The paper demonstrates that a self‑supervised Vision Transformer (ViT) pretrained on a fast, low‑cost semi‑numerical simulator can produce data summaries that transfer across different simulators without retraining. In 21cm cosmology, the ViT—named SKATR—pretrained on 67,000 21cmFAST lightcones is applied unchanged to hydrodynamical Loreli II lightcones, enabling accurate inference of five astrophysical parameters with fewer radiative‑transfer simulations than a fully‑supervised baseline. SKATR remains accurate, informative, and calibrated even under realistic SKA antenna array noise, outperforming supervised models retrained on noisy data.
By Yannic Pietschke, Caroline Heneka, Ayodele Ore, Romain Meriot
STRADAViT is a self‑supervised continued‑pretraining framework that adapts Vision Transformer (ViT) backbones for radio‑astronomy image analysis. It curates mixed‑survey data, generates radio‑astronomy‑aware training views, and initializes encoders with ViT‑MAE, optionally adding register tokens. Evaluations on three morphology benchmarks (MiraBest, LoTSS DR2, and Radio Galaxy Zoo) show that a register‑based two‑stage checkpoint improves linear‑probe Macro‑F1 scores over the ViT‑MAE baseline and enhances fine‑tuning on MiraBest and RGZ DR1, though performance on LoTSS DR2 fine‑tuning declines; these differences are statistically significant.
By Andrea DeMarco, Ian Fenech Conti, Hayley Camilleri, Ardiana Bushi, Simone Riggi
The paper presents a physics-informed neural network (PINN) that accelerates multilayer spectral inversion (MLSI) of solar chromospheric lines Hα 6562.8 Å and Ca II 8542.1 Å. The PINN predicts MLSI parameters from observed line profiles and uses a differentiable forward model to synthesize spectra, trained in two stages—first with spectral reconstruction loss, then fine‑tuned with conventional MLSI results on a single reference image. Applied to Fast Imaging Solar Spectrograph data, the method reproduces key spatial structures and achieves a 12–60× speedup, processing a raster in 5–15 s versus 3–5 min for traditional MLSI.
By Ziyang Zhang, Qin Li, Vasyl B. Yurchyshyn, Kangwoo Yi, Haimin Wang, Wenda Cao, Bo Shen
arXiv:2606. 28446v1 Announce Type: cross Abstract: Light curves describe temporal variations in the brightness of celestial objects.
By Yicheng Rui
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.
By Siddharth Chaini, Federica B. Bianco, Ashish Mahabal
The Meteosat Third Generation (MTG) Flexible Combined Imager (FCI) offers new opportunities for tropospheric temperature and humidity profiling, at higher spatio-temporal resolutions and expanded spec...
The study presents a spatially aware deep learning framework that retrieves all‑sky tropospheric temperature and humidity profiles from the Meteosat Third Generation Flexible Combined Imager (FCI) without relying on numerical weather prediction background fields. Using a Residual U‑Net trained on 14 months of collocated FCI observations and CERRA reanalysis data, the model achieves temperature biases below 0.4 K and relative humidity standard deviations between 12–20 %, with modest performance degradation under cloud cover. Ablation and feature‑sensitivity analyses confirm that incorporating spatial context across all 16 FCI channels, including visible and near‑infrared bands, improves retrieval accuracy, especially beneath cloud tops.
By Alejandro Salgueiro, Johannes Rausch, Julie Th\'er\`ese Villinger, Angela Meyer
The study audits the AION-1 foundation model, a 39‑modality transformer trained on over 200 million astronomical objects, and finds that its reliance on a survey detection channel—specifically the segmentation map—introduces a severe systematic bias. By keeping image tokens unchanged and editing only the segmentation map, all model outputs (flux, size, ellipticity, redshift) shift by factors of 110–4400 compared to a placebo, revealing that the model’s predictions are driven more by detection gating than by the actual light distribution. This bias propagates into cosmological analyses, shifting tomographic mean redshifts by a median 0.71 × the LSST DESC requirement and exceeding it in multiple assignments, while removing the detection channel eliminates the effect without measurable cost.
whyItMatters":"The bias in the detection channel directly inflates errors in key astronomical measurements, potentially compromising the precision of cosmological studies that rely on accurate redshift estimates."
By Ihor Kendiukhov
arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.
By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga
The paper introduces a deep‑learning pipeline that improves detection of faint moving objects in optical space situational awareness imagery. It combines a Tiny‑U‑Net segmentation network to mask stars with a partial‑convolution variational autoencoder (astro‑VAE) that learns background statistics and performs context‑aware inpainting. The reconstructed, star‑free backgrounds are used as a preprocessing step before detection, and when integrated with a shift‑and‑stack scheme, the method shows high‑fidelity background reconstruction and significant enhancement of moving‑target detectability in real ground‑based telescope data.
By Angela Cratere, Luca Ghilardi, Vishnu Reddy, Francesco Dell'Olio, Charalampos S. Kouzinopoulos, Roberto Furfaro
The paper presents a probabilistic machine‑learning framework for classifying low‑ and high‑resolution stellar and extragalactic spectra, targeting the upcoming 4MOST survey. Four approaches were evaluated—CNNs, Dirichlet distribution, Monte Carlo dropout (MCD), and Bayesian neural networks with variational inference—using SDSS data and a 4MOST mock dataset. The MCD‑augmented CNN achieved the highest accuracies (92.6% on SDSS, 93.9% on mock data) while also delivering well‑calibrated uncertainty estimates with minimal extra computational cost.
By Simon Barton, Martin Sahl\'en, Andreas Korn, Christian Glaser
DELOS is a deep‑learning framework that uses contrastive scoring to perform blind searches for shallow transits in Kepler photometry. It combines GPU‑accelerated phase folding, optimized phase binning, and a custom one‑dimensional convolutional encoder to produce a transit‑likeness score periodogram without relying on pre‑detected events. In tests on synthetic data and controlled injection‑recovery experiments, DELOS outperforms traditional methods (BLS and TLS) in precision‑recall and speed, and successfully recovers all known shallow intermediate‑to‑long‑period transit signals in a selected Kepler sample.
By Qingtian Liu, Jian Ge, XingChen Yan, Kevin Willis, Xinyu Yao, QuanQuan Hu, Jiapeng Zhu