Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments
The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Aartsen, M.G. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020transfer abstract |
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| Schlagwörter: |
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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:977 ; year:2020 ; day:11 ; month:10 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.nima.2020.164332 |
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| Katalog-ID: |
ELV050909207 |
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| 024 | 7 | |a 10.1016/j.nima.2020.164332 |2 doi | |
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| 100 | 1 | |a Aartsen, M.G. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments |
| 264 | 1 | |c 2020transfer abstract | |
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. | ||
| 520 | |a The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. | ||
| 650 | 7 | |a Data analysis |2 Elsevier | |
| 650 | 7 | |a Detector |2 Elsevier | |
| 650 | 7 | |a Monte Carlo |2 Elsevier | |
| 650 | 7 | |a Smoothing |2 Elsevier | |
| 650 | 7 | |a MC |2 Elsevier | |
| 650 | 7 | |a Neutrino mass ordering |2 Elsevier | |
| 650 | 7 | |a VLV ν T |2 Elsevier | |
| 650 | 7 | |a Statistics |2 Elsevier | |
| 650 | 7 | |a KDE |2 Elsevier | |
| 650 | 7 | |a Neutrino |2 Elsevier | |
| 700 | 1 | |a Ackermann, M. |4 oth | |
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| 700 | 1 | |a Unger, E. |4 oth | |
| 700 | 1 | |a Unland Elorrieta, M.A. |4 oth | |
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10.1016/j.nima.2020.164332 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001082.pica (DE-627)ELV050909207 (ELSEVIER)S0168-9002(20)30729-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Aartsen, M.G. verfasserin aut Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino Elsevier Ackermann, M. oth Adams, J. oth Aguilar, J.A. oth Ahlers, M. oth Ahrens, M. oth Alispach, C. oth Andeen, K. oth Anderson, T. oth Ansseau, I. oth Anton, G. oth Argüelles, C. oth Arlen, T.C. oth Auffenberg, J. oth Axani, S. oth Backes, P. oth Bagherpour, H. oth Bai, X. oth A.V., Balagopal oth Barbano, A. oth Barwick, S.W. oth Bastian, B. oth Baum, V. oth Baur, S. oth Bay, R. oth Beatty, J.J. oth Becker, K.-H. oth Becker Tjus, J. oth BenZvi, S. oth Berley, D. oth Bernardini, E. oth Besson, D.Z. oth Binder, G. oth Bindig, D. oth Blaufuss, E. oth Blot, S. oth Bohm, C. oth Börner, M. oth Böser, S. oth Botner, O. oth Böttcher, J. oth Bourbeau, E. oth Bourbeau, J. oth Bradascio, F. oth Braun, J. oth Bron, S. oth Brostean-Kaiser, J. oth Burgman, A. oth Buscher, J. oth Busse, R.S. oth Carver, T. oth Chen, C. oth Cheung, E. oth Chirkin, D. oth Choi, S. oth Clark, K. oth Classen, L. oth Coleman, A. oth Collin, G.H. oth Conrad, J.M. oth Coppin, P. oth Correa, P. oth Cowen, D.F. oth Cross, R. oth Dave, P. oth De Clercq, C. oth DeLaunay, J.J. oth Dembinski, H. oth Deoskar, K. oth De Ridder, S. oth Desiati, P. oth de Vries, K.D. oth de Wasseige, G. oth de With, M. oth DeYoung, T. oth Diaz, A. oth Dáz-Vélez, J.C. oth Dujmovic, H. oth Dunkman, M. oth Dvorak, E. oth Eberhardt, B. oth Ehrhardt, T. oth Eller, P. oth Engel, R. oth Evans, J.J. oth Evenson, P.A. oth Fahey, S. oth Fazely, A.R. oth Felde, J. oth Filimonov, K. oth Finley, C. oth Fox, D. oth Franckowiak, A. oth Friedman, E. oth Fritz, A. oth Gaisser, T.K. oth Gallagher, J. oth Ganster, E. oth Garrappa, S. oth Gerhardt, L. oth Ghorbani, K. oth Glauch, T. oth Glüsenkamp, T. oth Goldschmidt, A. oth Gonzalez, J.G. oth Grant, D. oth Griffith, Z. oth Griswold, S. oth Günder, M. oth Gündüz, M. oth Haack, C. oth Hallgren, A. oth Halliday, R. oth Halve, L. oth Halzen, F. oth Hanson, K. oth Haungs, A. oth Hebecker, D. oth Heereman, D. oth Heix, P. oth Helbing, K. oth Hellauer, R. oth Henningsen, F. oth Hickford, S. oth Hignight, J. oth Hill, G.C. oth Hoffman, K.D. oth Hoffmann, R. oth Hoinka, T. oth Hokanson-Fasig, B. oth Hoshina, K. oth Huang, F. oth Huber, M. oth Huber, T. oth Hultqvist, K. oth Hünnefeld, M. oth Hussain, R. oth In, S. oth Iovine, N. oth Ishihara, A. oth Japaridze, G.S. oth Jeong, M. oth Jero, K. oth Jones, B.J.P. oth Jonske, F. oth Joppe, R. oth Kang, D. oth Kang, W. oth Kappes, A. oth Kappesser, D. oth Karg, T. oth Karl, M. oth Karle, A. oth Katori, T. oth Katz, U. oth Kauer, M. oth Kelley, J.L. oth Kheirandish, A. oth Kim, J. oth Kintscher, T. oth Kiryluk, J. oth Kittler, T. oth Klein, S.R. oth Koirala, R. oth Kolanoski, H. oth Köpke, L. oth Kopper, C. oth Kopper, S. oth Koskinen, D.J. oth Kowalski, M. oth Krings, K. oth Krückl, G. oth Kulacz, N. oth Kurahashi, N. oth Kyriacou, A. oth Lanfranchi, J.L. oth Larson, M.J. oth Lauber, F. oth Lazar, J.P. oth Leonard, K. oth Leszczyńska, A. oth Leuermann, M. oth Liu, Q.R. oth Lohfink, E. oth Lozano Mariscal, C.J. oth Lu, L. oth Lucarelli, F. oth Lünemann, J. oth Luszczak, W. oth Lyu, Y. oth Ma, W.Y. oth Madsen, J. oth Maggi, G. oth Mahn, K.B.M. oth Makino, Y. oth Mallik, P. oth Mallot, K. oth Mancina, S. oth Mandalia, S. oth Mariş, I.C. oth Maruyama, R. oth Mase, K. oth Maunu, R. oth McNally, F. oth Meagher, K. oth Medici, M. oth Medina, A. oth Meier, M. oth Meighen-Berger, S. oth Menne, T. oth Merino, G. oth Meures, T. oth Micallef, J. oth Mockler, D. oth Momenté, G. oth Montaruli, T. oth Moore, R.W. oth Morse, R. oth Moulai, M. oth Muth, P. oth Nagai, R. oth Naumann, U. oth Neer, G. oth Niederhausen, H. oth Nisa, M.U. oth Nowicki, S.C. oth Nygren, D.R. oth Obertacke Pollmann, A. oth Oehler, M. oth Olivas, A. oth O’Murchadha, A. oth O’Sullivan, E. oth Palczewski, T. oth