Data Science at LHCb
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Machine learning is used at all stages of the LHCb experiment. It is routinely used: in the process of deciding which data to record and which to reject forever, as part of the reconstruction algorithms (feature engineering), and in the extraction of physics results from our data. This talk will highlight current use cases, as well as ideas for ambitious future applications, and how we (machine learning expert + physicist) can collaborate on them.
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Data Science at LHCb
- 1. Tim Head - EPFL NIPS December 2015 Data Science at LHCb
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- 24. The Topological Trigger General purpose Robust Fast Gligorov, Wiliams, arXiv:1210.6861
- 26. “I’ve mostly figured out what the BDT does. In easy regions it does easy to understand things and in hard regions it does hard to understand things.”
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- 39. Publish! CMS and LHCb collaborations, doi:10.1038/nature14474, arXiv:1411.4413
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- 41. No work is faster than some work. Preempting the Trigger
- 42. Preempting the Trigger Benbouzid, Busa-Fekete, Kegl, arXiv:1206.6387
- 43. Simulation is the largest user of CPU time! Generative Models
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