Punzi-loss:
Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Abudinén, F. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: The European physical journal - Berlin : Springer, 1998, 82(2022), 2 vom: Feb. |
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| Übergeordnetes Werk: |
volume:82 ; year:2022 ; number:2 ; month:02 |
| Links: |
|---|
| DOI / URN: |
10.1140/epjc/s10052-022-10070-0 |
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| Katalog-ID: |
SPR046180052 |
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| 520 | |a Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. | ||
| 700 | 1 | |a Bertemes, M. |4 aut | |
| 700 | 1 | |a Bilokin, S. |4 aut | |
| 700 | 1 | |a Campajola, M. |4 aut | |
| 700 | 1 | |a Casarosa, G. |4 aut | |
| 700 | 1 | |a Cunliffe, S. |4 aut | |
| 700 | 1 | |a Corona, L. |4 aut | |
| 700 | 1 | |a De Nuccio, M. |4 aut | |
| 700 | 1 | |a De Pietro, G. |4 aut | |
| 700 | 1 | |a Dey, S. |4 aut | |
| 700 | 1 | |a Eliachevitch, M. |4 aut | |
| 700 | 1 | |a Feichtinger, P. |0 (orcid)0000-0003-3966-7497 |4 aut | |
| 700 | 1 | |a Ferber, T. |4 aut | |
| 700 | 1 | |a Gemmler, J. |4 aut | |
| 700 | 1 | |a Goldenzweig, P. |4 aut | |
| 700 | 1 | |a Gottmann, A. |4 aut | |
| 700 | 1 | |a Graziani, E. |4 aut | |
| 700 | 1 | |a Haigh, H. |4 aut | |
| 700 | 1 | |a Hohmann, M. |4 aut | |
| 700 | 1 | |a Humair, T. |4 aut | |
| 700 | 1 | |a Inguglia, G. |4 aut | |
| 700 | 1 | |a Kahn, J. |4 aut | |
| 700 | 1 | |a Keck, T. |4 aut | |
| 700 | 1 | |a Komarov, I. |4 aut | |
| 700 | 1 | |a Krohn, J.-F. |4 aut | |
| 700 | 1 | |a Kuhr, T. |4 aut | |
| 700 | 1 | |a Lacaprara, S. |4 aut | |
| 700 | 1 | |a Lieret, K. |4 aut | |
| 700 | 1 | |a Maiti, R. |4 aut | |
| 700 | 1 | |a Martini, A. |4 aut | |
| 700 | 1 | |a Meier, F. |4 aut | |
| 700 | 1 | |a Metzner, F. |4 aut | |
| 700 | 1 | |a Milesi, M. |4 aut | |
| 700 | 1 | |a Park, S.-H. |4 aut | |
| 700 | 1 | |a Prim, M. |4 aut | |
| 700 | 1 | |a Pulvermacher, C. |4 aut | |
| 700 | 1 | |a Ritter, M. |4 aut | |
| 700 | 1 | |a Sato, Y. |4 aut | |
| 700 | 1 | |a Schwanda, C. |4 aut | |
| 700 | 1 | |a Sutcliffe, W. |4 aut | |
| 700 | 1 | |a Tamponi, U. |4 aut | |
| 700 | 1 | |a Tenchini, F. |4 aut | |
| 700 | 1 | |a Urquijo, P. |4 aut | |
| 700 | 1 | |a Zani, L. |4 aut | |
| 700 | 1 | |a Žlebčík, R. |4 aut | |
| 700 | 1 | |a Zupanc, A. |4 aut | |
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10.1140/epjc/s10052-022-10070-0 doi (DE-627)SPR046180052 (SPR)s10052-022-10070-0-e DE-627 ger DE-627 rakwb eng Abudinén, F. verfasserin aut Punzi-loss: 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. Bertemes, M. aut Bilokin, S. aut Campajola, M. aut Casarosa, G. aut Cunliffe, S. aut Corona, L. aut De Nuccio, M. aut De Pietro, G. aut Dey, S. aut Eliachevitch, M. aut Feichtinger, P. (orcid)0000-0003-3966-7497 aut Ferber, T. aut Gemmler, J. aut Goldenzweig, P. aut Gottmann, A. aut Graziani, E. aut Haigh, H. aut Hohmann, M. aut Humair, T. aut Inguglia, G. aut Kahn, J. aut Keck, T. aut Komarov, I. aut Krohn, J.-F. aut Kuhr, T. aut Lacaprara, S. aut Lieret, K. aut Maiti, R. aut Martini, A. aut Meier, F. aut Metzner, F. aut Milesi, M. aut Park, S.-H. aut Prim, M. aut Pulvermacher, C. aut Ritter, M. aut Sato, Y. aut Schwanda, C. aut Sutcliffe, W. aut Tamponi, U. aut Tenchini, F. aut Urquijo, P. aut Zani, L. aut Žlebčík, R. aut Zupanc, A. aut Enthalten in The European physical journal Berlin : Springer, 1998 82(2022), 2 vom: Feb. (DE-627)253722934 (DE-600)1459069-4 1434-6052 nnns volume:82 year:2022 number:2 month:02 https://dx.doi.org/10.1140/epjc/s10052-022-10070-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 82 2022 2 02 |
