235 research outputs found
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Edna Ferber author of the book “Giant.” Miss Ferber visited the home of Dr. Hector P. Garcia before writing the book “Giant” (photograph)
No full textEdna Ferber author of the book “Giant.” Miss Ferber visited the home of Dr. Hector P. Garcia before writing the book “Giant.
Edna Ferber in St. Anthony Hotel, San Antonio, Tex., 1948
No full text''Ferber's novel, Giant, set on a fictitious south Texas ranch, was published in 1952.''''Edna Ferber was in San Antonio on Saturday for a search of the surrounding ranch country for material and characters for a new book. The author of Show boat was registered at the St. Anthony Hotel.'
Jack L. Warner, Edna Ferber, George Stevens, Henry Ginsberg, GIANT, 1956
No full textLeft to right: Studio head Jack L. Warner, Edna Ferber (the author of GIANT), producer-director George Stevens, and producer Henry Ginsber for the film GIANT, 1956. 11x14 b&w photographic print
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
Pius XII. und die Geistlichen im KZ Dachau
No full textThe article publishes—for the first time—a letter written in December 1942 in the Sedelhof, Emmenbrücke, near Lucerne, concerning the incarcerated priests in the Dachau concentration camp. Its author is the German refugee Walter Ferber (1907-1996) and its recipient the Apostolic Nuncio in Switzerland, Filippo Bernardini (1884-1954). Bernardini forwards the letter to the Vatican Secretary of State, Luigi Maglione (1877-1944). Maglione’s reply to Bernardini shows that the Vatican knew about the crimes committed in concentration camps since at least December 1942, and explains the lack of public intervention by Pope Pius XII concerning the incarcerated priests in Dachau and, in part, the Nazi extermination policy in general. Despite this lack of public intervention, Pope Pius XII uses the term “the holocaust” (l'olocausto) already in his Christmas message of December 24, 1942
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
No full textImproved 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
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