The optical and UV variability of the majority of active galactic nuclei may be related to the reprocessing of rapidly changing X-ray emission from a more compact region near the central black hole. Such a reprocessing model would be characterized by lags between X-ray and optical/UV emission due to differences in light travel time. Observationally, however, such lag features have been difficult to detect due to gaps in the lightcurves introduced through factors such as source visibility or limited telescope time. In this work, Gaussian process regression is employed to interpolate the gaps in the Swift X-ray and UV lightcurves of the narrow-line Seyfert 1 galaxy Mrk 335. In a simulation study of five commonly employed analytic Gaussian process kernels, we conclude that the Matern 1 2 and rational quadratic kernels yield the most well-specified models for the X-ray and UVW2 bands of Mrk 335. In analyzing the structure functions of the Gaussian process lightcurves, we obtain a broken power law with a break point at 125 days in the UVW2 band. In the X-ray band, the structure function of the Gaussian process lightcurve is consistent with a power law in the case of the rational quadratic kernel while a broken power law with a break point at 66 days is obtained from the Matern 1 2 kernel. The subsequent cross-correlation analysis is consistent with previous studies and furthermore shows tentative evidence for a broad X-ray-UV lag feature of up to 30 days in the lag-frequency spectrum where the significance of the lag depends on the choice of Gaussian process kernel.Unified Astronomy Thesaurus concepts: Accretion (14); Galaxy accretion disks (562); Active galaxies (17); Gaussian Processes regression (1930); Bayesian statistics (1900); Galaxy nuclei (609)
Fast track article for IS&T International Symposium on Electronic Imaging 2021: Computer Vision and Image Analysis of Art 2021 proceedings.
Fast track article for IS&T International Symposium on Electronic Imaging 2021: Computer Vision and Image Analysis of Art 2021 proceedings.
Fast track article for IS&T International Symposium on Electronic Imaging 2021: Computer Vision and Image Analysis of Art 2021 proceedings.
<p><strong>Abstract</strong></p> <p>A novel application of the transfer of style via deep convolutional neural networks (CNN) is presented, utilising an ensemble of paintings, by Vincent van Gogh to stylise Martian landscape imagery. Neural style transfer (NST)<sup>[1][2] </sup>can be used to create artistic work through rendering a content image in the form of a style image. Through applying NST to Martian landscape imagery, a collection of artwork is presented that emulates what van Gogh may have painted had the artist visited Mars.</p> <p><strong>Introduction</strong></p> <p>Since the dawn of humanity celestial phenomena have inspired art, science and exploration. Throughout history these celestial phenomena have been viewed beneath the terrestrial sky. In the 21<sup>st</sup> century, through technological advances in spacecraft reusability and artificial intelligence, new celestial phenomena may be viewed by astronauts beneath extra-terrestrial skies for the first time.<sup>[3]</sup></p> <p>The Dutch post-impressionist painter Vincent van Gogh (1853-1890) is widely regarded as one of the most influential artists in Western art. Many of van Gogh&#8217;s most famous artwork includes celestial objects, such as in stars in The Starry Night (1889)<sup>[4] </sup>and The Starry Night over the Rh&#244;ne (1888)<sup>[5] </sup>and the Moon in Landscape with Wheat Sheaves and Rising Moon, (1889)<sup>[6]</sup>. Here a collection of work highlighting the achievements of the planetary science community in exploring the Martian environment is presented. This collection imagines what Vincent van Gogh would have painted had the artist have visited the Red Planet.</p> <p><strong>Methods</strong></p> <p>NST was&#160;developed by<sup>[1]</sup> and can be used to create novel artistic work by rendering one image in the&#160;style&#160;of another.&#160;NST uses a previously trained CNN called the VGG-19 network; a 19 layer&#160;neural&#160;network that has been trained on a large dataset of ImageNet<sup>[7]</sup> images. Training on this large dataset of images allows the VGG-19&#160;neural&#160;network to recognise low and high-level features in images.&#160;</p> <p>A key aspect of NST involves defining and minimising the content and&#160;style&#160;cost functions.&#160;Once these functions are defined, they are added together to create a total cost function. Using the Adam<sup>[8]</sup> optimisation algorithm, the&#160;aforementioned total cost function, which makes the generated image follow the content of the content image and the&#160;style&#160;of the&#160;style&#160;image simultaneously, which after several iterations creates stylised images. For further details the author refers the reader to the literature<sup>[1][2]</sup>.</p> <p>In this research 30 paintings by Vincent van Gogh were identified that included the Sun, Moon and stars, these artworks contribute to the collection of style images used with NST. Similarly, 30 famous images of Martian landscape imagery were selected, including images from NASA&#8217;s Spirit, Opportunity and Curiosity Rovers and Viking 1/2 landers, these artworks contribute to the collection of content images used with NST. The author found that creating a montage of a particular feature, i.e. the sky or cobbled streets, followed by applying NST from <sup>[2]</sup> with a linear colour transfer provided aesthetically better results than utilising semantic style transfer with guided gram matrices and masks.</p> <p><strong>Results</strong></p> <p><strong>&#160;&#160;<img src="data:image/png;base64, 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