Neutrino interaction vertex reconstruction and particle identification in the MicroBooNE detector

This thesis presents the results of a study measuring and improving the quality of neutrino interaction vertex reconstruction and particle identification (PID) in the MicroBooNE detector. The detector comprises a liquid argon time-projection chamber (LArTPC) with a light-collection system, permitting precise tracking of neutrino interaction final states. MicroBooNE's primary physics goal is to resolve the low-energy electron neutrino appearance anomalies observed at MiniBooNE and LSND. The experiment therefore requires high-quality neutrino interaction vertex reconstruction and PID, which together strongly influence event reconstruction quality and energy/momentum estimation. Improvements to the vertex reconstruction are made through the development of powerful new variables and the application of machine learning techniques; these algorithms are now the default used at MicroBooNE and have enabled new studies of neutrino interactions with up to six charged particles in the final state. A robust PID method (FOMA) is developed using a novel analytic approximation to the mode of the dE/dx distribution. A deep learning PID method (PidNet) is also proposed, based on convolutional neural networks (CNNs) and a semi-supervised representation learning method. The performance of the two approaches is compared and contrasted with PIDA, the default PID algorithm used at MicroBooNE. This work concludes by assessing the impact of the tools and methods developed in this work on particle energy estimation in MicroBooNE

Multivariate methods and the search for single top-quark production in association with a <em>W</em> boson in ATLAS

This thesis describes three machine learning algorithms that can be used for physics analyses. The first is a density estimator that was derived from the Green’s function identity of the Laplace operator and is capable of tagging data samples according to the signal purity. This latter task can also be performed with regression methods, and such an algorithm was implemented based on fast multi-dimensional polynomial regression. The accuracy was improved with a decision tree using smooth boundaries. Both methods apply rigorous checks against overtraining to make sure the results are drawn from statistically significant features. These two methods were applied in the search for the single top-quark production with a W boson. Their separation power differ highly in favour for the regression method, mainly be- cause it can exploit the extra information available during training. The third method is an unsupervised learning algorithm that offers finding an optimal coordinate system for a sample in the sense of maximal information entropy, which may aid future methods to model data

W Boson Polarization Studies for Vector Boson Scattering at LHC: from Classical Approaches to Quantum Computing

The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) has, in the recent years, delivered unprecedented high-energy proton-proton collisions that have been collected and studied by two multi-purpose experiments, ATLAS and CMS. In this thesis, we focus on one physics process in particular, the Vector Boson Scattering (VBS), which is one of the keys to probe the ElectroWeak sector of the Standard Model in the TeV regime and to shed light on the mechanism of ElectroWeak symmetry breaking. VBS measurement is extremely challenging, because of its low signal yields, complex final states and large backgrounds. Its understanding requires a coordinated effort of theorists and experimentalists, to explore all possible information about inclusive observables, kinematics and background isolation. The present work wants to contribute to Vector Boson Scattering studies by exploring the possibility to disentangle among W boson polarizations when analyzing a pure VBS sample. This work is organized as follows. In Chapter1, we overview the main concepts related to the Standard Model of particle physics. We introduce the VBS process from a theoretical perspective in Chapter2, underlying its role with respect to the known mechanism of ElectroWeak Symmetry Breaking. We emphasize the importance of regularizing the VBS amplitude by canceling divergences arising from longitudinally polarized vector bosons at high energy. In the same Chapter, we discuss strategies to explore how to identify the contribution of longitudinally polarized W bosons in the VBS process. We investigate the possibility to reconstruct the event kinematics and to thereby develop a technique that would efficiently discriminate between the longitudinal contribution and the rest of the participating processes in the VBS. In Chapter 3, we perform a Montecarlo generator comparison at different orders in perturbation theory, to explore the state-of-art of VBS Montecarlo programs and to provide suggestions and limits to the experimental community. In the last part of the same Chapter we provide an estimation of PDF uncertainty contribution to VBS observables. Chapter 4 introduces the phenomenological study of this work. We perform an extensive study on polarization fraction extraction and on reconstruction of the W boson reference frame. We first make use of traditional kinematic approaches, moving then to a Deep Learning strategy. Finally, in Chapter 5, we test a new technological paradigm, the Quantum Computer, to evaluate its potential in our case study and overall in the HEP sector. This work has been carried on in the framework of a PhD Executive project, in partnership between the University of Pavia and IBM Italia, and has therefore received supports from both the institutions. This work has been funded by the European Community via the COST Action VBSCan, created with the purpose of connecting all the main players involved in Vector Boson Scattering studies at hadron colliders, gathering a solid and multidisciplinary community and aiming at providing the worldwide phenomenological reference on this fundamental process

