Projects / Programmes source: ARIS

Advancing precision flavour studies with machine learning

Research activity

Code Science Field Subfield
1.02.00  Natural sciences and mathematics  Physics   

Code Science Field
1.03  Natural Sciences  Physical sciences 
Flavour Physics, Collider physics, Machine learning, Physics beyond the standard model
Evaluation (rules)
source: COBISS
Data for the last 5 years (citations for the last 10 years) on May 28, 2024; A3 for period 2018-2022
Data for ARIS tenders ( 04.04.2019 – Programme tender, archive )
Database Linked records Citations Pure citations Average pure citations
WoS  177  9,422  8,853  50.02 
Scopus  192  10,749  10,143  52.83 
Researchers (4)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  24264  PhD Jernej Fesel Kamenik  Physics  Head  2021 - 2024  277 
2.  26459  PhD Nejc Košnik  Physics  Researcher  2021 - 2024  79 
3.  25656  PhD Miha Nemevšek  Physics  Researcher  2021 - 2024  158 
4.  54617  PhD Michele Tammaro  Physics  Researcher  2021 - 2023  11 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  91,025 
The discovery of the Higgs boson in 2012 by the LHC collaborations confirmed the long- standing theoretical framework that we call the Standard Model (SM) of particle physics. However the SM as we know it is incomplete and can thus be considered only an Effective Field Theory (EFT) of fundamental particles and interactions. The SM does not account for neutrino masses nor cosmological dark matter, while the existence of both have long been confirmed experimentally. It also offers no explanation for the non-trivial spectrum of quarks and leptons (the so-called flavour structure of the SM): this is determined by the interactions of the SM fermions with the Higgs field and the strengths of these interactions span orders of magnitude, from O(10-6) for the electron to O(1) for the top quark. The central aim of high energy physics is now to find what lies Beyond the SM (BSM) with hopes of unravelling what underpins these unexplained phenomena. Quantum field theoretic arguments based on ‘naturalness’ indicate that this BSM physics should lie not far from scale at which the Higgs boson was discovered, an energy regime being directly explored by the LHC collaborations. However, we can also probe this new physics indirectly via precision flavour studies, i.e. precision studies of flavour-changing processes. In recent years the advances made by the Belle, BaBar and LHCb experiments have pushed the measurements of low-energy flavour observables to an unprecedented level of precison. New physics effects in these observables can be parameterised through appropriate sets of local operators which describe in an effective way the BSM interactions among SM fields. Experimental bounds on precision observables are then presented as bounds on the operator coefficients. Despite the recent progress, there are limitations to the information that can be currently extracted from these measurements due to difficulties in: • designing observables to optimally probe effects from multiple EFT operators, • disentangling non-perturbative SM (QCD) contributions from genuine BSM effects. Furthermore, a potentially richer set of observables is offered by flavor studies of high energy phenomena in the ATLAS and CMS detectors at the LHC. This is also the only venue, where interactions of the heaviest SM particles, the top quark and the Higgs boson, can be studied directly. This potentially promising complementary set of observables is currently hindered by: • inefficient identification (tagging) of the light (u, d, s) quark flavours; • difficulties in extracting detailed kinematical information in quark decays at high energies. The aim of this research proposal is to investigate and develop new state-of-the-art Machine Learning (ML) tools to address these issues. There is already abundant evidence, both in the recent literature and in the current analysis strategies in use at the LHC, that support the importance of this endeavour. In particular, recent works have shown that machine learning approaches can significantly improve the current state-of-the-art tools for top-tagging, and for quark-gluon discrimination. The techniques used in these studies are inspired by ML applications in computer vision, natural language processing, and genotype modeling. Machine-learning has also inspired the recent development of simulation-based inference to improve constraints on EFT coefficients extracted from experimental measurements. In this proposal we will develop new techniques based on existing ML tools to address the chal- lenging issues facing precision flavour physics, both at low and high energies listed above (which currently are not treated using ML techniques). This project is particularly motivated at present, given the upcoming expected experimental progress, by both Belle-II and LHC phase-III experiments.
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