collaborators

15 papers

astro-ph.GA2026

NEXUS: Spectral Variability of Little Red Dots and Blue Active Galactic Nuclei at

Zachary Stone, Yue Shen, Ming-Yang Zhuang +4

We present spectral measurements for 17 Little Red Dots (LRDs) and 14 blue broad-line active galactic nuclei (AGNs) at using multi-epoch JWST NIRSpec MSA s…

astro-ph.GA2026

NEXUS: Abundance, Environments, and Spectral Diversity of Little Red Dots from the NIRSpec MSA Sample

Zhiwei Pan, Ming-Yang Zhuang, Yue Shen +6

We present a comprehensive study of Little Red Dots (LRDs) at 2.3 < z < 7.4 using NIRCam photometry and NIRSpec MSA/PRISM spectra from the ongoing NEXUS program. Photometric select…

astro-ph.GA2026

Extreme Galaxy-scale Outflows Are Frequent among Luminous Early Quasars

Weizhe Liu, Xiaohui Fan, Huan Li +23

The existence of abundant post-starburst/quiescent galaxies just 1-2 Gyrs after the Big Bang challenges our current paradigm of galaxy evolution. Cosmological simulations sug…

astro-ph.GA2026

DeepDISC-Euclid: Source Classification and Photometric Redshifts in Euclid Deep Field North With a Pixel-Level Deep Learning Approach

Yuanzhe Jiang, Yue Shen, Grant Merz +8

The first Euclid Quick Data Release (Q1) provides extensive imaging and spectroscopic data for hundreds of millions of photometric objects across several deep fields. Accurate clas…

astro-ph.GA2026

Hidden in Pixels I: Discovery of dual "little red dots" indicates excess clustering on kilo-parsec scales

Takumi S. Tanaka, John D. Silverman, Kazuhiro Shimasaku +56

``Little Red Dots'' (LRDs) are an abundant high-redshift population newly discovered by the James Webb Space Telescope (JWST) and considered to be an early growth phase of supermas…

astro-ph.IM2026

Photometric Redshifts in JWST Deep Fields: A Pixel-Based Alternative with DeepDISC

Grant Merz, Ming-Yang Zhuang, Junyao Li +4

Photo-z algorithms that utilize SED template fitting have matured, and are widely adopted for use on high-redshift near-infrared data that provides a unique window into the early u…