Discovery in Physics

Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

Improvement in the gamma-ray energy reconstruction of MAGIC and impact on the spectral analysis of the first Gamma Ray Burst detected at TeV energies

This thesis is about the development of a novel methodology, the Random Forest based Energy reconstruction (RF-Erec), to determine the energy of the very high energy (VHE, energy larger than 50 GeV) gamma-ray events detected with the MAGIC telescopes. RF-Erec improves the energy reconstruction of gamma-rays, and thereby extends the capabilities of the MAGIC telescopes, compared to the previous methodology for energy reconstruction, which is based on Look-Up-Tables (LUTs-Erec), and has been used over the last decade. When the energy reconstruction is evaluated in the energy resolution, which is the width of Gaussian fit to the distribution of estimation error normalised with energy, RF-Erec is better by a factor more than around 2 in a very wide range of the energies and pointing Zenith distances (Zd). Such improvement is even larger for high Zd observations. Moreover, the standard deviation of the error distribution is substantially smaller, as the long tail seen in the LUTs-Erec disappears in the RF-Erec. This means the energy migration matrix becomes tighter, and the energy estimation of each event becomes more robust. Consequently, RF-Erec enables a reliable spectral measurement even in situations with poor statistics, an event-wise analysis like for Lorentz Invariance Violation (LIV) studies, and a search for anomalies in the spectral shape. The benefit is not only a better accuracy, but also a wider applicability, such as for observations at high Zenith distance, and morphological together with spectral studies. As a side-product of my studies, I also identified the major source of systematic uncertainties in the LUTs-Erec, clarified its mechanism, and confirmed that it is insignificant in the RF-Erec. I evaluated the actual performance improvement in the spectrum reconstruction for different realistic scenarios. One of the cases with the biggest improvement is on a high Zd observation of a gamma-ray source with very steep spectrum. In such spectrum, the energy mis-reconstruction error, namely the spillover to higher energies, complicates substantially the spectral analysis and reduces its reliability. While spillover extends to at most factor of a few in the RF-Erec, it extends to more than one order of magnitude in the LUTs-Erec. Therefore the high energy events estimated using LUTs-Erec are dominated by spillover events, but the RF-Erec keeps the fraction of genuine events to be more than half. I show that the RF-Erec has better ability than LUTs-Erec in estimating the slope and amplitude, as well as more reliable and consistent results among the available strategies for spectral analysis. I have implemented this novel methodology into the standard MAGIC Analysis and Reconstruction Software (MARS). It is now available to the MAGIC collaboration and, starting from year 2020, regarded as part of the standard data analysis framework. The first scientific application of the novel energy estimation was on the data from the MAGIC observations of the gamma-ray burst (GRB) GRB 190114C. It was the first GRB detected significantly at VHE gamma-rays, after more than 15 years of intense searches with the MAGIC telescopes. The spectrum has the steepest shape over one decade in energy (from 0.2 TeV to 2 TeV) that has been ever measured with MAGIC, and with any VHE gamma-ray instrument to date. The steep spectrum is due to the absorption by the Extragalactic Background Light (EBL), that reduces the gamma-ray flux by factors of several hundreds at the highest energies. Moreover the observation was performed at high Zd. Under these observing conditions, the previous method for energy reconstruction, LUTs-Erec, would not have been able to provide a reliable characterization of the VHE gamma-ray spectral shape from GRB 190114C. However, based on my novel methodology, the MAGIC GRB data were analyzed successfully, leading to two Nature papers reporting this historical discovery (Nature, vol.575, p455-458 and p459-463). The rich photon statistics enabled the characterization of the VHE spectra on timescales as short as 1 minute. The analysis revealed the existence of a new emission component extending to about 2 TeV. This new component could be explained as SSC from the external forward shock of the GRB outflow, which has been long predicted by several theorists. The data reveal that the SSC component has approximately the same power-law temporal behavior as the synchrotron component that decreases as the shock decelerates, and that it accounts for substantial amount of the kinetic energy deposited in the outflow from the GRB. Despite the technical difficulties in detecting TeV gamma-rays from GRBs, these results indicate that the SSC emission may be a common process among GRBs, which implies the need to substantially update our knowledge about these extreme phenomena.Diese Arbeit behandelt die Entwicklung einer neuartigen Methodik (Random Forest basierte Energierekonstruktion, RF-Erec) zur Bestimmung der Energie von sehr hochenergetischen (VHE, Energie mehr als 50 GeV) Gamma-Strahlen-Ereignissen, welche