Pandya, H. oth Pankova, D.V. oth Park, N. oth Peiffer, P. oth Pérez de los Heros, C. oth Philippen, S. oth Pieloth, D. oth Pinat, E. oth Pizzuto, A. oth Plum, M. oth Porcelli, A. oth Price, P.B. oth Przybylski, G.T. oth Raab, C. oth Raissi, A. oth Rameez, M. oth Rauch, L. oth Rawlins, K. oth Rea, I.C. oth Reimann, R. oth Relethford, B. oth Renschler, M. oth Renzi, G. oth Resconi, E. oth Rhode, W. oth Richman, M. oth Robertson, S. oth Rongen, M. oth Rott, C. oth Ruhe, T. oth Ryckbosch, D. oth Rysewyk, D. oth Safa, I. oth Sanchez Herrera, S.E. oth Sandrock, A. oth Sandroos, J. oth Santander, M. oth Sarkar, S. oth Sarkar, S. oth Satalecka, K. oth Schaufel, M. oth Schieler, H. oth Schlunder, P. oth Schmidt, T. oth Schneider, A. oth Schneider, J. oth Schröder, F.G. oth Schulte, L. oth Schumacher, L. oth Sclafani, S. oth Seckel, D. oth Seunarine, S. oth Shefali, S. oth Silva, M. oth Snihur, R. oth Soedingrekso, J. oth Soldin, D. oth Söldner-Rembold, S. oth Song, M. oth Spiczak, G.M. oth Spiering, C. oth Stachurska, J. oth Stamatikos, M. oth Stanev, T. oth Stein, R. oth Steinmüller, P. oth Stettner, J. oth Steuer, A. oth Stezelberger, T. oth Stokstad, R.G. oth Stößl, A. oth Strotjohann, N.L. oth Stürwald, T. oth Stuttard, T. oth Sullivan, G.W. oth Taboada, I. oth Tenholt, F. oth Ter-Antonyan, S. oth Terliuk, A. oth Tilav, S. oth Tollefson, K. oth Tomankova, L. oth Tönnis, C. oth Toscano, S. oth Tosi, D. oth Trettin, A. oth Tselengidou, M. oth Tung, C.F. oth Turcati, A. oth Turcotte, R. oth Turley, C.F. oth Ty, B. oth Unger, E. oth Unland Elorrieta, M.A. oth Usner, M. oth Vandenbroucke, J. oth Van Driessche, W. oth van Eijk, D. oth van Eijndhoven, N. oth van Santen, J. oth Verpoest, S. oth Vraeghe, M. oth Walck, C. oth Wallace, A. oth Wallraff, M. oth Wandkowsky, N. oth Watson, T.B. oth Weaver, C. oth Weindl, A. oth Weiss, M.J. oth Weldert, J. oth Wendt, C. oth Werthebach, J. oth Whelan, B.J. oth Whitehorn, N. oth Wiebe, K. oth Wiebusch, C.H. oth Wille, L. oth Williams, D.R. oth Wills, L. oth Wolf, M. oth Wood, J. oth Wood, T.R. oth Woschnagg, K. oth Wrede, G. oth Wren, S. oth Xu, D.L. oth Xu, X.W. oth Xu, Y. oth Yanez, J.P. oth Yodh, G. oth Yoshida, S. oth Yuan, T. oth Zöcklein, M. 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:977 year:2020 day:11 month:10 pages:0 https://doi.org/10.1016/j.nima.2020.164332 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 977 2020 11 1011 0 |
| spelling |
10.1016/j.nima.2020.164332 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001082.pica (DE-627)ELV050909207 (ELSEVIER)S0168-9002(20)30729-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Aartsen, M.G. verfasserin aut Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino Elsevier Ackermann, M. oth Adams, J. oth Aguilar, J.A. oth Ahlers, M. oth Ahrens, M. oth Alispach, C. oth Andeen, K. oth Anderson, T. oth Ansseau, I. oth Anton, G. oth Argüelles, C. oth Arlen, T.C. oth Auffenberg, J. oth Axani, S. oth Backes, P. oth Bagherpour, H. oth Bai, X. oth A.V., Balagopal oth Barbano, A. oth Barwick, S.W. oth Bastian, B. oth Baum, V. oth Baur, S. oth Bay, R. oth Beatty, J.J. oth Becker, K.-H. oth Becker Tjus, J. oth BenZvi, S. oth Berley, D. oth Bernardini, E. oth Besson, D.Z. oth Binder, G. oth Bindig, D. oth Blaufuss, E. oth Blot, S. oth Bohm, C. oth Börner, M. oth Böser, S. oth Botner, O. oth Böttcher, J. oth Bourbeau, E. oth Bourbeau, J. oth Bradascio, F. oth Braun, J. oth Bron, S. oth Brostean-Kaiser, J. oth Burgman, A. oth Buscher, J. oth Busse, R.S. oth Carver, T. oth Chen, C. oth Cheung, E. oth Chirkin, D. oth Choi, S. oth Clark, K. oth Classen, L. oth Coleman, A. oth Collin, G.H. oth Conrad, J.M. oth Coppin, P. oth Correa, P. oth Cowen, D.F. oth Cross, R. oth Dave, P. oth De Clercq, C. oth DeLaunay, J.J. oth Dembinski, H. oth Deoskar, K. oth De Ridder, S. oth Desiati, P. oth de Vries, K.D. oth de Wasseige, G. oth de With, M. oth DeYoung, T. oth Diaz, A. oth Dáz-Vélez, J.C. oth Dujmovic, H. oth Dunkman, M. oth Dvorak, E. oth Eberhardt, B. oth Ehrhardt, T. oth Eller, P. oth Engel, R. oth Evans, J.J. oth Evenson, P.A. oth Fahey, S. oth Fazely, A.R. oth Felde, J. oth Filimonov, K. oth Finley, C. oth Fox, D. oth Franckowiak, A. oth Friedman, E. oth Fritz, A. oth Gaisser, T.K. oth Gallagher, J. oth Ganster, E. oth Garrappa, S. oth Gerhardt, L. oth Ghorbani, K. oth Glauch, T. oth Glüsenkamp, T. oth Goldschmidt, A. oth Gonzalez, J.G. oth Grant, D. oth Griffith, Z. oth Griswold, S. oth Günder, M. oth Gündüz, M. oth Haack, C. oth Hallgren, A. oth Halliday, R. oth Halve, L. oth Halzen, F. oth Hanson, K. oth Haungs, A. oth Hebecker, D. oth Heereman, D. oth Heix, P. oth Helbing, K. oth Hellauer, R. oth Henningsen, F. oth Hickford, S. oth Hignight, J. oth Hill, G.C. oth Hoffman, K.D. oth Hoffmann, R. oth Hoinka, T. oth Hokanson-Fasig, B. oth Hoshina, K. oth Huang, F. oth Huber, M. oth Huber, T. oth Hultqvist, K. oth Hünnefeld, M. oth Hussain, R. oth In, S. oth Iovine, N. oth Ishihara, A. oth Japaridze, G.S. oth Jeong, M. oth Jero, K. oth Jones, B.J.P. oth Jonske, F. oth Joppe, R. oth Kang, D. oth Kang, W. oth Kappes, A. oth Kappesser, D. oth Karg, T. oth Karl, M. oth Karle, A. oth Katori, T. oth Katz, U. oth Kauer, M. oth Kelley, J.L. oth Kheirandish, A. oth Kim, J. oth Kintscher, T. oth Kiryluk, J. oth Kittler, T. oth Klein, S.R. oth Koirala, R. oth Kolanoski, H. oth Köpke, L. oth Kopper, C. oth Kopper, S. oth Koskinen, D.J. oth Kowalski, M. oth Krings, K. oth Krückl, G. oth Kulacz, N. oth Kurahashi, N. oth Kyriacou, A. oth Lanfranchi, J.L. oth Larson, M.J. oth Lauber, F. oth Lazar, J.P. oth Leonard, K. oth Leszczyńska, A. oth Leuermann, M. oth Liu, Q.R. oth Lohfink, E. oth