Uzay Aydin | Searcharxiv

arXiv2026

Rubin LSST DP2 unveils almost-dark galaxies in the Virgo Cluster

Galaxies with the faintest surface brightness are currently known only in the Local Group. Similar objects should exist beyond our vicinity and are crucial for understanding galaxy evolution, structure, and dark matter content, yet surveys have not reached the depth required to detect them systematically. We present a population of seven almost-dark galaxies identified in Data Preview 2 of the Rubin Legacy Survey of Space and Time. They surround M49 in the vicinity of the Virgo Cluster, and exhibit central surface brightnesses in the range of $26.9 - 28.5 \, \mathrm{mag \, arcsec^{-2}}$ in the $g$-band, with half-light radii of $0.6 - 4.6 \, \mathrm{kpc}$ at the distance of Virgo, and stellar masses of $10^6 - 10^7 \, \mathrm{M_\odot}$. Their characteristics are analogous to those of the faintest and low-mass galaxies identified among satellite galaxies And XXI, And XXIII, and And XXV in the Local Group. This discovery demonstrates the power of the forthcoming Rubin LSST 10-year survey to uncover extremely faint galaxies at scale, promising the large statistical samples needed to constrain the faint-end luminosity function and the nature of dark matter.

astro-ph.GA↗

arXiv2026

Cross Subtype Transferability of Machine Learning Photometric Redshift Relations in Low Redshift Seyfert AGN

Photometric redshift estimation for active galactic nuclei (AGN) is complicated by the combined effects of host-galaxy light, nuclear emission, dust attenuation, and broadband spectral diversity. We investigate whether machine learning photo-z relations trained on one low-redshift Seyfert subtype remain valid when transferred to another, and whether probabilistic subtype classification can be used to identify sources for which a specialised regressor is reliable. Using spectroscopically selected Seyfert I and Seyfert II samples from SDSS, matched to AllWISE photometry over 0 < z_spec <= 0.6, we constructed a common 45-feature representation from SDSS ugriz and WISE W1-W4 data. Random Forest and XGBoost regressors were evaluated within each subtype, followed by controlled cross-subtype transfer tests, redshift and sample size-matched experiments, feature ablations, and an independent classifier-gated regression test. The subtype specific models achieved strong within-sample performance, with the Seyfert II model reaching R2 = 0.965 and sigma_NMAD = 0.0169. However, transfer between Seyfert I and Seyfert II produced a clear and asymmetric degradation in accuracy that persisted after matching the samples and restricting the photometric inputs. A probabilistic Seyfert classifier further identified subsets for which the Seyfert II regressor was more reliable, while extrapolation beyond the redshift range represented in training produced systematic underestimation. These results demonstrate that AGN photo-z performance depends strongly on the population and redshift domain represented in the training data, supporting subtype-aware calibration and applicability-based source selection.

astro-ph.GA↗