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We present a new method to estimate the boundary of extended sources in high-energy photon lists and to quantify the uncertainty in the boundary. This method extends the graphed seeded region growing method developed by M. Fan et al. Here, we describe how an unambiguous boundary of a centrally concentrated astronomical source may be defined by first spatially segmenting the photon list, then forcibly merging the segments until only two segments—an extended source and its background—remain, and finally constructing a boundary as the connected outer edges of the Voronoi tessellation of the photons included in the source segment. The resulting boundary is then modeled using Fourier descriptors to generate a smooth curve, and this curve is bootstrapped to generate uncertainties. We apply the method to photon event lists obtained during the observations of galaxies NGC 2300 and Arp 299. We demonstrate how the derived extent and enclosed flux of NGC 2300 obtained with Chandra and XMM-Newton are comparable. We also show how complex internal structure, as in the case of Arp 299, may be subsumed to construct a compact boundary of the object. 

Abstract We present results from the Chandra X-ray Observatory Large Project (878 ks in 28 observations) of the Large Magellanic Cloud supernova remnant N132D. We measure the expansion of the forward shock in the bright southern rim to be $0\stackrel{″}{.}10\pm 0\stackrel{″}{.}02$over the ∼14.5 yr baseline, which corresponds to a velocity of 1620 ± 400 km s⁻¹after accounting for several instrumental effects. We measure an expansion of $0\stackrel{″}{.}23\pm 0\stackrel{″}{.}02$and a shock velocity of 3840 ± 260 km s⁻¹for two features in an apparent blowout region in the northeast. The emission-measure-weighted average temperature inferred from X-ray spectral fits to regions in the southern rim is 0.95 ± 0.17 keV, consistent with the electron temperature implied by the shock velocity after accounting for Coulomb equilibration and adiabatic expansion. In contrast, the emission-measure-weighted average temperature for the northeast region is 0.77 ± 0.04 keV, which is significantly lower than the value inferred from the shock velocity. We fit 1D evolutionary models for the shock in the southern rim and northeast region, using the measured radius and propagation velocity into constant density and power-law profile circumstellar media. We find good agreement with the age of ∼2500 yr derived from optical expansion measurements for explosion energies of 1.5–3.0 × 10⁵¹erg, ejecta masses of 2–6M_⊙, and ambient medium densities of ∼0.33–0.66 amu cm⁻³in the south and ∼0.01–0.02 amu cm⁻³in the northeast assuming a constant density medium. These results are consistent with previous studies that suggested the progenitor of N132D was an energetic supernova that exploded into a preexisting cavity. 

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. 

Abstract 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 callSeeded Region Growing on Graph, after which the overall method is namedSRGonG. Starting with a set of seed locations, this results in an oversegmented data set, whichSRGonGthen coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. UsingSRGonGwe are able to identify point-like and diffuse extended sources in the data with equal facility. We validateSRGonGusing 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 demonstrateSRGonG’s use on the Chandra data of the Antennae galaxies and show that it segments the complex structures appropriately. 

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. 

Abstract We describe a process for cross-calibrating the effective areas of X-ray telescopes that observe common targets. The targets are not assumed to be “standard candles” in the classic sense, in that we assume that the source fluxes have well-defined, but a priori unknown values. Using a technique developed by Chen et al. that involves a statistical method called shrinkage estimation , we determine effective area correction factors for each instrument that bring estimated fluxes into the best agreement, consistent with prior knowledge of their effective areas. We expand the technique to allow unique priors on systematic uncertainties in effective areas for each X-ray astronomy instrument and to allow correlations between effective areas in different energy bands. We demonstrate the method with several data sets from various X-ray telescopes.