Splitting the Gravitational Atom: Instabilities of Black Holes with Synchronized/Resonant Hair. Jordan Nicoules et. al. https://arxiv.org/abs/2509.20450
Small Progenitors, Large Couplings: Type Ic Supernova Constraints on Radiatively Decaying Particles. Francisco R. Candón et. al. https://arxiv.org/abs/2509.18253
Accelerated inference of binary black-hole populations from the stochastic gravitational-wave background. G. Giarda et. al. https://arxiv.org/abs/2506.12572
Electroluminescence and charge multiplication in liquid xenon with a VCC-like Microstrip Plate. Gonzalo Martínez-Lema et. al. https://arxiv.org/abs/2505.24611
Verifiable type-III seesaw and dark matter in a gauged $\boldsymbol{U(1)_{\rm B-L}}$ symmetric model. Satyabrata Mahapatra et. al. https://arxiv.org/abs/2504.00109
Anatomy of singlet-doublet dark matter relic: annihilation, co-annihilation, co-scattering, and freeze-in. Partha Kumar Paul et. al. https://arxiv.org/abs/2412.02607
The redshift distribution of Einstein Probe transients supports their relation to gamma-ray bursts. Brendan O'Connor et. al. https://arxiv.org/abs/2509.07141
DIPLODOCUS I: Framework for the evaluation of relativistic transport equations with continuous forcing and discrete particle interactions. Christopher N. Everett et. al. https://arxiv.org/abs/2508.13296
Prospects for probing dark matter particles and primordial black holes with the Square Kilometre Array using the 21 cm power spectrum at cosmic dawn. Meng-Lin Zhao et. al. https://arxiv.org/abs/2507.02651
HeII emitters at cosmic noon and beyond. Characterising the HeII λ1640 emission with MUSE and JWST/NIRSpec. R. González-Díaz et. al. https://arxiv.org/abs/2506.11685
Clustering analysis of BOSS-CMASS galaxies with semi-analytical model for galaxy formation and halo occupation distribution. Zhongxu Zhai et. al. https://arxiv.org/abs/2505.18748
Unveiling the trends between dust attenuation and galaxy properties at z ~ 2 - 12 with the James Webb Space Telescope. V. Markov et. al. https://arxiv.org/abs/2504.12378
Enhancing the reliability of machine learning for gravitational wave parameter estimation with attention-based models. Hibiki Iwanaga et. al. https://arxiv.org/abs/2501.10486