activity
20172026
most citedChemo-kinematics of the Milky Way from the SDSS-III MARVELS Survey

37 citations · 125 across the 12 of their papers we have counts for

collaborators

12 papers

astro-ph.EP2026

TransitNet: A Compact Attention-Augmented Deep Learning Framework for Low-SNR Transit Blind Searches

Xingchen Yan, Jian Ge, Qingtian Liu +3

Motivated by the observational incompleteness of intermediate-to-long-period Earth-size planets, we present TransitNet, a compact attention-augmented deep-learning framework for lo…

astro-ph.IM2026

GTLS: A GPU-accelerated method for periodic transit detection

Quanquan Hu, Jian Ge, Luoxi Jin +1

Computational efficiency is a critical requirement for transit searches in modern large-scale photometric surveys. We present Graphics Processing Units Transit Least Squares (GTLS)…

astro-ph.EP2026

DELOS: Contrastive Deep Learning for Low-SNR Blind Transit Searches in Kepler Photometry

Qingtian Liu, Jian Ge, XingChen Yan +4

We present DEtection in phase-folded Light curves with cOntrastive Scoring (DELOS), a deep-learning framework that uses contrastive scoring to perform blind searches for shallow tr…

astro-ph.GA2025

Detecting Ca II Absorption Lines with a Fe II assisted Dual Neural Network

Lucas Wang, Jian Ge, Kevin Willis

Ca II absorbers, characterized by dusty and metal-rich environments, provide unique insights into the interstellar medium of galaxies. However, their rarity and weak absorption fea…

astro-ph.EP2024★ 7 cited

The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits

Kaitlyn Wang, Jian Ge, Kevin Willis +2

This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devis…

astro-ph.EP2023

Discovery of Small Ultra-short-period Planets Orbiting KG Dwarfs in Kepler Survey Using GPU Phase Folding and Deep Learning Detection System

Kaitlyn Wang, Jian Ge, Kevin Willis +3

Of over 5,000 exoplanets identified so far, only a few hundred possess sub-Earth radii. The formation processes of these sub-Earths remain elusive, and acquiring additional samples…

astro-ph.GA2022★ 6 cited

Newly discovered Ca II absorbers in the early universe: statistics, element abundances and dust

Hannah Fang, Iona Xia, Jian Ge +2

We report discoveries of 165 new quasar Ca II absorbers from the Sloan Digital Sky Survey (SDSS) Data Release 7 and 12. Our Ca II rest frame equivalent width distribution supports…

astro-ph.GA2022★ 4 cited

Discovering Ca II Absorption Lines With a Neural Network

Iona Xia, Jian Ge, Kevin Willis +1

Quasar absorption line analysis is critical for studying gas and dust components and their physical and chemical properties as well as the evolution and formation of galaxies in th…

astro-ph.IM2022★ 17 cited

Discovering Faint and High Apparent Motion Rate Near-Earth Asteroids Using A Deep Learning Program

Franklin Wang, Jian Ge, Kevin Willis

Although many near-Earth objects have been found by ground-based telescopes, some fast-moving ones, especially those near detection limits, have been missed by observatories. We de…

astro-ph.IM2022★ 18 cited

ET White Paper: To Find the First Earth 2.0

Jian Ge, Hui Zhang, Weicheng Zang +163

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time,…

astro-ph.SR2018★ 37 cited

Chemo-kinematics of the Milky Way from the SDSS-III MARVELS Survey

Nolan Grieves, Jian Ge, Neil Thomas +20

Combining stellar atmospheric parameters, such as effective temperature, surface gravity, and metallicity, with barycentric radial velocity data provides insight into the chemo-dyn…

astro-ph.SR2017★ 36 cited

Exploring the Brown Dwarf Desert: New Substellar Companions from the SDSS-III MARVELS Survey

Nolan Grieves, Jian Ge, Neil Thomas +27

Planet searches using the radial velocity technique show a paucity of companions to solar-type stars within ~5 AU in the mass range of ~10 - 80 M. This deficit, know…