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Abstract The acquisition 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. 

Abstract We report superluminal jet motion with an apparent speed ofβ_app= 1.65 ± 0.57 in the radio-quiet (RQ) low-ionization nuclear emission-line region (LINER) galaxy KISSR 872. This result comes from two-epoch phase-referenced very long baseline interferometry observations at 5 GHz. The detection of bulk relativistic motion in the jet of this extremely radio-faint active galactic nucleus (AGN), with a total 1.4 GHz flux density of 5 mJy in the 5.″4 resolution Very Large Array FIRST survey image and 1.5 mJy in the ∼5 mas resolution Very Long Baseline Array image, is the first of its kind in an RQ LINER galaxy. The presence of relativistic jets in lower accretion rate objects like KISSR 872, with an Eddington ratio of 0.04, reveals that even RQ AGN can harbor relativistic jets and provides evidence of their universality over a wide range of accretion powers. 

ABSTRACT The analysis of individual X-ray sources that appear in a crowded field can easily be compromised by the misallocation of recorded events to their originating sources. Even with a small number of sources, which none the less have overlapping point spread functions, the allocation of events to sources is a complex task that is subject to uncertainty. We develop a Bayesian method designed to sift high-energy photon events from multiple sources with overlapping point spread functions, leveraging the differences in their spatial, spectral, and temporal signatures. The method probabilistically assigns each event to a given source. Such a disentanglement allows more detailed spectral or temporal analysis to focus on the individual component in isolation, free of contamination from other sources or the background. We are also able to compute source parameters of interest like their locations, relative brightness, and background contamination, while accounting for the uncertainty in event assignments. Simulation studies that include event arrival time information demonstrate that the temporal component improves event disambiguation beyond using only spatial and spectral information. The proposed methods correctly allocate up to 65$${{\ \rm per\ cent}}$$ more events than the corresponding algorithms that ignore event arrival time information. We apply our methods to two stellar X-ray binaries, UV Cet and HBC 515 A, observed with Chandra. We demonstrate that our methods are capable of removing the contamination due to a strong flare on UV Cet B in its companion ≈40× weaker during that event, and that evidence for spectral variability at times-scales of a few ks can be determined in HBC 515 Aa and HBC 515 Ab.