A spectral library and census of near-infrared stellar large-amplitude variables from Palomar Gattini-IR

TL;DR

This paper provides a comprehensive near-infrared spectral and photometric catalog of large-amplitude stellar variables from the Palomar Gattini-IR survey, including spectra and classifications of diverse variable star types.

Contribution

It introduces a new spectral and photometric library of 128 large-amplitude variables, combining multi-epoch infrared observations with medium-resolution spectra, expanding resources for stellar variability studies.

Findings

Catalog of 128 large-amplitude variables with detailed spectral classifications.

Identification of diverse stellar types including AGB, RCB, young stars, and binaries.

Spectral and photometric data serve as a resource for future infrared surveys.

Abstract

We present a near-infrared census of stellar large-amplitude variables (LAVs) observed by the Palomar Gattini-IR (PGIR) surveyor from 2019-2021. Over the three-year time period, PGIR performed a brightness-limited survey of the Northern sky (18,000 sq. deg) to J-band AB magnitudes of $\sim 13$ within and $\sim 15$ outside the Galactic plane. From 70 million stars detected in PGIR reference images, we provide a spectral and photometric library of the 128 largest amplitude stellar variables detected to median SNR > 10 for more than 50 epochs with more than 5 high-amplitude detections, peak-to-peak magnitudes $\geq$ 2, and von Neumann ratios $\leq$ 0.2. We obtained medium-resolution near-infrared spectra with TripleSpec on the 200-inch Hale Telescope at Palomar Observatory and SpeX at NASA's Infrared Telescope Facility. The spectral census consists of 82 evolved and dust-obscured Asymptotic Giant Branch stars, 16 R Coronae Borealis stars, 13 young-stellar or pre-main-sequence objects, 8 symbiotic binaries, 7 erratic carbon- and oxygen-rich giants, and 2 RV Tauri supergiants. The spectral and photometric dataset serves as an atlas of near-infrared LAVs and a repository of evolved stars, eruptive variables, and binary systems for future deeper infrared surveys.