then be explained in detail, together with its connection to differentiable programming.

Speaker: Prof. Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

12:00 → 13:30 Lunch break (lunch not provided)

13:30 → 15:30 Exercise 1: Classification and anomaly detection in S-top searches

Classification is a category of supervised learning where the goal is to classify the data into different categories. For the CMS search of the supersymmetric partner of the top quark in the compressed mass scenario a Boosted Decision Tree (BDT) algorithm was used to distinguish between signal-like and background-like events. In this exercise, a neural network will be implemented to achieve this task and performance will be compared with the BDT approach. If time allows, a further attempt will be made with an autoencoder neural network, where only background simulated events are used for training, the performance of this approach will also be compared to the previous two approaches. For comparison of the approaches a simplified limit setting via the pyhf library will be used.

Speaker: Cristóvão da Cruz e Silva (LIP)

15:30 → 16:00 Coffee Break

16:00 → 18:00 Exercise 2: Network structure and inductive bias in Higgs physics (ttH)

Inductive bias refers to the process of encoding into the learning process some properties of the data known a priori: this can happen by manipulating the training data (augmentation), by modifying the structure of the algorithm (e.g. dense vs convolutional networks), or by modifying the learning target (loss function). The exercise will consist in comparing the performance of generic algorithms with that of algorithms targeted to specific structures (e.g. convolutional networks).

Speaker: Prof. Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

Friday 14 March

09:00 → 09:30 Into the belly of Transformers: mathematical formalism and inner workings (lecture)

Transformers are an architecture that powers up most Large Language Models in the market nowadays. This lecture will explain the inner structure of a transformer.

Speaker: Prof. Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

09:30 → 10:30 Exercise 3: Flavour tagging with Transformers

Flavour tagging allows us to identify jets that originate from b- and c-quarks, and is a crucial tool for the physics programme of LHC experiments. The jet flavour can be predicted based on the characteristics of the charged particle tracks associated with it. This set of variable number and unordered tracks lends itself to a graph representation, which can be exploited by transformers. In this exercise, we train and evaluate a transformer for identifying b-jets.

Speaker: Inês Ochoa (LIP)

10:30 → 11:00 Coffee Break

11:00 → 12:00 Unsupervised learning (lecture)

When the data set is unlabelled, that is when the target quantity for learning is not known, traditional supervised learning techniques cannot be used. This lecture will explain the corresponding techniques to obtain learning algorithms without an explicitly known target, such as reinforcement learning.

Speaker: Prof. Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

12:00 → 13:30 Lunch break (lunch not provided)

13:30 → 15:30 Exercise 4: probing the substructure of boosted jets with unsupervised learning

Speakers: Inês Ochoa (LIP), Prof. Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

15:30 → 16:00 Coffee Break

16:00 → 18:00 Data challenge!!!

The data challenge will consist in solving a machine learning problem on a given data set.

The participants will be provided access to the data set, and skeleton code to set up the study.

Participants will have to submit a series of predictions for an evaluation data set, as well as the code and an explanation of the logic behind it.

The models faring the best in the evaluation dataset will be declared winner: the first three models will be presented by their developers (1st: 5 minutes, 2nd and 3rd: 2 minutes) during the awards ceremony.

Certificates for the first three classified will be provided.

Speakers: Cristóvão da Cruz e Silva (LIP), Inês Ochoa (LIP), Prof. Pietro Vischia (Universidad de Oviedo and Instituto de Ciencias y Tecnologías Espaciales de Asturias (ICTEA))

18:00 → 18:30 Wrap-up, Awards, and Group Photo