mit den MAGIC-Teleskopen aufgezeichnet wurden. RF-Erec verbessert die Energierekonstruktion von Gamma-Strahlen und erweitert damit die Möglichkeiten der MAGIC-Teleskope im Vergleich zur früheren Methodik der Energierekonstruktion, die auf Lookup-Tabellen (LUTs-Erec) basiert und in den letzten zehn Jahren verwendet wurde. Wenn die Energierekonstruktion in Bezug auf die Energieauflösung bewertet wird, d.h. der Breite der Gaußschen Anpassung an die Verteilung des mit der Energie normalisierten Schätzfehlers, ist RF-Erec in einem sehr breiten Energie- und Beobachtungszenitwinkelbereich (Zd) um einen Faktor mehr als 2 besser, und diese Verbesserung ist bei Beobachtungen unter hohen Zd sogar noch größer. Außerdem ist die Standardabweichung der Fehlverteilung wesentlich kleiner, da der lange Ausläufer, der in der LUTs-Erec zu sehen sind, in der RF-Erec verschwindet. Dies bedeutet, dass die Energiemigrationsmatrix schmaler wird und die Abschätzung der Energie jedes Ereignisses robuster wird. Folglich ermöglicht RF-Erec eine zuverlässige Messung des Spektrums auch in Situationen mit niedriger Statistik, eine ereignisbezogene Analyse wie für Untersuchungen zur Verletzungen der Lorentzinvarianz (LIV) und eine Suche nach spektralen Anomalien. Der Vorteil ist nicht nur eine verbesserte Genauigkeit, sondern auch eine breitere Anwendbarkeit, wie z.B. für Beobachtungen unter hohen Zenitwinkeln und gemeinsame morphologische und spektrale Untersuchungen. Als Nebenprodukt meiner Forschung identifizierte ich auch die Hauptquelle systematischer Unsicherheiten in der LUTs-Erec, legte ihren Mechanismus dar und bestätigte, dass dieser in der RF-Erec unbedeutend ist. Ich bewertete die tatsächliche Leistungsverbesserung bei der Rekonstruktion der Spektren für verschiedene realistische Szenarien. Einer der Fälle mit der größten Verbesserung ist die Beobachtung einer Gamma-Strahlenquelle mit sehr steilem Spektrum unter hohen Zd-Winkeln. Für ein solches Spektrum erschwert die falsche Energierekonstruktion, genauer gesagt das Verschieben von Ereignissen in Bins höherer Energie, die Spektralanalyse erheblich und verringert ihre Zuverlässigkeit. Während sich die Anzahl falsch zu richtig rekonstruierten Ereignissen bei der RF-Erec höchstens auf einen Faktor von einigen wenigen erstreckt, dehnt er sich bei der LUTs-Erec auf mehr als eine Gro ̈ßenordnung aus. Daher werden die mit LUTs-Erec abgeschätzten hochenergetischen Ereignisse von Überlaufereignissen dominiert, wohingegen die RF-Erec den Anteil der echten Ereignisse bei mehr als der Hälfte ha ̈lt. Ich zeige, dass die RF-Erec bessere Fähigkeiten als die LUTs-Erec bei der Abschätzung der spektralen Steigung und Amplitude hat sowie zuverlässigere und einheitlichere Ergebnisse als andere verfügbare Strategien für die Spektralanalyse liefert. Ich habe diese neuartige Methodik in das Standard-Softwarepaket zur MAGIC-Analyse (MARS) implementiert. Es steht nun der MAGIC-Kollaboration zur Verfügung und wird seit dem Jahr 2020 als Teil des Standard-Daten-analysesystems betrachtet. Die erste wissenschaftliche Anwendung der neuartigen Energieschätzung geschah auf die Daten der MAGIC-Beobachtungen des Gammastrahlenausbruchs (GRB) GRB 180114C. Dies war, nach 15 Jahren intensiver Suche mit den MAGIC-Teleskopen, der erste GRB, der bei VHE Gamma-Strahlen signifikant nach- gewiesen wurde. Das Spektrum hat die steilste Steigung über eine Energiedekade (von 0,2 TeV bis 2 TeV), die jemals mit MAGIC oder einem anderen VHE Gamma-Strahleninstrument gemessen wurde. Das steile Spektrum ist auf die Absorption durch das extragalaktische Hintergrundlicht (EBL) zurückzuführen, das den Strahlenfluss von Gamma-Strahlen bei den höchsten Energien auf weniger als ein Hundertstel reduziert. Außerdem wurde die Beobachtung bei hohem Zd durchgeführt. Unter diesen Beobachtungsbedingungen wäre die bisherige Methode zur Energierekonstruktion, LUTs-Erec, nicht in der Lage gewesen, eine zuverlässige Charakterisierung der Form des VHE Gamma-Strahlenspektrums von GRB 190114C zu liefern. Basierend auf meiner neuartigen Methodik wurden die MAGIC GRB-Daten jedoch erfolgreich analysiert, was zu zwei Nature- Veröffentlichungen führte, die über diese historische Entdeckung berichten (Nature, Bd. 575, p455-458 und p459-463). Die ergiebige Photonenstatistik ermöglicht die Charakterisierung der VHE-Spektren auf kurzen Zeitskalen von nur einer Minute. Die Analyse offenbarte die Existenz einer neuen Emissionskomponente, die sich bis etwa 2 TeV erstreckt. Diese neue Komponente könnte als Synchrotron-Selbst-Compton-Prozess (SSC) aus dem externen Vorwärtsschock des GRB-Ausstroms erklärt werden, der von mehreren Theoretikern seit langem vorhergesagt wurde. Die Daten offenbaren, dass die SSC-Komponente ungefähr das gleiche zeitliche Potenzverhalten wie die Synchrotron-Komponente aufweist, welche mit der Verlangsamung des Schocks abnimmt, und dass sie für einen beträchtlichen Anteil der kinetischen Energie verantwortlich ist, die im Ausstrom des GRBs deponiert wird. Trotz der technischen Schwierigkeiten beim Nachweis von TeV Gamma-Strahlen aus GRBs deuten diese Ergebnisse darauf hin, dass die SSC-Emission ein bei GRBs üblicher Prozess sein könnte, was die Notwendigkeit impliziert, unser Wissen über diese extremen Phänomene grundlegend zu aktualisieren

