Speaker
Description
We present a modular, data-driven framework for calibration and performance correction in the ALICE experiment. The method addresses time- and parameter-dependent effects in high-occupancy heavy-ion environments, where evolving detector conditions (e.g., occupancy and cluster overlaps, gain drift, space charge, dynamic distortions, and reconstruction or calibration deficiencies) require calibration techniques that go beyond static models and are difficult to reproduce accurately in Monte Carlo simulations of fundamental processes.
In contrast to traditional machine learning (ML) approaches that rely on large monolithic neural networks, our strategy is based on small, composable models — each representing a well-defined correction level. These models are interpretable, testable, and validated individually, allowing robust assembly into a global calibration pipeline. Many of these components are already in active use in ALICE reconstruction and calibration workflows.
Post-calibration corrections and MC/data mappings are performed using representative sampling, reweighting/remapping, and interactive multidimensional diagnostics. We use Python-based statistical libraries, RootInteractive for interactive QA and visualization of group-by statistics and parametric biases, and fastMCKalman to compute numerical derivatives of performance observables with respect to calibration parameters — enabling the propagation of imperfections and uncertainties, and optimization in high-dimensional spaces.
By combining modular ML techniques, effective modeling, and robust performance diagnostics, our framework offers a scalable and physically grounded alternative to end-to-end black-box models, enabling resilient calibration and analysis in evolving detector environments.
References
https://arxiv.org/abs/2403.19330
| Experiment context, if any | ALICE CERN |
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