Safi, S., Farhang, M., Mena, O., & Di Valentino, E. (2024). Semiblind reconstruction of the history of effective number of neutrinos using CMB data. Phys. Rev. D, 110(10), 103513–7pp.
Abstract: We explore the possibility of redshift-dependent deviations in the contribution of relativistic degrees of freedom to the radiation budget of the cosmos, conventionally parametrized by the effective number of neutrinos Neff, from the predictions of the standard model. We expand the deviations 0Neff(z) in terms of top-hat functions and treat their amplitudes as the free parameters of the theory to be measured alongside the standard cosmological parameters by the Planck measurements of the cosmic microwave background (CMB) anisotropies and baryonic acoustic oscillations, as well as performing forecasts for futuristic CMB surveys such as PICO and CMB-S4. We reconstruct the history of 0Neff and find that with the current data the history is consistent with the standard scenario. Inclusion of the new degrees of freedom in the analysis increases H0 to 68.71 +/- 0.44, slightly reducing the Hubble tension. With the smaller forecasted errors on the 0Neff(z) parametrization modes from future CMB surveys, very accurate bounds are expected within the possible range of dark radiation models.
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Baran, J. et al, & Brzezinski, K. (2024). Feasibility of the J-PET to monitor the range of therapeutic proton beams. Phys. Medica, 118, 103301–9pp.
Abstract: Purpose: The aim of this work is to investigate the feasibility of the Jagiellonian Positron Emission Tomography (J -PET) scanner for intra-treatment proton beam range monitoring. Methods: The Monte Carlo simulation studies with GATE and PET image reconstruction with CASToR were performed in order to compare six J -PET scanner geometries. We simulated proton irradiation of a PMMA phantom with a Single Pencil Beam (SPB) and Spread -Out Bragg Peak (SOBP) of various ranges. The sensitivity and precision of each scanner were calculated, and considering the setup's cost-effectiveness, we indicated potentially optimal geometries for the J -PET scanner prototype dedicated to the proton beam range assessment. Results: The investigations indicate that the double -layer cylindrical and triple -layer double -head configurations are the most promising for clinical application. We found that the scanner sensitivity is of the order of 10-5 coincidences per primary proton, while the precision of the range assessment for both SPB and SOBP irradiation plans was found below 1 mm. Among the scanners with the same number of detector modules, the best results are found for the triple -layer dual -head geometry. The results indicate that the double -layer cylindrical and triple -layer double -head configurations are the most promising for the clinical application, Conclusions: We performed simulation studies demonstrating that the feasibility of the J -PET detector for PET -based proton beam therapy range monitoring is possible with reasonable sensitivity and precision enabling its pre -clinical tests in the clinical proton therapy environment. Considering the sensitivity, precision and cost-effectiveness, the double -layer cylindrical and triple -layer dual -head J -PET geometry configurations seem promising for future clinical application.
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Barrientos, L., Borja-Lloret, M., Casaña, J. V., Dendooven, P., Garcia Lopez, J. G., Hueso-Gonzalez, F., et al. (2024). Gamma-ray sources imaging and test-beam results with MACACO III Compton camera. Phys. Medica, 117, 103199–10pp.
Abstract: Hadron therapy is a radiotherapy modality which offers a precise energy deposition to the tumors and a dose reduction to healthy tissue as compared to conventional methods. However, methods for real-time monitoring are required to ensure that the radiation dose is deposited on the target. The IRIS group of IFIC-Valencia developed a Compton camera prototype for this purpose, intending to image the Prompt Gammas emitted by the tissue during irradiation. The system detectors are composed of Lanthanum (III) bromide scintillator crystals coupled to silicon photomultipliers. After an initial characterization in the laboratory, in order to assess the system capabilities for future experiments in proton therapy centers, different tests were carried out in two facilities: PARTREC (Groningen, The Netherlands) and the CNA cyclotron (Sevilla, Spain). Characterization studies performed at PARTREC indicated that the detectors linearity was improved with respect to the previous version and an energy resolution of 5.2 % FWHM at 511 keV was achieved. Moreover, the imaging capabilities of the system were evaluated with a line source of 68Ge and a point-like source of 241Am-9Be. Images at 4.439 MeV were obtained from irradiation of a graphite target with an 18 MeV proton beam at CNA, to perform a study of the system potential to detect shifts at different intensities. In this sense, the system was able to distinguish 1 mm variations in the target position at different beam current intensities for measurement times of 1800 and 600 s.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Bariego-Quintana, A., Calvo, D., Carretero, V., Cecchini, V., et al. (2024). Differential Sensitivity of the KM3NeT/ARCA detector to a diffuse neutrino flux and to point-like source emission: Exploring the case of the Starburst Galaxies. Astropart Phys., 162, 102990–9pp.
Abstract: KM3NeT/ARCA is a Cherenkov neutrino telescope under construction in the Mediterranean sea, optimised for the detection of astrophysical neutrinos with energies above similar to 1 TeV. In this work, using Monte Carlo simulations including all-flavour neutrinos, the integrated and differential sensitivities for KM3NeT/ARCA are presented considering the case of a diffuse neutrino flux as well as extended and point-like neutrino sources. This analysis is applied to Starburst Galaxies demonstrating that the detector has the capability of tracing TeV neutrinos from these sources. Remarkably, after eight years, a hard power-law spectrum from the nearby Small Magellanic Cloud can be constrained. The sensitivity and discovery potential for NGC 1068 is also evaluated showing that KM3NeT/ARCA will discriminate between different astrophysical components of the measured neutrino flux after 3 years of data taking.
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Hirn, J., Sanz, V., Garcia Navarro, J. E., Goberna, M., Montesinos-Navarro, A., Navarro-Cano, J. A., et al. (2024). Transfer learning of species co-occurrence patterns between plant communities. Ecol. Inform., 83, 102826–8pp.
Abstract: Aim: The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. Location: Semiarid plant communities in Spain and Me<acute accent>xico. Time period: 2016-2022. Major taxa studied: Angiosperms. Methods: Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). Results: When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusions: This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates.
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