We investigate the impact of millicharged particles (MCPs) on massive stars undergoing pulsational pair-instability supernovae and on the location of the lower edge of the black hole mass gap. We find that energy losses due to MCP emission weaken the pulsations, allowing the star to retain more mass and thereby shifting the lower edge of the mass gap to higher black hole masses. The mass gap is sensitive to a region of MCP parameter space with masses $35\,{\rm keV}\lesssim m_\chi \lesssim 200\,{\rm keV}$ and charges $10^{-10}\lesssim q \lesssim 10^{-9}$, which remains unconstrained by existing astrophysical probes. If confirmed, recent gravitational wave observations placing the lower edge of the mass gap near $45\,{\rm M}_\odot$ would translate directly into bounds on this parameter space.
The South Pole Telescope (SPT) collaboration has recently embarked upon a campaign to monitor the brightness of a sample of active galactic nuclei (AGN), both in real time and in archival SPT data. The original design of the SPT was optimized for observations of the cosmic microwave background (CMB) at arc-minute and larger angular scales, and it has been used for this purpose for nearly twenty years, using three generations of CMB cameras. Recently it has been recognized that data from CMB experiments have the potential to be used for AGN monitoring. In this paper, we present the first public release of data from a full sample of SPT-monitored AGN, comprising 158 AGN light curves and associated data from the SPTpol camera, which was operational from 2012-2016. These light curves were created using observations from the SPTpol 500 deg$^{2}$ survey, in which the instrument was used to scan a 500 deg$^2$ patch of the sky several times per day with detectors sensitive to radiation in bands centered at 90 and 150 GHz. We provide a comprehensive description of the observations, the data processing methods, and the resulting light curve catalog. As an example of analyses that these data enable, we searched for a correlation between variability and spectral index, and we looked for ``bluer-when-brighter'' trends in the sample. Our analysis finds $> 10 \sigma$ correlation between fractional intrinsic variance and mean spectral index in the sample, but no significant evidence for bluer-when-brighter trends. The datasets from this study can be accessed through the SPT Treasury Record of AGN With Historical Activity and Time-Series or STRAWHAT catalog. This initial data release includes SPTpol light curves at 90 and 150 GHz, focusing on total intensity. In later updates, SPTpol polarization data and new observations from the SPT-3G instrument at 90, 150, and 220 GHz will be included.
We investigate the synthetic model of globular cluster (GC) systems of 17 compact massive galaxies (CMGs) from the Illustris TNG100 simulation to explore their connection with massive relic galaxies, systems that have undergone little structural evolution across cosmic time. The co-evolution of the GC systems and their host galaxies is based on a GC formation and evolution model that assigns clusters to stellar particles according to age and local conditions, providing positional, kinematic, and chemical information for individual GCs. By combining stellar assembly histories, effective radius evolution, and GC properties such as in-situ vs. ex-situ origin, metallicity, and spatial distribution, we identify consistent signatures of early formation and late-time accretion. We find that the GC mass fraction traces the host assembly history more robustly than the GC number fraction, as massive clusters better preserve the imprint of the early accretion history. Three CMGs from TNG100 emerge as strong massive relic analogs, exhibiting high in-situ GC fractions, narrow metallicity distributions, and compact spatial distributions. A tight correlation between the host stripped fraction and the extent of the ex-situ GC population further reveals the possibility to consider GC spatial profiles as a signature to identify tidal stripping processes. These results indicate that the combined analysis of GC populations and host stellar assembly offers a robust diagnostic for identifying massive relic galaxies and constraining their evolutionary histories.
The first Euclid Quick Data Release (Q1) provides extensive imaging and spectroscopic data for hundreds of millions of photometric objects across several deep fields. Accurate classifications and photometric redshifts (photo-z) for these sources are crucial to maximizing the value of these data. In this work, we perform source classification and photo-z estimation for the Euclid Deep Field North (EDF-N) around the North Ecliptic Pole, using a deep learning framework (DeepDISC) that learns and infers using 9-band images simultaneously. We train three dedicated models for (1) source detection and classification, (2) galaxy photo-z, and (3) quasar photo-z. The Euclid Q1 input source catalog, and classifications and spectroscopic redshifts (spec-z) from the Dark Energy Spectroscopic Instrument Data Release 1 are adopted as our training data. DeepDISC source detection achieves overall completeness of ~93% and purity of ~80% if using the Euclid source catalog as the ground truth. Using a JWST source catalog within EDF-N as the reference, we estimate a true purity of ~ 90% for DeepDISC sources. About 99.2%, 99.0%, and 84.8% of stars, galaxies, and quasars, respectively, are correctly recovered with their spectroscopic classifications. The DeepDISC photo-zs show good agreement with spectroscopic redshifts, for both galaxies and quasars. Comparisons with other Euclid Q1 products demonstrate that DeepDISC provides comparable or improved performance in source detection/deblending, classification and photo-z, especially for quasars. These results demonstrate the potential of pixel-level deep learning approaches for large-scale sky surveys such as Euclid and Roman, which will continue to improve with better training labels. We release the full DeepDISC source catalog (~13 million objects) for EDF-N with classifications and photo-zs, including photo-z probability distributions.
arXiv:2604.01112 . Comments appreciated