Artificial radio emissions have historically been a major focus of technosignature searches. Here, we apply an anthropological lens to expand this scope to surface technosignatures: non-random, intentional patterning on planetary terrain, similar to long-lasting human-made landscapes and monuments on Earth. On geologically inactive bodies such as the Moon and Mars, artificial traces and patterns could potentially remain visible for hundreds of millions of years. We discuss two recent developments that make this search timely: high-resolution imaging datasets, such as those produced by the HiRISE camera on the Mars Reconnaissance Orbiter (MRO), and ML-based anomaly-detection methods. Finally, we list two possible approaches to developing a model capable of searching for these surface technosignatures: a supervised and an unsupervised one. Complemented by LiDAR, ground-penetrating radar, and reflectance spectroscopy, these methods would enable the first quantitative upper limits on surface technosignatures while yielding ancillary benefits for planetary science.
Physics seldom makes breakthroughs when we consider objects only in isolation. Instead we model and measure how objects sit among their neighbours, spatial, temporal, or otherwise: a galaxy in its environment, a halo in its history, a photon hit in a detector. Graphs are ideally suited for encoding exactly these relations, and Graph Neural Networks (GNNs) learn the geometric functions that respect them. This review presents the use of GNNs for astrophysics and cosmology, and argues that GNNs often suit physics and physical data better than any alternative: they fit the native geometry of the domain and the signal, enable the incorporation of the symmetries and prior knowledge we already trust and have toiled to build, and stay flexible enough to capture what we do not yet understand. GNNs' physically intuitive workings also render them more interpretable than other deep-learning architectures. We show how we can begin from general principles of the geometry of deep learning and build the graph and the graph neural network from the ground up, and how other architectures, like the convolutional network and the transformer, can be seen as special cases of one unified geometric blueprint. We survey applications across cosmology, galaxy formation, dynamics, and instrumentation; and we chart the frontier. From this survey, we find that only one in six articles outside instrumentation applies GNNs to data rather than just simulations. We also distil a set of best practices, including when not to reach for a graph. Throughout, we will prioritise physical intuition and foundational principles over mathematical machinery whenever possible.
The Laser Interferometer Space Antenna will observe up to $\sim 10^{7}$ overlapping signals, which have to be characterised simultaneously with a global fit. This is a challenge for model comparison since rerunning the global fit for every physical model of interest, including deviations from vacuum General Relativity, is computationally demanding. An alternative is a two-step post-processing strategy: a first global fit with vacuum models only, after which a selected group of target sources is reanalysed under a different model with all other sources subtracted. We show that in this strategy a beyond-vacuum effect in a target source can be partly absorbed by the overlapping nuisance sources in the first fit, subtracted with them, and never reach the reanalysis. In the linear-signal approximation, the loss is captured by a single number, the masking ratio $\mathcal{R}\in[0,1]$. A deviation the global fit would detect at $n_σ$ is reported by post-processing at $\mathcal{R}\,n_σ$, and bounds in null tests are also reported too tight by the same factor. We show how to compute $\mathcal{R}$ from the posterior samples of the vacuum global fit, and give an algorithm that ranks the nuisance sources by how much of the effect each can absorb, so that only those most correlated with it need to be refitted with the target in a down-scaled reanalysis with a few sources. We validate our method with full Markov-chain Monte Carlo inference on a toy model of 21 overlapping chirping sinusoids, where one carries a small non-linear chirp that represents the missed physics, while the other 20 act as nuisance sources. This method can serve as a basis for tests of General Relativity in realistic LISA mock datasets and can be adapted to study the impact of systematics between different gravitational-wave sources.
Bayes factors are a widely used model comparison criterion, but are often quite challenging and computationally expensive to estimate well. We propose a variation of the model-switch thermodynamic integration method for estimating Bayes factors that decreases the variance of the estimator and essentially eliminates integration error. We choose samples whose density in $β$ is proportional to the standard deviation of previously drawn samples. Rather than averaging many samples at each $β$, we use a very dense set of $β$ values, collecting one sample at each in order of increasing $β$ and one sample at each in order of decreasing $β$ to minimize "thermic lag". We study our procedure with a toy model (based loosely on some signal recovery problems). In this model we find that adaptive sampling gives us a 2.7-fold decrease in the spread of the estimated Bayes factor. We test our procedure both drawing directly from the posterior distribution and using a Markov Chain Monte Carlo sampler.
