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Anomaly detection in hadronic final states with ATLAS Run-3 data
This thesis proposes a comprehensive study on the application of anomaly detection techniques to the analysis of the Run-3 data from the Large Hadron Collider (LHC), specifically focusing on resonant final states from the hadronic decay of boosted particles. The goal is to identify potential signs of new physics by analyzing anomalies in the data that do not conform to the Standard Model predictions. This work will leverage advanced statistical and machine learning (ML) methodologies to enhance the sensitivity of high-energy particle experiments to rare or unexpected events. Additionally, the thesis will explore the development of novel ML techniques aimed at identifying anomalous jets originating from new physics particles, which could be key in discovering beyond the Standard Model phenomena.
Contacts: Stefano Giagu, Valerio Ippolito, Giuliano Gustavino
Anomaly Detection Analysis Using Calorimeter Clusters
The search for new physics at the LHC increasingly relies on data-driven techniques to identify unexpected signatures beyond Standard Model predictions. This thesis focuses on an anomaly detection analysis leveraging calorimeter cluster information to identify unconventional jet signatures. The approach employs unsupervised machine learning methods trained on calorimeter observables to distinguish anomalous jets from the dominant QCD background. The next steps involve defining background and signal regions using statistical analysis techniques, performing a fit of the dijet invariant mass distribution of anomalous jets, and interpreting potential excesses in the context of physics beyond the Standard Model.
Contacts: Giuliano Gustavino
Development of an Anomaly Detection Tagger Based on Tracking Variables
The identification of anomalous jets is a crucial challenge in searches for new physics at the LHC, particularly in scenarios involving non-standard signatures. This thesis focuses on the development of a jet tagging algorithm based on anomaly detection techniques, leveraging tracking variables to enhance discrimination power. The approach exploits information from both prompt and displaced tracks to identify deviations from the Standard Model expectations. The study will involve training and evaluating machine learning models, particularly unsupervised learning methods, to detect rare and unexpected features in jet substructure. The tagger’s performance will be assessed in simulated datasets, with potential applications in real-time trigger selection and offline analyses within ATLAS.
Contacts: Giuliano Gustavino
Pheomenological Studies of Dark QCD: Connecting Models and Reinterpreting Current Searches
Dark QCD scenarios introduce a rich phenomenology, often leading to signatures distinct from conventional new physics searches. This thesis aims to bridge the gap between different theoretical models by studying their common phenomenological features and exploring how current experimental results can be reinterpreted in this context. The work will involve simulating various dark QCD scenarios, identifying key observables, and comparing them with existing searches at the LHC. Additionally, a systematic approach to reinterpret experimental exclusions and sensitivities will be developed to assess their impact on a wide range of theoretical frameworks. The study will provide insights into potential refinements of search strategies to better capture the signatures of dark QCD dynamics.
Contacts: Giuliano Gustavino
High-precision modelling of irreducible backgrounds in the Higgs to dimuon search with the ATLAS Experiment
The search for the Higgs boson decaying to a pair of muons is a crucial test of the Standard Model, given the rarity of this process and its sensitivity to new physics. A key challenge in this analysis is the precise parameterisation of the dimuon invariant mass background, which must be controlled at the permille level to distinguish the signal effectively.
This project explores novel machine learning approaches to improve background modelling and enhance the overall analysis reach. It will focus on developing and evaluating machine learning methods such as Gaussian processes for background modelling, investigating the integration of background modelling into the analysis optimisation pipeline, and implementing adversarial neural networks to refine the description of background distributions.
The study will leverage ATLAS data and Monte Carlo simulations to train and validate machine learning models, evaluating their performance in terms of fit quality, uncertainty reduction, and impact on the Higgs to dimuon search. By improving background parameterisation to the required permille-level precision, this work aims to enhance the sensitivity of the search.
Contacts: Alessandra Betti, Giacomo Artoni
Development and application of a novel generation of Deep-learning models for applications in High Energy Physics
- Intrinsically explainable modular AI based on transformer and foundation models for analysis, interpretation and recasting of HEP analyses at LHC.
- Large-Language Model based representation learning via multimodal Transformers of physical objects in a High Energy Physics Detector and applications to multiboson measurements and searches.
These two projects focused in innovative research, aiming at either developing a new generation of deep learning models that are intrinsically explainable, based on the recent field of the modular AI, and realizing a digital twin of a collider experiment that can be used for different tasks: generative, anomaly detections, reconstruction etc… The digital twin will be based on a large language model in which the language is represented by the particles’ interactions with the experiment’s detectors.
Contacts: Stefano Giagu
Search for non-resonant Higgs boson pair production in the bbττ final state with ATLAS Run 3 data
After the discovery of the Higgs boson by the ATLAS and CMS Collaborations in 2012, both experiments have started a vaste programme of measurements of the Higgs boson properties to test if they all agree with the Standard Model (SM) predictions, as any deviation of the observed values from the expected ones could be a hint of the presence of new physics beyond the SM (BSM). One very interesting property of the Higgs boson to study is its self-coupling, that is one of the parameters determining the shape of the Higgs potential around the minimum, and that is still largely unconstrained by experimental measurements. The Higgs boson self-coupling can be directly measured at the Large Hadron Collider through the study of non-resonant Higgs boson pair (HH) production, a very rare process predicted by the SM that has not yet been observed and whose properties can strongly be affected by possible BSM values of the Higgs boson self-coupling.
The proposed thesis project will focus on the search for HH production in the bbττ final state, that is a final state with a very good compromise between relatively high branching ratio and relatively low background, resulting in a good signal over background ratio that makes it one of the most sensitive channels for the HH measurements. The thesis project will be carried out using ATLAS Run 3 data and simulations and will have the goal of testing possible improvements of the sensitivity of the HH->bbττ analysis through the inclusion of additional data recorded via new triggers in new analysis categories and the development of new machine learning algorithms for signal and background classification.
Contacts: Alessandra Betti
Entangled in Tops: Quantum Information tests at LHC
Short Abstract: Building on the first experimental observation of top-quark quantum entanglement, the research investigates top-quark-pair correlations with a quantum information approach.
Foreseen Activities: Using ATLAS collision data, the student will develop advanced analysis techniques combining machine learning (ML) for event reconstruction with unfolding methods and statistical inference to extract quantum observables. She/He will search for new physics signatures beyond the Standard Model, pushing the boundaries of quantum measurements at unprecedented energy scales.
Required Skills: Good knowledge of Python and C++, Python libraries for ML and Quantum Information (not strictly required) Quantum Information Theory (not strictly required)
First observation of Quantum Entanglement at LHC (Nature 633 (2024) 542)
Contacts: Nello Bruscino, Luca Martinelli.
Precise measurements of the Top Yukawa coupling using advanced machine learning techniques
Short Abstract: The top Yukawa coupling to the Higgs boson is a crucial parameter to test the Standard Model and search for new physics signs. This thesis will investigate through Higgs boson production with top-quarks (ttH and tH), with particular focus on the yet-unobserved tH process that controls the relative sign of .
Foreseen Activities: The student will develop a deep-learning algorithm designed to improve identification of prompt leptons from weak boson (Prompt Lepton Isolation Tagger – PLIT). She/He will train and calibrate PLIT, then integrate it into the analysis workflow to enhance signal sensitivity and reduce systematic uncertainties in the top-Higgs coupling measurement.
Required Skills: Good knowledge of Python and C++, Python libraries for Machine Learning (not strictly required)
Contacts: Nello Bruscino, Luca Martinelli, Simonetta Gentile