Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques

Babiano-Suárez, V.V.Babiano-SuárezLerendegui-Marco, J.J.Lerendegui-MarcoBalibrea-Correa, J.J.Balibrea-CorreaCaballero, L.L.CaballeroCalvo, D.D.CalvoLadarescu, I.I.LadarescuReal, D.D.RealDomingo-Pardo, C.C.Domingo-PardoCalviño, F.F.CalviñoCasanovas, A.A.CasanovasTarifeño-Saldivia, A.A.Tarifeño-SaldiviaKnapova, I.I.KnapovaKokkoris, M.M.KokkorisKopatch, Y.Y.KopatchKrtička, M.M.KrtičkaKurtulgil, D.D.KurtulgilLederer-Woods, C.C.Lederer-WoodsLeeb, H.H.LeebLonsdale, S. J.S. J.LonsdaleMacina, D.D.MacinaManna, A.A.MannaQuesada, J. M.J. M.QuesadaMartinez, T.T.MartinezMasi, A.A.MasiMassimi, C.C.MassimiMastinu, P.P.MastinuMastromarco, M.M.MastromarcoMaugeri, E. A.E. A.MaugeriMazzone, A.A.MazzoneMendoza, E.E.MendozaMengoni, A.A.MengoniMichalopoulou, V.V.MichalopoulouRamos-Doval, D.D.Ramos-DovalMilazzo, P. M.P. M.MilazzoMingrone, F.F.MingroneMoreno-Soto, J.J.Moreno-SotoMusumarra, A.A.MusumarraNegret, A.A.NegretOgállar, F.F.OgállarOprea, A.A.OpreaPatronis, N.N.PatronisPavlik, A.A.PavlikPerkowski, J.J.PerkowskiRauscher, T.T.RauscherPersanti, L.L.PersantiPetrone, C.C.PetronePirovano, E.E.PirovanoPorras, I.I.PorrasPraena, J.J.PraenaReifarth, R.R.ReifarthRochman, D.D.RochmanRubbia, C.C.RubbiaSabaté-Gilarte, M.M.Sabaté-GilarteSAXENA, AAYUSHAAYUSHSAXENASchillebeeckx, P.P.SchillebeeckxSchumann, D.D.SchumannAlcayne, V.V.AlcayneSekhar, A.A.SekharSmith, A. G.A. G.SmithSosnin, N. V.N. V.SosninSprung, P.P.SprungStamatopoulos, A.A.StamatopoulosTagliente, G.G.TaglienteTain, J. L.J. L.TainTassan-Got, L.L.Tassan-GotThomas, Th.Th.ThomasTorres-Sánchez, P.P.Torres-SánchezGuerrero, C.C.GuerreroTsinganis, A.A.TsinganisUlrich, J.J.UlrichUrlass, S.S.UrlassValenta, S.S.ValentaVannini, G.G.VanniniVariale, V.V.VarialeVaz, P.P.VazVentura, A.A.VenturaVESCOVI, DiegoDiegoVESCOVIVlachoudis, V.V.VlachoudisMillán-Callado, M. A.M. A.Millán-CalladoVlastou, R.R.VlastouWallner, A.A.WallnerWoods, P. J.P. J.WoodsWright, T.T.WrightŽugec, P.P.ŽugecRodríguez-González, T.T.Rodríguez-GonzálezBarbagallo, M.M.BarbagalloAberle, O.O.AberleAmaducci, S.S.AmaducciAndrzejewski, J.J.AndrzejewskiAudouin, L.L.AudouinBacak, M.M.BacakBennett, S.S.BennettBerthoumieux, E.E.BerthoumieuxBillowes, J.J.BillowesBosnar, D.D.BosnarBrown, A.A.BrownBusso, M.M.BussoCaamaño, M.M.CaamañoCalviani, M.M.CalvianiCano-Ott, D.D.Cano-OttCerutti, F.F.CeruttiChiaveri, E.E.ChiaveriColonna, N.N.ColonnaCortés, G.G.CortésCortés-Giraldo, M. A.M. A.Cortés-GiraldoCosentino, L.L.CosentinoCRISTALLO, SergioSergioCRISTALLODamone, L. A.L. A.DamoneDavies, P. J.P. J.DaviesDiakaki, M.M.DiakakiDietz, M.M.DietzDressler, R.R.DresslerDucasse, Q.Q.DucasseDupont, E.E.DupontDurán, I.I.DuránEleme, Z.Z.ElemeFernández-Domínguez, B.B.Fernández-DomínguezFerrari, A.A.FerrariFinocchiaro, P.P.FinocchiaroFurman, V.V.FurmanGöbel, K.K.GöbelGarg, R.R.GargGawlik, A.A.GawlikGilardoni, S.S.GilardoniGonçalves, I. F.I. F.GonçalvesGonzález-Romero, E.E.González-RomeroGunsing, F.F.GunsingHarada, H.H.HaradaHeinitz, S.S.HeinitzHeyse, J.J.HeyseJenkins, D. G.D. G.JenkinsJunghans, A.A.JunghansKäppeler, F.F.KäppelerKadi, Y.Y.KadiKimura, A.A.Kimura2022-03-112022-03-1120211434-6001http://hdl.handle.net/20.500.12386/31550i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n ,γ ) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the 197Au(n ,γ ) and 56Fe(n ,γ ) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two C6D6 detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of ∼3 higher detection sensitivity than state-of-the-art C6D6 detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and new analysis methodologies based on Machine-Learning techniques.STAMPAenImaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniquesArticle10.1140/epja/s10050-021-00507-72-s2.0-85103664089WOS:000662881100001https://link.springer.com/article/10.1140/epja/s10050-021-00507-7https://api.elsevier.com/content/abstract/scopus_id/851036640892021EPJA...57..197BFIS/02 - FISICA TEORICA, MODELLI E METODI MATEMATICI