Wieduwilt, P., Paschen, B., Schreeck, H., Schwenker, B., Soltau, J., Ahlburg, P., et al. (2021). Performance of production modules of the Belle II pixel detector in a high-energy particle beam. Nucl. Instrum. Methods Phys. Res. A, 991, 164978–15pp.
Abstract: The Belle II experiment at the Super B factory SuperKEKB, an asymmetric e(+) e(-) collider located in Tsukuba, Japan, is tailored to perform precision B physics measurements. The centre of mass energy of the collisions is equal to the rest mass of the gamma (4S) resonance of m(gamma(4S)) = 10.58 GeV. A high vertex resolution is essential for measuring the decay vertices of B mesons. Typical momenta of the decay products are ranging from a few tens of MeV to a few GeV and multiple scattering has a significant impact on the vertex resolution. The VerteX Detector (VXD) for Belle II is therefore designed to have as little material as possible inside the acceptance region. Especially the innermost two layers, populated by the PiXel Detector (PXD), have to be ultra-thin. The PXD is based on DEpleted P-channel Field Effect Transistors (DEPFETs) with a thickness of only 75 μm. Spatial resolution and hit efficiency of production detector modules were studied in beam tests performed at the DESY test beam facility. The spatial resolution was investigated as a function of the incidence angle and improvements due to charge sharing are demonstrated. The measured module performance is compatible with the requirements for Belle II.
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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Pajtler, M. V. et al, & Gadea, A. (2021). Excited states of Y-90,Y-92,Y-94 populated in Zr-90+Pb-208 multinucleon transfer reaction. Phys. Scr., 96(3), 035305–7pp.
Abstract: Multinucleon transfer reactions in Zr-90+Pb-208 have been studied via fragment-gamma coincidences, employing the PRISMA magnetic spectrometer coupled to the CLARA gamma-array. An analysis on Y isotopes has been carried out incorporating spectroscopic as well as reaction mechanism aspects. New gamma transitions have been observed in Y-94, confirming the findings of recent studies where nuclei were produced via fission of uranium, and a comparison with near-by Y-90,Y-92 isotopes populated in the same reaction has been discussed. Experimental cross sections have been extracted and compared with the GRAZING calculations, showing a fair agreement along the neutron pick-up side. The results confirm how multinucleon transfer reactions are a suitable mechanism for the study of neutron-rich nuclei.
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Otten, S., Caron, S., de Swart, W., van Beekveld, M., Hendriks, L., van Leeuwen, C., et al. (2021). Event generation and statistical sampling for physics with deep generative models and a density information buffer. Nat. Commun., 12(1), 2985–16pp.
Abstract: Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies.
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Orrigo, S. E. A. et al, Rubio, B., Gelletly, W., Aguilera, P., Algora, A., Morales, A. I., et al. (2021). beta decay of the very neutron-deficient Ge-60 and Ge-62 nuclei. Phys. Rev. C, 103(1), 014324–12pp.
Abstract: We report here the results of a study of the beta decay of the proton-rich Ge isotopes, Ge-60 and Ge-62, produced in an experiment at the RIKEN Nishina Center. We have improved our knowledge of the half-lives of Ge-62 [73.5(1) ms] and Ge-60 [25.0(3) ms] and its daughter nucleus, Ga-60 [69.4(2) ms]. We measured individual beta-delayed proton and gamma emissions and their related branching ratios. Decay schemes and absolute Fermi and Gamow-Teller transition strengths have been determined. The mass excesses of the nuclei under study have been deduced. A total beta-delayed proton-emission branching ratio of 67(3)% has been obtained for Ge-60. New information has been obtained on the energy levels populated in Ga-60 and on the 1/2(-) excited state in the beta p daughter Zn-59. We extracted a ground state-to-ground state feeding of 85.3(3)% for the decay of Ge-62. Eight new y lines have been added to the deexcitation of levels populated in the Ga-62 daughter.
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