Primary vertex finding in proton-antiproton events with a neural network simulation
In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with t...
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1991 |
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| Reproduktion: |
Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 |
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| Übergeordnetes Werk: |
in: Nuclear Instruments and Methods in Physics Research Section A: - Amsterdam : Elsevier, 302(1991), 2, Seite 217-226 |
| Übergeordnetes Werk: |
volume:302 ; year:1991 ; number:2 ; pages:217-226 |
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| 520 | |a In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. | ||
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(DE-627)NLEJ180941224 (DE-599)GBVNLZ180941224 DE-627 ger DE-627 rakwb eng Primary vertex finding in proton-antiproton events with a neural network simulation 1991 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Lindsey, C.S. oth Denby, B. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 302(1991), 2, Seite 217-226 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:302 year:1991 number:2 pages:217-226 http://linkinghub.elsevier.com/retrieve/pii/0168-9002(91)90405-F GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 302 1991 2 217-226 |
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(DE-627)NLEJ180941224 (DE-599)GBVNLZ180941224 DE-627 ger DE-627 rakwb eng Primary vertex finding in proton-antiproton events with a neural network simulation 1991 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Lindsey, C.S. oth Denby, B. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 302(1991), 2, Seite 217-226 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:302 year:1991 number:2 pages:217-226 http://linkinghub.elsevier.com/retrieve/pii/0168-9002(91)90405-F GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 302 1991 2 217-226 |
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(DE-627)NLEJ180941224 (DE-599)GBVNLZ180941224 DE-627 ger DE-627 rakwb eng Primary vertex finding in proton-antiproton events with a neural network simulation 1991 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Lindsey, C.S. oth Denby, B. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 302(1991), 2, Seite 217-226 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:302 year:1991 number:2 pages:217-226 http://linkinghub.elsevier.com/retrieve/pii/0168-9002(91)90405-F GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 302 1991 2 217-226 |
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(DE-627)NLEJ180941224 (DE-599)GBVNLZ180941224 DE-627 ger DE-627 rakwb eng Primary vertex finding in proton-antiproton events with a neural network simulation 1991 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Lindsey, C.S. oth Denby, B. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 302(1991), 2, Seite 217-226 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:302 year:1991 number:2 pages:217-226 http://linkinghub.elsevier.com/retrieve/pii/0168-9002(91)90405-F GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 302 1991 2 217-226 |
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(DE-627)NLEJ180941224 (DE-599)GBVNLZ180941224 DE-627 ger DE-627 rakwb eng Primary vertex finding in proton-antiproton events with a neural network simulation 1991 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Lindsey, C.S. oth Denby, B. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 302(1991), 2, Seite 217-226 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:302 year:1991 number:2 pages:217-226 http://linkinghub.elsevier.com/retrieve/pii/0168-9002(91)90405-F GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 302 1991 2 217-226 |
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primary vertex finding in proton-antiproton events with a neural network simulation |
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Primary vertex finding in proton-antiproton events with a neural network simulation |
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In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. |
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In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. |
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In a test of neural networks in high energy physics pattern recognition problems, drift chamber data from experiment E735 at the Tevatron proton-antiproton collider was used in a neural network simulation to find the primary event vertex. A three layer, feed-forward neural network was trained with the back-propagation technique to give the beam line vertices of tracks traversing subsections of the chamber. Summing the outputs of all subsection networks then gives the primary vertex along the beam line. Results are compared to conventional methods. |
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