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Ripples in the OCEANS: Broad Line Variability of Little Red Dots

Authors: Madisyn Brooks, Kelcey Davis, Jonathan R. Trump, Raymond C. Simons, Erini Lambrides, Pablo Arrabal Haro, Bren E. Backhaus, Nikko J. Cleri, Steven L. Finkelstein, Mauro Giavalisco, Norman A. Grogin, Michaela Hirschmann, Dale D. Kocevski, Anton M. Koekemoer, Rebecca L. Larson, Ray A. Lucas, Stephen M. Wilkins, Stijn Wuyts, Jorge A. ZavalaPublished: 2026-08-12Paper ID: 2608.12487Category: astro-ph.GALicense: CC0 1.0

Abstract

Little Red Dots (LRDs) are a unique class of compact, red sources discovered in the JWST extragalactic deep fields. Determining if they are indeed powered by accreting supermassive black holes (SMBHs) is one of the main drivers of the intense study of these objects. Evidence for variability in these objects provides a direct test for the active galactic nucleus (AGN) nature of their central engine. In this study, we present a variability analysis of 6 LRDs observed by the $R \sim 2700$ OCEANS survey and leverage archival $R \sim 1000$ spectroscopic data from the CEERS and RUBIES surveys. We report marginal detections of $\rm H\alpha$ broad-line (BL) variability in the LRDs OCEANS-100424/RUBIES-42232 (27\% variability at 2.1$\sigma$ significance) and OCEANS-35829/RUBIES-49140 (GlimmIr/Irony; 50\% variability at 1.5$\sigma$ significance). The other 4 LRDs in our sample do not show evidence for BL variability, with a 1$\sigma$ upper limit of $4.8 \% - 30\%$ variability between their epochs of observations. We also find no evidence ($<1\sigma$) for continuum variability in our LRD sample. We compare our results to a sample of SDSS-RM quasars to determine the probability of our broad $\rm H\alpha$ variability detections. We find that the probability of reproducing 2 variable and 4 nonvariable quasars is $4.71\%$, corresponding to $\sim 2 \sigma$ departure from typical quasar variability. The detection of BL $\rm H\alpha$ variability in 2 LRDs provides some evidence for the AGN nature of these objects as opposed to pure scattering models.

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