论文标题

来自IceCube数据的天体中微子源人群的贝叶斯限制

Bayesian constraints on the astrophysical neutrino source population from IceCube data

论文作者

Capel, Francesca, Mortlock, Daniel J., Finley, Chad

论文摘要

我们对Icecube Neutminino天文台的最新数据施加的中微子来源的天体物理种群提出了限制。通过使用ICECUBE点源搜索方法对源的检测进行建模,我们的检测标准比使用高能中微子多重组进行源识别更为敏感。我们将问题作为贝叶斯层次模型将高水平的总体参数与ICECUBE数据联系起来,从而使我们能够始终如一地说明模型假设中的所有不确定性来源。我们的结果表明,本地密度为$ n_0 \ gtrsim 10^{ - 7} $ $ $ \ rm {mpc}^{ - 3} $ and Luminosity $ l \ lyssim 10^{43} $ erg/s的来源$ \ rm {mpc}^{ - 3} $和$ l \ simeq 10^{45} $ erg/s仍然可以与IceCube观测值一致。我们证明,这些结论在很大程度上取决于所考虑的源演化,我们考虑了广泛的模型。在此过程中,我们对反映我们当前的天体中微子观察的知识状态的人口参数呈现了现实的,独立的约束。在可能的源检测和未来仪器升级的情况下,我们还使用框架来研究约束。我们的方法是灵活的,可用于建模特定的源案例,并扩展到包含多通信的信息。

We present constraints on an astrophysical population of neutrino sources imposed by recent data from the IceCube neutrino observatory. By using the IceCube point source search method to model the detection of sources, our detection criterion is more sensitive than using the observation of high-energy neutrino multiplets for source identification. We frame the problem as a Bayesian hierarchical model to connect the high-level population parameters to the IceCube data, allowing us to consistently account for all relevant sources of uncertainty in our model assumptions. Our results show that sources with a local density of $n_0 \gtrsim 10^{-7}$ $\rm{Mpc}^{-3}$ and luminosity $L \lesssim 10^{43}$ erg/s are the most likely candidates, but that populations of rare sources with $n_0 \simeq 10^{-9}$ $\rm{Mpc}^{-3}$ and $L \simeq 10^{45}$ erg/s can still be consistent with the IceCube observations. We demonstrate that these conclusions are strongly dependent on the source evolution considered, for which we consider a wide range of models. In doing so, we present realistic, model-independent constraints on the population parameters that reflect our current state of knowledge from astrophysical neutrino observations. We also use our framework to investigate constraints in the case of possible source detections and future instrument upgrades. Our approach is flexible and can be used to model specific source cases and extended to include multi-messenger information.

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