Methodological Synergies between Technosignature and UHE Neutrino Searches

TL;DR

This paper explores methodological synergies between technosignature searches and UHE neutrino experiments, proposing shared data analysis techniques to improve detection of rare signals amidst noise.

Contribution

It introduces a joint workflow combining techniques from both fields, enabling cross-community collaboration for rare-event discovery.

Findings

Catalog-based sine subtraction improves broadband transient visibility.

Structural equivalence identified between clustering methods in both fields.

Background-only anomaly ranking aids in candidate triage.

Abstract

Radio technosignature searches and radio-based ultra-high energy (UHE) neutrino experiments address different scientific questions, but share a closely related data analysis problem: identifying rare signals of unknown morphology within large datasets dominated by thermal noise and anthropogenic radio-frequency interference (RFI). UHE neutrino radio experiments (including ARA, RNO-G, ANITA, and PUEO) have developed advanced methodologies for continuous-wave (CW) mitigation and background characterization. This invited contribution makes that connection concrete through three points. First, we demonstrate that catalog-based time-domain sine subtraction -- the CW mitigation technique used in ANITA and ARA -- can be adapted for technosignature pipelines by restricting subtraction to documented persistent contaminants, improving broadband transient visibility while preserving uncataloged narrowband candidates. Second, we identify a structural equivalence between spatiotemporal clustering used in UHE neutrino experiments and direction/cadence-based RFI rejection in radio SETI, proposing a joint feature space incorporating direction, time, frequency, bandwidth, duration, and polarization. Third, we argue that background-only anomaly ranking is the natural second stage of this workflow, providing morphology-agnostic candidate triage. Together, these ideas motivate a 'preserve-then-rank' workflow for commensal rare-event discovery, opening a near-term path toward cross-community collaboration.