Characterizing a supernova’s standing accretion shock instability with neutrinos and gravitational waves
Characterizing a supernova’s standing accretion shock instability with neutrinos and gravitational waves
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DOI：
10.1103/physrevd.107.083017
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发表时间：
2022-11
影响因子：
5
通讯作者：
Zidu Lin;Abhinav Rijal;C. Lunardini;M. Morales;M. Zanolin
中科院分区：
文献类型：
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作者：
Zidu Lin;Abhinav Rijal;C. Lunardini;M. Morales;M. Zanolin
我们进行了一种新颖的多信使分析，用于识别和估计具有中微子和引力波（GW）信号的核心塌陷超新星中的固定吸积激波不稳定性（SASI）。在中微子通道中，该方法对频域中 SASI 的存在进行似然比测试。对于引力波信号，我们使用改进的约束似然法来处理事件。利用模拟超新星信号、超神冈中微子探测器的特性和 O3 LIGO 干涉数据，我们生成 SASI 活动指示器的二维概率密度函数 (PDF)，并计算探测概率 $P_\mathrm{D}$ 以及错误识别概率 $P_\mathrm{FI}$。我们讨论了确定 SASI 存在的概率作为每个观测通道中源距离的函数，以及联合的概率。与单信使方法相比，联合分析结果表明 SASI 活动的 $P_\mathrm{D}$ （在 $P_\mathrm{FI}=0.1$ 处）对于距超新星 5 kpc 的距离而言高达 $\approx~40\%$。我们还讨论了如何准确地分别估计每个通道中 SASI 活动的频率和持续时间。我们的方法适合在现实数据分析和多信使环境中实施。
We perform a novel multi-messenger analysis for the identification and parameter estimation of the Standing Accretion Shock Instability (SASI) in a core collapse supernova with neutrino and gravitational wave (GW) signals. In the neutrino channel, this method performs a likelihood ratio test for the presence of SASI in the frequency domain. For gravitational wave signals we process an event with a modified constrained likelihood method. Using simulated supernova signals, the properties of the Hyper-Kamiokande neutrino detector, and O3 LIGO Interferometric data, we produce the two-dimensional probability density function (PDF) of the SASI activity indicator and calculate the probability of detection $P_\mathrm{D}$ as well as the false identification probability $P_\mathrm{FI}$. We discuss the probability to establish the presence of the SASI as a function of the source distance in each observational channel, as well as jointly. Compared to a single-messenger approach, the joint analysis results in $P_\mathrm{D}$ (at $P_\mathrm{FI}=0.1$) of SASI activities that is larger by up to $\approx~40\%$ for a distance to the supernova of 5 kpc. We also discuss how accurately the frequency and duration of the SASI activity can be estimated in each channel separately. Our methodology is suitable for implementation in a realistic data analysis and a multi-messenger setting.