Knowhow and experience derived from early adoption of neural network techniques by particle physics community.
- Particle physicists were among the first to use machine learning (ML) in software for analysis & simulations
- First AI HENP seminar in 1990
- Already in 2010, the CMS and LHCb experiments successfully introduced machine learning algorithms to its trigger system
- Higgs boson discovery earlier than expected (2012), also with help of ML
Facts & Figures:
- <10 μsec: ML applied for extremely fast decision making in CERN detector trigger systems
- AUC> 90%: High true positive / low false positive rates achieved even in sparse images with little datapoints
- >1000 times faster: Convolutional neural networks have dramatically decreased computing time in physics (vs traditional computing)
- ~100% efficient: Highly reliable trace reconstruction algorithms using tailored neural network techniques, even with multiple tracks in one sensor
Read more about Machine Learning and Deep Learning here.
Designing & Training Neural Networks:
CERN has a long history in the design and training of neural networks in for example classification, filtering, event and particle detection, regression, clustering and anomaly detection. Most of the ML/DL codes are tailor made using C++, Phyton, TensorFlow and Keras and applied in software or hardware (FPGAs).
Fast neural network inference in FPGAs:
CERN needs ultra fast machine learning interference (execution in μsec), requiring compact code for FPGAs. A companion compiler package for this work is developed based on High-Level Synthesis (HLS) called hls4ml to build machine learning models in FPGAs, allowing for fast prototyping and shorter time to results.
TMVA as Open Source ML / DL Toolkit:
The open source Toolkit for Multivariate Data Analysis (TMVA) developed by CERN provides a machine learning environment for the processing and evaluation of multivariate classification, both binary and multi class, and regression techniques. It is integrated in ROOT, a modular scientific software toolkit (C++).