Technosignature Searches of Interstellar Objects

James R. A. Davenport; Sofia Z. Sheikh; Steve Croft; Brian C. Lacki; Jason T. Wright; Chris Lintott; Adam Frank; T. Joseph W. Lazio; Colin Orion Chandler; Andrew P. V. Siemion; Stephen DiKerby; Ellie White; Valeria Garcia Lopez; Emma E. Yu; Maxwell K. Frissell; Peter Yoachim; Naomi Morato; Devanshi Singh; Jinshuo Zhang; Darryl Z. Seligman

arXiv · 2508.16825

Technosignature Searches of Interstellar Objects

View original source ↗Read source document ↗Export metadata ↓

Abstract

With the discovery of the third confirmed interstellar object (ISO), 3I/ATLAS, we have entered a new phase in the exploration of these long-predicted objects. Though confirmed discovery of ISOs is quite recent, their utility as targets in the search for technosignatures (historically known as the Search for Extraterrestrial Intelligence -- SETI) has been discussed for many decades. With the upcoming NSF-DOE Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), the discovery and tracking of such objects is expected to become routine, and thus so must our examination of these objects for possible technosignatures. Here we review the literature surrounding ISOs as targets for technosignatures, which provides a well-developed motivation for such exploration. We outline four broad classes of technosignatures that are well suited for ISO follow-up, including the type of data needed and the best timing for study. Given the limitations in the current understanding of ISOs, we show that care must be taken in identifying technosignatures based primarily on comparison to objects in the Solar System. We therefore provide a roadmap for careful and consistent study of the population of ISOs in the hope of identifying technosignatures.

Explore related subjects

astro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

James R. A. Davenport, Sofia Z. Sheikh, Steve Croft, Brian C. Lacki, Jason T. Wright, Chris Lintott, Adam Frank, T. Joseph W. Lazio, Colin Orion Chandler, Andrew P. V. Siemion, Stephen DiKerby, Ellie White, Valeria Garcia Lopez, Emma E. Yu, Maxwell K. Frissell, Peter Yoachim, Naomi Morato, Devanshi Singh, Jinshuo Zhang, Darryl Z. Seligman. 2025-08-22. Technosignature Searches of Interstellar Objects. https://arxiv.org/abs/2508.16825

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Radial Pulsations in Polaris: A Secondary Science Application of Cherenkov Telescopes via Intensity Interferometry

Ground-based Cherenkov telescopes, which are typically inoperative during moonlit nights for gamma-ray observations, offer a valuable opportunity during this time for secondary scientific applications through Intensity Interferometry (II). Recent developments and observations suggest that implementing II instrumentation on existing and planned Imaging Atmospheric Cherenkov Telescopes (IACTs) can significantly advance optical stellar measurements. Motivated by the resurgence of II efforts over the past two decades, this work presents simulations demonstrating the estimation of stellar parameters for a radially pulsating star, such as Polaris, using either a single telescope or multiple telescopes. For single-telescope simulations, we assume that the photon pixels in the camera are mapped onto four distinct regions of the aperture, generating multiple baselines and enabling enhanced observational plane coverage. These results highlight the potential of Cherenkov telescopes in India for high-resolution optical astronomy during otherwise inoperative periods and offer promising insights into the characterization of bright stellar objects with unprecedented precision.

astro-ph.IM↗

arXiv2026

Parallel adaptive reweighting importance sampling for Bayesian astrophysics

Efficient sampling from high-dimensional, multi-modal posteriors is a central challenge in Bayesian inference across the physical sciences. Gravitational-wave (GW) astronomy, where likelihood evaluations are computationally expensive and posteriors are complex, is one application motivating this work. Popular families of methods like Markov-chain Monte Carlo, nested sampling, and importance sampling all rely on proposal distributions to guide exploration. Because prior knowledge of the target is often limited, practitioners can adopt adaptive proposals that iteratively refine themselves using information gained from previously drawn samples. Traditional adaptive strategies, however, struggle in high-dimensional multi-modal settings: complex, non-linear correlations are hard to capture, and hyperparameters typically require tedious, problem-specific tuning. To address these issues, we introduce Parallel Adaptive Reweighting Importance Sampling (PARIS; descriptively, ``seed sampling''). PARIS models its proposal as a Gaussian mixture whose component centers are the existing samples and whose component weights match the current importance weights. New draws from the proposal therefore concentrate around high-weight regions, while candidate points in unexplored areas receive intentionally inflated weights. As the algorithm continuously reweights all samples up to the latest proposal, any initial over-weighting self-corrects once additional neighbor samples are collected. To enable rapid reweighting, we present an efficient update scheme and evaluate PARIS on illustrative toy problems and more realistic gravitational-wave parameter estimation tasks. PARIS achieves accurate posterior reconstruction and evidence estimation with substantially fewer function evaluations than competing approaches, highlighting its promise for widespread use in astrophysical data analysis.

astro-ph.IM↗

arXiv2026

Ising noise filter: physics-informed filtering for particle detectors

We present the Ising noise filter, a highly portable, graph-based pre-filtering algorithm for early-stage background suppression in particle accelerators and astrophysical detectors. Standard noise rejection methods relying on track fitting suffer from severe combinatorial explosion. Our method bypasses this by mapping individual detector hits to a network of binary spins and minimizing an energy functional. The interaction kernels are physics-informed, tailored to the underlying physics and geometry of the experiment. We demonstrate the efficacy of this approach in two distinct experimental regimes. Applied to the Baikal-GVD neutrino telescope the filter yields fast, standard-quality noise rejection with 96.8% recall for astrophysical neutrinos. For the SPD detector at the NICA collider the filter attains recall of 97% on a toy Monte Carlo sample. Furthermore, when combined with a Peterson--Hopfield network for track finding, our physics-informed coupling improves the TrackML score from 0.5 to 0.95.

astro-ph.IM↗