AI 中文总结
本研究应用统计数据同化框架，结合太阳与核心坍缩超新星的中微子味演化简化模型，成功提取两类场景下物质密度涨落振幅信息，在太阳案例中可靠性更高，高涨落振幅下方法同样有效。
AI 中文摘要
中微子在天体物理源（如太阳和核心坍缩超新星）中产生后，在到达地面探测器的过程中会发生显著的味演化。这种味演化受中微子相互作用的环境强烈影响，使中微子成为有用的天体信使，可能携带其产生源的性质信息。本研究应用统计数据同化（SDA）框架，确定探测到的中微子信号能在多大程度上携带关于其从源到探测器路径上物质密度涨落的信息。通过使用太阳和核心坍缩超新星（CCSN）中中微子味演化与传播的简化模型，研究发现SDA方法能够提取太阳和CCSN场景下密度涨落振幅的信息，该方法在太阳中微子案例中表现出相对更高的可靠性；即便在CCSN中微子模型中，该方法在高涨落振幅下也被证明是有效的。
英文摘要
Neutrinos can undergo substantial flavor evolution between their production in astrophysical sources-such as the Sun and core-collapse supernovae-and their subsequent detection in terrestrial detectors. This flavor evolution is strongly influenced by the environment that the neutrinos interact with, making them useful astrophysical messengers capable of potentially carrying information about the properties of the source wherein they are produced. In this work, we apply the framework of statistical data assimilation (SDA) in order to ascertain the extent to which a neutrino signal at detection may contain information about matter density fluctuations along their path from the source. Using simplified models of neutrino flavor evolution and propagation in the sun and in a core-collapse supernova (CCSN), we find that the SDA method is able to extract information about the amplitude of density fluctuations in both the solar and CCSN scenarios, with the method showing relatively greater reliability in the solar neutrino case. Nonetheless, even in the CCSN neutrino model, the method proved effective at high fluctuation amplitudes
