Mergers of binary neutron stars (BNSs) emit signals in both the
gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017
multi-messenger observation of GW170817 led to scientific discoveries across
cosmology, nuclear physics, and gravity. Central to these results were the sky
localization and distance obtained from GW data, which, in the case of
GW170817, helped to identify the associated EM transient, AT 2017gfo, 11 hours
after the GW signal. Fast analysis of GW data is critical for directing
time-sensitive EM observations; however, due to challenges arising from the
length and complexity of signals, it is often necessary to make approximations
that sacrifice accuracy. Here, we develop a machine learning approach that
performs complete BNS inference in just one second without making any such
approximations. This is enabled by a new method for explicit integration of
physical domain knowledge into neural networks. Our approach enhances
multi-messenger observations by providing (i) accurate localization even before
the merger; (ii) improved localization precision by $\sim30\%$ compared to
approximate low-latency methods; and (iii) detailed information on luminosity
distance, inclination, and masses, which can be used to prioritize expensive
telescope time. Additionally, the flexibility and reduced cost of our method
open new opportunities for equation-of-state and waveform systematics studies.
Finally, we demonstrate that our method scales to extremely long signals, up to
an hour in length, thus serving as a blueprint for data analysis for
next-generation ground- and space-based detectors.

融合的双中子星 （Binary Neutron Stars，BNSs）在引力波（Gravitational Wave，GW）和电磁（Electromagnetic，EM）光谱中发射信号，在该论文中，作者开发了一种机器学习方法，能够在一秒钟内完成完整的 BNS 推理，无需进行任何近似计算。

利用机器学习进行二进制中子星的实时引力波推断

Real-time gravitational-wave inference for binary neutron stars using  machine learning

On June 8, 2017 at 02:01:16.49 UTC, a gravitational-wave signal from the
merger of two stellar-mass black holes was observed by the two Advanced LIGO
detectors with a network signal-to-noise ratio of 13. This system is the
lightest black hole binary so far observed, with component masses
$12^{+7}_{-2}\,M_\odot$ and $7^{+2}_{-2}\,M_\odot$ (90% credible intervals).
These lie in the range of measured black hole masses in low-mass X-ray
binaries, thus allowing us to compare black holes detected through
gravitational waves with electromagnetic observations. The source's luminosity
distance is $340^{+140}_{-140}$ Mpc, corresponding to redshift
$0.07^{+0.03}_{-0.03}$. We verify that the signal waveform is consistent with
the predictions of general relativity.

2017 年 6 月 8 日 02:01:16.49 UTC，使用两个 Advanced LIGO 探测器观测到了两个恒星质量黑洞合并的引力波信号，其质量处于低质量 X 射线二元系统测量的黑洞质量范围之内，对比了通过引力波检测到的黑洞与电磁观测结果，并验证了该信号波形与广义相对论预测一致。

GW170608：观测到一对质量为 19 太阳质量的二进制黑洞合并

GW170608: Observation of a 19-solar-mass Binary Black Hole Coalescence

The source of the gravitational-wave signal GW170817, very likely a binary
neutron star merger, was also observed electromagnetically, providing the first
multi-messenger observations of this type. The two week long electromagnetic
counterpart had a signature indicative of an r-process-induced optical
transient known as a kilonova. This Letter examines how the mass of the
dynamical ejecta can be estimated without a direct electromagnetic observation
of the kilonova, using gravitational-wave measurements and a phenomenological
model calibrated to numerical simulations of mergers with dynamical ejecta.
Specifically, we apply the model to the binary masses inferred from the
gravitational-wave measurements, and use the resulting mass of the dynamical
ejecta to estimate its contribution (without the effects of wind ejecta) to the
corresponding kilonova light curves from various models. The distributions of
dynamical ejecta mass range between $M_{ej} = 10^{-3} - 10^{-2} M_{\odot}$ for
various equations of state, assuming the neutron stars are rotating slowly. In
addition, we use our estimates of the dynamical ejecta mass and the neutron
star merger rates inferred from GW170817 to constrain the contribution of
events like this to the r-process element abundance in the Galaxy when ejecta
mass from post-merger winds is neglected. We find that if $\gtrsim10\%$ of the
matter dynamically ejected from BNS mergers is converted to r-process elements,
GW170817-like BNS mergers could fully account for the amount of r-process
material observed in the Milky Way.