Lozano Mariscal, C.J. oth Lu, L. oth Lucarelli, F. oth Lünemann, J. oth Luszczak, W. oth Lyu, Y. oth Ma, W.Y. oth Madsen, J. oth Maggi, G. oth Mahn, K.B.M. oth Makino, Y. oth Mallik, P. oth Mallot, K. oth Mancina, S. oth Mandalia, S. oth Mariş, I.C. oth Maruyama, R. oth Mase, K. oth Maunu, R. oth McNally, F. oth Meagher, K. oth Medici, M. oth Medina, A. oth Meier, M. oth Meighen-Berger, S. oth Menne, T. oth Merino, G. oth Meures, T. oth Micallef, J. oth Mockler, D. oth Momenté, G. oth Montaruli, T. oth Moore, R.W. oth Morse, R. oth Moulai, M. oth Muth, P. oth Nagai, R. oth Naumann, U. oth Neer, G. oth Niederhausen, H. oth Nisa, M.U. oth Nowicki, S.C. oth Nygren, D.R. oth Obertacke Pollmann, A. oth Oehler, M. oth Olivas, A. oth O’Murchadha, A. oth O’Sullivan, E. oth Palczewski, T. oth Pandya, H. oth Pankova, D.V. oth Park, N. oth Peiffer, P. oth Pérez de los Heros, C. oth Philippen, S. oth Pieloth, D. oth Pinat, E. oth Pizzuto, A. oth Plum, M. oth Porcelli, A. oth Price, P.B. oth Przybylski, G.T. oth Raab, C. oth Raissi, A. oth Rameez, M. oth Rauch, L. oth Rawlins, K. oth Rea, I.C. oth Reimann, R. oth Relethford, B. oth Renschler, M. oth Renzi, G. oth Resconi, E. oth Rhode, W. oth Richman, M. oth Robertson, S. oth Rongen, M. oth Rott, C. oth Ruhe, T. oth Ryckbosch, D. oth Rysewyk, D. oth Safa, I. oth Sanchez Herrera, S.E. oth Sandrock, A. oth Sandroos, J. oth Santander, M. oth Sarkar, S. oth Sarkar, S. oth Satalecka, K. oth Schaufel, M. oth Schieler, H. oth Schlunder, P. oth Schmidt, T. oth Schneider, A. oth Schneider, J. oth Schröder, F.G. oth Schulte, L. oth Schumacher, L. oth Sclafani, S. oth Seckel, D. oth Seunarine, S. oth Shefali, S. oth Silva, M. oth Snihur, R. oth Soedingrekso, J. oth Soldin, D. oth Söldner-Rembold, S. oth Song, M. oth Spiczak, G.M. oth Spiering, C. oth Stachurska, J. oth Stamatikos, M. oth Stanev, T. oth Stein, R. oth Steinmüller, P. oth Stettner, J. oth Steuer, A. oth Stezelberger, T. oth Stokstad, R.G. oth Stößl, A. oth Strotjohann, N.L. oth Stürwald, T. oth Stuttard, T. oth Sullivan, G.W. oth Taboada, I. oth Tenholt, F. oth Ter-Antonyan, S. oth Terliuk, A. oth Tilav, S. oth Tollefson, K. oth Tomankova, L. oth Tönnis, C. oth Toscano, S. oth Tosi, D. oth Trettin, A. oth Tselengidou, M. oth Tung, C.F. oth Turcati, A. oth Turcotte, R. oth Turley, C.F. oth Ty, B. oth Unger, E. oth Unland Elorrieta, M.A. oth Usner, M. oth Vandenbroucke, J. oth Van Driessche, W. oth van Eijk, D. oth van Eijndhoven, N. oth van Santen, J. oth Verpoest, S. oth Vraeghe, M. oth Walck, C. oth Wallace, A. oth Wallraff, M. oth Wandkowsky, N. oth Watson, T.B. oth Weaver, C. oth Weindl, A. oth Weiss, M.J. oth Weldert, J. oth Wendt, C. oth Werthebach, J. oth Whelan, B.J. oth Whitehorn, N. oth Wiebe, K. oth Wiebusch, C.H. oth Wille, L. oth Williams, D.R. oth Wills, L. oth Wolf, M. oth Wood, J. oth Wood, T.R. oth Woschnagg, K. oth Wrede, G. oth Wren, S. oth Xu, D.L. oth Xu, X.W. oth Xu, Y. oth Yanez, J.P. oth Yodh, G. oth Yoshida, S. oth Yuan, T. oth Zöcklein, M. 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:977 year:2020 day:11 month:10 pages:0 https://doi.org/10.1016/j.nima.2020.164332 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 977 2020 11 1011 0 |
| allfields_unstemmed |
10.1016/j.nima.2020.164332 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001082.pica (DE-627)ELV050909207 (ELSEVIER)S0168-9002(20)30729-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Aartsen, M.G. verfasserin aut Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino Elsevier Ackermann, M. oth Adams, J. oth Aguilar, J.A. oth Ahlers, M. oth Ahrens, M. oth Alispach, C. oth Andeen, K. oth Anderson, T. oth Ansseau, I. oth Anton, G. oth Argüelles, C. oth Arlen, T.C. oth Auffenberg, J. oth Axani, S. oth Backes, P. oth Bagherpour, H. oth Bai, X. oth A.V., Balagopal oth Barbano, A. oth Barwick, S.W. oth Bastian, B. oth Baum, V. oth Baur, S. oth Bay, R. oth Beatty, J.J. oth Becker, K.-H. oth Becker Tjus, J. oth BenZvi, S. oth Berley, D. oth Bernardini, E. oth Besson, D.Z. oth Binder, G. oth Bindig, D. oth Blaufuss, E. oth Blot, S. oth Bohm, C. oth Börner, M. oth Böser, S. oth Botner, O. oth Böttcher, J. oth Bourbeau, E. oth Bourbeau, J. oth Bradascio, F. oth Braun, J. oth Bron, S. oth Brostean-Kaiser, J. oth Burgman, A. oth Buscher, J. oth Busse, R.S. oth Carver, T. oth Chen, C. oth Cheung, E. oth Chirkin, D. oth Choi, S. oth Clark, K. oth Classen, L. oth Coleman, A. oth Collin, G.H. oth Conrad, J.M. oth Coppin, P. oth Correa, P. oth Cowen, D.F. oth Cross, R. oth Dave, P. oth De Clercq, C. oth DeLaunay, J.J. oth Dembinski, H. oth Deoskar, K. oth De Ridder, S. oth Desiati, P. oth de Vries, K.D. oth de Wasseige, G. oth de With, M. oth DeYoung, T. oth Diaz, A. oth Dáz-Vélez, J.C. oth Dujmovic, H. oth Dunkman, M. oth Dvorak, E. oth Eberhardt, B. oth Ehrhardt, T. oth Eller, P. oth Engel, R. oth Evans, J.J. oth Evenson, P.A. oth Fahey, S. oth Fazely, A.R. oth Felde, J. oth Filimonov, K. oth Finley, C. oth Fox, D. oth Franckowiak, A. oth Friedman, E. oth Fritz, A. oth Gaisser, T.K. oth Gallagher, J. oth Ganster, E. oth Garrappa, S. oth Gerhardt, L. oth Ghorbani, K. oth Glauch, T. oth Glüsenkamp, T. oth Goldschmidt, A. oth Gonzalez, J.G. oth Grant, D. oth Griffith, Z. oth Griswold, S. oth Günder, M. oth Gündüz, M. oth Haack, C. oth Hallgren, A. oth Halliday, R. oth Halve, L. oth Halzen, F. oth Hanson, K. oth Haungs, A. oth Hebecker, D. oth Heereman, D. oth Heix, P. oth Helbing, K. oth Hellauer, R. oth Henningsen, F. oth Hickford, S. oth Hignight, J. oth Hill, G.C. oth Hoffman, K.D. oth Hoffmann, R. oth Hoinka, T. oth Hokanson-Fasig, B. oth Hoshina, K. oth Huang, F. oth Huber, M. oth