Optimising Underwater Neutrino Telescopes for All-Flavour Point Source Sensitivity

TL;DR

This study tackles how to optimise underwater neutrino telescopes for all-flavour point-source sensitivity, using TRIDENT as a case study. It employs a full-chain simulation framework, TRIDENTSim, to evaluate detector geometries (string spacing, string height, and clustering) and sea-water optical properties, measuring performance via effective area, angular resolution, and the time to reach a $5\sigma$ discovery for steady point sources across hard and soft spectra. The results show that simply increasing instrumented volume does not guarantee better discovery potential; taller strings and balanced, uniform layouts generally outperform larger, clustered, or overly sparse configurations, with water attenuation length emerging as a critical design driver. The findings emphasize the importance of Phase-I deployments for in-situ optical measurements and motivate multi-channel optimisation focusing on both track and cascade channels to maximize discovery potential and flavour sensitivity in next-generation deep-sea neutrino telescopes.

Abstract

High-energy neutrino astronomy has advanced rapidly in recent years, with IceCube, KM3NeT, and Baikal-GVD establishing a diffuse astrophysical flux and pointing to promising source candidates. These achievements mark the transition from first detections to detailed source studies, motivating next-generation detectors with larger volumes, improved angular resolution, and full neutrino-flavour sensitivity. We present a performance study of large underwater neutrino telescopes, taking the proposed TRIDENT array in the South China Sea as a case study, with a focus on comparing the performance of various detector configurations against the TRIDENT baseline design. Both track-like events primarily from muon neutrinos, which provide precise directional information, and cascade events from all flavours, which offer superior energy resolution, diffuse-source sensitivity, and all-sky flavour coverage, are included to achieve a balanced performance across source types. The time to discover potential astrophysical sources with both track- and cascade-like events is used as the figure of merit to compare a variety of detector design choices. Our results show that, for a fixed number of optical modules, simply enlarging the instrumented volume does not inherently lead to improved performance, while taller strings can provide modest gains across all detector channels, within engineering constraints. Distributing dense clusters of strings over a large volume is found to generally worsen discovery potential compared to the baseline layout. Finally, the optical properties of the sea-water emerge as the key factor dictating the optimisation of detector layout, highlighting the need for in-situ measurements and early deployment of optical modules to guide the final array configuration.

Figures (7)

• Figure 1: Visualization of simulated neutrino interactions using the TRIDENTSim framework, showing an example $\nu_{\mu}$-CC track event (a) and $\nu_{e}$-CC cascade event (b). Coloured spheres and their radii measure the number of hits detected on a given hDOM, where the colour scale indicates the time of detected photons, where red corresponds to early times and blue to late.
• Figure 2: Example detector geometries tested in this study, where each point represents an hDOM. (a - c) String spacing: top-down views of TRIDENT detector geometries, for string-to-string distances from 60 to 160 m, adhering to a Penrose tiling distribution of strings. (d - f) String spacing: side-on views of detector geometries varying hDOM vertical spacing, where each string contains 20 hDOMs, separated by 15, 30 and 45 m. (g - i) String clustering: Varied levels of string clustering, where the radius of the entire instrumented volume coincides with the largest string spacing detector is seen in (c). Figures (b) and (e) correspond to the reference TRIDENT geometry used as a comparison point reference in sections \ref{['sec:reconstruction']} and \ref{['sec:point_source_discovery potential']}.
• Figure 3: Effective area (top) and angular resolution (bottom) for $\nu_\mu$ CC track events (left) and $\nu_e$ CC cascade events (right), for the reference TRIDENT geometry, applying the trigger and reconstruction cuts described in section \ref{['sec:Aeff']}.
• Figure 4: Ratios of the effective area for $\nu_{\mu}$-CC track events and $\nu_e$-CC cascade events in (left top and left bottom, respectively) with respect to the reference TRIDENT geometry, shown as a function of string spacing and consequently detector volume. Angular resolution ratios are similarly shown (right). Uncertainty bars reflect the simulated event statistics at a given energy. Values larger than unity in all plots indicate improved performance for the tested layout compared to the reference geometry.
• Figure 5: Ratios of the effective area for $\nu_{\mu}$-CC track events and $\nu_e$-CC cascade events in (left) with respect to the reference TRIDENT geometry, shown as a function of detector volume, varying the string height. Angular resolution ratios are similarly shown (right). Values larger than unity in all plots indicate improved performance for the tested layout compared to the reference geometry.