The color gradients of spiral disks in the Sloan Digital Sky Survey

We investigate the radial color gradients of galactic disks using a sample of about 20,000 face-on spiral galaxies selected from the fourth data release of the Sloan Digital Sky Survey (SDSS-DR4). We combine galaxies with similar concentration, size and luminosity to construct composite galaxies, and then measure their color profiles by stacking the azimuthally averaged radial color profiles of all the member galaxies. Except for the smallest galaxies (R_{50}<3 kpc), almost all galaxies show negative disk color gradients with mean g-r gradient G_{gr}=-0.006 mag kpc^{-1} and r-z gradient G_{rz}=-0.018 mag kpc^{-1}. The disk color gradients are independent of the morphological types of galaxies and strongly dependent on the disk surface brightness \mu_{d}, with lower surface brightness galactic disks having steeper color gradients. We quantify the intrinsic correlation between color gradients and surface brightness as G_{gr}=-0.011\mu_{d}+0.233 and G_{rz}=-0.015\mu_{d}+0.324. These quantified correlations provide tight observational constraints on the formation and evolution models of spiral galaxies.Comment: 20 pages, 5 figures, Accepted for publication in RAA (Research in Astronomy and Astrophysics

Corrigendum to: The TianQin project: current progress on science and technology

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

ATHENA detector proposal - a totally hermetic electron nucleus apparatus proposed for IP6 at the Electron-Ion Collider

ATHENA has been designed as a general purpose detector capable of delivering the full scientific scope of the Electron-Ion Collider. Careful technology choices provide fine tracking and momentum resolution, high performance electromagnetic and hadronic calorimetry, hadron identification over a wide kinematic range, and near-complete hermeticity.This article describes the detector design and its expected performance in the most relevant physics channels. It includes an evaluation of detector technology choices, the technical challenges to realizing the detector and the R&D required to meet those challenges