RAD@home citizen science discovery of two spiral galaxies where the 30-220 kpc radio lobes are possibly shaped by ram pressure stripping

TL;DR

This study reports the discovery of two rare spiral galaxies with asymmetric, large-scale radio lobes, suggesting environmental ram pressure effects influence their radio morphology, highlighting citizen science's role in astrophysical research.

Contribution

The paper presents the first citizen science discovery of spiral-host radio galaxies with asymmetric lobes and proposes environmental ram pressure stripping as a shaping mechanism.

Findings

Both galaxies exhibit asymmetric radio lobes extending over 30-220 kpc.

Host galaxies are young, star-forming, with distorted stellar disks.

Environmental ram pressure likely influences radio lobe morphology.

Abstract

We report the RAD@home citizen science discovery of two rare spiral-host radio galaxies (NGC 3898 and WISEA J221656.57-132042434.1 or RAD-"Thumbs up" galaxy), both exhibiting asymmetric radio lobes extending over 30 to 220 kiloparsec scales. We present a multi-wavelength image analysis of these two sources using radio, optical, and ultraviolet data. Both host galaxies are young, star-forming systems with asymmetric or distorted stellar disks. These disks show similarities to those in galaxies undergoing ram pressure stripping, and the radio morphologies resemble those of asymmetric or bent FR-II and wide-angle-tailed radio galaxies. We suggest that non-uniform gas density in the environment surrounding the ram pressure-stripped disks may contribute to the observed asymmetry in the size, shape, and brightness of the bipolar radio lobes. Such environmental effects, when properly accounted for, could help explain many of the non-standard radio morphologies observed in Seyfert galaxies and in recently identified populations of galaxies with galaxy-scale radio jets, now being revealed through deep and sensitive radio surveys with uGMRT, MeerKAT, LOFAR, and, in the future, the SKAO. These findings also underscore the potential of citizen science to complement professional research and data-driven approaches involving machine learning and artificial intelligence in the analysis of complex radio sources.