← Back to all news
arXiv Machine Learning By Joshua Williamson, Niall Jeffrey, Lorne Whiteway

Learning without relearning physical symmetry: an SO(3)-equivariant convolutional network (CNN) on the sphere

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 21

Graph Neural Networks for Astrophysics and Cosmology

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.

By Christian Kragh Jespersen
llms
More like this →
arXiv AI
Sep 10

Field-level weak lensing cosmology with $60$ simulations using multifidelity simulation-based inference

arXiv:2606.23346v2 Announce Type: replace-cross Abstract: We perform a realistic KiDS-Legacy mock analysis with field-level neural compression and simulation-based inference using just 60 $N$-body si...

By Alex A. Saoulis, Kiyam Lin, Niall Jeffrey, Maximilian von Wietersheim-Kramsta, Davide Piras, Alessio Spurio Mancini, Ana M. G. Ferreira, Benjamin Joachimi
fine-tuning
More like this →