Huber, T. oth Hultqvist, K. oth Hünnefeld, M. oth Hussain, R. oth In, S. oth Iovine, N. oth Ishihara, A. oth Japaridze, G.S. oth Jeong, M. oth Jero, K. oth Jones, B.J.P. oth Jonske, F. oth Joppe, R. oth Kang, D. oth Kang, W. oth Kappes, A. oth Kappesser, D. oth Karg, T. oth Karl, M. oth Karle, A. oth Katori, T. oth Katz, U. oth Kauer, M. oth Kelley, J.L. oth Kheirandish, A. oth Kim, J. oth Kintscher, T. oth Kiryluk, J. oth Kittler, T. oth Klein, S.R. oth Koirala, R. oth Kolanoski, H. oth Köpke, L. oth Kopper, C. oth Kopper, S. oth Koskinen, D.J. oth Kowalski, M. oth Krings, K. oth Krückl, G. oth Kulacz, N. oth Kurahashi, N. oth Kyriacou, A. oth Lanfranchi, J.L. oth Larson, M.J. oth Lauber, F. oth Lazar, J.P. oth Leonard, K. oth Leszczyńska, A. oth Leuermann, M. oth Liu, Q.R. oth Lohfink, E. oth Lozano Mariscal, C.J. oth Lu, L. oth Lucarelli, F. oth Lünemann, J. oth Luszczak, W. oth Lyu, Y. oth Ma, W.Y. oth Madsen, J. oth Maggi, G. oth Mahn, K.B.M. oth Makino, Y. oth Mallik, P. oth Mallot, K. oth Mancina, S. oth Mandalia, S. oth Mariş, I.C. oth Maruyama, R. oth Mase, K. oth Maunu, R. oth McNally, F. oth Meagher, K. oth Medici, M. oth Medina, A. oth Meier, M. oth Meighen-Berger, S. oth Menne, T. oth Merino, G. oth Meures, T. oth Micallef, J. oth Mockler, D. oth Momenté, G. oth Montaruli, T. oth Moore, R.W. oth Morse, R. oth Moulai, M. oth Muth, P. oth Nagai, R. oth Naumann, U. oth Neer, G. oth Niederhausen, H. oth Nisa, M.U. oth Nowicki, S.C. oth Nygren, D.R. oth Obertacke Pollmann, A. oth Oehler, M. oth Olivas, A. oth O’Murchadha, A. oth O’Sullivan, E. oth Palczewski, T. oth Pandya, H. oth Pankova, D.V. oth Park, N. oth Peiffer, P. oth Pérez de los Heros, C. oth Philippen, S. oth Pieloth, D. oth Pinat, E. oth Pizzuto, A. oth Plum, M. oth Porcelli, A. oth Price, P.B. oth Przybylski, G.T. oth Raab, C. oth Raissi, A. oth Rameez, M. oth Rauch, L. oth Rawlins, K. oth Rea, I.C. oth Reimann, R. oth Relethford, B. oth Renschler, M. oth Renzi, G. oth Resconi, E. oth Rhode, W. oth Richman, M. oth Robertson, S. oth Rongen, M. oth Rott, C. oth Ruhe, T. oth Ryckbosch, D. oth Rysewyk, D. oth Safa, I. oth Sanchez Herrera, S.E. oth Sandrock, A. oth Sandroos, J. oth Santander, M. oth Sarkar, S. oth Sarkar, S. oth Satalecka, K. oth Schaufel, M. oth Schieler, H. oth Schlunder, P. oth Schmidt, T. oth Schneider, A. oth Schneider, J. oth Schröder, F.G. oth Schulte, L. oth Schumacher, L. oth Sclafani, S. oth Seckel, D. oth Seunarine, S. oth Shefali, S. oth Silva, M. oth Snihur, R. oth Soedingrekso, J. oth Soldin, D. oth Söldner-Rembold, S. oth Song, M. oth Spiczak, G.M. oth Spiering, C. oth Stachurska, J. oth Stamatikos, M. oth Stanev, T. oth Stein, R. oth Steinmüller, P. oth Stettner, J. oth Steuer, A. oth Stezelberger, T. oth Stokstad, R.G. oth Stößl, A. oth Strotjohann, N.L. oth Stürwald, T. oth Stuttard, T. oth Sullivan, G.W. oth Taboada, I. oth Tenholt, F. oth Ter-Antonyan, S. oth Terliuk, A. oth Tilav, S. oth Tollefson, K. oth Tomankova, L. oth Tönnis, C. oth Toscano, S. oth Tosi, D. oth Trettin, A. oth Tselengidou, M. oth Tung, C.F. oth Turcati, A. oth Turcotte, R. oth Turley, C.F. oth Ty, B. oth Unger, E. oth Unland Elorrieta, M.A. oth Usner, M. oth Vandenbroucke, J. oth Van Driessche, W. oth van Eijk, D. oth van Eijndhoven, N. oth van Santen, J. oth Verpoest, S. oth Vraeghe, M. oth Walck, C. oth Wallace, A. oth Wallraff, M. oth Wandkowsky, N. oth Watson, T.B. oth Weaver, C. oth Weindl, A. oth Weiss, M.J. oth Weldert, J. oth Wendt, C. oth Werthebach, J. oth Whelan, B.J. oth Whitehorn, N. oth Wiebe, K. oth Wiebusch, C.H. oth Wille, L. oth Williams, D.R. oth Wills, L. oth Wolf, M. oth Wood, J. oth Wood, T.R. oth Woschnagg, K. oth Wrede, G. oth Wren, S. oth Xu, D.L. oth Xu, X.W. oth Xu, Y. oth Yanez, J.P. oth Yodh, G. oth Yoshida, S. oth Yuan, T. oth Zöcklein, M. 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:977 year:2020 day:11 month:10 pages:0 https://doi.org/10.1016/j.nima.2020.164332 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 977 2020 11 1011 0 |
| allfieldsGer |
10.1016/j.nima.2020.164332 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001082.pica (DE-627)ELV050909207 (ELSEVIER)S0168-9002(20)30729-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Aartsen, M.G. verfasserin aut Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino Elsevier Ackermann, M. oth Adams, J. oth Aguilar, J.A. oth Ahlers, M. oth Ahrens, M. oth Alispach, C. oth Andeen, K. oth Anderson, T. oth Ansseau, I. oth Anton, G. oth Argüelles, C. oth Arlen, T.C. oth Auffenberg, J. oth Axani, S. oth Backes, P. oth Bagherpour, H. oth Bai, X. oth A.V., Balagopal oth Barbano, A. oth Barwick, S.W. oth Bastian, B. oth Baum, V. oth Baur, S. oth Bay, R. oth Beatty, J.J. oth Becker, K.-H. oth Becker Tjus, J. oth BenZvi, S. oth Berley, D. oth Bernardini, E. oth Besson, D.Z. oth Binder, G. oth Bindig, D. oth Blaufuss, E. oth Blot, S. oth Bohm, C. oth Börner, M. oth Böser, S. oth Botner, O. oth Böttcher, J. oth Bourbeau, E. oth Bourbeau, J. oth Bradascio, F. oth Braun, J. oth Bron, S. oth Brostean-Kaiser, J. oth Burgman, A. oth Buscher, J. oth Busse, R.S. oth Carver, T. oth Chen, C. oth Cheung, E. oth Chirkin, D. oth Choi, S. oth Clark, K. oth Classen, L. oth Coleman, A. oth Collin, G.H. oth Conrad, J.M. oth Coppin, P. oth Correa, P. oth Cowen, D.F. oth Cross, R. oth Dave, P. oth De Clercq, C. oth DeLaunay, J.J. oth Dembinski, H. oth Deoskar, K. oth De Ridder, S. oth Desiati, P. oth de Vries, K.D. oth de Wasseige, G. oth de With, M. oth DeYoung, T. oth Diaz, A. oth Dáz-Vélez, J.C. oth Dujmovic, H. oth Dunkman, M. oth Dvorak, E. oth Eberhardt, B. oth Ehrhardt, T. oth Eller, P. oth Engel, R. oth