| spelling |
10.1140/epjc/s10052-022-10070-0 doi (DE-627)SPR046180052 (SPR)s10052-022-10070-0-e DE-627 ger DE-627 rakwb eng Abudinén, F. verfasserin aut Punzi-loss: 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. Bertemes, M. aut Bilokin, S. aut Campajola, M. aut Casarosa, G. aut Cunliffe, S. aut Corona, L. aut De Nuccio, M. aut De Pietro, G. aut Dey, S. aut Eliachevitch, M. aut Feichtinger, P. (orcid)0000-0003-3966-7497 aut Ferber, T. aut Gemmler, J. aut Goldenzweig, P. aut Gottmann, A. aut Graziani, E. aut Haigh, H. aut Hohmann, M. aut Humair, T. aut Inguglia, G. aut Kahn, J. aut Keck, T. aut Komarov, I. aut Krohn, J.-F. aut Kuhr, T. aut Lacaprara, S. aut Lieret, K. aut Maiti, R. aut Martini, A. aut Meier, F. aut Metzner, F. aut Milesi, M. aut Park, S.-H. aut Prim, M. aut Pulvermacher, C. aut Ritter, M. aut Sato, Y. aut Schwanda, C. aut Sutcliffe, W. aut Tamponi, U. aut Tenchini, F. aut Urquijo, P. aut Zani, L. aut Žlebčík, R. aut Zupanc, A. aut Enthalten in The European physical journal Berlin : Springer, 1998 82(2022), 2 vom: Feb. (DE-627)253722934 (DE-600)1459069-4 1434-6052 nnns volume:82 year:2022 number:2 month:02 https://dx.doi.org/10.1140/epjc/s10052-022-10070-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 82 2022 2 02 |
| allfields_unstemmed |
10.1140/epjc/s10052-022-10070-0 doi (DE-627)SPR046180052 (SPR)s10052-022-10070-0-e DE-627 ger DE-627 rakwb eng Abudinén, F. verfasserin aut Punzi-loss: 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. Bertemes, M. aut Bilokin, S. aut Campajola, M. aut Casarosa, G. aut Cunliffe, S. aut Corona, L. aut De Nuccio, M. aut De Pietro, G. aut Dey, S. aut Eliachevitch, M. aut Feichtinger, P. (orcid)0000-0003-3966-7497 aut Ferber, T. aut Gemmler, J. aut Goldenzweig, P. aut Gottmann, A. aut Graziani, E. aut Haigh, H. aut Hohmann, M. aut Humair, T. aut Inguglia, G. aut Kahn, J. aut Keck, T. aut Komarov, I. aut Krohn, J.-F. aut Kuhr, T. aut Lacaprara, S. aut Lieret, K. aut Maiti, R. aut Martini, A. aut Meier, F. aut Metzner, F. aut Milesi, M. aut Park, S.-H. aut Prim, M. aut Pulvermacher, C. aut Ritter, M. aut Sato, Y. aut Schwanda, C. aut Sutcliffe, W. aut Tamponi, U. aut Tenchini, F. aut Urquijo, P. aut Zani, L. aut Žlebčík, R. aut Zupanc, A. aut Enthalten in The European physical journal Berlin : Springer, 1998 82(2022), 2 vom: Feb. (DE-627)253722934 (DE-600)1459069-4 1434-6052 nnns volume:82 year:2022 number:2 month:02 https://dx.doi.org/10.1140/epjc/s10052-022-10070-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 82 2022 2 02 |
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10.1140/epjc/s10052-022-10070-0 doi (DE-627)SPR046180052 (SPR)s10052-022-10070-0-e DE-627 ger DE-627 rakwb eng Abudinén, F. verfasserin aut Punzi-loss: 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. Bertemes, M. aut Bilokin, S. aut Campajola, M. aut Casarosa, G. aut Cunliffe, S. aut Corona, L. aut De Nuccio, M. aut De Pietro, G. aut Dey, S. aut Eliachevitch, M. aut Feichtinger, P. (orcid)0000-0003-3966-7497 aut Ferber, T. aut Gemmler, J. aut Goldenzweig, P. aut Gottmann, A. aut Graziani, E. aut Haigh, H. aut Hohmann, M. aut Humair, T. aut Inguglia, G. aut Kahn, J. aut Keck, T. aut Komarov, I. aut Krohn, J.-F. aut Kuhr, T. aut Lacaprara, S. aut Lieret, K. aut Maiti, R. aut Martini, A. aut Meier, F. aut Metzner, F. aut Milesi, M. aut Park, S.-H. aut Prim, M. aut Pulvermacher, C. aut Ritter, M. aut Sato, Y. aut Schwanda, C. aut Sutcliffe, W. aut Tamponi, U. aut Tenchini, F. aut Urquijo, P. aut Zani, L. aut Žlebčík, R. aut Zupanc, A. aut Enthalten in The European physical journal Berlin : Springer, 1998 82(2022), 2 vom: Feb. (DE-627)253722934 (DE-600)1459069-4 1434-6052 nnns volume:82 year:2022 number:2 month:02 https://dx.doi.org/10.1140/epjc/s10052-022-10070-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 82 2022 2 02 |
