Towards Improved Heliosphere Sky Map Estimation with Theseus

The Interstellar Boundary Explorer (IBEX) satellite has been in orbit since 2008 and detects energy-resolved energetic neutral atoms (ENAs) originating from the heliosphere. Different regions of the heliosphere generate ENAs at different rates. It is of scientific interest to take the data collected by IBEX and estimate spatial maps of heliospheric ENA rates (referred to as sky maps) at higher resolutions than before. These sky maps will subsequently be used to discern between competing theories of heliosphere properties that are not currently possible. The data IBEX collects present challenges to sky map estimation. The two primary challenges are noisy and irregularly spaced data collection and the IBEX instrumentation’s point spread function. In essence, the data collected by IBEX are both noisy and biased for the underlying sky map of inferential interest. In this paper, we present a two-stage sky map estimation procedure called Theseus. In Stage 1, Theseus estimates a blurred sky map from the noisy and irregularly spaced data using an ensemble approach that leverages projection pursuit regression and generalized additive models. In Stage 2, Theseus deblurs the sky map by deconvolving the PSF with the blurred map using regularization. Unblurred sky map uncertainties are computed via bootstrapping. We compare Theseus to a method closely related to the one operationally used today by the IBEX Science Operation Center (ISOC) on both simulated and real data. Theseus outperforms ISOC in nearly every considered metric on simulated data, indicating that Theseus is an improvement over the current state of the art.

星际边界探测器（Interstellar Boundary Explorer, IBEX）自2008年起便在轨运行，可探测源自日球层的能量分辨型高能中性原子（energy-resolved energetic neutral atoms, ENAs）。日球层不同区域产生高能中性原子的速率各不相同。利用IBEX采集的数据，以高于以往的分辨率估算日球层高能中性原子产生速率的空间分布（又称天图），具有重要的科学意义。后续可借助这类天图，甄别当前尚无法区分的多种日球层特性竞争性理论。但IBEX采集的数据给天图估算带来了两项核心挑战：一是数据采集存在噪声且采样间隔不规则，二是IBEX仪器的点扩散函数（point spread function, PSF）效应。本质而言，IBEX采集的数据既包含噪声，又对我们所要推断的真实天图存在偏倚。本文提出了一种名为Theseus的两阶段天图估算流程。第一阶段中，Theseus采用集成学习方法，结合投影寻踪回归与广义加性模型，从含噪声且采样不规则的数据中估算出模糊天图。第二阶段中，Theseus通过正则化方法将点扩散函数与模糊天图进行反卷积，从而实现天图去模糊。借助自助法（bootstrapping）计算去模糊后天图的不确定性。我们分别在模拟数据与真实数据上，将Theseus与IBEX科学运行中心（IBEX Science Operation Center, ISOC）当前实际使用的方法进行了对比。在模拟数据上，Theseus在几乎所有评估指标上均优于ISOC的方法，表明Theseus相较于当前主流技术水平实现了性能提升。