Graph Neural Networks (GNNs) are presented as a natural framework for modeling the relational structure inherent in astrophysical and cosmological data, such as galaxies in their environments or photons in detectors. The review argues that GNNs align with the native geometry of these domains, incorporate existing symmetries and prior knowledge, and remain flexible enough to capture unknown phenomena, offering greater interpretability than other deep‑learning models. It surveys applications across cosmology, galaxy formation, dynamics, and instrumentation, noting that only about one in six non‑instrumentation papers apply GNNs to real data rather than simulations, and provides best‑practice guidelines for when to use or avoid graph approaches.
arXiv:2607. 27320v1 Announce Type: cross Abstract: Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable.
arXiv:2512. 10222v2 Announce Type: replace-cross Abstract: Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions.
We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset, which contains about 40,000 objects...
arXiv:2607. 21607v1 Announce Type: cross Abstract: Graph Neural Networks propagate information through local message passing, but the graph topologies themselves can silently prevent any amount of training from solving long-range tasks.
arXiv:2512. 07420v3 Announce Type: replace-cross Abstract: Jet identification plays a central role in analyzing data from high-energy collider experiments.
The paper introduces a novel SO(3)-equivariant convolutional neural network designed for data on the sphere, specifically using the Hierarchical Equal Area isoLatitude Pixelization (HEALPix). By employing steerable filters, parallel transport, and spin-weighted features, the architecture guarantees exact equivariance and avoids the need for the network to learn spherical symmetry from data. Benchmarks show the model achieves percent-level SO(3)-invariance error and outperforms existing methods in dense gradient regression, cosmic microwave background foreground removal, and denoising tasks.
The paper presents a deep ensemble graph neural network (GNN) approach for reconstructing the arrival direction and energy of ultra‑high‑energy cosmic rays from voltage traces recorded by ground‑based radio detector arrays. Triggered antennas are modeled as a graph input to the GNN, and physical knowledge is embedded in both the architecture and data to improve precision while reducing training set size. The method achieves an angular resolution of 0.092° and an electromagnetic energy resolution of 16.4% on simulated data with realistic noise, and includes uncertainty estimation and robustness checks for real‑world applicability.
arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.
ASTROLABE is a flow‑matching generative emulator that operates directly on the 6N phase‑space coordinates of individual particles, avoiding voxel grids and allowing any observable to be measured from inference. It uses a field‑particle hybrid transformer so that compute and memory scale linearly with the number of particles, and incorporates physical flow matching by basing the prior on COLA‑derived coordinates with momentum and mass conservation enforced throughout the flow. In Lambda‑CDM tests, ASTROLABE reproduces realistic fields, accurate power spectra, halo mass functions, velocity profiles, and halo density profiles, and it generalizes to out‑of‑distribution dark‑acoustic oscillation initial conditions while also being fine‑tuned to emulate large‑scale simulations such as TNG50‑3‑dark at a fraction of the cost.
arXiv:2609.17061v1 Announce Type: cross Abstract: Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn g...
arXiv:2607. 27767v1 Announce Type: new Abstract: Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques for even larger graphs.
The paper presents a method for training Lagrangian Neural Networks (LNNs) to learn Schwarzschild geodesic motion. By introducing a decoupled position–velocity normalization and an energy-constrained log-uniform sampling strategy, the authors achieve accurate reproduction of perihelion precession, relativistic time dilation, and conservation of energy and angular momentum across diverse orbital regimes. This work marks the first successful training of LNNs on Schwarzschild geodesics and demonstrates their potential for data-driven modeling of complex relativistic systems.