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Development of Continuous Muon Momentum Calibrations for the ATLAS Experiment
The precise calibration of muon momentum is essential for many physics analyses in ATLAS, particularly those involving high-precision measurements and searches for rare processes. Traditional calibration methods rely on data-driven techniques and simulation-based corrections, but achieving optimal performance across the detector requires a detailed understanding of resolution effects and scale biases. This project explores the use of Machine Learning to model detector performance in muon momentum reconstruction, focusing on the prediction of resolution, scale biases, and other systematic effects. Additionally, it will investigate the feasibility of an ML-based calibration pipeline for muons, aiming for a continuous parameterisation of the relevant scale and resolution correction factor. The study will leverage simulated and real ATLAS data to train and validate ML models, assessing their ability to improve momentum reconstruction across different detector regions and kinematic regimes. By refining the precision of muon momentum calibration, this work has the potential to enhance the sensitivity of key physics analyses, including Higgs boson studies and new physics searches.
Contacts: Luca Martinelli, Giacomo Artoni
Development of new machine-learning based methods for di-τ mass reconstruction in ATLAS
Many interesting measurements and searches performed by the ATLAS experiment involve final states with τ-lepton pairs coming from the decay of resonances, including measurements of the properties of the Higgs boson decaying into a pair of τ-leptons (e.g. measurement of the H->ττ coupling, differential cross section measurements and CP measurements), searches for new particles decaying into a pair of τ-leptons (e.g. X->ττ), or searches for new particles decaying into a pair of intermediate particles that can then decay into a pair of τ-leptons (e.g. X->HH->bbττ, X->ZZ->4l, etc.). All these measurements and searches require the reconstruction of the invariant mass of the di-τ system in order to reconstruct the mass of the mother particle and use this information for discriminating signal and background events. Accurate reconstruction of the mass of a resonance decaying into a pair of τ-leptons is very challenging because of the presence of multiple neutrinos from the τ-lepton decays that result in missing transverse energy. Currently, the ATLAS experiment is using a likelihood-based technique to estimate the di-τ mass starting from information on the visible decay products of the τ-leptons and the missing transverse energy. This method provides a mass resolution of about 15% for di-τ masses around 100 GeV, that degrades for higher masses or for Lorentz-boosted systems.
The proposed thesis project will focus on the development of new machine-learning based algorithms to improve the reconstruction of the di-τ mass in terms of improved resolution and improved stability versus the mass values and the Lorentz-boost of the resonance, as well as improved usage of the computational resources.
Contacts: Alessandra Betti
Study of the data collected by ATLAS in 2025
Analyses involving muons in the final state are crucial to the ATLAS physics program. Ensuring excellent performance in muon reconstruction, identification, and measurement is essential not only for precision studies of fundamental particles but also for the potential discovery of new physics.
This thesis will focus on the first evaluation of muon reconstruction performance using the new data collected by ATLAS in 2025. By providing real-time feedback on the quality of the data-taking process, this work will play a key role in identifying potential issues and assessing whether the detector and reconstruction algorithms behave as expected. Such early-stage analysis is essential for optimizing performance and ensuring that the collected data meets the requirements for high-precision physics measurements.
Contacts: Luca Martinelli
DL and RL based real-time monitoring of ATLAS detector anomalies
The ATLAS detector at the Large Hadron Collider generates an immense volume of data, requiring robust real-time monitoring to detect anomalies that could indicate hardware failures, calibration drifts, or unexpected physical phenomena. This thesis explores the application of reinforcement learning techniques for anomaly detection in ATLAS, leveraging deep RL agents to dynamically adapt to evolving detector conditions. Unlike traditional rule-based or supervised learning approaches, RL can autonomously learn optimal strategies for identifying and classifying anomalies by interacting with real-time detector data streams. The proposed methodology involves training an RL-based anomaly detection system using both simulated and historical data, optimizing its reward function for early detection and minimal false positives. The project aims to enhance the efficiency and reliability of ATLAS operations, contributing to the broader effort of automating high-energy physics data analysis with machine learning-driven solutions.
Contacts: Stefano Giagu