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Abstract We present a mosaic of those co-added Full Frame Images acquired by the TESS satellite that had been released in 2020 April. The mosaic shows substantial stray light over the sky. Yet over spatial scales of a few degrees, the background appears uniform. This result indicates that TESS has considerable potential as a Low Surface Brightness Observatory. The co-added images are freely available as a High Level Science Product (HLSP) at MAST and accessible through a Jupyter Notebook. 

We describe a case study to use the Montage image mosaic engine to create maps of the ALLWISE image data set in the Hierarchical Progressive Survey (HiPS) sky-tesselation scheme. Our approach demonstrates that Montage reveals the science content of infrared images in greater detail than has hitherto been possible in HiPS maps. The approach exploits two unique (to our knowledge) characteristics of the Montage image mosaic engine: background modeling to rectify the time variable image backgrounds to common levels; and an adaptive image stretch to present images for visualization. The creation of the maps is supported by the development of four new tools that when fully tested will become part of the Montage distribution. The compute intensive part of the processing lies in the reprojection of the images, and we show how we optimized the processing for efficient creation of mosaics that are used in turn to create maps in the HiPS tiling scheme. We plan to apply our methodology to infrared image data sets such a those delivered by Spitzer, 2MASS, IRAS and Planck. 

e describe the use of Montage to create all-sky astronomy maps compliant with the Hierarchical Progressive Survey (HiPS) sky-tesselation scheme. These maps support panning and zooming across the sky to progressively smaller scales, and are used widely for visualization in astronomy. They are, however, difficult to create at infrared wavelengths because of high background emission. Montage is an ideal tool for creating infrared maps for two reasons: it uses background modeling to rectify the time variable image backgrounds to a common level; and it uses an adaptive image stretch algorithm to convert the image data to display values for visualization. The creation of the maps involves the use of existing Montage tools in tandem with four new tools to support HiPS. We wil present images of infrared sky surveys in the HiPS scheme. 

Presentation given by invitation at the NASA HyperWall exhibit. 

This paper describes how we have sustained the Montage image mosaic engine (http://montage.ipac.caltech.edu) first released in 2002, to support the ever-growing scale and complexity of modern data sets. The key to its longevity has been its design as a toolkit written in ANSI-C, with each tool performing one distinct task, for easy integration into scripts, pipelines and workflows. The same code base now supports Windows, JavaScript and Python by taking advantage of recent advances in compilers. The design has led to applicability of Montage far beyond what was anticipated when Montage was first built, such as supporting observation planning for the JWST. Moreover, Montage is highly scalable and is in wide use within the IT community to develop advanced, fault-tolerant cyber-infrastructure, such as job schedulers for grids, workflow orchestration, and restructuring techniques for processing complex workflows and pipelines.