Roser, J., Barrientos, L., Bernabeu, J., Borja-Lloret, M., Muñoz, E., Ros, A., et al. (2022). Joint image reconstruction algorithm in Compton cameras. Phys. Med. Biol., 67(15), 155009–15pp.
Abstract: Objective. To demonstrate the benefits of using an joint image reconstruction algorithm based on the List Mode Maximum Likelihood Expectation Maximization that combines events measured in different channels of information of a Compton camera. Approach. Both simulations and experimental data are employed to show the algorithm performance. Main results. The obtained joint images present improved image quality and yield better estimates of displacements of high-energy gamma-ray emitting sources. The algorithm also provides images that are more stable than any individual channel against the noisy convergence that characterizes Maximum Likelihood based algorithms. Significance. The joint reconstruction algorithm can improve the quality and robustness of Compton camera images. It also has high versatility, as it can be easily adapted to any Compton camera geometry. It is thus expected to represent an important step in the optimization of Compton camera imaging.
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LHCb Collaboration(Aaij, R. et al), Jashal, B. K., Martinez-Vidal, F., Oyanguren, A., Remon Alepuz, C., & Ruiz Vidal, J. (2022). Precision measurement of forward Z boson production in proton-proton collisions at root s=13 TeV. J. High Energy Phys., 07(7), 026–57pp.
Abstract: A precision measurement of the Z boson production cross-section at root s = 13 TeV in the forward region is presented, using pp collision data collected by the LHCb detector, corresponding to an integrated luminosity of 5.1 fb(-1). The production cross-section is measured using Z -> mu(+)mu(-) events within the fiducial region defined as pseudorapidity 2.0 < eta < 4.5 and transverse momentum p(T) > 20 GeV/c for both muons and dimuon invariant mass 60 < M-mu μ< 120 GeV/c(2). The integrated cross-section is determined to be sigma(Z -> mu(+)mu(-)) = 196.4 +/- 0.2 +/- 1.6 +/- 3.9 pb, where the first uncertainty is statistical, the second is systematic, and the third is due to the luminosity determination. The measured results are in agreement with theoretical predictions within uncertainties.
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Mandal, S., Miranda, O. G., Sanchez Garcia, G., Valle, J. W. F., & Xu, X. J. (2022). High-energy colliders as a probe of neutrino properties. Phys. Lett. B, 829, 137110–5pp.
Abstract: The mediators of neutrino mass generation can provide a probe of neutrino properties at the next round of high-energy hadron (FCC-hh) and lepton colliders (FCC-ee/ILC/CEPC/CLIC). We show how the decays of the Higgs triplet scalars mediating the simplest seesaw mechanism can shed light on the neutrino mass scale and mass-ordering, as well as the atmospheric octant. Four-lepton signatures at the high-energy frontier may provide the discovery-site for charged lepton flavor non-conservation in nature, rather than low-energy intensity frontier experiments.
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Albiol, A., Albiol, F., Paredes, R., Plasencia-Martinez, J. M., Blanco Barrio, A., Garcia Santos, J. M., et al. (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights Imaging, 13(1), 122–12pp.
Abstract: Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.
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Pich, A., & Rodriguez-Sanchez, A. (2022). Violations of quark-hadron duality in low-energy determinations of alpha(s). J. High Energy Phys., 07(7), 145–42pp.
Abstract: Using the spectral functions measured in tau decays, we investigate the actual numerical impact of duality violations on the extraction of the strong coupling. These effects are tiny in the standard alpha(s)(m(tau)(2)) determinations from integrated distributions of the hadronic spectrum with pinched weights, or from the total tau hadronic width. The pinched-weight factors suppress very efficiently the violations of duality, making their numerical effects negligible in comparison with the larger perturbative uncertainties. However, combined fits of alpha(s) and duality-violation parameters, performed with non-protected weights, are subject to large systematic errors associated with the assumed modelling of duality-violation effects. These uncertainties have not been taken into account in the published analyses, based on specific models of quark-hadron duality.
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