FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4280491297> ?p ?o ?g. }

• W4280491297 endingPage "085102" @default.
• W4280491297 startingPage "085102" @default.
• W4280491297 abstract "Abstract Residual production cross sections in spallation reactions are key data for nuclear physics and related applications. Spallation reactions are very complex due to the wide range of incident energies and abundant fragments involved. Therefore, it is challenging to obtain accurate and complete energy-dependent residual cross sections. With the guidance of a simplified EPAX formula (sEPAX), the Bayesian neural network (BNN) technique is applied to form a new machine learning model (BNN + sEPAX) for predicting fragment cross sections in proton-induced nuclear spallation reactions. Three types of sample dataset for measured residual production cross sections in proton-induced nuclear spallation reactions are made, i.e. D1 consists of isotopic cross sections in reactions below 1 GeV/u, D2 consists of fragments excitation functions of reactions up to 2.6 GeV/u, and D3 is a hybrid of D1 and D2. With the constructed BNN and BNN + sEPAX models, the isotopic and mass cross section distributions are compared for the 356 MeV/u 40 Ca + p and 1 GeV/u 136 Xe + p reactions, and fragment excitation functions in 40 Ca + p , 56 Fe + p , 138 Ba + p and 197 Au + p reactions. It is found that the BNN model needs sufficient information to achieve good extrapolations, while the BNN + sEPAX model performs better extrapolations based on less information due to the physical guidance of the sEPAX formulas. It is suggested that the BNN + sEPAX model provides a new approach to predict the energy-dependent residual production cross sections produced in proton-induced nuclear spallation reactions of incident energies from tens of MeV/u up to several GeV/u." @default.
• W4280491297 created "2022-05-22" @default.
• W4280491297 creator A5015052331 @default.
• W4280491297 creator A5026244166 @default.
• W4280491297 creator A5026720577 @default.
• W4280491297 creator A5041966023 @default.
• W4280491297 creator A5044553276 @default.
• W4280491297 creator A5045015284 @default.
• W4280491297 creator A5056977715 @default.
• W4280491297 creator A5069709646 @default.
• W4280491297 date "2022-06-28" @default.
• W4280491297 modified "2023-10-17" @default.
• W4280491297 title "Bayesian evaluation of residual production cross sections in proton-induced nuclear spallation reactions" @default.
• W4280491297 cites W1568790255 @default.
• W4280491297 cites W1902812956 @default.
• W4280491297 cites W1992566495 @default.
• W4280491297 cites W2001804412 @default.
• W4280491297 cites W2005635297 @default.
• W4280491297 cites W2017109418 @default.
• W4280491297 cites W2017529805 @default.
• W4280491297 cites W2017894826 @default.
• W4280491297 cites W2031252220 @default.
• W4280491297 cites W2031332723 @default.
• W4280491297 cites W2041113191 @default.
• W4280491297 cites W2043415528 @default.
• W4280491297 cites W2046369473 @default.
• W4280491297 cites W2065300367 @default.
• W4280491297 cites W2069164913 @default.
• W4280491297 cites W2070691265 @default.
• W4280491297 cites W2074264475 @default.
• W4280491297 cites W2092806482 @default.
• W4280491297 cites W2103506815 @default.
• W4280491297 cites W2133172768 @default.
• W4280491297 cites W2168343117 @default.
• W4280491297 cites W2239526712 @default.
• W4280491297 cites W2522637443 @default.
• W4280491297 cites W2530031830 @default.
• W4280491297 cites W2584822331 @default.
• W4280491297 cites W2596804226 @default.
• W4280491297 cites W2599606797 @default.
• W4280491297 cites W2606610124 @default.
• W4280491297 cites W2752820893 @default.
• W4280491297 cites W2783931264 @default.
• W4280491297 cites W2807207171 @default.
• W4280491297 cites W2888029233 @default.
• W4280491297 cites W2888962121 @default.
• W4280491297 cites W2894703730 @default.
• W4280491297 cites W2920930727 @default.
• W4280491297 cites W2933349571 @default.
• W4280491297 cites W2951597345 @default.
• W4280491297 cites W2951667073 @default.
• W4280491297 cites W2952844526 @default.
• W4280491297 cites W2957140590 @default.
• W4280491297 cites W2979671133 @default.
• W4280491297 cites W2997880847 @default.
• W4280491297 cites W3004846186 @default.
• W4280491297 cites W3006994943 @default.
• W4280491297 cites W3018621017 @default.
• W4280491297 cites W3045726496 @default.
• W4280491297 cites W3103464473 @default.
• W4280491297 cites W3121458348 @default.
• W4280491297 cites W3121532553 @default.
• W4280491297 cites W3129712460 @default.
• W4280491297 cites W3144673880 @default.
• W4280491297 cites W3162355500 @default.
• W4280491297 cites W3165785024 @default.
• W4280491297 cites W3195216589 @default.
• W4280491297 cites W3203822839 @default.
• W4280491297 cites W4206983114 @default.
• W4280491297 cites W4220836549 @default.
• W4280491297 cites W4232762411 @default.
• W4280491297 cites W4375844723 @default.
• W4280491297 doi "https://doi.org/10.1088/1361-6471/ac7069" @default.
• W4280491297 hasPublicationYear "2022" @default.
• W4280491297 type Work @default.
• W4280491297 citedByCount "7" @default.
• W4280491297 countsByYear W42804912972022 @default.
• W4280491297 countsByYear W42804912972023 @default.
• W4280491297 crossrefType "journal-article" @default.
• W4280491297 hasAuthorship W4280491297A5015052331 @default.
• W4280491297 hasAuthorship W4280491297A5026244166 @default.
• W4280491297 hasAuthorship W4280491297A5026720577 @default.
• W4280491297 hasAuthorship W4280491297A5041966023 @default.
• W4280491297 hasAuthorship W4280491297A5044553276 @default.
• W4280491297 hasAuthorship W4280491297A5045015284 @default.
• W4280491297 hasAuthorship W4280491297A5056977715 @default.
• W4280491297 hasAuthorship W4280491297A5069709646 @default.
• W4280491297 hasBestOaLocation W42804912972 @default.
• W4280491297 hasConcept C11413529 @default.
• W4280491297 hasConcept C11608111 @default.
• W4280491297 hasConcept C121332964 @default.
• W4280491297 hasConcept C152568617 @default.
• W4280491297 hasConcept C155512373 @default.
• W4280491297 hasConcept C159985019 @default.
• W4280491297 hasConcept C185544564 @default.
• W4280491297 hasConcept C192562407 @default.
• W4280491297 hasConcept C204323151 @default.
• W4280491297 hasConcept C206191943 @default.

• next