The Parameter Goodness-of-fit (PG) test [hep-ph/0304176] offers a simple test statistic to study the compatibility of different data sets within a given theoretical model. In its original formulation it assumes that the data sets are statistically independent. We propose an extension of the PG test for the situation when some parameters of the model are constrained by an auxiliary data set called prior here. As the prior is imposed for all data sets, they are no longer statistically independent. We show that under the Gaussian approximation, the prior-constrained PG test statistic follows a generalised $χ^2$ distribution, which can be used to obtain $p$-values in a straight-forward way. In the approximation of a strong prior constraint, the test converges towards the standard PG test for the unconstrained model parameters.
Deep learning (DL) models are increasingly being deployed into radiation-challenging environments in spacecraft, particle accelerators, and, soon, space-based data centers. Instantiated as a learned orchestration of numbers stored in persistent memory, models are susceptible to continual degradation as high-energy particles corrupt data. At inference time, new data passed through the network fundamentally break the off-line assumptions and guarantees of a static model determined during training. To preserve the quality and efficacy of the models, fault tolerance against bit flips is a clear objective. In this work we developed a runtime method for repairing the quantized weights of neural networks (NNs) after bit flips on commodity, commercial off-the-shelf (COTS) hardware, without storing extra replicas. Our work extends the well-established Fault-Aware Training (FAT) paradigm by adding an alignment penalty to concentrate undamaged activations and weights into a low-rank subspace, and we then reconstruct flagged corrupted coordinates from the uncorrupted survivors. We call this method {\it Projection Healing} and apply it to six regression benchmarks on quantized NNs. Holding a FAT checkpoint fixed and toggling only the runtime heal gives a further ${\sim}23\%$ reduction in the native-unit excess integral. Across those datasets the method extends the operational scrub interval by a median of $2.04\times$ at a $+5\%$ RMSE degradation and $1.96\times$ at $+50\%$, compared with $1.62\times$ and $1.07\times$ for classical Single Error Correction, Double Error Detection (SECDED). This introduces a new path towards designing resilient data-driven models, complementing established hardware redundancy, ECC, and FAT-based techniques.
Accurate short-term forecasting of atmospheric seeing is of significant interest for astronomical observations and free-space optical communication systems, where rapid variations in optical turbulence can affect system performance and operational planning. In this work, we benchmark machine learning models for predicting atmospheric seeing up to two hours in advance, with a particular focus on probabilistic forecasting and uncertainty quantification. We compare two probabilistic approaches, Gaussian Processes (GPs) and a normalizing-flow- based model (FloTS), against deterministic recurrent neural network (RNN) and long short-term memory (LSTM) baselines. All models are trained and evaluated on seeing measurements from the Maunakea summit. We assess the models using both deterministic metrics, including the root mean square error and the Pearson correlation coefficient, and probabilistic metrics that evaluate the quality and calibration of predictive uncertainty estimates. The results show that deep learning approaches outperform standard recurrent networks for short-term forecasting, with LSTM and FloTS achieving the best overall predictive accuracy. While Gaussian Processes provide a useful probabilistic baseline, their predictive distributions are limited by the Gaussian assumption. FloTS combines competitive forecasting accuracy with flexible uncertainty quantification and produces the most informative probabilistic forecasts.
Ammonia and hydrogen are major constituents of planets within and beyond the solar system, yet how they interact in planetary envelopes is poorly known. We perform large-scale two-phase coexistence simulations of the ammonia-hydrogen system from 500 to 4000 K and 0.1 to 100 GPa, driven by machine-learning interatomic potentials trained on density functional theory calculations. From these we map the compositions of coexisting hydrogen-rich and ammonia-rich phases, and the critical curve that separates conditions of complete miscibility from those of phase separation. The critical temperature increases with pressure, from 500 K at 0.1 GPa to 2000 K near 30 GPa, and we find no phase separation at 3000 and 4000 K up to 100 GPa. The slope of the critical curve decreases substantially between 0.3 and 30 GPa, and the curve nearly flattens above about 30 GPa and 2000 K, where molecules begin to give way to atomic species. The coexistence curves are asymmetric with an ammonia-rich critical composition. For the expected envelope compositions, the phase boundary lies close to, and intersects, published thermal profiles of Uranus and Neptune, implying ammonia may be raining out in these planets today. In contrast, in warm sub-Neptunes such as TOI-270 d, ammonia and hydrogen are likely miscible in all proportions. Together with previous studies, our results suggest that silicates, water and ammonia separate from hydrogen in sequence as planets cool, with implications for their compositional, thermal and structural evolution.