collaborators

6 papers

astro-ph.SR2026

Nature of HD 251108: an RS CVn binary with a long-term evolving spot

Xinlin Zhao, Song Wang, B. Fuhrmeister +5

Recently, the Lobster Eye Imager for Astronomy (LEIA) detected the longest-lasting and most energetic stellar X-ray flare event from HD 251108. In this work, we re-determined the a…

astro-ph.SR2026

SN 2023fyq: direct detection of a Type Ibn supernova progenitor and its multi-wavelength environmental constraints

Xinyi Hong, Ning-Chen Sun, Yali Shao +9

Context. Type Ibn supernovae (SNe) are characterized by narrow helium emission lines arising from ejecta-circumstellar medium interaction, yet their progenitors remain debated, wit…

astro-ph.GA2026

J-PAS: unprecedented precision in stellar populations of diffuse tidal features

Sepideh Eskandarlou, Mohammad Akhlaghi, Francisco Arizo-Borillo +38

Galaxies frequently interact with nearby systems, a process that can significantly alter their morphology and star formation activity. However, spectroscopic studies of their faint…

astro-ph.CO2026

J-PAS: forecast on the primordial power spectrum reconstruction

Guillermo Martínez-Somonte, Airam Marcos-Caballero, Enrique Martínez-González +27

We investigate the capability of the J-PAS survey to constrain the primordial power spectrum using a non-parametric Bayesian method. Specifically, we analyze simulated power spectr…

astro-ph.IM2026

J-PAS: Semi-Supervised Sim-to-Obs Transfer for Robust Star--Galaxy--Quasar Classification

Daniel López-Cano, L. Raul Abramo, L. Nakazono +28

Modern studies in astrophysics and cosmology increasingly rely on simulations and cross-survey analyses, yet differences in data generation, instrumentation, calibration, and unmod…

astro-ph.IM2025

The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation

Ana Paula Jeakel, Gabriel Vieira dos Santos, Valerio Marra +27

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJP…