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Author ANTARES Collaboration (Albert, A. et al); Colomer, M.; Gozzini, R.; Hernandez-Rey, J.J.; Illuminati, G.; Khan-Chowdhury, N.R.; Manczak, J.; Thakore, T.; Zornoza, J.D.; Zuñiga, J. url  doi
openurl 
  Title Constraining the contribution of Gamma-Ray Bursts to the high-energy diffuse neutrino flux with 10 yr of ANTARES data Type Journal Article
  Year 2021 Publication (down) Monthly Notices of the Royal Astronomical Society Abbreviated Journal Mon. Not. Roy. Astron. Soc.  
  Volume 500 Issue 4 Pages 5614-5628  
  Keywords acceleration of particles; neutrinos; transients: gamma-ray bursts; astroparticle physics  
  Abstract Addressing the origin of the astrophysical neutrino flux observed by IceCube is of paramount importance. Gamma-Ray Bursts (GRBs) are among the few astrophysical sources capable of achieving the required energy to contribute to such neutrino flux through p gamma interactions. In this work, ANTARFS data have been used to search for upward going muon neutrinos in spatial and temporal coincidence with 784 GRBs occurred from 2007 to 2017. For each GRB, the expected neutrino flux has been calculated in the framework of the internal shock model and the impact of the lack of knowledge on the majority of source redshifts and on other intrinsic parameters of the emission mechanism has been quantified. It is found that the model parameters that set the radial distance where shock collisions occur have the largest impact on neutrino flux expectations. In particular, the bulk Lorentz factor of the source ejecta and the minimum variability time-scale are found to contribute significantly to the GRB-neutrino flux uncertainty. For the selected sources, ANTARES data have been analysed by maximizing the discovery probability of the stacking sample through an extended maximum-likelihood strategy. Since no neutrino event passed the quality cuts set by the optimization procedure, 90 per cent confidence level upper limits (with their uncertainty) on the total expected diffuse neutrino flux have been derived, according to the model. The GRB contribution to the observed diffuse astrophysical neutrino flux around 100 TeV is constrained to be less than 10 percent.  
  Address [Albert, A.; Drouhin, D.; Huang, F.; Organokov, M.; Pradier, T.] Univ Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France, Email: silvia.celli@roma1.infn.it;  
  Corporate Author Thesis  
  Publisher Oxford Univ Press Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0035-8711 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000606297700092 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 4677  
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Author Gammaldi, V.; Zaldivar, B.; Sanchez-Conde, M.A.; Coronado-Blazquez, J. url  doi
openurl 
  Title A search for dark matter among Fermi-LAT unidentified sources with systematic features in machine learning Type Journal Article
  Year 2023 Publication (down) Monthly Notices of the Royal Astronomical Society Abbreviated Journal Mon. Not. Roy. Astron. Soc.  
  Volume 520 Issue 1 Pages 1348-1361  
  Keywords astroparticle physics – methods; data analysis – methods; observational – methods; statistical – dark matter – gamma-rays; general  
  Abstract Around one-third of the point-like sources in the Fermi-LAT catalogues remain as unidentified sources (unIDs) today. Indeed, these unIDs lack a clear, univocal association with a known astrophysical source. If dark matter (DM) is composed of weakly interacting massive particles (WIMPs), there is the exciting possibility that some of these unIDs may actually be DM sources, emitting gamma-rays from WIMPs annihilation. We propose a new approach to solve the standard, machine learning (ML) binary classification problem of disentangling prospective DM sources (simulated data) from astrophysical sources (observed data) among the unIDs of the 4FGL Fermi-LAT catalogue. We artificially build two systematic features for the DM data which are originally inherent to observed data: the detection significance and the uncertainty on the spectral curvature. We do it by sampling from the observed population of unIDs, assuming that the DM distributions would, if any, follow the latter. We consider different ML models: Logistic Regression, Neural Network (NN), Naive Bayes, and Gaussian Process, out of which the best, in terms of classification accuracy, is the NN, achieving around 93 . 3 per cent +/- 0 . 7 per cent performance. Other ML evaluation parameters, such as the True Ne gativ e and True Positive rates, are discussed in our work. Applying the NN to the unIDs sample, we find that the de generac y between some astrophysical and DM sources can be partially solved within this methodology. None the less, we conclude that there are no DM source candidates among the pool of 4FGL Fermi-LAT unIDs.  
  Address [Gammaldi, V; Sanchez-Conde, M. A.; Coronado-Blazquez, J.] Univ Autonoma Madrid, Departamentode Fis Teor, E-28049 Madrid, Spain, Email: viviana.gammaldi@uam.es;  
  Corporate Author Thesis  
  Publisher Oxford Univ Press Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0035-8711 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000937053400014 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ pastor @ Serial 5489  
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Author Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. url  doi
openurl 
  Title A deep Generative Artificial Intelligence system to predict species coexistence patterns Type Journal Article
  Year 2022 Publication (down) Methods in Ecology and Evolution Abbreviated Journal Methods Ecol. Evol.  
  Volume 13 Issue Pages 1052-1061  
  Keywords artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders  
  Abstract Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.  
  Address [Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es  
  Corporate Author Thesis  
  Publisher Wiley Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2041-210x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000765239700001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5155  
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Author Assam, I.; Vijande, J.; Ballester, F.; Perez-Calatayud, J.; Poppe, B.; Siebert, F.A. doi  openurl
  Title Evaluation of dosimetric effects of metallic artifact reduction and tissue assignment on Monte Carlo dose calculations for I-125 prostate implants Type Journal Article
  Year 2022 Publication (down) Medical Physics Abbreviated Journal Med. Phys.  
  Volume 49 Issue Pages 6195-6208  
  Keywords metallic artifact reduction; Monte Carlo dosimetry; post-implant CT; prostate brachytherapy; tissue assignment schemes; voxelized virtual patient model  
  Abstract Purpose Monte Carlo (MC) simulation studies, aimed at evaluating the magnitude of tissue heterogeneity in I-125 prostate permanent seed implant brachytherapy (BT), customarily use clinical post-implant CT images to generate a virtual representation of a realistic patient model (virtual patient model). Metallic artifact reduction (MAR) techniques and tissue assignment schemes (TAS) are implemented on the post-implant CT images to mollify metallic artifacts due to BT seeds and to assign tissue types to the voxels corresponding to the bright seed spots and streaking artifacts, respectively. The objective of this study is to assess the combined influence of MAR and TAS on MC absorbed dose calculations in post-implant CT-based phantoms. The virtual patient models used for I-125 prostate implant MC absorbed dose calculations in this study are derived from the CT images of an external radiotherapy prostate patient without BT seeds and prostatic calcifications, thus averting the need to implement MAR and TAS. Methods The geometry of the IsoSeed I25.S17plus source is validated by comparing the MC calculated results of the TG-43 parameters for the line source approximation with the TG-43U1S2 consensus data. Four MC absorbed dose calculations are performed in two virtual patient models using the egs_brachy MC code: (1) TG-43-based D-w,w-TG(43), (2) D-w,D-w-MBDC that accounts for interseed scattering and attenuation (ISA), (3) D-m,D-m that examines ISA and tissue heterogeneity by scoring absorbed dose in tissue, and (4) D-w,D-m that unlike D-m,D-m scores absorbed dose in water. The MC absorbed doses (1) and (2) are simulated in a TG-43 patient phantom derived by assigning the densities of every voxel to 1.00 g cm(-3) (water), whereas MC absorbed doses (3) and (4) are scored in the TG-186 patient phantom generated by mapping the mass density of each voxel to tissue according to a CT calibration curve. The MC absorbed doses calculated in this study are compared with VariSeed v8.0 calculated absorbed doses. To evaluate the dosimetric effect of MAR and TAS, the MC absorbed doses of this work (independent of MAR and TAS) are compared to the MC absorbed doses of different I-125 source models from previous studies that were calculated with different MC codes using post-implant CT-based phantoms generated by implementing MAR and TAS on post-implant CT images. Results The very good agreement of TG-43 parameters of this study and the published consensus data within 3% validates the geometry of the IsoSeed I25.S17plus source. For the clinical studies, the TG-43-based calculations show a D-90 overestimation of more than 4% compared to the more realistic MC methods due to ISA and tissue composition. The results of this work generally show few discrepancies with the post-implant CT-based dosimetry studies with respect to the D-90 absorbed dose metric parameter. These discrepancies are mainly Type B uncertainties due to the different I-125 source models and MC codes. Conclusions The implementation of MAR and TAS on post-implant CT images have no dosimetric effect on the I-125 prostate MC absorbed dose calculation in post-implant CT-based phantoms.  
  Address [Assam, Isong; Siebert, Frank-Andre] UKSH, Clin Radiotherapy Radiooncol, Campus Kiel, Kiel, Germany, Email: Isong.Assam@uksh.de  
  Corporate Author Thesis  
  Publisher Wiley Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0094-2405 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000835807200001 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5321  
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Author Rodriguez-Alvarez, M.J.; Sanchez, F.; Soriano, A.; Iborra, A.; Mora, C. doi  openurl
  Title Exploiting symmetries for weight matrix design in CT imaging Type Journal Article
  Year 2011 Publication (down) Mathematical and Computer Modelling Abbreviated Journal Math. Comput. Model.  
  Volume 54 Issue 7-8 Pages 1655-1664  
  Keywords Computerized tomography imaging; Polar grid; System matrix; Rotation symmetries; ART  
  Abstract In this paper we propose several methods of constructing the system matrix (SM) of a Computed Tomography (CT) scanner with two objectives: (1) to construct SMs in the shortest possible time and store them in an ordinary PC without losing quality, (2) to analyze the possible applications of the proposed method to 3D, taking into account SMs' sizes, computing time and reconstructed image quality. In order to build the SM, we propose two new field of view (FOV) pixellation schemes, based on a polar coordinate system (polar grid) by taking advantage of the polar rotation symmetries of CT devices. Comparisons between the SMs proposed are performed using two phantom and a real CT-simulator images. Global error, contrast, noise and homogeneity of the reconstructed images are discussed.  
  Address [Rodriguez-Alvarez, MJ; Iborra, A; Mora, C] Univ Politecn Valencia, Inst Matemat Multidisciplinar, Valencia 46022, Spain, Email: mjrodri@imm.upv.es  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0895-7177 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000293269200007 Approved no  
  Is ISI yes International Collaboration no  
  Call Number IFIC @ elepoucu @ Serial 708  
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