1,721,052 research outputs found
Search for Axion-Like Particles produced in ee collisions and photon energy resolution studies at Belle II
No full textDespite the great successes achieved by the Standard Model (SM) in explaining and predicting the behavior and existence of particles, multiple phenomena are yet to be given a satisfying explanation. Amongst these is Dark Matter (DM), a kind of matter that would permeate the whole Universe and that so far has been observed only via its gravitational interactions.One possible extension of the SM, which may contribute to solve the mystery of DM and/or explain some astrophysical anomalies, are Axion-Like Particles (ALPs). The model taken into consideration in this thesis is of an ALP interacting with SM photons with a coupling strength g and having mass m. This thesis describes a search for the direct production of such ALP via the process e+e− → γa(a → γγ), in the mass range 0.2 < m < 9.7 GeV/c. This search is performed using 0.445 fb of data collected in 2018 by the Belle II detector.No evidence for ALPs is found, and a 95%-confidence-level upper limit is set on the coupling constant g at the level of 10 GeV. These limits are the strongest to date for 0.2 < m < 1 GeV/c.Given that the final state of the e+e− → γa(a → γγ) process is fully neutral, being made up by three photons, a proper kinematic fit with neutral particles may be a powerful tool to improve signal resolution. To achieve such a kinematic fit, a precise knowledge of the photon covariance matrix is needed. Such matrix is obtained from the results of photon resolution studies, whose status and results are presented in this thesis
Improved Particle Identification with the Belle II Calorimeter Using Machine Learning
No full textThis dissertation revolves around the utilization of Convolutional Neural Networks (CNNs) to advance Particle Identification (PID) within the Belle II Electromagnetic Calorimeter (ECL). The core goal of the research is to refine the differentiation process between low-momentum muons and charged pions. The ECL plays a significant role in the PID system as it is engineered to measure the energy deposition by both charged and neutral particles. The task of identifying low-momentum muons and charged pions within the ECL becomes particularly vital when they fail to reach the outer muon detector. In order to provide optimal data, the study employs track-seeded cluster energy images. The energy deposition patterns for muons and charged pions, as detected within crystals surrounding an extrapolated track at the ECL's entry point, are integrated with crystal positions in the plane along with the track's transverse momentum. This amalgamation of information is then utilized to train the CNN, capitalizing on the distinctiveness between the dispersed energy depositions of pion hadronic interactions and the more localized muon electromagnetic interactions. The study includes a comparison of the CNN algorithm's performance with other PID methods currently in use at Belle II, which predominantly rely on track-matched clustering information. The findings imply that the CNN PID method improves the separation between muons and charged pions in low-momentum regions. The research includes samples with varying beam backgrounds, including no beam background. The effectiveness of the CNN method has been assessed with different energy thresholds for ECL crystals, utilizing 21.5 fb data from 2020 and 2021 and Monte Carlo (MC) samples. To substantiate the CNN method with real data, clean samples of muons and charged pions have been singled out using and , respectively. Finally, recognizing that the CNN is sensitive to tracks in close proximity within a single event, additional research was conducted to evaluate the CNN's performance with isolated and non-isolated tracks within the ECL
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Optimization of the reconstruction selections for the Belle II experiment
No full textThe purpose of this thesis is to provide optimized selections for the reconstruction in the Belle II analysis software framework (basf2). basf2 provides generic selections on photons and on pi0s reconstructed via pi0->gg, which are designed to provide a certain pi0 reconstruction efficiency. The goal is to define optimized selections for 60%, 50%, 40%, 30%, 20%, and 10% π0 reconstruction efficiency, the optimization criterion being the maximization of the purity of the sample
A Real-Time Graph Neural Network Trigger Algorithm for the Belle II Electromagnetic Calorimeter
Full text linkImproved Particle Identification with the Belle II Calorimeter Using Machine Learning
