Physics Informed Neural Networks for design optimisation of diamond particle detectors for charged particle fast-tracking at high luminosity hadron colliders
This work addresses the need for fast-tracking detectors in future high-luminosity colliders, but it is incremental as it extends existing formalisms and methods for a specific domain.
The paper tackled the problem of signal propagation delays in 3D diamond particle detectors for high-luminosity hadron colliders, caused by high-resistivity electrodes, by developing a Mixture-of-Experts Physics-Informed Neural Network to model timing degradation, achieving a meshless solver that assesses this effect.
Future high-luminosity hadron colliders demand tracking detectors with extreme radiation tolerance, high spatial precision, and sub-nanosecond timing. 3D diamond pixel sensors offer these capabilities due to diamond's radiation hardness and high carrier mobility. Conductive electrodes, produced via femtosecond IR laser pulses, exhibit high resistivity that delays signal propagation. This effect necessitates extending the classical Ramo-Shockley weighting potential formalism. We model the phenomenon through a 3rd-order, 3+1D PDE derived as a quasi-stationary approximation of Maxwell's equations. The PDE is solved numerically and coupled with charge transport simulations for realistic 3D sensor geometries. A Mixture-of-Experts Physics-Informed Neural Network, trained on Spectral Method data, provides a meshless solver to assess timing degradation from electrode resistance.