The upcoming Large Synoptic Survey Telescope (LSST) will observe 18 000
deg$^2$ of the Southern sky and is expected to discover thousands of transients
every night due to its large coverage of the sky and its observing strategy. In
this work we address the prospects of the LSST in discovering Tidal Disruption
Events (TDEs) and in probing the supermassive black hole (SMBH) mass
distribution in the Universe. We used the LSST simulation framework and defined
TDE catalogs on 20 fields of 20.25 deg$^2$ size. TDE properties were defined by
randomly chosen impact factors and SMBH masses drawn from six different mass
distributions. Observations of TDEs in 10 years of LSST operations were
simulated by querying the simulated observing strategy database minion_1016.
Based on the results of our simulations we estimate that the LSST should
discover between 35 000 and 80 000 TDEs in 10 years of operations, depending on
the assumed SMBH mass distribution. We also find that probing the SMBH mass
distribution with TDE observations will not be straightforward due to the fact
that TDEs caused by low mass black holes ($10^ 5 M_\odot$) are expected to be
less luminous and shorter than TDEs by heavier SMBHs ($> 10^6 M_\odot$), and
therefore will mostly be missed by the irregular LSST cadence minion_1016.

LSST 采用模拟观测策略在 20 个 20.25 平方度的场上定义了 TDE 目录，预计在 10 年的运营期间发现 35000 到 80000 个 TDE。但是，受不规则的观测节律影响，探测 SMBH 质量分布会面临一定的困难。

利用 LSST 观测潮汐破坏事件的前景

The Prospects of Observing Tidal Disruption Events with the LSST

We investigate the ability of the Large Synoptic Survey Telescope (LSST) to
discover kilonovae (kNe) from binary neutron star (BNS) and neutron star-black
hole (NSBH) mergers, focusing on serendipitous detections in the Wide-Fast-Deep
(WFD) survey. We simulate observations of kNe with proposed LSST survey
strategies, paying particular attention to cadence choices that are compatible
with the broader LSST cosmology programme. We find that if all kNe are
identical to GW170817, the baseline survey strategy will yield 58 kNe over the
survey lifetime. If we instead assume a representative population model of BNS
kNe, we expect to detect only 27 kNe. However, we find the choice of survey
strategy significantly impacts these numbers and can increase them to 254 kNe
and 82 kNe over the survey lifetime, respectively. This improvement arises from
an increased cadence of observations between different filters with respect to
the baseline. We then consider the ability of the Advanced LIGO/Virgo (ALV)
detector network to detect these BNS mergers. If the optimal survey strategy is
adopted, 202 of the GW170817-like kNe and 56 of the BNS population model kNe
are detected with LSST but are below the threshold for detection by the ALV
network. This represents, for both models, an increase by a factor greater than
4.5 in the number of detected sub-threshold events over the baseline survey
strategy. Such a population of sub-threshold events would provide an
opportunity to conduct electromagnetic-triggered searches for signals in
gravitational-wave detector data and assess selection effects in measurements
of the Hubble constant from standard sirens, e.g., related to viewing angle
effects.

本研究研究了大型望远镜可以如何发现双中子星和中子星 - 黑洞合并后的重力波事件，探究了合适的观测策略对于发现类似事件的影响以及其对测量哈勃常数的影响。

LSST 主观测中的偶发性千分光新星的发现：最大化亚阈重力波事件的探测

Serendipitous Discoveries of Kilonovae in the LSST Main Survey:  Maximising Detections of Sub-Threshold Gravitational Wave Events

The unprecedented volume and rate of transient events that will be discovered
by the Large Synoptic Survey Telescope (LSST) demands that the astronomical
community update its followup paradigm. Alert-brokers -- automated software
system to sift through, characterize, annotate and prioritize events for
followup -- will be critical tools for managing alert streams in the LSST era.
The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is
one such broker. In this work, we develop a machine learning pipeline to
characterize and classify variable and transient sources only using the
available multiband optical photometry. We describe three illustrative stages
of the pipeline, serving the three goals of early, intermediate and
retrospective classification of alerts. The first takes the form of variable vs
transient categorization, the second, a multi-class typing of the combined
variable and transient dataset, and the third, a purity-driven subtyping of a
transient class. While several similar algorithms have proven themselves in
simulations, we validate their performance on real observations for the first
time. We quantitatively evaluate our pipeline on sparse, unevenly sampled,
heteroskedastic data from various existing observational campaigns, and
demonstrate very competitive classification performance. We describe our
progress towards adapting the pipeline developed in this work into a real-time
broker working on live alert streams from time-domain surveys.

