Mitigating Deadtime in Distributed Optical Arrays: A Liveness-Aware Trigger Approach for High-Energy Neutrino Detection

TL;DR

The paper addresses detector deadtime in large-scale distributed optical arrays where conventional coincidence triggers lose sensitivity when channels are temporarily non-live. It introduces a liveness-aware trigger that maintains a continuity-preserving effective observable at each sensor node via a recursive IIR update with k = exp(-alpha Delta t), decoupling measurement construction from trigger decision and using a coherence-based aggregation for triggering. The approach is validated with a hybrid pipeline that combines IceCube Open Data topologies, a parametric signal/readout model, and controlled deadtime, showing sustained event recovery, up to two orders of magnitude improvement in effective SNR, and robustness to saturation under high deadtime. The method is FPGA-friendly, offering O(1) per-sample processing, deterministic latency, and modularity that supports deployment across next-generation large-scale detectors while preserving nominal performance in low-deadtime regimes. Overall, the work provides a practical, hardware-ready framework for robust trigger design in noise-dominated, deadtime-prone distributed optical arrays with significant impact for high-energy neutrino detection and multimessenger observatories.

Abstract

Large-scale neutrino observatories operate under unavoidable detector deadtime arising from photomultiplier saturation, digitizer limits, and front-end readout constraints. Conventional coincidence-based trigger logic implicitly assumes continuous sensor availability and therefore suffers systematic efficiency loss when channels become temporarily non-live. This work presents the design of a liveness-aware trigger architecture targeting low-latency FPGA deployment in distributed optical arrays. We introduce a recursive Infinite Impulse Response (IIR) update law implemented as a fully synthesizable pipeline that constructs a continuity-preserving effective observable at each sensor node. Rather than collapsing during non-liveness intervals, the observable decays smoothly while retaining phase and amplitude information relevant for network-level coherence estimation. By explicitly separating continuous measurement construction from discrete trigger decision logic, the proposed architecture enables graceful degradation under partial channel non-liveness. Performance is evaluated using a hybrid validation framework that combines representative event topologies derived from IceCube Open Data with a hardware-accurate signal and noise model spanning a wide dynamic range. Simulation results demonstrate that the proposed trigger sustains high event recovery efficiency in regimes of elevated deadtime probability, where conventional coincidence logic degrades substantially. Furthermore, the continuity-preserving observable yields up to a two-order-of-magnitude improvement in effective signal-to-noise ratio, enabling robust detection under strong saturation and non-ideal operating conditions. This method provides a robust foundation for next-generation firmware-level trigger strategies in large-scale, noise-dominated detector systems.

Figures (14)

• Figure 1: Three complementary views of a representative distributed optical detector geometry: (a) the horizontal layout in the $xy$-plane, (b) the vertical deployment profile in the $xz$-plane, and (c) a 3D isometric rendering illustrating the overall footprint and depth extent.
• Figure 2: Pipeline architecture of the proposed liveness-aware trigger system.
• Figure 3: Hybrid validation pipeline architecture.
• Figure 4: Comparison between naive gating (Baseline) and the proposed IIR-based effective observable (Proposed).
• Figure 5: Aggregation of node-level effective observables into a global coherence score $\mathcal{G}[n]$.