CLASS Survey Description: Coronal Line Needles in the SDSS Haystack

TL;DR

This study conducts a large-scale survey of coronal lines in nearly one million SDSS galaxy spectra, revealing new insights into AGN activity and establishing correlations with mid-infrared properties using advanced detection techniques.

Contribution

Introduces a novel survey of coronal lines in SDSS galaxies, employing a new pre-selection method, MCMC fitting, and neural networks, significantly expanding the known coronal line population.

Findings

Discovered that ~60% of coronal line activity is missed in previous surveys.

Established a strong correlation between coronal line and WISE W2 luminosities.

Achieved a 75.49% true positive rate in line detection with neural networks.

Abstract

Coronal lines are a powerful, yet poorly understood, tool to identify and characterize Active Galactic Nuclei (AGNs). There have been few large scale surveys of coronal lines in the general galaxy population in the literature so far. Using a novel pre-selection technique with a flux-to-RMS ratio $F$, followed by Markov-Chain Monte Carlo (MCMC) fitting, we searched for the full suite of 20 coronal lines in the optical spectra of almost 1 million galaxies from the Sloan Digital Sky Survey (SDSS) Data Release 8. We present a catalog of the emission line parameters for the resulting 258 galaxies with detections. The Coronal Line Activity Spectroscopic Survey (CLASS) includes line properties, host galaxy properties, and selection criteria for all galaxies in which at least one line is detected. This comprehensive study reveals that a significant fraction of coronal line activity is missed in past surveys based on a more limited set of coronal lines; $\sim$60% of our sample do not display the more widely surveyed [Fe X] $\lambda$6374. In addition, we discover a strong correlation between coronal line and WISE W2 luminosities, suggesting that the mid-infrared flux can be used to predict coronal line fluxes. For each line we also provide a confidence level that the line is present, generated by a novel neural network, trained on fully simulated data. We find that after training the network to detect individual lines using 100,000 simulated spectra, we achieve an overall true positive rate of 75.49% and a false positive rate of only 3.96%.