本文介绍了一种机器学习方法，采用可用的光学多波段光度测量特征对宇宙学事件进行分类、表征和优先级排序，该方法适用于实时监听和处理大型天文调查过程中产生的异常事件流。

基于机器学习的经纪人用于 LSST 警报流的实时分类

Machine Learning-based Brokers for Real-time Classification of the LSST  Alert Stream

We apply a convolutional neural network (CNN) to classify and detect quasars
in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric
redshifts of quasars. The network takes the variability of objects into account
by converting light curves into images. The width of the images, noted w,
corresponds to the five magnitudes ugriz and the height of the images, noted h,
represents the date of the observation. The CNN provides good results since its
precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for
the same recall with a random forest classifier. Moreover 175 new quasar
candidates are found with the CNN considering a fixed recall of 0.97. The
combination of probabilities given by the CNN and the random forest makes good
performance even better with a precision of 0.99 for a recall of 0.90.
For the redshift predictions, the CNN presents excellent results which are
higher than those obtained with a feature extraction step and different
classifiers (a K-nearest-neighbors, a support vector machine, a random forest
and a gaussian process classifier). Indeed, the accuracy of the CNN within
|\Delta z|<0.1 can reach 78.09%, within |\Delta z|<0.2 reaches 86.15%, within
|\Delta z|<0.3 reaches 91.2% and the value of rms is 0.359. The performance of
the KNN decreases for the three |\Delta z| regions, since within the accuracy
of |\Delta z|<0.1, |\Delta z|<0.2 and |\Delta z|<0.3 is 73.72%, 82.46% and
90.09% respectively, and the value of rms amounts to 0.395. So the CNN
successfully reduces the dispersion and the catastrophic redshifts of quasars.
This new method is very promising for the future of big databases like the
Large Synoptic Survey Telescope.

本研究使用卷积神经网络（CNN）对斯隆数字天空调查中的类星体进行分类和检测，以及预测类星体的光度红移，并结合随机森林分类器提高准确性，相较于其他分类器表现显著提高，在未来的大数据库例如大型巡天望远镜中具有很好的应用前景。

用于分类、检测和预测斯隆数字天区 Stripe 82 类星体光度红移的深度学习方法

Deep learning Approach for Classifying, Detecting and Predicting  Photometric Redshifts of Quasars in the Sloan Digital Sky Survey Stripe 82

The Large Synoptic Survey Telescope is designed to provide an unprecedented
optical imaging dataset that will support investigations of our Solar System,
Galaxy and Universe, across half the sky and over ten years of repeated
observation. However, exactly how the LSST observations will be taken (the
observing strategy or "cadence") is not yet finalized. In this
dynamically-evolving community white paper, we explore how the detailed
performance of the anticipated science investigations is expected to depend on
small changes to the LSST observing strategy. Using realistic simulations of
the LSST schedule and observation properties, we design and compute diagnostic
metrics and Figures of Merit that provide quantitative evaluations of different
observing strategies, analyzing their impact on a wide range of proposed
science projects. This is work in progress: we are using this white paper to
communicate to each other the relative merits of the observing strategy choices
that could be made, in an effort to maximize the scientific value of the
survey. The investigation of some science cases leads to suggestions for new
strategies that could be simulated and potentially adopted. Notably, we find
motivation for exploring departures from a spatially uniform annual tiling of
the sky: focusing instead on different parts of the survey area in different
years in a "rolling cadence" is likely to have significant benefits for a
number of time domain and moving object astronomy projects. The communal
assembly of a suite of quantified and homogeneously coded metrics is the vital
first step towards an automated, systematic, science-based assessment of any
given cadence simulation, that will enable the scheduling of the LSST to be as
well-informed as possible.

