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The production of complex astronomical data is accelerating, especially with newer telescopes producing ever more large-scale surveys. The increased quantity, complexity, and variety of astronomical data demand a parallel increase in skill and sophistication in developing, deciding, and deploying statistical methods. Understanding limitations and appreciating nuances in statistical and machine learning methods and the reasoning behind them is essential for improving data-analytic proficiency and acumen. Aiming to facilitate such improvement in astronomy, we delineate cautionary tales in statistics via six maxims, with examples drawn from the astronomical literature. Inspired by the significant quality improvement in business and manufacturing processes by the routine adoption of Six Sigma, we hope the routine reflection on these Six Maxims will improve the quality of both data analysis and scientific findings in astronomy. 

Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may be superposed. We have developed a novel nonparametric event list segmentation algorithm to divide up the field of view into distinct emission components. We use photon location data directly, without binning them into an image. We first construct a graph from the Voronoi tessellation of the observed photon locations and then grow segments using a new adaptation of seeded region growing that we call Seeded Region Growing on Graph, after which the overall method is named SRGonG. Starting with a set of seed locations, this results in an oversegmented data set, which SRGonG then coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. Using SRGonG we are able to identify point-like and diffuse extended sources in the data with equal facility. We validate SRGonG using simulations, demonstrating that it is capable of discerning irregularly shaped low-surface-brightness emission structures as well as point-like sources with strengths comparable to that seen in typical X-ray data. We demonstrate SRGonG's use on the Chandra data of the Antennae galaxies and show that it segments the complex structures appropriately. 

The impact of the dynamic evolution of the Storm-Enhanced Density (SED) on the upward ion fluxes during the March 06, 2016 geomagnetic storm is studied using comprehensive multi-scale datasets. This storm was powered by a Corotating Interaction Region (CIR), and the minimum Sym-H reached ∼−110 nT. During the ionospheric positive storm phase, the SED formed and the associated plume and polar cap patches occasionally drifted anti-sunward across the polar cap. When these high-density structures encountered positive vertical flows, large ion upward fluxes were produced, with the largest upward flux reaching 3 × 10¹⁴ m⁻²s⁻¹. These upflows were either the type-1 ion upflow associated with fast flow channels, such as the subauroral polarization stream (SAPS) channel, or the type-2 ion upflow due to soft particle precipitations in the cusp region. The total SED-associated upflow flux in the dayside cusp can be comparable to the total upflow flux in the nightside auroral zone despite the much smaller cusp area compared with the auroral zone. During the ionospheric negative storm phase, the ionospheric densities within the SED and plume decreased significantly and thus led to largely reduced upward fluxes. This event analysis demonstrates the critical role of the ionospheric high-density structures in creating large ion upward fluxes. It also suggests that the dynamic processes in the coupled ionosphere-thermosphere system and the resulting state of the ionospheric storm are crucial for understanding the temporal and spatial variations of ion upflow fluxes and thus should be incorporated into coupled geospace models for improving our holistic understanding of the role of ionospheric plasma in the geospace system.