Information Retrieval and Recommendation System for Astronomical Observatories

TL;DR

This paper introduces a machine learning-based information retrieval system designed for astronomical observatories to efficiently access technical information, thereby enhancing detector maintenance, increasing uptime, and supporting future detector development.

Contribution

It presents a novel online information retrieval system tailored for astronomical observatories, integrating existing documentation to assist users in troubleshooting and knowledge dissemination.

Findings

System successfully implemented for LIGO and Virgo observatories.

Improves access to technical information and troubleshooting resources.

Potential to enhance detector uptime and support future observatory projects.

Abstract

We present a machine learning based information retrieval system for astronomical observatories that tries to address user defined queries related to an instrument. In the modern instrumentation scenario where heterogeneous systems and talents are simultaneously at work, the ability to supply with the right information helps speeding up the detector maintenance operations. Enhancing the detector uptime leads to increased coincidence observation and improves the likelihood for the detection of astrophysical signals. Besides, such efforts will efficiently disseminate technical knowledge to a wider audience and will help the ongoing efforts to build upcoming detectors like the LIGO-India etc even at the design phase to foresee possible challenges. The proposed method analyses existing documented efforts at the site to intelligently group together related information to a query and to present it on-line to the user. The user in response can further go into interesting links and find already developed solutions or probable ways to address the present situation optimally. A web application that incorporates the above idea has been implemented and tested for LIGO Livingston, LIGO Hanford and Virgo observatories.