The NASA Exoplanet Archive (NEA) and the Exoplanet Follow-up Observing Program service are two widely used resources for the exoplanet community. The NEA provides a complete and accurate accounting of exoplanetary systems published by NASA missions and by the community in the refereed literature. In anticipation of continued exponential growth in the number of exoplanetary systems and the increasing complexity in our characterization of these systems, the NEA has restructured its primary tables and interfaces, as well as extending and standardizing their modes of access. The Exoplanet Follow-up Observing Program service provides the exoplanet community with a venue for coordinating and sharing follow-up and precursor data for exoplanets, their host stars, and stars that might eventually be targets for future planet searches and recently reached 1 million files uploaded by the community. In this paper, we describe the updates to our data holdings, functionality, accessibility, and tools, as well as future priorities for these two services.

For all active instruments, the Keck Observatory Archive (KOA) now ingests raw data from the Keck Telescopes within 1 minute of acquisition, quick-look reduced data within 5 minutes of creation, and science ready reduced data for four instruments as they are created by their automated pipelines. On August 1, 2023, KOA released the Observers Data Access Portal (ODAP), which enables observers at the telescope and their collaborators anywhere in the world to securely monitor and download science, calibration, and quick-look data as they are ingested into the archive. The portal is built using Python Socket IO.WebSockets that ensure metadata appear in the portal as the data themselves are ingested. The portal itself is a dynamic web interface built with React. It enables users to view and customize metadata fields, filter metadata according to data type, and download data as they are ingested or in bulk through wget scripts. Observers have used the ODAP since its release and have provided feedback that will guide future releases.

The W. M. Keck Observatory Archive (KOA) has released the Observers' Data Access Portal (ODAP), a web-application that delivers astronomical data from the W. M. Keck Observatory to the scheduled program's principal investigator and their collaborators anywhere in the world in near real-time. Data files and their associated metadata are streamed to a user's desktop machine moments after they are written to disk and archived in KOA. The ODAP User Interface is built in React and uses the WebSocket protocol to stream data between KOA and the user. This document describes the design of the tool, challenges encountered, shows how ODAP is integrated into the Keck observing model, and provides an analysis of usage metrics.

We report here on a project that has developed a practical approach to processing all-sky image collections on cloud platforms, using as an exemplar application the creation of three-color Hierarchical Progressive Survey (HiPS) maps of the 2MASS data set with the Montage Image Mosaic Engine on Amazon Web Services. We will emphasize issues that must be considered by scientists wishing to use cloud platforms to perform such parallel processing, so providing a guide for scientists wishing to exploit cloud platforms for similar large-scale processing. A HiPS map is based on the HEALPix sky-tiling scheme. Progressive zooming of a HiPS map reveals an image sampled at ever smaller or larger spatial scales that are defined by the HEALPix standard. Briefly, the approach used by Montage involves creating a base mosaic at the lowest required HEALPix level, usually chosen to match as closely as possible the spatial sampling of the input images, then cutting out the HiPS cells in PNG format from this mosaic. The process is repeated at successive HEALPix levels to create a nested collection of FITS files, from which PNG files are created that are shown in HiPS viewers. Stretching FITS files to produce PNGs is based on an image histogram. For composite regions (up and including the whole sky), the histograms for each tile can be combined to create a composite histogram for the region. Using this single histogram for each of the individual FITS files means all the PNGs are on the same brightness scale and displaying them side by side in a HiPS viewer produces a continuous uniform map across the entire sky.

The Montage Image Mosaic Engine, first released in 2002, has found applicability across the electromagnetic spectrum to support data processing and visualization. This broad applicability has come about through its design as an Open Source ANSI-C toolkit (and Python binary extensions), with independent components to perform each step in the creation of a mosaic and with support for all WCS extensions. This design enables easy integration into custom environments, workflows and pipelines, and is the principal reason for its long lifetime. Here we emphasize the growing use of Montage in radio astronomy (37 peer-reviewed papers since 2020), and will focus on three high-profile applications: (1) Analysis of observations made with SKA precursor experiments, such as MeerKAT and the Murchison Wide-field Array, (2) Faraday tomography of LOFAR Two-Metre Sky Survey data (LoTSS-DR2), which explores the structure of the local interstellar medium, and (3) Identification of fast radio bursts.