本文研究了如何在没有直接电磁观测到光新星的情况下估算动力喷射物的质量，使用引力波观测和与动力喷射物对应的光曲线。研究表明，二元中子星合并释放出的动力喷射物可能占到系内流星云物质丰度的至少 10%。

估计动力喷射物在与 GW170817 相关的千新星中的贡献

Estimating the Contribution of Dynamical Ejecta in the Kilonova  Associated with GW170817

We present a method for detection and reconstruction of the
gravitational-wave (GW) transients with the networks of advanced detectors.
Originally designed to search for transients with the initial GW detectors, it
uses significantly improved algorithms, which enhances both the low-latency
searches with rapid localization of GW events for the electro-magnetic followup
and high confidence detection of a broad range of the transient GW sources. In
the paper we present the analytic framework of the method. Following a short
description of the core analysis algorithms, we introduce a novel approach to
the reconstruction of the GW polarization from a pattern of detector responses
to a GW signal. This polarization pattern is a unique signature of an arbitrary
GW signal that can be measured independent from the other source parameters.
The polarization measurements enable rapid reconstruction of the GW waveforms,
sky localization and helps identification of the source origin.

本文提出了一种用于检测和重构引力波瞬变的方法，该方法利用先进探测器网络，通过改进的算法增强了电磁跟踪的低延迟搜索和广泛范围瞬变引力波源的可靠检测。该方法包括核心分析算法、引力波极化的重构等，极化测量可以实现快速重建引力波波形、定位天空和帮助确定源的起源。

利用先进探测网络进行引力波瞬变的检测和重建方法

Method for detection and reconstruction of gravitational wave transients  with networks of advanced detectors

We introduce a highly-parallelizable architecture for estimating parameters
of compact binary coalescence using gravitational-wave data and waveform
models. Using a spherical harmonic mode decomposition, the waveform is
expressed as a sum over modes that depend on the intrinsic parameters (e.g.
masses) with coefficients that depend on the observer dependent extrinsic
parameters (e.g. distance, sky position). The data is then prefiltered against
those modes, at fixed intrinsic parameters, enabling efficiently evaluation of
the likelihood for generic source positions and orientations, independent of
waveform length or generation time. We efficiently parallelize our intrinsic
space calculation by integrating over all extrinsic parameters using a Monte
Carlo integration strategy. Since the waveform generation and prefiltering
happens only once, the cost of integration dominates the procedure. Also, we
operate hierarchically, using information from existing gravitational-wave
searches to identify the regions of parameter space to emphasize in our
sampling. As proof of concept and verification of the result, we have
implemented this algorithm using standard time-domain waveforms, processing
each event in less than one hour on recent computing hardware. For most events
we evaluate the marginalized likelihood (evidence) with statistical errors of
less than about 5%, and even smaller in many cases. With a bounded runtime
independent of the waveform model starting frequency, a nearly-unchanged
strategy could estimate NS-NS parameters in the 2018 advanced LIGO era. Our
algorithm is usable with any noise curve and existing time-domain model at any
mass, including some waveforms which are computationally costly to evolve.

本文提出一种并行化体系结构，使用引力波数据和波形模型估计紧凑二进制合并参数，通过球形谐波模式分解来将波形表达为取决于固有参数的模的总和，并通过固定固有参数的模来预先筛选数据，并使用 Monte Carlo 积分策略整合所有外在参数，从而高效评估通用源位置和方向的可能性，独立于波形长度或产生时间。

用于紧凑双星并合引力波的快速并行参数估计新方案

A novel scheme for rapid parallel parameter estimation of gravitational  waves from compact binary coalescences

Combined gravitational-wave (GW) and electromagnetic (EM) observations of
compact binary mergers should enable detailed studies of astrophysical
processes in the strong-field gravity regime. Networks of GW interferometers
have poor angular resolution on the sky and their EM signatures are predicted
to be faint. Therefore, a challenging goal will be to unambiguously pinpoint
the EM counterparts to GW mergers. We perform the first comprehensive
end-to-end simulation that focuses on: i) GW sky localization, distance
measures and volume errors with two compact binary populations and four
different GW networks, ii) subsequent detectability by a slew of
multiwavelength telescopes and, iii) final identification of the merger
counterpart amidst a sea of possible astrophysical false-positives. First, we
find that double neutron star (NS) binary mergers can be detected out to a
maximum distance of 400 Mpc (or 750 Mpc) by three (or five) detector GW
networks respectively. NS -- black-hole (BH) mergers can be detected a factor
of 1.5 further out. The sky localization uncertainties for NS-BH mergers are
50--170 sq. deg. (or 6--65 sq. deg.) for a three (or five detector) GW network
respectively. Second, we quantify relative fractions of optical counterparts
that are detectable by different size telescopes. Third, we present five case
studies to illustrate the diversity of challenges in secure identification of
the EM counterpart at low and high Galactic latitudes. For the first time, we
demonstrate how construction of low-latency GW volumes in conjunction with
local universe galaxy catalogs can help solve the problem of false positives.

本研究模拟 GW 和 EM 观测点源的情况，定量地研究了 EM 对应物探测的挑战性，并提出了构建低延迟 GW 体积和当地星系目录相结合，以解决错误阳性问题的方法。