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GRS 1747−312 is a bright Low-Mass X-ray Binary in the globular cluster Terzan 6, located at a distance of 9.5 kpc from the Earth. It exhibits regular outbursts approximately every 4.5 months, during which periodic eclipses are known to occur. These eclipses have only been observed in the outburst phase, and are not clearly seen when the source is quiescent. Recent Chandra observations of the source were performed in 2019 June and 2021 April, June, and August. Two of these observations captured the source during its outburst, and showed clear flux decreases at the expected time of eclipse. The other two observations occurred when the source was quiescent. We present the discovery of a dip that occurred during the quiescent state. The dip is of longer duration and its time of occurrence does not fit the ephemeris of the shorter eclipses. We study the physical characteristics of the dip and determine that it has all the properties of an eclipse by an object with a well defined surface. We find that there are several possibilities for the nature of the object causing the 5.3 ks eclipse. First, GRS 1747−312 may be an X-ray triple, with an LMXB orbited by an outer third object, which could be an M-dwarf, brown dwarf, or planet. Secondly, there could be two LMXBs in close proximity to each other, potentially bound together. Whatever the true nature of the eclipser, its presence suggests that the GRS 1747−312 system is exotic.

 

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 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. 

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.