We describe the development of the Keck Observatory Archive (KOA) Data Discovery Service, a web-based dashboard that returns metadata for wide-area queries of the entire archive in seconds. Currently in beta, this dashboard will support exploration, visualization, and data access across multiple instruments. This effort is underpinned by an open-source, VO-compliant query infrastructure and will offer services that can be hosted on web pages or in Jupyter notebooks. The effort also informs the design of a new, modern landing page that meets the expectations of accessibility and ease of use. The new query infrastructure is based on nexsciTAP, a component-based, DBMS-agnostic Python implementation of the IVOA Table Access Protocol, developed at NExScI and integrated into the NASA Exoplanet Archive and the NEID Archive, and into the PyKOA Python client. This infrastructure incorporates R-tree spatial indexing, built as memory-mapped files as part of Montage, a software toolkit used to create composite astronomical images. Although R-trees are used most often in geospatial analysis, here they enable searches of the entire KOA archive, an eclectic collection of 100 million records of imaging and spectroscopic data, in 2 seconds, and they speed up spatial searches by x20. The front end is built on the open-source Plotly-Dash framework, which allows users to build an interactive user interface based on a single Python file.

Thoth is a free desktop/laptop software application with a friendly graphical user interface that facilitates routine data-visualization and statistical-calculation tasks for astronomy and astrophysical research (and other fields where numbers are visualized). This software has been upgraded with many significant improvements and new capabilities. The major upgrades consist of: (1) six new graph types, including 3D stacked-bar charts and 3D surface plots, made by the Orson 3D Charts library; (2) new saving and loading of graph settings; (3) a new batch-mode or command-line operation; (4) new graph-data annotation functions; (5) new options for data-file importation; and (6) a new built-in FITS-image viewer. There is now the requirement that Thoth be run under Java 1.8 or higher. Many other miscellaneous minor upgrades and bug fixes have also been made to Thoth. The newly implemented plotting options generally make possible graph construction and reuse with relative ease, without resorting to writing computer code. The illustrative astronomy case study of this paper demonstrates one of the many ways the software can be utilized. These new software features and refinements help make astronomers more efficient in their work of elucidating data.

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.

Image processing at scale is a powerful tool for creating new data sets and integrating them with existing data sets and performing analysis and quality assurance investigations. Workflow managers offer advantages in this type of processing, which involves multiple data access and processing steps. Generally, they enable automation of the workflow by locating data and resources, recovery from failures, and monitoring of performance. In this focus demo we demonstrate how the Pegasus Workflow Manager Python API manages image processing to create mosaics with the Montage Image Mosaic engine. Since 2001, Pegasus has been developed and maintained at USC/ISI. Montage was in fact one of the first applications used to design Pegasus and optimize its performance. Pegasus has since found application in many areas of science. LIGO exploited it in making discoveries of black holes. The Vera C. Rubin Observatory used it to compare the cost and performance of processing images on cloud platforms. While these are examples of projects at large scale, small team investigations on local clusters of machines can benefit from Pegasus as well.

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.

The Montage image mosaic engine has found wide applicability in astronomy research, integration into processing environments, and is an examplar application for the development of advanced cyber-infrastructure. It is written in C to provide performance and portability. Linking C/C++ libraries to the Python kernel at run time as binary extensions allows them to run under Python at compiled speeds and enables users to take advantage of all the functionality in Python. We have built Python binary extensions of the 59 ANSI-C modules that make up version 5 of the Montage toolkit. This has involved a turning the code into a C library, with driver code fully separated to reproduce the calling sequence of the command-line tools; and then adding Python and C linkage code with the Cython library, which acts as a bridge between general C libraries and the Python interface. We will demonstrate how to use these Python binary extensions to perform image processing, including reprojecting and resampling images, rectifying background emission to a common level, creation of image mosaics that preserve the calibration and astrometric fidelity of the input images, creating visualizations with an adaptive stretch algorithm, processing HEALPix images, and analyzing and managing image metadata.

