Astrophysical Parameters from Gaia DR2, 2MASS, and AllWISE

M. Fouesneau,R. Andrae,T. Dharmawardena,J. Rybizki,C. A. L. Bailer-Jones,M. Demleitner

Astronomy and Astrophysics（2022）SCI 2区

Max Planck Inst Astron | Heidelberg Univ

Cited 18|Views28

Abstract

Stellar physical and dynamical properties are essential knowledge to understanding the structure, formation, and evolution of our Galaxy. We produced an all-sky uniformly derived catalog of stellar astrophysical parameters (APs; age, mass, temperature, bolometric luminosity, distance, dust extinction) to give insight into the physical properties of Milky-Way stars. Exploiting the power of multi-wavelength and multi-survey observations from Gaia DR2 parallaxes and integrated photometry along with 2MASS and AllWISE photometry, we introduce an all-sky uniformly derived catalog of stellar astrophysical parameters, including dust extinction (A0) and average grain size (R0) along the line of sight, for 123,097,070 stars. In contrast with previous works, we do not use a Galactic model as prior in our analysis. We validate our results against other literature (e.g., benchmark stars, interferometry, Bayestar, StarHorse). The limited optical information in the Gaia photometric bands or the lack of ultraviolet or spectroscopic information renders the chemistry inference prior dominated. We demonstrate that Gaia parallaxes bring sufficient leverage to explore the detailed structures of the interstellar medium in our Milky Way. In Gaia DR3, we will obtain the dispersed optical light information to break through some limitations of this analysis, allowing us to infer stellar chemistry in particular. Gaia promises us data to construct the most detailed view of the chemo-dynamics of field star populations in our Galaxy. Our catalog is available from GAVO at http://dc.g-vo.org/tableinfo/gdr2ap.main (soon Gaia Archive and VizieR)

Translated text

Key words

methods: statistical,catalogs,stars: fundamental parameters,Galaxy: stellar content,stars: distances,dust, extinction

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper