Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors
The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Flores, J.L. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Umfang: |
7 |
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| Übergeordnetes Werk: |
Enthalten in: The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol - Ide, C.V. ELSEVIER, 2017, a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics, Amsterdam |
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| Übergeordnetes Werk: |
volume:830 ; year:2016 ; day:11 ; month:09 ; pages:287-293 ; extent:7 |
| Links: |
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| DOI / URN: |
10.1016/j.nima.2016.05.107 |
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ELV019165471 |
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| 520 | |a The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. | ||
| 520 | |a The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. | ||
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10.1016/j.nima.2016.05.107 doi GBV00000000000162A.pica (DE-627)ELV019165471 (ELSEVIER)S0168-9002(16)30509-5 DE-627 ger DE-627 rakwb eng 530 530 DE-600 610 VZ 44.90 bkl Flores, J.L. verfasserin aut Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. HYDE Elsevier Silicon detectors Elsevier FAIR Elsevier Neural networks Elsevier Digital pulse shape analysis Elsevier Martel, I. oth Jiménez, R. oth Galán, J. oth Salmerón, P. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:830 year:2016 day:11 month:09 pages:287-293 extent:7 https://doi.org/10.1016/j.nima.2016.05.107 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 830 2016 11 0911 287-293 7 045F 530 |
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10.1016/j.nima.2016.05.107 doi GBV00000000000162A.pica (DE-627)ELV019165471 (ELSEVIER)S0168-9002(16)30509-5 DE-627 ger DE-627 rakwb eng 530 530 DE-600 610 VZ 44.90 bkl Flores, J.L. verfasserin aut Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. HYDE Elsevier Silicon detectors Elsevier FAIR Elsevier Neural networks Elsevier Digital pulse shape analysis Elsevier Martel, I. oth Jiménez, R. oth Galán, J. oth Salmerón, P. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:830 year:2016 day:11 month:09 pages:287-293 extent:7 https://doi.org/10.1016/j.nima.2016.05.107 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 830 2016 11 0911 287-293 7 045F 530 |
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10.1016/j.nima.2016.05.107 doi GBV00000000000162A.pica (DE-627)ELV019165471 (ELSEVIER)S0168-9002(16)30509-5 DE-627 ger DE-627 rakwb eng 530 530 DE-600 610 VZ 44.90 bkl Flores, J.L. verfasserin aut Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. HYDE Elsevier Silicon detectors Elsevier FAIR Elsevier Neural networks Elsevier Digital pulse shape analysis Elsevier Martel, I. oth Jiménez, R. oth Galán, J. oth Salmerón, P. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:830 year:2016 day:11 month:09 pages:287-293 extent:7 https://doi.org/10.1016/j.nima.2016.05.107 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 830 2016 11 0911 287-293 7 045F 530 |
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10.1016/j.nima.2016.05.107 doi GBV00000000000162A.pica (DE-627)ELV019165471 (ELSEVIER)S0168-9002(16)30509-5 DE-627 ger DE-627 rakwb eng 530 530 DE-600 610 VZ 44.90 bkl Flores, J.L. verfasserin aut Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors 2016transfer abstract 7 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. HYDE Elsevier Silicon detectors Elsevier FAIR Elsevier Neural networks Elsevier Digital pulse shape analysis Elsevier Martel, I. oth Jiménez, R. oth Galán, J. oth Salmerón, P. oth Enthalten in North-Holland Publ. Co Ide, C.V. ELSEVIER The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol 2017 a journal on accelerators, instrumentation and techniques applied to research in nuclear and atomic physics, materials science and related fields in physics Amsterdam (DE-627)ELV000874671 volume:830 year:2016 day:11 month:09 pages:287-293 extent:7 https://doi.org/10.1016/j.nima.2016.05.107 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 830 2016 11 0911 287-293 7 045F 530 |
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Flores, J.L. |
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The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol |
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The efficacy of EEG-biofeedback for acute pain management, a randomized sham-controlled study of a tailored protocol |
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application of neural networks to digital pulse shape analysis for an array of silicon strip detectors |
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Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors |
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The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. |
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The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. |
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The new generation of nuclear physics detectors that used to study nuclear reactions is considering the use of digital pulse shape analysis techniques (DPSA) to obtain the (A,Z) values of the reaction products impinging in solid state detectors. This technique can be an important tool for selecting the relevant reaction channels at the HYDE (HYbrid DEtector ball array) silicon array foreseen for the Low Energy Branch of the FAIR facility (Darmstadt, Germany). In this work we study the feasibility of using artificial neural networks (ANNs) for particle identification with silicon detectors. Multilayer Perceptron networks were trained and tested with recent experimental data, showing excellent identification capabilities with signals of several isotopes ranging from 12C up to 84Kr, yielding higher discrimination rates than any other previously reported. |
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Application of neural networks to digital pulse shape analysis for an array of silicon strip detectors |
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Martel, I. Jiménez, R. Galán, J. Salmerón, P. |
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