Novel inflation and black hole dynamics with applications

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Black holes are interesting astrophysical objects that have been studied as systems sensitive to quantum gravitational data. The accelerated geometry in the exterior of extremal black holes can induce large center-of-mass energies between particles with particular momenta at the horizon. This is known as the Bañados-Silk-West (BSW) effect. For point particles, the BSW effect requires tuning to have the collision coincide with the horizon. However, this tuning is relaxed for string-theoretic objects. String scattering amplitudes are large in the Regge limit which occurs at large center-of-mass energies and shallow scattering angles, parametrically surpassing quantum field theoretic amplitudes. In this limit, longitudinal string spreading is induced between strings with a large difference in light-cone momenta, and this spread can be used to 'detune' the BSW effect. With this in mind, quantum gravitational data, as described by string theory, may play an important role in near horizon dynamics of extremal Kerr black holes. Further, though it may be hard to realize astrophysically, this system acts as a natural particle accelerator for probing the nature of small-scale physics at Planckian energies. The inflationary epoch of the early universe described by the rapid acceleration of space, was introduced to explain the distribution of matter in our universe. In particular, models using 'slow-roll inflation' have been successful in predicting this distribution, as have more general models with stronger interactions, such as DBI inflation obtained from a gauge theory with many 'color' degrees of freedom. We find a novel analogous mechanism for inflation by coupling the scalar field governing the expansion, the inflaton, to a family of flavor fields. Its effective action leads to a Coleman-Wienberg potential term that generates viable inflation, via a speed limit deriving from a logarithmic-- rather than a square root-- branch cut. Interestingly, a version of the model can be cast as an energy-conserving-descent optimization algorithm. A machine learning algorithm utilizing this logarithmic Hamiltonian has interesting properties for finding minimal loss solutions while effectively spanning large regions of phase space.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2023; ©2023
Publication date 2023; 2023
Issuance monographic
Language English


Author Mathis, Dayshon Tyree
Degree supervisor Silverstein, Eva
Thesis advisor Silverstein, Eva
Thesis advisor Graham, Peter
Thesis advisor Kachru, Shamit
Degree committee member Graham, Peter
Degree committee member Kachru, Shamit
Associated with Stanford University, School of Humanities and Sciences
Associated with Stanford University, Department of Physics


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Dayshon Mathis.
Note Submitted to the Department of Physics.
Thesis Thesis Ph.D. Stanford University 2023.

Access conditions

© 2023 by Dayshon Tyree Mathis
This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).

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