Toward the End-to-End Optimization of Particle Detectors with Differentiable Programming

Particle detectors are complex instruments, as their design involves the specification of hundreds of geometric and material parameters. That choice in the past has been informed by robust paradigms (redundancy, symmetry, “track-first, destroy later”), which allowed us to build highly performant particle physics experiments. However, in the impossibility of optimizing our design choices to the final, true goals of our instruments (the highest sensitivity to a flagship parameter, or the discovery reach to some relevant phenomenon), we have until now consistently relied on manageable proxies as figures of merit: e.g., highest resolution, lowest backgrounds. While sensible, in a high-dimensional feature space of possible choices that modus operandi corresponds to potential huge losses of performance on those true goals.