Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2605.01019

The Outer Space Treaty Won't Save Us From Ourselves

John Barentine·Aparna Venkatesan
View original source ↗Read source document ↗Export metadata ↓

Abstract

The rapid growth of human activities in outer space sounds urgent alarms around ethical and philosophical issues, particularly concerning space militarization. The present international legal framework governing activities in space, the Outer Space Treaty (OST), views the peaceful exploration of space for scientific research as co-equal to other 'uses' entitled to "due regard" with respect to "potentially harmful interference" on the part of other space actors. The OST is deeply aspirational but has weak enforcement mechanisms, relying at its core on the goodwill of all involved parties as the fundamental basis for accountability. But that framework now faces unsustainable pressures from both public and private interests, and current agreements like the OST may be unable to exert timely, material protections. Terrestrial frameworks of "ethics of deterrence" versus the "ethics of agreements" are quickly expanding into cosmic environments. We argue for the legal recognition of space as an environment as the basis of any future approach to securing its integrity, and share examples of agreements grounded in peaceful cooperative stewardship of shared environments. These represent potential pathways forward that are ethical and also serve rational self-interest and self-preservation at this crucial juncture for humanity.

Explore related subjects

astro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

John Barentine, Aparna Venkatesan. 2026-05-01. The Outer Space Treaty Won't Save Us From Ourselves. https://doi.org/10.3389/frspt.2026.1748406

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

KEEP EXPLORING

Related papers

arXiv2026

Multi-Scale Contrastive Attention for Light-Curve Representation Learning

Current and next-generation time-domain surveys demand automated techniques capable of analyzing millions of light curves, observed in multiple filters, without relying on exhaustive human annotation or scarce spectroscopic follow-up. We present Astra-CLR, an attention-based, self-supervised contrastive learning framework which enables the representation of raw light curves into a highly discriminative latent space. Pre-trained on $\sim$2.1 million unlabeled Zwicky Transient Facility light curves, the framework utilizes partial light curves as input sequences to generate asymmetric, multi-scale temporal views (explicitly contrasting shorter sequences against longer ones) forcing the network to learn a robust "local-to-global" mapping strategy. Furthermore, we introduce a novel multi-view late fusion architecture that extends the model to efficiently handle longer light curves with larger numbers of observations while accommodating the different cadences associated with each filter. The discriminatory power of the resulting representations was evaluated by using them as input to a Multinomial Logistic Regression classifier, trained to identify 12 broad classes of variability. Final accuracy achieved $\sim 0.70$. When applying a label-efficient, partial top-layer fine-tuning strategy, the topological structure of the latent space is significantly refined, boosting results to $\sim$0.77. Astra-CLR is the first publicly available multi-filter time-series Transformer trained exclusively on real ZTF light curves. Results presented here demonstrate that it provides an ideal foundation for the development of end-to-end pipelines, taking into account color evolution and respecting the inhomogeneous nature of astronomical light curve sampling.

astro-ph.IM↗
arXiv2026

Combining astrometry with pulsar timing: the first joint analysis of very low frequency gravitational waves

The pHz to sub-nHz GW regime remains largely unexplored but is crucial for mapping the early inspiral stage of SMBHBs and probing early-Universe physics. Astrometry and pulsar timing offer orthogonal and deeply complementary secular observables to investigate this frequency band. We aim to present the first joint data analysis combining real astrometric proper motions with binary pulsar timing, to search for and constrain continuous gravitational waves (CWs) sourced by SMBHBs in the ultra-low-frequency regime ($10^{-12} \le f_{GW} \le10^{-9} Hz$). We employ a Bayesian model selection and upper limit estimation framework to combine apparent proper motion displacements of $\sim 1.5 \times 10^6$ quasars from the Gaia CRF3 catalog with the line-of-sight orbital period derivatives ($\dot{P}_b$) of 11 high-precision binary pulsars. To prevent spurious detections, we heavily model instrumental and astrophysical systematics: we propagate the Galactic potential uncertainty for pulsars via Monte Carlo simulations and perform a Vector Spherical Harmonics (VSH) decomposition up to the octupole order (l=3) for quasars. We find no statistically significant evidence for a CWs signal in the joint analysis ($\ln B_{joint} = -0.42 \pm 0.03$). In the absence of detection, we set the tightest constraints to date on CW strain in the pHz band, yielding a 95% upper limit of $h_0\le6.4x10^{-11}$ at a reference frequency of $f_{ref} = 4 \times 10^{-10}$ Hz. The combined dataset achieves full sky coverage and improves single-dataset upper limits by 20%-30%. Combining orthogonal observables successfully breaks spatial degeneracies intrinsic to isolated searches. Furthermore, forecasts from inj.-rec. indicate that with the extended temporal baseline and reduced uncertainties of the upcoming Gaia DR4, this joint framework is poised to break the $h_0 < 10^{-11}$ upper limit barrier for sub-nHz CWs.

astro-ph.IM↗
arXiv2026

Bayesian classification of astronomical spectra with class uncertainties

Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Aims: Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Methods: A CNN with simple architecture and about 20,000 parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.

astro-ph.IM↗