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Inferring the point spread function (PSF) at galaxy positions is one of the crucial steps of the shear measurement. We introduce a novel method to estimate the PSFs at the galaxy positions by using the galaxy images, which could provide additional constrains for the PSF field variations. We construct the PSF for each star image by using Principal-Components-Analysis (PCA) method, which can capture the most significant characteristics of the data. Our method utilizes the image difference of the same object between multi-exposures to probe the coefficients of the principal components, in which the differences are mainly caused by PSFs. We apply our method to the observed data. The results show that the corresponding PSFs can be properly estimated from multiple images of different exposures. We then use the obtained principal components from the observations to mock multi-exposure images, where the PSFs field of each exposure is constructed by bivariate polynomial on coefficients. We find that our method can reproduce the PSFs consistently with mocked data. Our results show that the multi-exposed galaxy images could provide us additional constraints for the PSF fields in PCA scenario. It offers a promising prospect for combing the information of stars and galaxies together to construct the PSF field when the point sources are sparsely sampled. [ABSTRACT FROM AUTHOR]

Accurate shear measurement is a key topic in weak lensing community. Point spread function (PSF), which smears the observed galaxy image, plays one of the main roles in the systematic errors in shear measurement and must be treated carefully to avoid bias and errors in cosmological parameters. In this paper, we present new PSF measurement methods, Smooth-PCA (SPCA) and Improved-SPCA (iSPCA), which can reconstruct smooth PSFs with high efficiency. Our methods decompose the star images into smooth principal components by using the Expectation-Maximization-PCA (EMPCA) method, and the smooth principal components are composed by Moffatlets basis functions, which are derived from the Moffat function. We demonstrate our approaches based on simulated Moffat PSFs and phosim star images. The constructed smooth principal components show flexible and efficient as the same as EMPCA, and have more stable patterns than EMPCA under noises contamination. We then check the reconstruction accuracy on the shape of PSFs. We find that our methods are able to reconstruct the PSFs at the same precision as the EMPCA method that indicates and iSPCA are promising for weak lensing shear measurement. [ABSTRACT FROM AUTHOR]

We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn , is publicly available at https://github.com/burke86/astro_rcnn. [ABSTRACT FROM AUTHOR]

We present an X-ray spectroscopic study of the nuclear region of the M31 bulge, based on observations of the XMM–Newton Reflection Grating Spectrometers. The obtained high-resolution grating spectra clearly show individual emission lines of highly ionized iron and oxygen, which unambiguously confirm the presence of diffuse hot gas in the bulge, as indicated from previous X-ray CCD imaging studies. We model the spectra with detailed Monte Carlo simulations, which provide a robust spectroscopic estimate of the hot gas temperature ∼0.29 keV and the O/Fe ratio ∼0.3 solar. The results indicate that iron ejecta of Type Ia supernovae are partly mixed with the hot gas. The observed spectra show an intensity excess at the O vii triplet, which most likely represents emission from charge exchanges at the interface between the hot gas and a known cool gas spiral in the same nuclear region. [ABSTRACT FROM AUTHOR]