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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 Hisurveys, precursors to the Square Kilometre Array 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 Hiemitters unless accompanied by independent spectroscopic redshifts. Building on methods proposed by Roberts et al. for distinguishing these interloping OHMs via near- to mid-IR photometry and emission line frequencies, we apply these techniques to data from the Arecibo Legacy Fast ALFA [AreciboL-band Feed Array] (ALFALFA) survey and a preliminary Aperture Tile In Focus (Apertif) Hiemission line catalog from the Westerbork Synthesis Radio Telescope. Our study, utilizing the Apache Point Observatory 3.5 m telescope to obtain optical spectroscopic redshifts of 142 candidates (107 from ALFALFA and 35 from Apertif), confirms five new OHM host galaxies and reidentifies two previously catalogued OHMs misclassified as Hiemitters in ALFALFA. These findings support the predictions from Roberts et al. and underscore the evolving landscape of radio astronomy in the context of next-generation telescopes. 

In the era of rapidly growing astronomical data, the gap between data collection and analysis is a significant barrier, especially for teams searching for rare scientific objects. Although machine learning (ML) can quickly parse large data sets, it struggles to robustly identify scientifically interesting objects, a task at which humans excel. Human-in-the-loop (HITL) strategies that combine the strengths of citizen science (CS) and ML offer a promising solution, but first, we need to better understand the relationship between human- and machine-identified samples. In this work, we present a case study from the Galaxy Zoo: Weird & Wonderful project, where volunteers inspected ~200,000 astronomical images—processed by an ML-based anomaly detection model—to identify those with unusual or interesting characteristics. Volunteer-selected images with common astrophysical characteristics had higher consensus, while rarer or more complex ones had lower consensus. This suggests low-consensus choices shouldn’t be dismissed in further explorations. Additionally, volunteers were better at filtering out uninteresting anomalies, such as image artifacts, which the machine struggled with. We also found that a higher ML-generated anomaly score that indicates images’ low-level feature anomalousness was a better predictor of the volunteers’ consensus choice. Combining a locus of high volunteer-consensus images within the ML learnt feature space and anomaly score, we demonstrated a decision boundary that can effectively isolate images with unusual and potentially scientifically interesting characteristics. Using this case study, we lay important guidelines for future research studies looking to adapt and operationalize human-machine collaborative frameworks for efficient anomaly detection in big data. 

Abstract We study the evolution of the bar fraction in disk galaxies between 0.5 < z < 4.0 using multiband colored images from JWST Cosmic Evolution Early Release Science Survey (CEERS). These images were classified by citizen scientists in a new phase of the Galaxy Zoo (GZ) project called GZ CEERS. Citizen scientists were asked whether a strong or weak bar was visible in the host galaxy. After considering multiple corrections for observational biases, we find that the bar fraction decreases with redshift in our volume-limited sample (n= 398); from $25_{-4}^{+6}$% at 0.5 <z< 1.0 to $3_{-1}^{+6}$% at 3.0 < z < 4.0. However, we argue it is appropriate to interpret these fractions as lower limits. Disentangling real changes in the bar fraction from detection biases remains challenging. Nevertheless, we find a significant number of bars up toz= 2.5. This implies that disks are dynamically cool or baryon dominated, enabling them to host bars. This also suggests that bar-driven secular evolution likely plays an important role at higher redshifts. When we distinguish between strong and weak bars, we find that the weak bar fraction decreases with increasing redshift. In contrast, the strong bar fraction is constant between 0.5 <z< 2.5. This implies that the strong bars found in this work are robust long-lived structures, unless the rate of bar destruction is similar to the rate of bar formation. Finally, our results are consistent with disk instabilities being the dominant mode of bar formation at lower redshifts, while bar formation through interactions and mergers is more common at higher redshifts. 

ABSTRACT We present the discovery of the most distant OH megamaser (OHM) to be observed in the main lines, using data from the MeerKAT International Giga-Hertz Tiered Extragalactic Exploration (MIGHTEE) survey. At a newly measured redshift of z = 0.7092, the system has strong emission in both the 1665 MHz (L ≈ 2500 L⊙) and 1667 MHz (L ≈ 4.5 × 104 L⊙) transitions, with both narrow and broad components. We interpret the broad line as a high-velocity-dispersion component of the 1667 MHz transition, with velocity v ∼ 330 km s−1 with respect to the systemic velocity. The host galaxy has a stellar mass of M⋆ = 2.95 × 1010 M⊙ and a star formation rate of SFR = 371 M⊙ yr−1, placing it ∼1.5 dex above the main sequence for star-forming galaxies at this redshift, and can be classified as an ultraluminous infrared galaxy. Alongside the optical imaging data, which exhibit evidence for a tidal tail, this suggests that the OHM arises from a system that is currently undergoing a merger, which is stimulating star formation and providing the necessary conditions for pumping the OH molecule to saturation. The OHM is likely to be lensed, with a magnification factor of ∼2.5, and perhaps more if the maser emitting region is compact and suitably offset relative to the centroid of its host galaxy’s optical light. This discovery demonstrates that spectral line mapping with the new generation of radio interferometers may provide important information on the cosmic merger history of galaxies. 

Abstract In the local universe, OH megamasers (OHMs) are detected almost exclusively in infrared-luminous galaxies, with a prevalence that increases with IR luminosity, suggesting that they trace gas-rich galaxy mergers. Given the proximity of the rest frequencies of OH and the hyperfine transition of neutral atomic hydrogen (Hi), radio surveys to probe the cosmic evolution of Hiin galaxies also offer exciting prospects for exploiting OHMs to probe the cosmic history of gas-rich mergers. Using observations for the Looking At the Distant Universe with the MeerKAT Array (LADUMA) deep Hisurvey, we report the first untargeted detection of an OHM atz> 0.5, LADUMA J033046.20−275518.1 (nicknamed “Nkalakatha”). The host system, WISEA J033046.26−275518.3, is an infrared-luminous radio galaxy whose optical redshiftz≈ 0.52 confirms the MeerKAT emission-line detection as OH at a redshiftz_OH= 0.5225 ± 0.0001 rather than Hiat lower redshift. The detected spectral line has 18.4σpeak significance, a width of 459 ± 59 km s⁻¹, and an integrated luminosity of (6.31 ± 0.18 [statistical] ± 0.31 [systematic]) × 10³L_⊙, placing it among the most luminous OHMs known. The galaxy’s far-infrared luminosityL_FIR= (1.576 ±0.013) × 10¹²L_⊙marks it as an ultraluminous infrared galaxy; its ratio of OH and infrared luminosities is similar to those for lower-redshift OHMs. A comparison between optical and OH redshifts offers a slight indication of an OH outflow. This detection represents the first step toward a systematic exploitation of OHMs as a tracer of galaxy growth at high redshifts.