Speaker
Description
Particle identification is a major task in any high energy physics experiment. With the challenging environments encountered in Run 3 & 4 particle identification for the ALICE TPC tracks has become a machine learning based task, showing significant improvements and flexibility compared to previous approaches. This projects aims to extend this idea in an experiment-agnostic way.
All LHC experiments utilize different detection systems for various purposes. Improving momentum resolutions, energy measurements or detections of weakly interacting particles, all detectors share a common goal: Separating and identifying particles. With many detectors organized in close spatial proximity to each other, their information is often correlated and can be combined. Bayseian PID is a classic way of approaching this problem, while it still relies on individual identifications made within each detection system. Multiheaded attention mechanisms can in contrast make use of a flexible set of input information. This information can even be incomplete or partial. Extending this thought, information from run conditions, detector conditions or even LHC specific information can be passed to a global machine learning algorithm which combines the information and creates a PID prediction as an output. Concretely, the input and output can be formulated as a set of tokens, like
Input: {(ITS, pT = ...), (ITS, eta = 0), ..., (TPC, dE/dx = ...), ..., (FT0, occupancy = ...), ..., (LHC, beamtype = ...)}
Output: {(PID = [0-N]), (Quality = [0,1]), ...}
This approach can be implemented with minimal effort on Monte-Carlo to prove the concept. It can then be extended in a data-driven way using cleaned environments, fine-tuning the model with real data to the use case (using e.g. V0, high-pT particles, cosmic tracks). A first step towards such a project was investigated within the collaboration (https://link.springer.com/article/10.1140/epjc/s10052-024-13047-3) but never reached production level application as refinement on the technique and quality assurance are needed.
CERN group/ Experiment
EP-AIP-SDS, ALICE
| Working area | Area 1" Cutting Edge AI for Offline Data Processing |
|---|---|
| Project goals | The final goal is a global, experiment-agnostic and potentially data-driven PID calibration suitable for ALICE in Run 4 and beyond. Intermediate goals will be the successful commissioning of transformer architectures in offline calibration, performance benchmarks and optimizations of the input, output and model architecture. Improvements on the physics quality shall then be demonstrated on MC and real data. |
| Timeline | 2 years |
| Available person power | 1 FTE |
| Additional person power request | 0 |
| Is this an already ongoing activity? | No |
| Indicative hardware resources needs | 0 |