The KiDS Strongly lensed QUAsar Detection project (KiDS-SQuaD) aims at
finding as many previously undiscovered gravitational lensed quasars as
possible in the Kilo Degree Survey. This is the second paper of this series
where we present a new, automatic object classification method based on machine
learning technique. The main goal of this paper is to build a catalogue of
bright extragalactic objects (galaxies and quasars), from the KiDS Data Release
4, with a minimum stellar contamination, preserving the completeness as much as
possible, to then apply morphological methods to select reliable
gravitationally lensed (GL) quasar candidates. After testing some of the most
used machine learning algorithms, decision trees based classifiers, we decided
to use CatBoost, that was specifically trained with the aim of creating a
sample of extragalactic sources as clean as possible from stars. We discuss the
input data, define the training sample for the classifier, give quantitative
estimates of its performances, and finally describe the validation results with
Gaia DR2, AllWISE, and GAMA catalogues. We have built and make available to the
scientific community the KiDS Bright EXtraGalactic Objects catalogue
(KiDS-BEXGO), specifically created to find gravitational lenses. This is made
of $\approx6$ millions of sources classified as quasars ($\approx 200\,000$)
and galaxies ($\approx 5.7$M), up to $r<22^m$. From this catalog we selected
'Multiplets': close pairs of quasars or galaxies surrounded by at least one
quasar, presenting the 12 most reliable gravitationally lensed quasar
candidates, to demonstrate the potential of the catalogue, which will be
further explored in a forthcoming paper. We compared our search to the previous
one, presented in the first paper from this series, showing that employing a
machine learning method decreases the stars-contaminators within the GL
candidates.

本文介绍了一种新的基于机器学习的自动分类方法，旨在找到尽可能多的重力透镜类星体，保持目录尽可能完整性，同时最小化恒星污染。通过对 KiDS 数据的分类与筛选，我们建立并公开了 KiDS Bright EXtraGalactic Objects catalog， 并挑选出 12 个最可靠的重力透镜类星体候选者，展示它们在目录中的潜力。

KiDS-SQuaD II：筛选明亮的星系外物体以搜索新的引力透镜类星体的机器学习

KiDS-SQuaD II: Machine learning selection of bright extragalactic  objects to search for new gravitationally lensed quasars

A quasar catalogue is presented with a total of 510764 objects including
424748 type 1 QSOs and 26623 type 1 AGN complete from the literature to 25
January 2015. Also included are 25015 high-confidence SDSS-based photometric
quasars with radio/X-ray associations, 1595 BL Lac objects, and 32783 type 2
objects. Each object is displayed with arcsecond-accurate astrometry, red and
blue photometry, redshift, citations, and radio and X-ray associations where
present. Also, 114 new spectroscopically confirmed quasars are presented.

该研究提出了一个包括超过 510764 个天体的类星体目录，涵盖了其精确测量的天文学数据，包括视差、光度、红移、雷达和 X 射线联合等。

半百万类星体（HMQ）目录

The Half Million Quasars (HMQ) Catalogue

In order to understand the formation and subsequent evolution of galaxies one
must first distinguish between the two main morphological classes of massive
systems: spirals and early-type systems. This paper introduces a project,
Galaxy Zoo, which provides visual morphological classifications for nearly one
million galaxies, extracted from the Sloan Digital Sky Survey (SDSS). This
achievement was made possible by inviting the general public to visually
inspect and classify these galaxies via the internet. The project has obtained
more than 40,000,000 individual classifications made by ~100,000 participants.
We discuss the motivation and strategy for this project, and detail how the
classifications were performed and processed. We find that Galaxy Zoo results
are consistent with those for subsets of SDSS galaxies classified by
professional astronomers, thus demonstrating that our data provides a robust
morphological catalogue. Obtaining morphologies by direct visual inspection
avoids introducing biases associated with proxies for morphology such as
colour, concentration or structual parameters. In addition, this catalogue can
be used to directly compare SDSS morphologies with older data sets. The
colour--magnitude diagrams for each morphological class are shown, and we
illustrate how these distributions differ from those inferred using colour
alone as a proxy for morphology.

通过 Galaxy Zoo 项目，从 Sloan 数字巨星勘测中提取了近一百万个星系，邀请公众通过互联网对其进行视觉形态分类研究，得到了早期数据集所没有的新的形态分类目录，避免了代理变量引入偏差的情况，同时还可以通过颜色 - 等级图表直接比较星系的基础形态特征。