arXiv Machine Learning
Closed-Form of the Local Galactic Potential and Stellar Distribution Function from Gaia DR3
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arXiv Machine Learning
Jun 2
21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables
arXiv:2606. 00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks.
arXiv Machine Learning
Aug 6
Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
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.
arXiv AI
Jun 9
Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model
arXiv:2606. 07771v1 Announce Type: cross Abstract: Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galaxy property estimation.
arXiv Machine Learning
Jun 16
On the Energy Distribution of the Galactic Center Excess' Sources
arXiv:2507. 17804v2 Announce Type: replace-cross Abstract: The Galactic Center Excess (GCE) may yet herald the discovery of annihilating dark matter.
arXiv Computer Vision
Sep 7
Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models
The paper presents a fully automated method for reconstructing the surface mass density of galaxy clusters using photometry and gravitational lensing data. It introduces DarkClusters-15k, a benchmark dataset of 15,000 simulated clusters with paired mass and photometry maps across multiple redshifts and simulation frameworks. By training a diffusion prior on this dataset, the authors generate posterior samples constrained by weak- and strong-lensing observables, achieving accurate, physics‑guided reconstructions with well‑calibrated uncertainties in minutes.
arXiv Machine Learning
Jun 29
High-dimensional inference for the $\gamma$-ray sky with differentiable programming
arXiv:2604. 08648v2 Announce Type: replace-cross Abstract: We motivate the use of differentiable probabilistic programming techniques in order to account for the large model-space inherent to astrophysical $\gamma$-ray analyses.