Quantitative Spectroscopic Diagnostics for FU Orionis-Type Young Stellar Objects

TL;DR

This paper develops near-infrared spectroscopic diagnostics to identify FU Orionis stars, characterized by extreme outbursts and distinct spectral features, aiding their detection in time-domain surveys.

Contribution

The study introduces specific spectral diagnostics and band ratios to distinguish FU Orionis objects from other stars using near-infrared spectra.

Findings

Identified key atomic and molecular features for FUOr characterization.

Designed spectral diagnostics to differentiate FUOrs from normal stars.

Validated diagnostics with spectra from 28 FUOrs and control samples.

Abstract

We present near-infrared spectroscopic diagnostics that can be used to identify FU Orionis stars (FUOrs). FUOrs are young stellar objects (YSOs) that are currently in a state of extreme outburst, caused by enhanced mass {inflow} from their accretion disks. The disks give FUOrs a distinct multi-temperature optical and infrared spectrum. Considering both the predicted spectrum from a disk atmosphere model, and existing spectral diagnostics from the literature, we identify key atomic and molecular features for characterizing FUOrs. Some of the chosen features are proxies for temperature, others are sensitive to surface gravity, and still others probe disk winds. Using the Palomar Observatory/Hale Telescope TripleSpec spectrograph, we gathered near-infrared spectra of 28 known FUOrs. We use standard equivalent widths to determine the strength of atomic lines and we design several band ratios for measuring molecular features. We compare the measurements between our spectra and a control sample of late-type dwarfs and evolved stars from the Infrared Telescope Facility Spectral Library. By considering the relative distributions of these samples in our defined spectral diagnostics, we propose a number of parameter spaces that can distinguish FUOr disks from normal stars. The rate of discovery of FUOr candidates has increased significantly in recent years, largely due to the increasing prevalence of time-domain surveys. Our proposed diagnostics will allow new photometric candidates to be confirmed or refuted as such.