The Montage image mosaic engine has found wide applicability in astronomy research, integration into processing environments, and is an examplar application for the development of advanced cyber-infrastructure. It is written in C to provide performance and portability. Linking C/C++ libraries to the Python kernel at run time as binary extensions allows them to run under Python at compiled speeds and enables users to take advantage of all the functionality in Python. We have built Python binary extensions of the 59 ANSI-C modules that make up version 5 of the Montage toolkit. This has involved a turning the code into a C library, with driver code fully separated to reproduce the calling sequence of the command-line tools; and then adding Python and C linkage code with the Cython library, which acts as a bridge between general C libraries and the Python interface. We will demonstrate how to use these Python binary extensions to perform image processing, including reprojecting and resampling images, rectifying background emission to a common level, creation of image mosaics that preserve the calibration and astrometric fidelity of the input images, creating visualizations with an adaptive stretch algorithm, processing HEALPix images, and analyzing and managing image metadata.

The primary goal of the two-year Transiting Exoplanet Sky Survey (TESS) mission is to discover new, nearby exoplanet systems (Ricker et al. 2015). The mission acquires images every 30 minutes, through a single broadband filter and with four cameras. It offers a unique opportunity to study the diffuse universe. Holwerda (2018) showed it can in principle allow studies of topics such as the derivation of the halo mass profiles of nearby galaxies (essentially those in the NGC and UGC catalogs); tests of Lambda-CDM galaxy formation scenarios; derivation of stellar halo fractions for galaxies of different masses and morphologies; identification of local stellar streams that cross over multiple TESS observing sectors and other galaxy cannibalism leftovers; detection of ultra-diffuse galaxies as companions to bigger galaxies; and searches for supernovae remnants and planetary nebulae. With such science goals in mind, we have constructed a first-look, science-ready mosaic of a subset of the images released by TESS, to inform the processing and storage requirements of a mosaic of the southern sky, planned for Fall 2019. The mosaic covers the continuous viewing zone near the south ecliptic pole. In response to community requests, the mosaic is freely available at https://doi.org/10.26134/ExoFOP4 along with tools for downloading the data. This paper describes the creation of the mosaic and its characteristics.

The Kilodegree Extremely Little Telescope (KELT) project has been conducting a photometric survey of transiting planets orbiting bright stars for over 10 years. The KELT images have a pixel scale of ∼23″ pixel −1 —very similar to that of NASA’s Transiting Exoplanet Survey Satellite ( TESS )—as well as a large point-spread function, and the KELT reduction pipeline uses a weighted photometric aperture with radius 3′. At this angular scale, multiple stars are typically blended in the photometric apertures. In order to identify false positives and confirm transiting exoplanets, we have assembled a follow-up network (KELT-FUN) to conduct imaging with spatial resolution, cadence, and photometric precision higher than the KELT telescopes, as well as spectroscopic observations of the candidate host stars. The KELT-FUN team has followed-up over 1600 planet candidates since 2011, resulting in more than 20 planet discoveries. Excluding ∼450 false alarms of non-astrophysical origin (i.e., instrumental noise or systematics), we present an all-sky catalog of the 1128 bright stars (6 < V < 13) that show transit-like features in the KELT light curves, but which were subsequently determined to be astrophysical false positives (FPs) after photometric and/or spectroscopic follow-up observations. The KELT-FUN team continues to pursue KELT and other planet candidates and will eventually follow up certain classes of TESS candidates. The KELT FP catalog will help minimize the duplication of follow-up observations by current and future transit surveys such as TESS .

