Speaker
Description
The LHCb experiment at CERN's Large Hadron Collider (LHC) relies on precise alignment of its tracking detectors comprising the Vertex Locator (VELO), Upstream Tracker (UT), and Scintillating Fibre (SciFi) Tracker to achieve the momentum, mass, and vertex resolutions demanded by the LHCb physics programme. During data-taking, detector alignment at LHCb is performed on CPUs using an iterative χ² minimisation procedure based on Kalman-filter track fits. This algorithm typically requires large event samples and substantial computational resources.
Within the ErUM-Data project DEEP, we investigate novel Deep Learning (DL) approaches for detector alignment at LHCb, with the long-term goal of deployment on GPUs, FPGAs, and System on Chip (SoC) devices. Such an approach could enable fast, energy-efficient detector alignment, supporting the increasing computing demands of LHCb Run 4 and future upgrades.
Detector alignment is a challenging inverse problem with inherent ambiguities. Translational and rotational misalignments can produce similar residual signatures. Moreover, the residual measured in a detector module depends not only on its own misalignment but also on the misalignments of all detector modules traversed by the track and on uncertainties in the track reconstruction. Learning these correlations directly from reconstructed tracks therefore represents a non-trivial machine learning task that requires carefully designed model architectures and representative training data.
We develop and evaluate DL architectures that infer VELO module misalignments directly from track-level information. Initial results on simulated data demonstrate the feasibility of a physics-guided, PointNet-based approach, achieving root mean square errors of approximately 5 μm for translations and 70 μrad for rotations using fewer than 300 events, even when all detector modules are simultaneously and randomly misaligned. These results demonstrate the potential of deep learning-based detector alignment and motivate further development towards deployment on heterogeneous computing platforms for real-time data processing and analyses.