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Diklic, J. et al, & Jurado, M. (2023). Transfer reactions in 206Pb+118Sn: From quasielastic to deep-inelastic processes. Phys. Rev. C, 107(1), 014619–8pp.
Abstract: We measured multinucleon transfer reactions for the 206Pb + 118Sn system at Elab = 1200 MeV by employing the large solid angle magnetic spectrometer PRISMA. Differential and total cross sections and Q-value distri-butions have been obtained for a variety of neutron and proton pick-up and stripping channels. The Q-value distributions show how the quasielastic and deep inelastic processes depend on the mass and charge of the transfer products. The corresponding cross sections have been compared with calculations performed with the GRAZING code. An overall good agreement is found for most of the few nucleon transfer channels. The underestimation of the data for channels involving a large number of transferred nucleons indicates that more complicated processes populate the given isotopes.
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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2023). Detailed Analysis of the TeV gamma-Ray Sources 3HWC J1928+178, 3HWC J1930+188, and the New Source HAWC J1932+192. Astrophys. J., 942(2), 96–18pp.
Abstract: The latest High Altitude Water Cherenkov (HAWC) point-like source catalog up to 56 TeV reported the detection of two sources in the region of the Galactic plane at galactic longitude 52 degrees < l < 55 degrees, 3HWC J1930+188 and 3HWC J1928+178. The first one is associated with a known TeV source, the supernova remnant SNR G054.1+00.3. It was discovered by one of the currently operating Imaging Atmospheric Cherenkov Telescope (IACT), the Very Energetic Radiation Imaging Telescope Array System (VERITAS), detected by the High Energy Stereoscopic System (H.E.S.S), and identified as a composite SNR. However, the source 3HWC J1928+178, discovered by HAWC and coincident with the pulsar PSR J1928+1746, was not detected by any IACT despite their long exposure on the region, until a recent new analysis of H.E.S.S. data was able to confirm it. Moreover, no X-ray counterpart has been detected from this pulsar. We present a multicomponent fit of this region using the latest HAWC data. This reveals an additional new source, HAWC J1932+192, which is potentially associated with the pulsar PSR J1932+1916, whose gamma-ray emission could come from the acceleration of particles in its pulsar wind nebula. In the case of 3HWC J1928+178, several possible explanations are explored, in an attempt to unveil the origins of the very-high-energy gamma-ray emission.
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Caron, S., Ruiz de Austri, R., & Zhang, Z. Y. (2023). Mixture-of-Theories training: can we find new physics and anomalies better by mixing physical theories? J. High Energy Phys., 03(3), 004–37pp.
Abstract: Model-independent search strategies have been increasingly proposed in recent years because on the one hand there has been no clear signal for new physics and on the other hand there is a lack of a highly probable and parameter-free extension of the standard model. For these reasons, there is no simple search target so far. In this work, we try to take a new direction and ask the question: bearing in mind that we have a large number of new physics theories that go beyond the Standard Model and may contain a grain of truth, can we improve our search strategy for unknown signals by using them “in combination”? In particular, we show that a signal hypothesis based on a large, intermingled set of many different theoretical signal models can be a superior approach to find an unknown BSM signal. Applied to a recent data challenge, we show that “mixture-of-theories training” outperforms strategies that optimize signal regions with a single BSM model as well as most unsupervised strategies. Applications of this work include anomaly detection and the definition of signal regions in the search for signals of new physics.
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Llosa, G., & Rafecas, M. (2023). Hybrid PET/Compton-camera imaging: an imager for the next generation. Eur. Phys. J. Plus, 138(3), 214–19pp.
Abstract: Compton cameras can offer advantages over gamma cameras for some applications, since they are well suited for multitracer imaging and for imaging high-energy radiotracers, such as those employed in radionuclide therapy. While in conventional clinical settings state-of-the-art Compton cameras cannot compete with well-established methods such as PET and SPECT, there are specific scenarios in which they can constitute an advantageous alternative. The combination of PET and Compton imaging can benefit from the improved resolution and sensitivity of current PET technology and, at the same time, overcome PET limitations in the use of multiple radiotracers. Such a system can provide simultaneous assessment of different radiotracers under identical conditions and reduce errors associated with physical factors that can change between acquisitions. Advances are being made both in instrumentation developments combining PET and Compton cameras for multimodal or three-gamma imaging systems, and in image reconstruction, addressing the challenges imposed by the combination of the two modalities or the new techniques. This review article summarizes the advances made in Compton cameras for medical imaging and their combination with PET.
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Gomez Ambrosio, R., ter Hoeve, J., Madigan, M., Rojo, J., & Sanz, V. (2023). Unbinned multivariate observables for global SMEFT analyses from machine learning. J. High Energy Phys., 03(3), 033–66pp.
Abstract: Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits.
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