No full textThis dissertation revolves around the utilization of Convolutional Neural Networks (CNNs) to advance Particle Identification (PID) within the Belle II Electromagnetic Calorimeter (ECL). The core goal of the research is to refine the differentiation process between low-momentum muons and charged pions. The ECL plays a significant role in the PID system as it is engineered to measure the energy deposition by both charged and neutral particles. The task of identifying low-momentum muons and charged pions within the ECL becomes particularly vital when they fail to reach the outer muon detector. In order to provide optimal data, the study employs track-seeded cluster energy images. The energy deposition patterns for muons and charged pions, as detected within crystals surrounding an extrapolated track at the ECL’s entry point, are integrated with crystal positions in the θ − φ plane along with the track’s transverse momentum. This amalgamation of information is then utilized to train the CNN, capitalizing on the distinctiveness between the dispersed energy depositions of pion hadronic interactions and the more localized muon electromagnetic interactions. The study includes a comparison of the CNN algorithm’s performance with other PID methods currently in use at Belle II, which predominantly rely on track-matched clustering information. The findings imply that the CNN PID method improves the separation between muons and charged pions in low-momentum regions. The research includes samples with varying beam backgrounds, including no beam background. The effectiveness of the CNN method has been assessed with different energy thresholds for ECL crystals, utilizing 21.5 fb−1 data from 2020 and 2021 and Monte Carlo (MC) samples. To substantiate the CNN method with real data, clean samples of muons and charged pions have been singled out using e+e− → μ+μ−γ and D∗+ → D0(→ K−π+)π+, respectively. Finally, recognizing that the CNN is sensitive to tracks in close proximity within a single event, additional research was conducted to evaluate the CNN’s performance with isolated and non-isolated tracks within the ECL
Search for a long-lived spin-0 particle in b → s quark transitions at the Belle II experiment
No full textPhenomena such as dark matter can be connected to the standard model via additional spin-0 mediators. This doctoral thesis describes a search for a long-lived spin-0 particle S in rare B-meson decays mediated by b → s quark transitions.
A dataset corresponding to an integrated luminosity of 189fb−1 of e+e− collisions collected at the Υ(4S) resonance energy by the Belle II experiment is analysed. The search is carried out in eight exclusive channels, the two modes of production B+ → K+S and B0 → K∗0 S with decays via S → e+e−/μ+μ−/π+π−/K+K−. Decays of the S to standard model particles are motivated if the decay to dark matter is kinematically not possible. Mediator masses between (0.025 − 4.78) GeV/c2 and lifetimes between (0.001 − 400) cm are probed. Long-lived particle reconstruction is studied and validated with K0S-mesons. Maximum likelihood fits to the reconstructed S mass distribution are S used to determine the signal yield.
No evidence for the signal process is found. Model-independent upper limits are derived on the branching fractions of the signal processes. The upper limits extend down to the order of O(10−7). These are the first constraints on hadronic final states of the S produced in B → KS and the most stringent bounds from a direct search for S → e+e− at e+e− colliders.
The results are interpreted in models that predict a dark Higgs-like scalar and an axionlike particle with fermion couplings. The model-dependent bounds are competitive with existing experimental constraints
Neural network based pulse shape analysis with the Belle II electromagnetic calorimeter
No full textThe Belle II experiment, located at the SuperKEKB e+e- collider inJapan, uses pulse shape analysis techniques to distinguish electromagneticallyand hadronically interacting particles within the CsI(Tl) electromagneticcalorimeter. The pulse shapes from the particle-dependent scintillationresponse are nominally analysed with a multi-template oine t to measurethe fraction of scintillation emission produced by hadrons. This ttingmethod allows for the determination of the total deposited energy, the totalscintillation emission by hadrons, and the time of energy deposit. This thesisreports on a new approach to extract the total deposited energy, and thehadronic component of the scintillation emission from the pulse shapes usingmachine learning techniques. For this, a neural network is trained on pulseshapes produced in crystals from calorimeter clusters from simulated photonsand pions, and is employed as a multivariate regression tool. Inferred onphotons, the neural network outperforms the current tting method in termsof crystal energy resolution and hadron intensity resolution. For pions theneural network shows a similar resolution compared with the current ttingmethod. Furthermore the neural network approach improves the discriminationof electromagnetic and hadronic interactions and is robust towardsuctuations in photon pile-up from beam backgrounds. Overall the neuralnetwork approach is promising, however additional ne tuning of the compositionof the training sample could further improve its performance androbustnes
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