Experiments at HL-LHC and beyond will have ever higher read-out rate. It is then essential to explore new hardware paradigms for large scale computations. We have considered the Optical Processing Unit (OPU) from LightOn https://lighton.ai , which is an analog device to multiply a binary 1 mega pixel image by a (fixed) 1E6x1E6 random matrix, resulting in a mega pixel image, at a 2kHz rate. It could be used for the whole branch of Machine Learning using random matrix in particular for dimensionality reduction. In this talk, we have explored the potential of OPU for two typical HEP use cases:
1) “Tracking”: high energy proton collisions at the LHC yield billions of records with typically 100,000 3D points corresponding to the trajectory of 10.000 particles. Using two datasets from previous tracking challenges, we investigate the OPU potential to solve similar or related problems in high-energy physics, in terms of dimensionality reduction, data representation, and preliminary results.
2) “Calorimeter Event classification”: high energy proton collision at the Large Hadron Collider have been simulated, each collision being recorded as an image representing the energy flux in the detector. The task is to train a classifier to separate signal from the background. The OPU allows fast end-to-end classification without building intermediate objects (like jets). This technique is presented, compared with more classical particle physics approaches.