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The Eskimo Nebula (NGC 2392) is a double-shell planetary nebula (PN) known for the exceptionally large expansion velocity of its inner shell, ~90 km s-1, and the existence of a fast bipolar outflow with a line-of-sight expansion velocity approaching 200 km s-1. We have obtained XMM-Newtonobservations of the Eskimo and detected diffuse X-ray emission within its inner shell. The X-ray spectra suggest thin plasma emission with a temperature of ~2 $\times$106K and an X-ray luminosity of $L_{\rm X} = (2.6\pm1.0)$$\times$$10^{31} (d/1150~{\rm pc})^2$erg s-1, where dis the distance in parsecs. The diffuse X-ray emission shows noticeably different spatial distributions between the 0.2–0.65 keV and 0.65–2.0 keV bands. High-resolution X-ray images of the Eskimo are needed to determine whether its diffuse X-ray emission originates from shocked fast wind or bipolar outflows.

XMM-NewtonEPIC observations of the planetary nebula (PN) NGC 7009, the Saturn Nebula, have detected extended X-ray emission in its central cavity. The diffuse X-ray emission must originate in the shocked fast stellar wind. The spectra show that the temperature of the hot gas is $1.8\times 10^6$K. The rms density derived from the volume emission measure is a few tens H-atom cm-3. The hot gas does not appear over-pressurized with respect to the nebular shell. NGC 7009 represents an evolutionary stage at which the influence of the hot gas in the PN interior starts to decline due to the diminishing strength of the fast stellar wind and the expansion of the central cavity.

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered "Planet 9", may be present in the outer solar system. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a data set consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimized, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) = 0.996 ± 0.001. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.

The Dark Energy Survey (DES) is a five-year optical imaging campaign with the goal of understanding the origin of cosmic acceleration. DES performs a ?5000 deg2survey of the southern sky in five optical bands (g, r, i, z, Y) to a depth of ?24th magnitude. Contemporaneously, DES performs a deep, time-domain survey in four optical bands (g, r, i, z) over ?27 deg2. DES exposures are processed nightly with an evolving data reduction pipeline and evaluated for image quality to determine if they need to be retaken. Difference imaging and transient source detection are also performed in the time domain component nightly. On a bi-annual basis, DES exposures are reprocessed with a refined pipeline and coadded to maximize imaging depth. Here we describe the DES image processing pipeline in support of DES science, as a reference for users of archival DES data, and as a guide for future astronomical surveys.

We characterize the variation in photometric response of the Dark Energy Camera (DECam) across its 520 Mpix science array during 4 years of operation. These variations are measured using high signal-to-noise aperture photometry of >107stellar images in thousands of exposures of a few selected fields, with the telescope dithered to move the sources around the array. A calibration procedure based on these results brings the rms variation in aperture magnitudes of bright stars on cloudless nights down to 2-3 mmag, with <1 mmag of correlated photometric errors for stars separated by ?20?. On cloudless nights, any departures of the exposure zeropoints from a secant airmass law exceeding 1 mmag are plausibly attributable to spatial/temporal variations in aperture corrections. These variations can be inferred and corrected by measuring the fraction of stellar light in an annulus between 6? and 8? diameter. Key elements of this calibration include: correction of amplifier nonlinearities; distinguishing pixel-area variations and stray light from quantum-efficiency variations in the flat fields; field-dependent color corrections; and the use of an aperture-correction proxy. The DECam response pattern across the 2° field drifts over months by up to ±9 mmag, in a nearly wavelength-independent low-order pattern. We find no fundamental barriers to pushing global photometric calibrations toward mmag accuracy.

We describe the model for mapping from sky brightness to the digital output of the Dark Energy Camera (DECam) and the algorithms adopted by the Dark Energy Survey (DES) for inverting this model to obtain photometric measures of celestial objects from the raw camera output. This calibration aims for fluxes that are uniform across the camera field of view and across the full angular and temporal span of the DES observations, approaching the accuracy limits set by shot noise for the full dynamic range of DES observations. The DES pipeline incorporates several substantive advances over standard detrending techniques, including principal-components-based sky and fringe subtraction; correction of the "brighter-fatter" nonlinearity; use of internal consistency in on-sky observations to disentangle the influences of quantum efficiency, pixel-size variations, and scattered light in the dome flats; and pixel-by-pixel characterization of instrument spectral response, through combination of internal-consistency constraints with auxiliary calibration data. This article provides conceptual derivations of the detrending/calibration steps, and the procedures for obtaining the necessary calibration data. Other publications will describe the implementation of these concepts for the DES operational pipeline, the detailed methods, and the validation that the techniques can bring DECam photometry and astrometry within mmag and mas, respectively, of fundamental atmospheric and statistical limits. The DES techniques should be broadly applicable to wide-field imagers.

We characterize the ability of the Dark Energy Camera (DECam) to perform relative astrometry across its 500 Mpix, 3-deg2science field of view and across four years of operation. This is done using internal comparisons of ?4 × 107measurements of high signal-to-noise ratio stellar images obtained in repeat visits to fields of moderate stellar density, with the telescope dithered to move the sources around the array. An empirical astrometric model includes terms for optical distortions; stray electric fields in the CCD detectors; chromatic terms in the instrumental and atmospheric optics; shifts in CCD relative positions of up to ?10 ?m when the DECam temperature cycles; and low-order distortions to each exposure from changes in atmospheric refraction and telescope alignment. Errors in this astrometric model are dominated by stochastic variations with typical amplitudes of 10-30 mas (in a 30 s exposure) and 5'-10' coherence length, plausibly attributed to Kolmogorov-spectrum atmospheric turbulence. The size of these atmospheric distortions is not closely related to the seeing. Given an astrometric reference catalog at density e.g., from Gaia, the typical atmospheric distortions can be interpolated to ?7 mas rms accuracy (for 30 s exposures) with coherence length in residual errors. Remaining detectable error contributors are 2-4 mas rms from unmodelled stray electric fields in the devices, and another 2-4 mas rms from focal plane shifts between camera thermal cycles. Thus the astrometric solution for a single DECam exposure is accurate to 3-6 mas (?0.02 pixels, or ?300 nm) on the focal plane, plus the stochastic atmospheric distortion.