Statistical Modelling

The book collects the proceedings of the 19th International Workshop on Statistical Modelling held in Florence on July 2004. Statistical modelling is an important cornerstone in many scientific disciplines, and the workshop has provided a rich environment for cross-fertilization of ideas from different disciplines. It consists in four invited lectures, 48 contributed papers and 47 posters. The contributions are arranged in sessions: Statistical Modelling; Statistical Modelling in Genomics; Semi-parametric Regression Models; Generalized Linear Mixed Models; Correlated Data Modelling; Missing Data, Measurement of Error and Survival Analysis; Spatial Data Modelling and Time Series and Econometrics

Modeling of Crash Risk for Realistic Artificial Data Generation: Application to Naturalistic Driving Study Data

Most safety performance analysis employs cross-sectional and time-series datasets, posing an important challenge to safety performance and crash modification analysis. The traditional safety model analysis paradigm relying on observed data only allows relative comparisons between analysis methods and is unable to establish how well the methods mimic the true underlying crash generation process. Assumptions are made about the data, but whether the assumptions truly characterize the safety data generation in the real world remains unknown. To address this issue, this thesis proposes the generation of realistic artificial data (RAD). In developing a prototype RAD generator for crash data, we mimic the process of crash occurrence, simulating daily traffic patterns and evaluating each trip for crash risk. For each crash, details such as crash location, crash type, and crash severity are also generated. As part of the artificial data generation, this thesis also proposes a framework for employing naturalistic driving study (NDS) data to understand and predict crash risk at a disaggregate trip level. This framework proposes a case-control study design for understanding trip level crash risk. The study also conducts a comparison of different case to control ratios and finds the model parameters estimated with these control ratios are reasonably similar. A multi-level random parameters binary logit model was estimated where multiple forms of unobserved variables were tested. This model was calibrated by modifying the constant parameter to generate a population conforming risk model, and then tested on a hold-out sample of data records. This thesis contributes to safety research through the development of a prototype RAD generator for traffic crash data, which will lead to new information about the underlying causes of crashes and ways to make roadways safer