Up to 150000 asteroids will be visible in the images of the ESA Euclid space
telescope, and the instruments of Euclid offer multiband visual to
near-infrared photometry and slitless spectra of these objects. Most asteroids
will appear as streaks in the images. Due to the large number of images and
asteroids, automated detection methods are needed. A non-machine-learning
approach based on the StreakDet software was previously tested, but the results
were not optimal for short and/or faint streaks. We set out to improve the
capability to detect asteroid streaks in Euclid images by using deep learning.
We built, trained, and tested a three-step machine-learning pipeline with
simulated Euclid images. First, a convolutional neural network (CNN) detected
streaks and their coordinates in full images, aiming to maximize the
completeness (recall) of detections. Then, a recurrent neural network (RNN)
merged snippets of long streaks detected in several parts by the CNN. Lastly,
gradient-boosted trees (XGBoost) linked detected streaks between different
Euclid exposures to reduce the number of false positives and improve the purity
(precision) of the sample.
The deep-learning pipeline surpasses the completeness and reaches a similar
level of purity of a non-machine-learning pipeline based on the StreakDet
software. Additionally, the deep-learning pipeline can detect asteroids
0.25-0.5 magnitudes fainter than StreakDet. The deep-learning pipeline could
result in a 50% increase in the number of detected asteroids compared to the
StreakDet software. There is still scope for further refinement, particularly
in improving the accuracy of streak coordinates and enhancing the completeness
of the final stage of the pipeline, which involves linking detections across
multiple exposures.

通过使用深度学习方法，我们建立、训练和测试了一种机器学习流程，可以提高在 Euclid 图像中检测小行星痕迹的能力，大大增加了检测到的小行星数量。

Euclid：使用深度学习在模拟图像中识别小行星轨迹

Euclid: Identification of asteroid streaks in simulated images using  deep learning

The accurate modelling of the Point Spread Function (PSF) is of paramount
importance in astronomical observations, as it allows for the correction of
distortions and blurring caused by the telescope and atmosphere. PSF modelling
is crucial for accurately measuring celestial objects' properties. The last
decades brought us a steady increase in the power and complexity of
astronomical telescopes and instruments. Upcoming galaxy surveys like Euclid
and LSST will observe an unprecedented amount and quality of data. Modelling
the PSF for these new facilities and surveys requires novel modelling
techniques that can cope with the ever-tightening error requirements. The
purpose of this review is three-fold. First, we introduce the optical
background required for a more physically-motivated PSF modelling and propose
an observational model that can be reused for future developments. Second, we
provide an overview of the different physical contributors of the PSF,
including the optic- and detector-level contributors and the atmosphere. We
expect that the overview will help better understand the modelled effects.
Third, we discuss the different methods for PSF modelling from the parametric
and non-parametric families for ground- and space-based telescopes, with their
advantages and limitations. Validation methods for PSF models are then
addressed, with several metrics related to weak lensing studies discussed in
detail. Finally, we explore current challenges and future directions in PSF
modelling for astronomical telescopes.

本篇论文旨在介绍天文观测中的点扩散函数（PSF）建模，包括背景知识、PSF 的物理贡献、建模方法与验证方法，并重点探讨 PSF 建模在未来天文望远镜中的挑战与未来发展方向。

天文望远镜点扩散函数建模：弱重力透镜研究综述

Point spread function modelling for astronomical telescopes: a review  focused on weak gravitational lensing studies

Euclid is a European Space Agency medium class mission selected for launch in
2019 within the Cosmic Vision 2015-2025 programme. The main goal of Euclid is
to understand the origin of the accelerated expansion of the Universe. Euclid
will explore the expansion history of the Universe and the evolution of cosmic
structures by measuring shapes and redshifts of galaxies as well as the
distribution of clusters of galaxies over a large fraction of the sky. Although
the main driver for Euclid is the nature of dark energy, Euclid science covers
a vast range of topics, from cosmology to galaxy evolution to planetary
research. In this review we focus on cosmology and fundamental physics, with a
strong emphasis on science beyond the current standard models. We discuss five
broad topics: dark energy and modified gravity, dark matter, initial
conditions, basic assumptions and questions of methodology in the data
analysis. This review has been planned and carried out within Euclid's Theory
Working Group and is meant to provide a guide to the scientific themes that
will underlie the activity of the group during the preparation of the Euclid
mission.

Euclid（欧几里德）是欧洲航天局在宇宙愿景计划 2015-2025 中选定的中型任务，旨在通过测量星系的形状和红移以及星系团的分布，从宇宙学到银河演化到行星研究等广泛领域，探究宇宙的扩张历史和宇宙结构的演化，以进一步了解宇宙加速膨胀的起源，该综述主要聚焦宇宙学和基本物理学两大领域，并讨论 “暗能量和修正引力”、“暗物质”、“初始条件”、“基本假设” 和 “数据分析方法论” 五个议题。