TL;DR
The paper introduces ${ m L{ iny I}M{ iny E}}$, a comprehensive library for analyzing large spectroscopic datasets, supporting various observational modes, with tools for line detection, fitting, and visualization, integrated into a virtual observatory for JWST data.
Contribution
It presents a new spectral analysis library with a human- and machine-readable notation system, extensive line database, machine learning training model, and online documentation, tailored for large spectroscopic datasets.
Findings
Successful implementation of ${ m L{ iny I}M{ iny E}}$ for JWST spectra
Enhanced accessibility and dissemination of spectroscopic data
Support for multidisciplinary observations and machine learning integration
Abstract
The upcoming generation of telescopes, instruments, and surveys is poised to usher in an unprecedented "Big Data" era in the field of astronomy. Within this context, even seemingly modest tasks such as spectral line analyses could become increasingly challenging for astronomers. In this paper, we announce the release of . This package is tailored for multidisciplinary observations with long-slit and integral field spectroscopy (IFS) support. functions encompass the reading of observational files, detecting lines, conditioned line fitting, and the plotting and storage of results. Most importantly, these measurements are structured to support the subsequent chemical and kinematic analyses. To reduce the coding effort required from users, we introduced a notation system for atomic transitions that is accessible to humans and…
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