Testing OH Megamaser Identification Methods in HI Surveys: Updated Source-Flagging Algorithms and New Detections in ALFALFA
Hayley Roberts, Jeremy Darling, Kelley M. Hess, Andrew J. Baker, Elizabeth A. K. Adams, and Helga D\'enes

TL;DR
This study applies updated source-flagging algorithms and IR-based identification methods to HI survey data, successfully confirming new OH megamaser hosts and correcting previous misclassifications, thus enhancing detection strategies for upcoming radio surveys.
Contribution
It demonstrates the effectiveness of new algorithms and IR techniques in identifying OH megamasers within large HI survey datasets, supporting predictions for future detections.
Findings
Confirmed five new OHM host galaxies.
Reidentified two previously misclassified OHMs.
Supported predictions for increased OHM detections in next-generation surveys.
Abstract
OH megamasers (OHMs) are extragalactic masers found primarily in gas-rich galaxy major mergers. To date, only 120 OHMs have been cataloged since their discovery in 1982, and efforts to identify distinct characteristics of OHM host galaxies have remained inconclusive. As radio astronomy advances with next-generation telescopes and extensive 21 cm HI surveys, precursors to the Square Kilometre Array (SKA) are expected to detect the 18 cm OH masing line with significantly increased frequency, potentially expanding the known OHM population tenfold. These detections, however, risk confusion with lower-redshift HI emitters unless accompanied by independent spectroscopic redshifts. Building on methods proposed by Roberts et al. (arXiv:2102.12486) for distinguishing these interloping OHMs via near- to mid-IR photometry and emission line frequencies, we apply these techniques to data from…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