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10.1140/epjc/s10052-022-10070-0 doi (DE-627)SPR046180052 (SPR)s10052-022-10070-0-e DE-627 ger DE-627 rakwb eng Abudinén, F. verfasserin aut Punzi-loss: 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. Bertemes, M. aut Bilokin, S. aut Campajola, M. aut Casarosa, G. aut Cunliffe, S. aut Corona, L. aut De Nuccio, M. aut De Pietro, G. aut Dey, S. aut Eliachevitch, M. aut Feichtinger, P. (orcid)0000-0003-3966-7497 aut Ferber, T. aut Gemmler, J. aut Goldenzweig, P. aut Gottmann, A. aut Graziani, E. aut Haigh, H. aut Hohmann, M. aut Humair, T. aut Inguglia, G. aut Kahn, J. aut Keck, T. aut Komarov, I. aut Krohn, J.-F. aut Kuhr, T. aut Lacaprara, S. aut Lieret, K. aut Maiti, R. aut Martini, A. aut Meier, F. aut Metzner, F. aut Milesi, M. aut Park, S.-H. aut Prim, M. aut Pulvermacher, C. aut Ritter, M. aut Sato, Y. aut Schwanda, C. aut Sutcliffe, W. aut Tamponi, U. aut Tenchini, F. aut Urquijo, P. aut Zani, L. aut Žlebčík, R. aut Zupanc, A. aut Enthalten in The European physical journal Berlin : Springer, 1998 82(2022), 2 vom: Feb. (DE-627)253722934 (DE-600)1459069-4 1434-6052 nnns volume:82 year:2022 number:2 month:02 https://dx.doi.org/10.1140/epjc/s10052-022-10070-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2119 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 82 2022 2 02 |
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Abudinén, F. @@aut@@ Bertemes, M. @@aut@@ Bilokin, S. @@aut@@ Campajola, M. @@aut@@ Casarosa, G. @@aut@@ Cunliffe, S. @@aut@@ Corona, L. @@aut@@ De Nuccio, M. @@aut@@ De Pietro, G. @@aut@@ Dey, S. @@aut@@ Eliachevitch, M. @@aut@@ Feichtinger, P. @@aut@@ Ferber, T. @@aut@@ Gemmler, J. @@aut@@ Goldenzweig, P. @@aut@@ Gottmann, A. @@aut@@ Graziani, E. @@aut@@ Haigh, H. @@aut@@ Hohmann, M. @@aut@@ Humair, T. @@aut@@ Inguglia, G. @@aut@@ Kahn, J. @@aut@@ Keck, T. @@aut@@ Komarov, I. @@aut@@ Krohn, J.-F. @@aut@@ Kuhr, T. @@aut@@ Lacaprara, S. @@aut@@ Lieret, K. @@aut@@ Maiti, R. @@aut@@ Martini, A. @@aut@@ Meier, F. @@aut@@ Metzner, F. @@aut@@ Milesi, M. @@aut@@ Park, S.-H. @@aut@@ Prim, M. @@aut@@ Pulvermacher, C. @@aut@@ Ritter, M. @@aut@@ Sato, Y. @@aut@@ Schwanda, C. @@aut@@ Sutcliffe, W. @@aut@@ Tamponi, U. @@aut@@ Tenchini, F. @@aut@@ Urquijo, P. @@aut@@ Zani, L. @@aut@@ Žlebčík, R. @@aut@@ Zupanc, A. @@aut@@ |
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Abudinén, F. Bertemes, M. Bilokin, S. Campajola, M. Casarosa, G. Cunliffe, S. Corona, L. De Nuccio, M. De Pietro, G. Dey, S. Eliachevitch, M. Feichtinger, P. Ferber, T. Gemmler, J. Goldenzweig, P. Gottmann, A. Graziani, E. Haigh, H. Hohmann, M. Humair, T. Inguglia, G. Kahn, J. Keck, T. Komarov, I. Krohn, J.-F. Kuhr, T. Lacaprara, S. Lieret, K. Maiti, R. Martini, A. Meier, F. Metzner, F. Milesi, M. Park, S.-H. Prim, M. Pulvermacher, C. Ritter, M. Sato, Y. Schwanda, C. Sutcliffe, W. Tamponi, U. Tenchini, F. Urquijo, P. Zani, L. Žlebčík, R. Zupanc, A. |
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Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. © The Author(s) 2022 |
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Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. © The Author(s) 2022 |
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Abstract We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example. © The Author(s) 2022 |
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