Evans, J.J. oth Evenson, P.A. oth Fahey, S. oth Fazely, A.R. oth Felde, J. oth Filimonov, K. oth Finley, C. oth Fox, D. oth Franckowiak, A. oth Friedman, E. oth Fritz, A. oth Gaisser, T.K. oth Gallagher, J. oth Ganster, E. oth Garrappa, S. oth Gerhardt, L. oth Ghorbani, K. oth Glauch, T. oth Glüsenkamp, T. oth Goldschmidt, A. oth Gonzalez, J.G. oth Grant, D. oth Griffith, Z. oth Griswold, S. oth Günder, M. oth Gündüz, M. oth Haack, C. oth Hallgren, A. oth Halliday, R. oth Halve, L. oth Halzen, F. oth Hanson, K. oth Haungs, A. oth Hebecker, D. oth Heereman, D. oth Heix, P. oth Helbing, K. oth Hellauer, R. oth Henningsen, F. oth Hickford, S. oth Hignight, J. oth Hill, G.C. oth Hoffman, K.D. oth Hoffmann, R. oth Hoinka, T. oth Hokanson-Fasig, B. oth Hoshina, K. oth Huang, F. oth Huber, M. oth Huber, T. oth Hultqvist, K. oth Hünnefeld, M. oth Hussain, R. oth In, S. oth Iovine, N. oth Ishihara, A. oth Japaridze, G.S. oth Jeong, M. oth Jero, K. oth Jones, B.J.P. oth Jonske, F. oth Joppe, R. oth Kang, D. oth Kang, W. oth Kappes, A. oth Kappesser, D. oth Karg, T. oth Karl, M. oth Karle, A. oth Katori, T. oth Katz, U. oth Kauer, M. oth Kelley, J.L. oth Kheirandish, A. oth Kim, J. oth Kintscher, T. oth Kiryluk, J. oth Kittler, T. oth Klein, S.R. oth Koirala, R. oth Kolanoski, H. oth Köpke, L. oth Kopper, C. oth Kopper, S. oth Koskinen, D.J. oth Kowalski, M. oth Krings, K. oth Krückl, G. oth Kulacz, N. oth Kurahashi, N. oth Kyriacou, A. oth Lanfranchi, J.L. oth Larson, M.J. oth Lauber, F. oth Lazar, J.P. oth Leonard, K. oth Leszczyńska, A. oth Leuermann, M. oth Liu, Q.R. oth Lohfink, E. oth Lozano Mariscal, C.J. oth Lu, L. oth Lucarelli, F. oth Lünemann, J. oth Luszczak, W. oth Lyu, Y. oth Ma, W.Y. oth Madsen, J. oth Maggi, G. oth Mahn, K.B.M. oth Makino, Y. oth Mallik, P. oth Mallot, K. oth Mancina, S. oth Mandalia, S. oth Mariş, I.C. oth Maruyama, R. oth Mase, K. oth Maunu, R. oth McNally, F. oth Meagher, K. oth Medici, M. oth Medina, A. oth Meier, M. oth Meighen-Berger, S. oth Menne, T. oth Merino, G. oth Meures, T. oth Micallef, J. oth Mockler, D. oth Momenté, G. oth Montaruli, T. oth Moore, R.W. oth Morse, R. oth Moulai, M. oth Muth, P. oth Nagai, R. oth Naumann, U. oth Neer, G. oth Niederhausen, H. oth Nisa, M.U. oth Nowicki, S.C. oth Nygren, D.R. oth Obertacke Pollmann, A. oth Oehler, M. oth Olivas, A. oth O’Murchadha, A. oth O’Sullivan, E. oth Palczewski, T. oth Pandya, H. oth Pankova, D.V. oth Park, N. oth Peiffer, P. oth Pérez de los Heros, C. oth Philippen, S. oth Pieloth, D. oth Pinat, E. oth Pizzuto, A. oth Plum, M. oth Porcelli, A. oth Price, P.B. oth Przybylski, G.T. oth Raab, C. oth Raissi, A. oth Rameez, M. oth Rauch, L. oth Rawlins, K. oth Rea, I.C. oth Reimann, R. oth Relethford, B. oth Renschler, M. oth Renzi, G. oth Resconi, E. oth Rhode, W. oth Richman, M. oth Robertson, S. oth Rongen, M. oth Rott, C. oth Ruhe, T. oth Ryckbosch, D. oth Rysewyk, D. oth Safa, I. oth Sanchez Herrera, S.E. oth Sandrock, A. oth Sandroos, J. oth Santander, M. oth Sarkar, S. oth Sarkar, S. oth Satalecka, K. oth Schaufel, M. oth Schieler, H. oth Schlunder, P. oth Schmidt, T. oth Schneider, A. oth Schneider, J. oth Schröder, F.G. oth Schulte, L. oth Schumacher, L. oth Sclafani, S. oth Seckel, D. oth Seunarine, S. oth Shefali, S. oth Silva, M. oth Snihur, R. oth Soedingrekso, J. oth Soldin, D. oth Söldner-Rembold, S. oth Song, M. oth Spiczak, G.M. oth Spiering, C. oth Stachurska, J. oth Stamatikos, M. oth Stanev, T. oth Stein, R. oth Steinmüller, P. oth Stettner, J. oth Steuer, A. oth Stezelberger, T. oth Stokstad, R.G. oth Stößl, A. oth Strotjohann, N.L. oth Stürwald, T. oth Stuttard, T. oth Sullivan, G.W. oth Taboada, I. oth Tenholt, F. oth Ter-Antonyan, S. oth Terliuk, A. oth Tilav, S. oth Tollefson, K. oth Tomankova, L. oth Tönnis, C. oth Toscano, S. oth Tosi, D. oth Trettin, A. oth Tselengidou, M. oth Tung, C.F. oth Turcati, A. oth Turcotte, R. oth Turley, C.F. oth Ty, B. oth Unger, E. oth Unland Elorrieta, M.A. oth Usner, M. oth Vandenbroucke, J. oth Van Driessche, W. oth van Eijk, D. oth van Eijndhoven, N. oth van Santen, J. oth Verpoest, S. oth Vraeghe, M. oth Walck, C. oth Wallace, A. oth Wallraff, M. oth Wandkowsky, N. oth Watson, T.B. oth Weaver, C. oth Weindl, A. oth Weiss, M.J. oth Weldert, J. oth Wendt, C. oth Werthebach, J. oth Whelan, B.J. oth Whitehorn, N. oth Wiebe, K. oth Wiebusch, C.H. oth Wille, L. oth Williams, D.R. oth Wills, L. oth Wolf, M. oth Wood, J. oth Wood, T.R. oth Woschnagg, K. oth Wrede, G. oth Wren, S. oth Xu, D.L. oth Xu, X.W. oth Xu, Y. oth Yanez, J.P. oth Yodh, G. oth Yoshida, S. oth Yuan, T. oth Zöcklein, M. 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:977 year:2020 day:11 month:10 pages:0 https://doi.org/10.1016/j.nima.2020.164332 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 977 2020 11 1011 0 |