本研究探讨了少量变化对 LSST 观测策略的影响，分析了不同的观测策略对众多提议的科学项目的影响，并采用一系列的诊断指标和优劣指标进行了量化评估，发现采用 “滚动节奏” 对于许多时间域和运动物体天文学项目具有重要意义。

LSST 观测策略的科学优化

Science-Driven Optimization of the LSST Observing Strategy

The Large Synoptic Survey Telescope (LSST) is a high etendue imaging facility
that is being constructed atop Cerro Pachon in Northern Chile. It is scheduled
to begin science operations in 2022. With an 8.4m (6.5m effective) aperture, a
three-mirror design achieving a seeing-limited 9.6deg^2 field of view, and a
3.2 Gigapixel camera, the LSST has the deep-wide-fast imaging capability to
carry out an 18,000deg^2 survey in six passbands (ugrizy) to a coadded depth of
r~27.5 over 10 years using 90% of its observational time. The remaining 10%
will be devoted to deeper and faster time-domain observations and smaller
surveys. In total, each patch of the sky in the main survey will receive 800
visits allocated across the six passbands with 30s exposure visits.
The huge volume of high-quality LSST data will provide a wide range of
science opportunities and open a new era of precision cosmology with
unprecedented statistical power and tight control of systematic errors. In this
review, we give a brief account of the LSST cosmology program with an emphasis
on dark energy investigations. The LSST will address dark energy physics and
cosmology in general by exploiting diverse precision probes including
large-scale structure, weak lensing, type Ia supernovae, galaxy clusters, and
strong lensing. Combined with the cosmic microwave background data, these
probes form interlocking tests on the cosmological model and the nature of dark
energy.
The LSST data products will be made available to the U.S. and Chilean
scientific communities and to international partners with no proprietary
period. Close collaborations with coeval surveys observing at a variety of
wavelengths, resolutions, depths, and timescales will be a vital part of the
LSST science program, which will not only enhance specific studies but also
allow a more complete understanding of the universe through different windows.

LSST 是一项高端成像设施，将在智利北部的 Cerro Pachon 上建成，以测量暗能量物理和宇宙论的事实为重点。

大型同步巡天望远镜宇宙学概述

Cosmology with the Large Synoptic Survey Telescope: an Overview

We show that multiple machine learning algorithms can match human performance
in classifying transient imaging data from the Sloan Digital Sky Survey (SDSS)
supernova survey into real objects and artefacts. This is a first step in any
transient science pipeline and is currently still done by humans, but future
surveys such as the Large Synoptic Survey Telescope (LSST) will necessitate
fully machine-enabled solutions. Using features trained from eigenimage
analysis (principal component analysis, PCA) of single-epoch g, r and
i-difference images, we can reach a completeness (recall) of 96 per cent, while
only incorrectly classifying at most 18 per cent of artefacts as real objects,
corresponding to a precision (purity) of 84 per cent. In general, random
forests performed best, followed by the k-nearest neighbour and the SkyNet
artificial neural net algorithms, compared to other methods such as na\"ive
Bayes and kernel support vector machine. Our results show that PCA-based
machine learning can match human success levels and can naturally be extended
by including multiple epochs of data, transient colours and host galaxy
information which should allow for significant further improvements, especially
at low signal-to-noise.

通过基于 PCA 的机器学习算法对短暂成像数据进行分类，可以实现 96% 的完整度和 84% 的准确度，并且在未来的天文调查中将大力发展完全机器化的解决方案。

SDSS 瞬变巡天图像的机器学习分类

Machine Learning Classification of SDSS Transient Survey Images

We report results from the Supernova Photometric Classification Challenge
(SNPCC), a publicly released mix of simulated supernovae (SNe), with types (Ia,
Ibc, and II) selected in proportion to their expected rate. The simulation was
realized in the griz filters of the Dark Energy Survey (DES) with realistic
observing conditions (sky noise, point-spread function and atmospheric
transparency) based on years of recorded conditions at the DES site.
Simulations of non-Ia type SNe are based on spectroscopically confirmed light
curves that include unpublished non-Ia samples donated from the Carnegie
Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan
Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was
provided for training. We challenged scientists to run their classification
algorithms and report a type and photo-z for each SN. Participants from 10
groups contributed 13 entries for the sample that included a host-galaxy
photo-z for each SN, and 9 entries for the sample that had no redshift
information. Several different classification strategies resulted in similar
performance, and for all entries the performance was significantly better for
the training subset than for the unconfirmed sample. For the spectroscopically
unconfirmed subset, the entry with the highest average figure of merit for
classifying SNe~Ia has an efficiency of 0.96 and an SN~Ia purity of 0.79. As a
public resource for the future development of photometric SN classification and
photo-z estimators, we have released updated simulations with improvements
based on our experience from the SNPCC, added samples corresponding to the
Large Synoptic Survey Telescope (LSST) and the SDSS, and provided the answer
keys so that developers can evaluate their own analysis.

