arXiv subjects

R. Moseley

Publications and source records attributed to R. Moseley.

3 recordsLinked to original sources

arXiv2026

A New Plotly-Dash-based Query Infrastructure for the Keck Observatory Archive

The Keck Observatory Archive (KOA) curates all observational data acquired at the W. M. Keck Observatory. The archive is expected to grow rapidly as complex new instruments are commissioned and as the expectations of archive users have expanded. In response, KOA has implemented a new Python-based, VO-compliant query infrastructure. This work is a continuation of the architectural design and technology selection identified at ADASS 2024. We have deployed real-time ingestion of newly acquired data and a dedicated interface for observers to manage these data. Our ADASS 2024 poster identified the new technologies chosen: Plotly-Dash, a low-code framework that exploits event-driven callbacks to simplify the handling of user interactions; R-tree spatial indexing to speed up spatial searches by x20; a VO-compliant TAP middleware, already in use at the NASA Exoplanet Archive and NEID archive; and mViewer, a visualization engine in the Montage Image Mosaic toolkit that is optimized for astronomy images. These technologies will underpin new services that can be hosted on web pages or in Jupyter notebooks, and when completed, will replace the current query infrastructure. We have completed two new services now in beta release. The first is the Data Discovery Service, a web-based dashboard that returns spatial and temporal queries of the entire archive in seconds. It supports filtering observations by keywords, previewing results in an interactive data grid, and visualizing images, and it offers data downloads. The second is a Jupyter notebook that performs interactive visualization of Keck observations of protostars in the Rho Oph Dark Cloud and uses data from CDS and IRSA, as well as KOA.

astro-ph.IM↗

arXiv2024

New Python-based Architecture for the Keck Observatory Archive

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.

astro-ph.IM↗

arXiv2024

Montage and Radio Astronomy

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.

astro-ph.IM↗