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10.1016/j.nima.2020.164332 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001082.pica (DE-627)ELV050909207 (ELSEVIER)S0168-9002(20)30729-4 DE-627 ger DE-627 rakwb eng 610 VZ 44.90 bkl Aartsen, M.G. verfasserin aut Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments 2020transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino Elsevier Ackermann, M. oth Adams, J. oth Aguilar, J.A. oth Ahlers, M. oth Ahrens, M. oth Alispach, C. oth Andeen, K. oth Anderson, T. oth Ansseau, I. oth Anton, G. oth Argüelles, C. oth Arlen, T.C. oth Auffenberg, J. oth Axani, S. oth Backes, P. oth Bagherpour, H. oth Bai, X. oth A.V., Balagopal oth Barbano, A. oth Barwick, S.W. oth Bastian, B. oth Baum, V. oth Baur, S. oth Bay, R. oth Beatty, J.J. oth Becker, K.-H. oth Becker Tjus, J. oth BenZvi, S. oth Berley, D. oth Bernardini, E. oth Besson, D.Z. oth Binder, G. oth Bindig, D. oth Blaufuss, E. oth Blot, S. oth Bohm, C. oth Börner, M. oth Böser, S. oth Botner, O. oth Böttcher, J. oth Bourbeau, E. oth Bourbeau, J. oth Bradascio, F. oth Braun, J. oth Bron, S. oth Brostean-Kaiser, J. oth Burgman, A. oth Buscher, J. oth Busse, R.S. oth Carver, T. oth Chen, C. oth Cheung, E. oth Chirkin, D. oth Choi, S. oth Clark, K. oth Classen, L. oth Coleman, A. oth Collin, G.H. oth Conrad, J.M. oth Coppin, P. oth Correa, P. oth Cowen, D.F. oth Cross, R. oth Dave, P. oth De Clercq, C. oth DeLaunay, J.J. oth Dembinski, H. oth Deoskar, K. oth De Ridder, S. oth Desiati, P. oth de Vries, K.D. oth de Wasseige, G. oth de With, M. oth DeYoung, T. oth Diaz, A. oth Dáz-Vélez, J.C. oth Dujmovic, H. oth Dunkman, M. oth Dvorak, E. oth Eberhardt, B. oth Ehrhardt, T. oth Eller, P. oth Engel, R. oth Evans, J.J. oth Evenson, P.A. oth Fahey, S. oth Fazely, A.R. oth Felde, J. oth Filimonov, K. oth Finley, C. oth Fox, D. oth Franckowiak, A. oth Friedman, E. oth Fritz, A. oth Gaisser, T.K. oth Gallagher, J. oth Ganster, E. oth Garrappa, S. oth Gerhardt, L. oth Ghorbani, K. oth Glauch, T. oth Glüsenkamp, T. oth Goldschmidt, A. oth Gonzalez, J.G. oth Grant, D. oth Griffith, Z. oth Griswold, S. oth Günder, M. oth Gündüz, M. oth Haack, C. oth Hallgren, A. oth Halliday, R. oth Halve, L. oth Halzen, F. oth Hanson, K. oth Haungs, A. oth Hebecker, D. oth Heereman, D. oth Heix, P. oth Helbing, K. oth Hellauer, R. oth Henningsen, F. oth Hickford, S. oth Hignight, J. oth Hill, G.C. oth Hoffman, K.D. oth Hoffmann, R. oth Hoinka, T. oth Hokanson-Fasig, B. oth Hoshina, K. oth Huang, F. oth Huber, M. oth Huber, T. oth Hultqvist, K. oth Hünnefeld, M. oth Hussain, R. oth In, S. oth Iovine, N. oth Ishihara, A. oth Japaridze, G.S. oth Jeong, M. oth Jero, K. oth Jones, B.J.P. oth Jonske, F. oth Joppe, R. oth Kang, D. oth Kang, W. oth Kappes, A. oth Kappesser, D. oth Karg, T. oth Karl, M. oth Karle, A. oth Katori, T. oth Katz, U. oth Kauer, M. oth Kelley, J.L. oth Kheirandish, A. oth Kim, J. oth Kintscher, T. oth Kiryluk, J. oth Kittler, T. oth Klein, S.R. oth Koirala, R. oth Kolanoski, H. oth Köpke, L. oth Kopper, C. oth Kopper, S. oth Koskinen, D.J. oth Kowalski, M. oth Krings, K. oth Krückl, G. oth Kulacz, N. oth Kurahashi, N. oth Kyriacou, A. oth Lanfranchi, J.L. oth Larson, M.J. oth Lauber, F. oth Lazar, J.P. oth Leonard, K. oth Leszczyńska, A. oth Leuermann, M. oth Liu, Q.R. oth Lohfink, E. oth Lozano Mariscal, C.J. oth Lu, L. oth Lucarelli, F. oth Lünemann, J. oth Luszczak, W. oth Lyu, Y. oth Ma, W.Y. oth Madsen, J. oth Maggi, G. oth Mahn, K.B.M. oth Makino, Y. oth Mallik, P. oth Mallot, K. oth Mancina, S. oth Mandalia, S. oth Mariş, I.C. oth Maruyama, R. oth Mase, K. oth Maunu, R. oth McNally, F. oth Meagher, K. oth Medici, M. oth Medina, A. oth Meier, M. oth Meighen-Berger, S. oth Menne, T. oth Merino, G. oth Meures, T. oth Micallef, J. oth Mockler, D. oth Momenté, G. oth Montaruli, T. oth Moore, R.W. oth Morse, R. oth Moulai, M. oth Muth, P. oth Nagai, R. oth Naumann, U. oth Neer, G. oth Niederhausen, H. oth Nisa, M.U. oth Nowicki, S.C. oth Nygren, D.R. oth Obertacke Pollmann, A. oth Oehler, M. oth Olivas, A. oth O’Murchadha, A. oth O’Sullivan, E. oth Palczewski, T. oth Pandya, H. oth Pankova, D.V. oth Park, N. oth Peiffer, P. oth Pérez de los Heros, C. oth Philippen, S. oth Pieloth, D. oth Pinat, E. oth Pizzuto, A. oth Plum, M. oth Porcelli, A. oth Price, P.B. oth Przybylski, G.T. oth Raab, C. oth Raissi, A. oth Rameez, M. oth Rauch, L. oth Rawlins, K. oth Rea, I.C. oth Reimann, R. oth Relethford, B. oth Renschler, M. oth Renzi, G. oth Resconi, E. oth Rhode, W. oth Richman, M. oth Robertson, S. oth Rongen, M. oth Rott, C. oth Ruhe, T. oth Ryckbosch, D. oth Rysewyk, D. oth Safa, I. oth Sanchez Herrera, S.E. oth Sandrock, A. oth Sandroos, J. oth Santander, M. oth Sarkar, S. oth Sarkar, S. oth Satalecka, K. oth Schaufel, M. oth Schieler, H. oth Schlunder, P. oth Schmidt, T. oth Schneider, A. oth Schneider, J. oth Schröder, F.G. oth Schulte, L. oth Schumacher, L. oth Sclafani, S. oth Seckel, D. oth Seunarine, S. oth Shefali, S. oth Silva, M. oth Snihur, R. oth Soedingrekso, J. oth Soldin, D. oth Söldner-Rembold, S. oth Song, M. oth Spiczak, G.M. oth Spiering, C. oth Stachurska, J. oth Stamatikos, M. oth Stanev, T. oth Stein, R. oth Steinmüller, P. oth Stettner, J. oth Steuer, A. oth Stezelberger, T. oth Stokstad, R.G. oth Stößl, A. oth Strotjohann, N.L. oth Stürwald, T. oth Stuttard, T. oth Sullivan, G.W. oth Taboada, I. oth Tenholt, F. oth Ter-Antonyan, S. oth Terliuk, A. oth Tilav, S. oth Tollefson, K. oth Tomankova, L. oth Tönnis, C. oth Toscano, S. oth Tosi, D. oth Trettin, A. oth Tselengidou, M. oth Tung, C.F. oth Turcati, A. oth Turcotte, R. oth Turley, C.F. oth Ty, B. oth Unger, E. oth Unland Elorrieta, M.A. oth Usner, M. oth Vandenbroucke, J. oth Van Driessche, W. oth van Eijk, D. oth van Eijndhoven, N. oth van Santen, J. oth Verpoest, S. oth Vraeghe, M. oth Walck, C. oth Wallace, A. oth Wallraff, M. oth Wandkowsky, N. oth Watson, T.B. oth Weaver, C. oth Weindl, A. oth Weiss, M.J. oth Weldert, J. oth Wendt, C. oth Werthebach, J. oth Whelan, B.J. oth Whitehorn, N. oth Wiebe, K. oth Wiebusch, C.H. oth Wille, L. oth Williams, D.R. oth Wills, L. oth Wolf, M. oth Wood, J. oth Wood, T.R. oth Woschnagg, K. oth Wrede, G. oth Wren, S. oth Xu, D.L. oth Xu, X.W. oth Xu, Y. oth Yanez, J.P. oth Yodh, G. oth Yoshida, S. oth Yuan, T. oth Zöcklein, M. 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:977 year:2020 day:11 month:10 pages:0 https://doi.org/10.1016/j.nima.2020.164332 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.90 Neurologie VZ AR 977 2020 11 1011 0 |
