Curvaton self-interactions in non-quadratic potentials produce a local non-Gaussian map that enables supermassive primordial black hole formation at peak amplitudes of order 10^{-5} while remaining consistent with μ-distortion bounds.
Constraints on primordial black holes from the first part of LIGO-Virgo-KAGRA fourth observing run
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abstract
We analyze PBH populations using state-of-the-art modeling of PBH binaries, deriving the strongest bounds on PBH abundance in the $0.6-100 M_\odot$ range from LIGO-Virgo-KAGRA O4a data and demonstrating sensitivity in the $10^{-4}-10^4 M_\odot$ range. The constraints are dominated by resolvable PBH mergers, while the associated gravitational wave background provides complementary but weaker limits. Allowing PBHs to account for a subset of the cataloged events slightly relaxes these bounds. However, a joint fit with astrophysical black holes shows no compelling evidence for a PBH contribution.
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EDE models increase inferred α_s from CMB data, strengthening tension with USR PBH models that predict negative running.
Cross-correlating CSST galaxies with mock GW catalogs from ET2CE and BDET2CE networks can detect PBH merger fractions above ~40% and ~20% respectively via clustering bias differences.
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Memoirs of the curvaton: non-perturbative non-Gaussianity and supermassive primordial black holes
Curvaton self-interactions in non-quadratic potentials produce a local non-Gaussian map that enables supermassive primordial black hole formation at peak amplitudes of order 10^{-5} while remaining consistent with μ-distortion bounds.
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Running into tension: primordial black holes from ultra-slow-roll inflation, spectral running, and the Hubble tension
EDE models increase inferred α_s from CMB data, strengthening tension with USR PBH models that predict negative running.
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Synergy between CSST and future gravitational-wave detectors: Probing primordial black holes by cross-correlating dark sirens with galaxies
Cross-correlating CSST galaxies with mock GW catalogs from ET2CE and BDET2CE networks can detect PBH merger fractions above ~40% and ~20% respectively via clustering bias differences.