Particle reconstruction in collider detectors is a multidimensional problem where machine learning algorithms offer the potential for significant improvements over traditional techniques. In the Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC), photons and electrons produced by the collisions at the interaction point are recorded by the CMS Electromagnetic Calorimeter (ECAL). The large number of collisions, coupled with the detector's complex geometry, make the reconstruction of clusters in the calorimeter a formidable challenge. Traditional algorithms struggle to distinguish between overlapping clusters created by proximate particles. In contrast, It has been shown that graph neural networks offer significant advantages, providing better differentiation between overlapping clusters without being negatively affected by the sparse topology of the events. However, it is crucial to understand which extracted features contribute to this superior performance and what kind of physics information they contain. This understanding is particularly important for testing the robustness of the algorithms under different operating conditions and for preventing any biases the network may introduce due to the difference between data and simulated samples (used to train the network).
In this project, we propose to use Gradient-weighted Class Activation Mapping (Grad-CAM) and its attention mechanism aware derivatives to interpret the algorithm's decisions. By evaluating the extracted features, we aim to derive analytical relationships that can be used to modify existing lightweight traditional algorithms.
Furthermore, with the upcoming High Luminosity upgrade of the LHC, events involving overlapping clusters are expected to become even more frequent, thereby increasing the need for advanced deep learning techniques. Additionally, precision timing information of the order of 30 ps will be made available to aid in particle reconstruction. In this PhD project, we also aim to explore deep learning techniques that utilize Graph and Attention mechanisms (Graph Attention Networks) to resolve spatially proximate clusters using timing information. We will integrate position and energy deposition data from the ECAL with precision timing measurements from both the ECAL and the new MIP Timing Detector (MTD). Ultimately, the developed techniques will be tested in the analysis of a Higgs boson decaying into two beyond-the-standard-model scalar particles.
We are seeking an enthusiastic PhD candidate who holds an MSc degree in particle physics and is eager to explore cutting-edge artificial intelligence techniques. The selected candidate will also work on the upgrade of the CMS detector for the high-luminosity LHC.