LHCb trigger streams optimization

  title={LHCb trigger streams optimization},
  author={Denis Derkach and Nikita Kazeev and R. Neychev and A. Panin and Ilya Trofimov and Andrey Ustyuzhanin and M. Vesterinen},
  journal={Journal of Physics: Conference Series},
The LHCb experiment stores around 1011 collision events per year. A typical physics analysis deals with a final sample of up to 107 events. Event preselection algorithms (lines) are used for data reduction. Since the data are stored in a format that requires sequential access, the lines are grouped into several output file streams, in order to increase the efficiency of user analysis jobs that read these data. The scheme efficiency heavily depends on the stream composition. By putting similar… 

New physics implications and searches at LHCb

The Standard Model of Particle Physics (henceforth, SM) is a very successful theory. Nevertheless, it fails to explain some important questions present in nature. Therefore, a new model beyond the SM

Machine learning at the energy and intensity frontiers of particle physics

The application and development of machine-learning methods used in experiments at the frontiers of particle physics (such as the Large Hadron Collider) are reviewed, including recent advances based on deep learning.



The LHCb trigger and its performance in 2011

The LHCb trigger and its performance based on data taken at the LHC in 2011 is presented. LHCb is designed to perform flavour physics measurements, and its trigger distinguishes charm and beauty

Theano: A Python framework for fast computation of mathematical expressions

The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.

Machine learning

Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.

Scikit-learn: Machine Learning in Python

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing

Journal of Machine Learning Research

  • Journal of Machine Learning Research
  • 2011

streams.py rev. 207995 URL https://svnweb.cern.ch/trac/lhcb/browser/DBASE/trunk/ TurboStreamProd/python/TurboStreamProd/streams.py?rev=207995

  • 2016

The Theano Development Team 2016 arXiv preprint arXiv:1605

  • The Theano Development Team 2016 arXiv preprint arXiv:1605

Conference Series 119 072026 URL http://stacks

  • Journal of Physics
  • 2008