Generating Primordial Black Holes Via Hilltop-Type Inflation Models
Laila Alabidi, Kazunori Kohri

TL;DR
This paper explores how Hilltop-type inflation models can produce primordial black holes by satisfying specific potential conditions, extending previous models and updating constraints with recent observational data.
Contribution
It demonstrates that Hilltop inflation models with certain self-interaction powers can generate primordial black holes, and refines parameter space constraints using latest WMAP data.
Findings
Hilltop models with self-interaction power ≤ 3 can produce black holes.
The analysis extends to the running mass model confirming black hole production.
Updated constraints on inflation parameters using WMAP Year 5 data.
Abstract
It has been shown that black holes would have formed in the early Universe if, on any given scale, the spectral amplitude of the Cosmic Microwave Background (CMB) exceeds 10^(-4). This value is within the bounds allowed by astrophysical phenomena for the small scale spectrum of the CMB, corresponding to scales which exit the horizon at the end of slow-roll inflation. Previous work by Kohri et. al. (2007) showed that for black holes to form from a single field model of inflation, the slope of the potential at the end of inflation must be flatter than it was at horizon exit. In this work we show that a phenomenological Hilltop model of inflation, satisfying the Kohri et. al. criteria, could lead to the production of black holes, if the power of the inflaton self-interaction is less than or equal to 3, with a reasonable number or e-folds. We extend our analysis to the running mass model,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCosmology and Gravitation Theories · Geophysics and Gravity Measurements · Computational Physics and Python Applications
