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Effective LHC measurements with matrix elements and machine learning
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| Zeitschriftentitel: | Journal of Physics: Conference Series |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Journal of Physics: Conference Series, 1525, 2020, 1, S. 012022 |
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IOP Publishing
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Brehmer, J. Cranmer, K. Espejo, I. Kling, F. Louppe, G. Pavez, J. Brehmer, J. Cranmer, K. Espejo, I. Kling, F. Louppe, G. Pavez, J. |
|---|---|
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Brehmer, J. Cranmer, K. Espejo, I. Kling, F. Louppe, G. Pavez, J. |
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Brehmer, J. Cranmer, K. Espejo, I. Kling, F. Louppe, G. Pavez, J. Journal of Physics: Conference Series Effective LHC measurements with matrix elements and machine learning General Physics and Astronomy |
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brehmer, j. |
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Brehmer, J. Cranmer, K. Espejo, I. Kling, F. Louppe, G. Pavez, J. 1742-6588 1742-6596 IOP Publishing General Physics and Astronomy http://dx.doi.org/10.1088/1742-6596/1525/1/012022 <jats:title>Abstract</jats:title> <jats:p>One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.</jats:p> Effective LHC measurements with matrix elements and machine learning Journal of Physics: Conference Series |
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| title |
Effective LHC measurements with matrix elements and machine learning |
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Effective LHC measurements with matrix elements and machine learning |
| title_full |
Effective LHC measurements with matrix elements and machine learning |
| title_fullStr |
Effective LHC measurements with matrix elements and machine learning |
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Effective LHC measurements with matrix elements and machine learning |
| title_short |
Effective LHC measurements with matrix elements and machine learning |
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effective lhc measurements with matrix elements and machine learning |
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General Physics and Astronomy |
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http://dx.doi.org/10.1088/1742-6596/1525/1/012022 |
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2020 |
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012022 |
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<jats:title>Abstract</jats:title>
<jats:p>One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.</jats:p> |
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| description | <jats:title>Abstract</jats:title> <jats:p>One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.</jats:p> |
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| spelling | Brehmer, J. Cranmer, K. Espejo, I. Kling, F. Louppe, G. Pavez, J. 1742-6588 1742-6596 IOP Publishing General Physics and Astronomy http://dx.doi.org/10.1088/1742-6596/1525/1/012022 <jats:title>Abstract</jats:title> <jats:p>One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies solve this issue, including the traditional histogram approach used in most particle physics analyses, the Matrix Element Method, Optimal Observables, and modern techniques based on neural density estimation. We then discuss powerful new inference methods that use a combination of matrix element information and machine learning to accurately estimate the likelihood function. The MadMiner package automates all necessary data-processing steps. In first studies we find that these new techniques have the potential to substantially improve the sensitivity of the LHC legacy measurements.</jats:p> Effective LHC measurements with matrix elements and machine learning Journal of Physics: Conference Series |
| spellingShingle | Brehmer, J., Cranmer, K., Espejo, I., Kling, F., Louppe, G., Pavez, J., Journal of Physics: Conference Series, Effective LHC measurements with matrix elements and machine learning, General Physics and Astronomy |
| title | Effective LHC measurements with matrix elements and machine learning |
| title_full | Effective LHC measurements with matrix elements and machine learning |
| title_fullStr | Effective LHC measurements with matrix elements and machine learning |
| title_full_unstemmed | Effective LHC measurements with matrix elements and machine learning |
| title_short | Effective LHC measurements with matrix elements and machine learning |
| title_sort | effective lhc measurements with matrix elements and machine learning |
| title_unstemmed | Effective LHC measurements with matrix elements and machine learning |
| topic | General Physics and Astronomy |
| url | http://dx.doi.org/10.1088/1742-6596/1525/1/012022 |