تقرير
Topology classification with deep learning to improve real-time event selection at the LHC
| العنوان: | Topology classification with deep learning to improve real-time event selection at the LHC |
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| المؤلفون: | Nguyen, Thong Q., Weitekamp III, Daniel, Anderson, Dustin, Castello, Roberto, Cerri, Olmo, Pierini, Maurizio, Spiropulu, Maria, Vlimant, Jean-Roch |
| المصدر: | Comput Softw Big Sci (2019) 3: 12 |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | High Energy Physics - Experiment, Computer Science - Machine Learning, High Energy Physics - Phenomenology, Physics - Data Analysis, Statistics and Probability |
| الوصف: | We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features are utilized. In the considered examples, a filter based on the classifier's score can be trained to retain ~99% of the interesting events and reduce the false-positive rate by as much as one order of magnitude for certain background processes. By operating such a filter as part of the online event selection infrastructure of the LHC experiments, one could benefit from a more flexible and inclusive selection strategy while reducing the amount of downstream resources wasted in processing false positives. The saved resources could be translated into a reduction of the detector operation cost or into an effective increase of storage and processing capabilities, which could be reinvested to extend the physics reach of the LHC experiments. Comment: This is a pre-print of an article published in Computing and Software for Big Science. The final authenticated version is available online at: https://doi.org/10.1007/s41781-019-0028-1 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/s41781-019-0028-1 |
| URL الوصول: | http://arxiv.org/abs/1807.00083 |
| رقم الأكسشن: | edsarx.1807.00083 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/s41781-019-0028-1 |
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