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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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Aartsen, M.G. @@aut@@ Ackermann, M. @@oth@@ Adams, J. @@oth@@ Aguilar, J.A. @@oth@@ Ahlers, M. @@oth@@ Ahrens, M. @@oth@@ Alispach, C. @@oth@@ Andeen, K. @@oth@@ Anderson, T. @@oth@@ Ansseau, I. @@oth@@ Anton, G. @@oth@@ Argüelles, C. @@oth@@ Arlen, T.C. @@oth@@ Auffenberg, J. @@oth@@ Axani, S. @@oth@@ Backes, P. @@oth@@ Bagherpour, H. @@oth@@ Bai, X. @@oth@@ A.V., Balagopal @@oth@@ Barbano, A. @@oth@@ Barwick, S.W. @@oth@@ Bastian, B. @@oth@@ Baum, V. @@oth@@ Baur, S. @@oth@@ Bay, R. @@oth@@ Beatty, J.J. @@oth@@ Becker, K.-H. @@oth@@ Becker Tjus, J. @@oth@@ BenZvi, S. @@oth@@ Berley, D. @@oth@@ Bernardini, E. @@oth@@ Besson, D.Z. @@oth@@ Binder, G. @@oth@@ Bindig, D. @@oth@@ Blaufuss, E. @@oth@@ Blot, S. @@oth@@ Bohm, C. @@oth@@ Börner, M. @@oth@@ Böser, S. @@oth@@ Botner, O. @@oth@@ Böttcher, J. @@oth@@ Bourbeau, E. @@oth@@ Bourbeau, J. @@oth@@ Bradascio, F. @@oth@@ Braun, J. @@oth@@ Bron, S. @@oth@@ Brostean-Kaiser, J. @@oth@@ Burgman, A. @@oth@@ Buscher, J. @@oth@@ Busse, R.S. @@oth@@ Carver, T. @@oth@@ Chen, C. @@oth@@ Cheung, E. @@oth@@ Chirkin, D. @@oth@@ Choi, S. @@oth@@ Clark, K. @@oth@@ Classen, L. @@oth@@ Coleman, A. @@oth@@ Collin, G.H. @@oth@@ Conrad, J.M. @@oth@@ Coppin, P. @@oth@@ Correa, P. @@oth@@ Cowen, D.F. @@oth@@ Cross, R. @@oth@@ Dave, P. @@oth@@ De Clercq, C. @@oth@@ DeLaunay, J.J. @@oth@@ Dembinski, H. @@oth@@ Deoskar, K. @@oth@@ De Ridder, S. @@oth@@ Desiati, P. @@oth@@ de Vries, K.D. @@oth@@ de Wasseige, G. @@oth@@ de With, M. @@oth@@ DeYoung, T. @@oth@@ Diaz, A. @@oth@@ Dáz-Vélez, J.C. @@oth@@ Dujmovic, H. @@oth@@ Dunkman, M. @@oth@@ Dvorak, E. @@oth@@ Eberhardt, B. @@oth@@ Ehrhardt, T. @@oth@@ Eller, P. @@oth@@ Engel, R. @@oth@@ Evans, J.J. @@oth@@ Evenson, P.A. @@oth@@ Fahey, S. @@oth@@ Fazely, A.R. @@oth@@ Felde, J. @@oth@@ Filimonov, K. @@oth@@ Finley, C. @@oth@@ Fox, D. @@oth@@ Franckowiak, A. @@oth@@ Friedman, E. @@oth@@ Fritz, A. @@oth@@ Gaisser, T.K. @@oth@@ Gallagher, J. @@oth@@ Ganster, E. @@oth@@ Garrappa, S. @@oth@@ Gerhardt, L. @@oth@@ Ghorbani, K. @@oth@@ Glauch, T. @@oth@@ Glüsenkamp, T. @@oth@@ Goldschmidt, A. @@oth@@ Gonzalez, J.G. @@oth@@ Grant, D. @@oth@@ Griffith, Z. @@oth@@ Griswold, S. @@oth@@ Günder, M. @@oth@@ Gündüz, M. @@oth@@ Haack, C. @@oth@@ Hallgren, A. @@oth@@ Halliday, R. @@oth@@ Halve, L. @@oth@@ Halzen, F. @@oth@@ Hanson, K. @@oth@@ Haungs, A. @@oth@@ Hebecker, D. @@oth@@ Heereman, D. @@oth@@ Heix, P. @@oth@@ Helbing, K. @@oth@@ Hellauer, R. @@oth@@ Henningsen, F. @@oth@@ Hickford, S. @@oth@@ Hignight, J. @@oth@@ Hill, G.C. @@oth@@ Hoffman, K.D. @@oth@@ Hoffmann, R. @@oth@@ Hoinka, T. @@oth@@ Hokanson-Fasig, B. @@oth@@ Hoshina, K. @@oth@@ Huang, F. @@oth@@ Huber, M. @@oth@@ Huber, T. @@oth@@ Hultqvist, K. @@oth@@ Hünnefeld, M. @@oth@@ Hussain, R. @@oth@@ In, S. @@oth@@ Iovine, N. @@oth@@ Ishihara, A. @@oth@@ Japaridze, G.S. @@oth@@ Jeong, M. @@oth@@ Jero, K. @@oth@@ Jones, B.J.P. @@oth@@ Jonske, F. @@oth@@ Joppe, R. @@oth@@ Kang, D. @@oth@@ Kang, W. @@oth@@ Kappes, A. @@oth@@ Kappesser, D. @@oth@@ Karg, T. @@oth@@ Karl, M. @@oth@@ Karle, A. @@oth@@ Katori, T. @@oth@@ Katz, U. @@oth@@ Kauer, M. @@oth@@ Kelley, J.L. @@oth@@ Kheirandish, A. @@oth@@ Kim, J. @@oth@@ Kintscher, T. @@oth@@ Kiryluk, J. @@oth@@ Kittler, T. @@oth@@ Klein, S.R. @@oth@@ Koirala, R. @@oth@@ Kolanoski, H. @@oth@@ Köpke, L. @@oth@@ Kopper, C. @@oth@@ Kopper, S. @@oth@@ Koskinen, D.J. @@oth@@ Kowalski, M. @@oth@@ Krings, K. @@oth@@ Krückl, G. @@oth@@ Kulacz, N. @@oth@@ Kurahashi, N. @@oth@@ Kyriacou, A. @@oth@@ Lanfranchi, J.L. @@oth@@ Larson, M.J. @@oth@@ Lauber, F. @@oth@@ Lazar, J.P. @@oth@@ Leonard, K. @@oth@@ Leszczyńska, A. @@oth@@ Leuermann, M. @@oth@@ Liu, Q.R. @@oth@@ Lohfink, E. @@oth@@ Lozano Mariscal, C.J. @@oth@@ Lu, L. @@oth@@ Lucarelli, F. @@oth@@ Lünemann, J. @@oth@@ Luszczak, W. @@oth@@ Lyu, Y. @@oth@@ Ma, W.Y. @@oth@@ Madsen, J. @@oth@@ Maggi, G. @@oth@@ Mahn, K.B.M. @@oth@@ Makino, Y. @@oth@@ Mallik, P. @@oth@@ Mallot, K. @@oth@@ Mancina, S. @@oth@@ Mandalia, S. @@oth@@ Mariş, I.C. @@oth@@ Maruyama, R. @@oth@@ Mase, K. @@oth@@ Maunu, R. @@oth@@ McNally, F. @@oth@@ Meagher, K. @@oth@@ Medici, M. @@oth@@ Medina, A. @@oth@@ Meier, M. @@oth@@ Meighen-Berger, S. @@oth@@ Menne, T. @@oth@@ Merino, G. @@oth@@ Meures, T. @@oth@@ Micallef, J. @@oth@@ Mockler, D. @@oth@@ Momenté, G. @@oth@@ Montaruli, T. @@oth@@ Moore, R.W. @@oth@@ Morse, R. @@oth@@ Moulai, M. @@oth@@ Muth, P. @@oth@@ Nagai, R. @@oth@@ Naumann, U. @@oth@@ Neer, G. @@oth@@ Niederhausen, H. @@oth@@ Nisa, M.U. @@oth@@ Nowicki, S.C. @@oth@@ Nygren, D.R. @@oth@@ Obertacke Pollmann, A. @@oth@@ Oehler, M. @@oth@@ Olivas, A. @@oth@@ O’Murchadha, A. @@oth@@ O’Sullivan, E. @@oth@@ Palczewski, T. @@oth@@ Pandya, H. @@oth@@ Pankova, D.V. @@oth@@ Park, N. @@oth@@ Peiffer, P. @@oth@@ Pérez de los Heros, C. @@oth@@ Philippen, S. @@oth@@ Pieloth, D. @@oth@@ Pinat, E. @@oth@@ Pizzuto, A. @@oth@@ Plum, M. @@oth@@ Porcelli, A. @@oth@@ Price, P.B. @@oth@@ Przybylski, G.T. @@oth@@ Raab, C. @@oth@@ Raissi, A. @@oth@@ Rameez, M. @@oth@@ Rauch, L. @@oth@@ Rawlins, K. @@oth@@ Rea, I.C. @@oth@@ Reimann, R. @@oth@@ Relethford, B. @@oth@@ Renschler, M. @@oth@@ Renzi, G. @@oth@@ Resconi, E. @@oth@@ Rhode, W. @@oth@@ Richman, M. @@oth@@ Robertson, S. @@oth@@ Rongen, M. @@oth@@ Rott, C. @@oth@@ Ruhe, T. @@oth@@ Ryckbosch, D. @@oth@@ Rysewyk, D. @@oth@@ Safa, I. @@oth@@ Sanchez Herrera, S.E. @@oth@@ Sandrock, A. @@oth@@ Sandroos, J. @@oth@@ Santander, M. @@oth@@ Sarkar, S. @@oth@@ Satalecka, K. @@oth@@ Schaufel, M. @@oth@@ Schieler, H. @@oth@@ Schlunder, P. @@oth@@ Schmidt, T. @@oth@@ Schneider, A. @@oth@@ Schneider, J. @@oth@@ Schröder, F.G. @@oth@@ Schulte, L. @@oth@@ Schumacher, L. @@oth@@ Sclafani, S. @@oth@@ Seckel, D. @@oth@@ Seunarine, S. @@oth@@ Shefali, S. @@oth@@ Silva, M. @@oth@@ Snihur, R. @@oth@@ Soedingrekso, J. @@oth@@ Soldin, D. @@oth@@ Söldner-Rembold, S. @@oth@@ Song, M. @@oth@@ Spiczak, G.M. @@oth@@ Spiering, C. @@oth@@ Stachurska, J. @@oth@@ Stamatikos, M. @@oth@@ Stanev, T. @@oth@@ Stein, R. @@oth@@ Steinmüller, P. @@oth@@ Stettner, J. @@oth@@ Steuer, A. @@oth@@ Stezelberger, T. @@oth@@ Stokstad, R.G. @@oth@@ Stößl, A. @@oth@@ Strotjohann, N.L. @@oth@@ Stürwald, T. @@oth@@ Stuttard, T. @@oth@@ Sullivan, G.W. @@oth@@ Taboada, I. @@oth@@ Tenholt, F. @@oth@@ Ter-Antonyan, S. @@oth@@ Terliuk, A. @@oth@@ Tilav, S. @@oth@@ Tollefson, K. @@oth@@ Tomankova, L. @@oth@@ Tönnis, C. @@oth@@ Toscano, S. @@oth@@ Tosi, D. @@oth@@ Trettin, A. @@oth@@ Tselengidou, M. @@oth@@ Tung, C.F. @@oth@@ Turcati, A. @@oth@@ Turcotte, R. @@oth@@ Turley, C.F. @@oth@@ Ty, B. @@oth@@ Unger, E. @@oth@@ Unland Elorrieta, M.A. @@oth@@ Usner, M. @@oth@@ Vandenbroucke, J. @@oth@@ Van Driessche, W. @@oth@@ van Eijk, D. @@oth@@ van Eijndhoven, N. @@oth@@ van Santen, J. @@oth@@ Verpoest, S. @@oth@@ Vraeghe, M. @@oth@@ Walck, C. @@oth@@ Wallace, A. @@oth@@ Wallraff, M. @@oth@@ Wandkowsky, N. @@oth@@ Watson, T.B. @@oth@@ Weaver, C. @@oth@@ Weindl, A. @@oth@@ Weiss, M.J. @@oth@@ Weldert, J. @@oth@@ Wendt, C. @@oth@@ Werthebach, J. @@oth@@ Whelan, B.J. @@oth@@ Whitehorn, N. @@oth@@ Wiebe, K. @@oth@@ Wiebusch, C.H. @@oth@@ Wille, L. @@oth@@ Williams, D.R. @@oth@@ Wills, L. @@oth@@ Wolf, M. @@oth@@ Wood, J. @@oth@@ Wood, T.R. @@oth@@ Woschnagg, K. @@oth@@ Wrede, G. @@oth@@ Wren, S. @@oth@@ Xu, D.L. @@oth@@ Xu, X.W. @@oth@@ Xu, Y. @@oth@@ Yanez, J.P. @@oth@@ Yodh, G. @@oth@@ Yoshida, S. @@oth@@ Yuan, T. @@oth@@ Zöcklein, M. @@oth@@ |
| publishDateDaySort_date |
2020-01-11T00:00:00Z |
| hierarchy_top_id |
ELV000874671 |
| dewey-sort |
3610 |
| id |
ELV050909207 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV050909207</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626031308.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.nima.2020.164332</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001082.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV050909207</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0168-9002(20)30729-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Aartsen, M.G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. 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Aartsen, M.G. |
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Aartsen, M.G. ddc 610 bkl 44.90 Elsevier Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments |
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610 VZ 44.90 bkl Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino Elsevier |
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ddc 610 bkl 44.90 Elsevier Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino |
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ddc 610 bkl 44.90 Elsevier Data analysis Elsevier Detector Elsevier Monte Carlo Elsevier Smoothing Elsevier MC Elsevier Neutrino mass ordering Elsevier VLV ν T Elsevier Statistics Elsevier KDE Elsevier Neutrino |
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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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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments |
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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments |
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Aartsen, M.G. |
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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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computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments |
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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments |
| abstract |
The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. |
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The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. |
| abstract_unstemmed |
The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources. |
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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV050909207</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230626031308.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">210910s2020 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.nima.2020.164332</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">/cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001082.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV050909207</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0168-9002(20)30729-4</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Aartsen, M.G.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020transfer abstract</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Data analysis</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Detector</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Monte Carlo</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Smoothing</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">MC</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Neutrino mass ordering</subfield><subfield code="2">Elsevier</subfield></datafield><datafield 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