arXiv:2510. 06200v4 Announce Type: replace-cross Abstract: Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling.
arXiv:2606. 28446v1 Announce Type: cross Abstract: Light curves describe temporal variations in the brightness of celestial objects.
arXiv:2607. 00228v1 Announce Type: cross Abstract: Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a central challenge in time-domain astronomy.
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.
arXiv:2608.29145v1 Announce Type: cross Abstract: The rapid expansion of low Earth orbit satellites such as Starlink is increasingly contaminating astronomical surveys. In practice, contaminated imag...
EncoTESS is a compact Time Series Foundation Model trained on TESS 2‑minute light curves that encodes stellar variability into a fixed‑size latent space, handling noise, irregular sampling, and data gaps. It improves age estimation for young K and M stars (≤100 Myr) and older M stars (≤1 Gyr) by outperforming traditional rotation period and variability amplitude indicators. The model’s lightweight architecture (~1 % of typical TSFMs) allows deployment on standard laptops and can be extended to other TESS cadences and missions like Kepler and PLATO.
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.
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.
arXiv:2606. 02778v1 Announce Type: cross Abstract: I present EXOVEIL, a transit detection system that learns what a star's brightness should look like and flags when reality disagrees.
arXiv:2504. 06176v4 Announce Type: replace-cross Abstract: Foundation Models, which leverage large neural networks pre-trained on unlabelled data before fine-tuning for specific tasks, are increasingly being applied to specialised domains.
arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
arXiv:2606. 10197v1 Announce Type: cross Abstract: Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution.
arXiv:2606. 05103v1 Announce Type: new Abstract: The Nancy Grace Roman Space Telescope (Roman), set for launch as early as September 2026, will conduct wide-field infrared imaging surveys with unprecedented spatial resolution and cadence, enabling the discovery of millions of astronomical transients.