Contact Thea Aarrestad (thea.aarrestad@cern.ch) for more details.

QONNX: Accurate resource and latency modeling for Neural Networks on FPGAs

Implement and commision tools for performing in-software estimates of FPGA resource consumption and latency estimates in the QONNX library. Mostly a software project (desigining tools for hardware acceleration), but will include a physics usecase component. Co-supervised with Yaman Umuroglu and Michaela Blott (AMD).

Level: Master Thesis

OPEN

Optimal Transport and Model Independent Statistical Tests for New Physics searches

Design ML-based model independent New Physics Analysis for Phase 2 scouting in CMS. Co-supervised with Gaia Grosso, Katya Govorkova and Phil Harris (MIT).

Level: Master Thesis

Zhengting He

Physics-inspired dynamic graph neural networks embedding approximate symmetries for the CMS Experiment at CERN

The project is to implement certain graph neural networks that are invariant to different symmetry groups, relevant to physics, and test them for jet tagging, which is a classification task in particle physics. The network will be analysed, and the number of FLOPS, tentative mathematical guarantees and comparison with current best models will be determined (LorentzNet, ParticleTransformer, PELICAN). Furthermore following Walters and Wang Approximately Equivariant Networks for Imperfectly Symmetric Dynamics the network equivariance is mitigated to better fit the real world data produced in the CMS detector in CERN.
Particle physics is an ideal playground for testing equivariant networks as the Standard Model is full of symmetries.
The input data can consist of the transversal momentum and two angles of a constituent particle. Therefore implementing networks is not a simple application of already existing architectures as the equivariance should exist for each input specifically and not globally on the entries.
Giving the network only to symmetry equivariant functions to learn should theoretically induce better performance, it could be more understandable in terms of mathematical analysis and should be more efficient for the inference.
The need for low latency models in particle physics is particularly important for the selection of stored events in the CMS Experiment, therefore developing a solution for jet tagging could potentially help to construct an algorithm that would only register interesting events during a collision.
It is a nice illustration of the usage of approximately equivariant networks in a real case scenario. Co-supervised with Prof. Siddhartha Mishra (Professor of Applied Mathematics, ETH).

Level: Master Thesis

Matthias Bonvin

Jet tagging for HL-LHC

Get inspiration from the work here and implement one of these algorithms for real data taking in CMS. Co-supervised with Sioni Summers (CERN). Details TBA.

Level: Semester Thesis

Asra Serinken

Incorporating physics-motivated symmetries into Neural Networks for high-energy particle physics experiments

Semester thesis of Matthias Bonvin

Co-supervised with Günther Dissertori at ETH Zurich

Status: Completed Fall 2023

Scouting for anomalous events with unsupervised AI in the CMS hardware trigger

PhD thesis of Patrick Odagiu

Co-supervised with Günther Dissertori at ETH Zurich

Status: Ongoing

AXOL1TL: Real-time anomaly detection in the CMS hardware trigger

Master thesis of Chang Sun

Co-supervised with Günther Dissertori at ETH Zürich

Presented at Fast Machine Learning for Science 2023, Grade: 6

Latency and resource-aware decision trees for faster FPGA inference at the LHC

Master thesis of Andrew Oliver

Co-supervised with Sioni Summers (CERN), M. Guillame-Bert (Google) and Prof. Dr. G. Dissertori (ETHZ)

Presented at Fast Machine Learning for Science 2023, Grade: 6

Deep Neural Network to Identify High-Energy B Hadrons via their Hit Multiplicity Increase through Pixel Detection Layers

UZH Bachelor Thesis by M. Sommerhalder

Main supervisor: M. Sommerhalder

Feb-Aug 2018, github.com/msommerh/bTag_HitCount

Explainable Anomaly Detection for New Physics searches at the LHC with PIDForest

Jessica Prendi

Co-supervised with Prof. Dr. G. Dissertori (ETHZ), Dr. S. Summers (CERN), Dr. M. Guillame-Bert and Dr. R. Stotz (Google)

Sep-Nov 2023

Detecting long-lived particles trapped in detector material at the LHC

CERN summer student project by Jasmine Simms

Co-supervised with Juliette Alimena

Published in Phys.Rev.D 105, L051701

Convolutional Autoencoders for Anomaly Detection in the L1 Trigger

CERN Student 2020, Sierra Weyhmiller

Co-supervisor