Meir E. Schochet | Searcharxiv

arXiv2026

KRONOS II: Solar-like Umbra and Penumbra Properties on the Young Sun V1298~Tau

Transiting exoplanets provide a unique laboratory for studying stellar surface heterogeneities via starspot or facular occultations. When observed at multiple wavelengths, this configuration enables spectroscopic characterization of spot thermal contrasts, distributions, and morphology. In this work, we leverage JWST NIRISS/SOSS transit observations of the 20--30~Myr planets V1298~Tau~bcd to study the surface properties of their solar analog host star V1298~Tau. We identify 14 starspot crossing events across two visits. We derive $0.8-2.8μ$m starspot contrast spectra and demonstrate the contrasts can only be explained when accounting for the umbral and penumbral components of the starspots, robust to which stellar model grid is assumed. The spot temperatures are broadly consistent between visits, suggesting that V1298~Tau ($T_\mathrm{phot}=4866\pm33$\,K) has starspots with $T_{\mathrm{umbra}}$ = 3300--3600\,K umbrae and $T_{\mathrm{penumbra}}$ = 4400--4600\,K penumbrae, and are $\sim$25--30\% umbrae by area. The differences between these spot components and the stellar photosphere are consistent with sunspots. Additionally, the relation between the spot contrast and the ratio of umbral to penumbral area is similar to that of the Sun. Combining these JWST observations with long baseline multi-band photometry from the Las Cumbres Observatory, we also estimated the global unocculted spot distribution, revealing at least 5 additional large unocculted active regions. Altogether, these measurements suggest that while the total spot coverage evolves in time, the relative temperatures of surface heterogeneities on Sun-like stars may be consistent throughout their lifetimes. Furthermore, these results demonstrate that JWST exoplanet transit observations can be useful for starspot substructure characterization.

astro-ph.SR↗

arXiv2025

200,000+ Deep Learning-inferred Periods of Stellar Variability from the All-Sky Automated Survey for Supernovae

Stars exhibit a range of variability periods that depend on their mass, age, and evolutionary stage. For space-based photometric data, convolutional neural networks (CNNs) have demonstrated success in recovering and measuring periodic variability from photometric missions like Kepler and TESS. All-sky ground-based surveys can have similar if not longer baselines than space-based missions; however, these datasets are more challenging to work with due to irregular sampling, more complex systematics, and larger data gaps. In this work, we demonstrate that CNNs can be used to derive variability periods from ground-based surveys. From the All-Sky Automated Survey for Supernovae, we recover 208,260 variability periods between 1 and 30 days, approximately 60% of which are new detections. We recover periods for active RSCVn, anomalous sub-subgiants, and cool dwarfs that are consistent with previously measured rotation periods, while periods for stars above the Kraft break are generally spurious. We also identify periodic signals in tens of thousands of giant stars that correspond to frequencies of stellar oscillations rather than rotation. Our results highlight that CNNs can be used on sparsely sampled ground-based photometry to recover periodicity. We conclude that the findings of our work are very promising for the potential recovery of hundreds of thousands of stellar rotation periods in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time and the Nancy Grace Roman Space Telescopes Galactic Bulge Time Domain Survey.

astro-ph.SR↗