该研究报告介绍了超新星光度分类的公开挑战，其中模拟了不同类型的 SNe，包括未发表的非 Ia 样本，评估了分类算法的效果，并发布了更新的模拟数据作为未来研究的公共资源。

超新星光度分类挑战赛结果

Results from the Supernova Photometric Classification Challenge

A survey that can cover the sky in optical bands over wide fields to faint
magnitudes with a fast cadence will enable many of the exciting science
opportunities of the next decade. The Large Synoptic Survey Telescope (LSST)
will have an effective aperture of 6.7 meters and an imaging camera with field
of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over
20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with
fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a
total point-source depth of r~27.5. The LSST Science Book describes the basic
parameters of the LSST hardware, software, and observing plans. The book
discusses educational and outreach opportunities, then goes on to describe a
broad range of science that LSST will revolutionize: mapping the inner and
outer Solar System, stellar populations in the Milky Way and nearby galaxies,
the structure of the Milky Way disk and halo and other objects in the Local
Volume, transient and variable objects both at low and high redshift, and the
properties of normal and active galaxies at low and high redshift. It then
turns to far-field cosmological topics, exploring properties of supernovae to
z~1, strong and weak lensing, the large-scale distribution of galaxies and
baryon oscillations, and how these different probes may be combined to
constrain cosmological models and the physics of dark energy.

LSST 是一项为期 10 年的成像调查，光学带可以覆盖大面积并对光强度进行快速检测，它可以革命性地映射我们的宇宙，探索不同宇宙模型以及黑暗能量的物理学问题。

LSST 科学书，版本 2.0

LSST Science Book, Version 2.0

(Abridged) We describe here the most ambitious survey currently planned in
the optical, the Large Synoptic Survey Telescope (LSST). A vast array of
science will be enabled by a single wide-deep-fast sky survey, and LSST will
have unique survey capability in the faint time domain. The LSST design is
driven by four main science themes: probing dark energy and dark matter, taking
an inventory of the Solar System, exploring the transient optical sky, and
mapping the Milky Way. LSST will be a wide-field ground-based system sited at
Cerro Pach\'{o}n in northern Chile. The telescope will have an 8.4 m (6.5 m
effective) primary mirror, a 9.6 deg$^2$ field of view, and a 3.2 Gigapixel
camera. The standard observing sequence will consist of pairs of 15-second
exposures in a given field, with two such visits in each pointing in a given
night. With these repeats, the LSST system is capable of imaging about 10,000
square degrees of sky in a single filter in three nights. The typical 5$\sigma$
point-source depth in a single visit in $r$ will be $\sim 24.5$ (AB). The
project is in the construction phase and will begin regular survey operations
by 2022. The survey area will be contained within 30,000 deg$^2$ with
$\delta<+34.5^\circ$, and will be imaged multiple times in six bands, $ugrizy$,
covering the wavelength range 320--1050 nm. About 90\% of the observing time
will be devoted to a deep-wide-fast survey mode which will uniformly observe a
18,000 deg$^2$ region about 800 times (summed over all six bands) during the
anticipated 10 years of operations, and yield a coadded map to $r\sim27.5$. The
remaining 10\% of the observing time will be allocated to projects such as a
Very Deep and Fast time domain survey. The goal is to make LSST data products,
including a relational database of about 32 trillion observations of 40 billion
objects, available to the public and scientists around the world.

大型同步巡天望远镜 (LSST) 是目前最具野心的光学巡天项目，驱动其设计的四大科学主题是探测暗能量和暗物质、对太阳系进行清单式调查、探测短暂的光学天体和绘制银河系地图，它能够在深度、范围和速度方面实现独特的巡天能力，并将在未来十年内观测 18000 平方度的天区，生成 40 亿天体的 320 万亿条数据记录。