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Chiara Pinto

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+49 89 289-12488
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Dense and Strange Hadronic Matter

Offered Bachelor’s or Master’s Theses Topics

Absorption of antinuclei in ALICE Time Projection Chamber using machine learning algorithms

Dark Matter (DM) is believed to account for roughly 27% of the mass-energy of our Universe, and its nature remains one of the most intriguing unsolved questions of modern physics. Multiple balloon- and space-borne experiments are searching for the traces of DM using the idea of possible annihilation or decay of DM particles into ordinary (anti)particles, including light (anti)nuclei. The latter (such as antideuterons and antihelium nuclei) are considered as especially promising probe for such indirect DM searches, as the background stemming from ordinary collisions between cosmic rays and the interstellar medium is expected to be very low with respect to the DM signal. In order to reliably estimate the fluxes of antinuclei near Earth stemming from DM and from background, it is necessary to know the probability for antinuclei to interact inelastically with ordinary matter on their way to the detectors (e.g. with interstellar medium and Earth's atmosphere). This probability is driven by the inelastic cross section of corresponding processes, which for antinuclei are still poorly (or not) known. This fact hinders precise calculations of antinuclei fluxes near Earth and forces existing estimates to rely on extrapolations and modelling. The here advertised master project will deal with the analysis of inelastic interactions of antinuclei inside the gas volume of the Time Projection Chamber of the ALICE detector. Such interactions typically create a bunch of secondary (charged) particles with low momentum with a characteristic topology of secondary vertex inside the TPC volume. The pattern can be recognised by machine learning algorithms trained on simulated events, in which such annihilation processes happen in a controlled environment. After the validation of algorithms with simulated events, one can analyse real experimental data and tag the annihilation events of interest, which in turn can be used to evaluate the effective antinuclei + A inelastic cross section. This project will be structured in the following way: - simulation of the inelastic interactions of antinuclei with the TPC gas using Geant4 toolkit - training and validation of neural networks to reliably recognise antinuclei annihilation events - Analysis of the ALICE experimental data from pp collisions at sqrt(s) = 13 TeV - Evaluation of the effective antinuclei + A inelastic cross sections

suitable as
  • Master’s Thesis Nuclear, Particle, and Astrophysics
Supervisor: Laura Fabbietti
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