The Keck Observatory Archive (KOA) (https://koa.ipac.caltech.edu) curates all observations acquired at the W. M. Keck Observatory (WMKO) since it began operations in 1994, including data from eight active instruments and two decommissioned instruments. The archive is a collaboration between WMKO and the NASA Exoplanet Science Institute (NExScI). Since its inception in 2004, the science information system used at KOA has adopted an architectural approach that emphasizes software re-use and adaptability. This paper describes how KOA is currently leveraging and extending open source software components to develop new services and to support delivery of a complete set of instrument metadata, which will enable more sophisticated and extensive queries than currently possible.In August 2015, KOA deployed a program interface to discover public data from all instruments equipped with an imaging mode. The interface complies with version 2 of the Simple Imaging Access Protocol (SIAP), under development by the International Virtual Observatory Alliance (IVOA), which defines a standard mechanism for discovering images through spatial queries. The heart of the KOA service is an R-tree-based, database-indexing mechanism prototyped by the Virtual Astronomical Observatory (VAO) and further developed by the Montage Image Mosaic project, designed to provide fast access to large imaging data sets as a first step in creating wide-area image mosaics (such as mosaics of subsets of the 4.7 million images of the SDSS DR9 release). The KOA service uses the results of the spatial R-tree search to create an SQLite data database for further relational filtering. The service uses a JSON configuration file to describe the association between instrument parameters and the service query parameters, and to make it applicable beyond the Keck instruments.The images generated at the Keck telescope usually do not encode the image footprints as WCS fields in the FITS file headers. Because SIAP searches are spatial, much of the effort in developing the program interface involved processing the instrument and telescope parameters to understand how accurately we can derive the WCS information for each instrument. This knowledge is now being fed back into the KOA databases as part of a program to include complete metadata information for all imaging observations.The R-tree program was itself extended to support temporal (in addition to spatial) indexing, in response to requests from the planetary science community for a search engine to discover observations of Solar System objects. With this 3D-indexing scheme, the service performs very fast time and spatial matches between the target ephemerides, obtained from the JPL SPICE service. Our experiments indicate these matches can be more than 100 times faster than when separating temporal and spatial searches. Images of the tracks of the moving targets, overlaid with the image footprints, are computed with a new command-line visualization tool, mViewer, released with the Montage distribution. The service is currently in test and will be released in late summer 2016.

The Flexible Image Transport System (FITS) standard has been a great boon to astronomy, allowing observatories, scientists and the public to exchange astronomical information easily. The FITS standard, however, is showing its age. Developed in the late 1970s, the FITS authors made a number of implementation choices that, while common at the time, are now seen to limit its utility with modern data. The authors of the FITS standard could not anticipate the challenges which we are facing today in astronomical computing. Difficulties we now face include, but are not limited to, addressing the need to handle an expanded range of specialized data product types (data models), being more conducive to the networked exchange and storage of data, handling very large datasets, and capturing significantly more complex metadata and data relationships. There are members of the community today who find some or all of these limitations unworkable, and have decided to move ahead with storing data in other formats. If this fragmentation continues, we risk abandoning the advantages of broad interoperability, and ready archivability, that the FITS format provides for astronomy. In this paper we detail some selected important problems which exist within the FITS standard today. These problems may provide insight into deeper underlying issues which reside in the format and we provide a discussion of some lessons learned. It is not our intention here to prescribe specific remedies to these issues; rather, it is to call attention of the FITS and greater astronomical computing communities to these problems in the hope that it will spur action to address them.

The Flexible Image Transport System (FITS) standard has been a great boon to astronomy, allowing observatories, scientists, and the public to exchange astronomical information easily. The FITS standard is, however, showing its age. Developed in the late 1970s the FITS authors made a number of implementation choices for the format that, while common at the time, are now seen to limit its utility with modern data. The authors of the FITS standard could not appreciate the challenges which we would be facing today in astronomical computing. Difficulties we now face include, but are not limited to, having to address the need to handle an expanded range of specialized data product types (data models), being more conducive to the networked exchange and storage of data, handling very large datasets and the need to capture significantly more complex metadata and data relationships.There are members of the community today who find some (or all) of these limitations unworkable, and have decided to move ahead with storing data in other formats. This reaction should be taken as a wakeup call to the FITS community to make changes in the FITS standard, or to see its usage fall. In this paper we detail some selected important problems which exist within the FITS standard today. It is not our intention to prescribe specific remedies to these issues; rather, we hope to call attention of the FITS and greater astronomical computing communities to these issues in the hopes that it will spur action to address them.