Optimal Inflationary Potentials
- URL: http://arxiv.org/abs/2310.16786v2
- Date: Mon, 15 Apr 2024 07:36:00 GMT
- Title: Optimal Inflationary Potentials
- Authors: Tomás Sousa, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira,
- Abstract summary: Inflation is a favoured theory for the early Universe.
It is highly under-determined with a large number of candidate implementations.
We use a new method in symbolic regression to generate all possible simple scalar field potentials.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Inflation is a highly favoured theory for the early Universe. It is compatible with current observations of the cosmic microwave background and large scale structure and is a driver in the quest to detect primordial gravitational waves. It is also, given the current quality of the data, highly under-determined with a large number of candidate implementations. We use a new method in symbolic regression to generate all possible simple scalar field potentials for one of two possible basis sets of operators. Treating these as single-field, slow-roll inflationary models we then score them with an information-theoretic metric ("minimum description length") that quantifies their efficiency in compressing the information in current data. We explore two possible priors on the parameter space of potentials, one related to the functions' structural complexity and one that uses a Katz back-off language model to prefer functions that may be theoretically motivated. This enables us to identify the inflaton potentials that optimally balance simplicity with accuracy at explaining current data, which may subsequently find theoretical motivation. Our exploratory study opens the door to extraction of fundamental physics directly from data, and may be augmented with more refined theoretical priors in the quest for a complete understanding of the early Universe.
Related papers
- Gradient-Based Feature Learning under Structured Data [57.76552698981579]
In the anisotropic setting, the commonly used spherical gradient dynamics may fail to recover the true direction.
We show that appropriate weight normalization that is reminiscent of batch normalization can alleviate this issue.
In particular, under the spiked model with a suitably large spike, the sample complexity of gradient-based training can be made independent of the information exponent.
arXiv Detail & Related papers (2023-09-07T16:55:50Z) - DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained
Diffusion [66.21290235237808]
We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states.
We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs.
Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
arXiv Detail & Related papers (2023-01-23T15:18:54Z) - Large-Scale $2+1$D $\mathrm{U}(1)$ Gauge Theory with Dynamical Matter in
a Cold-Atom Quantum Simulator [3.1192594881563127]
A major driver of quantum-simulator technology is the prospect of probing high-energy phenomena in synthetic quantum matter setups at a high level of control and tunability.
Here, we propose an experimentally feasible realization of a large-scale $2+1$D $mathrmU(1)$ gauge theory with dynamical matter and gauge fields in a cold-atom quantum simulator with spinless bosons.
arXiv Detail & Related papers (2022-11-02T18:00:00Z) - Data-Efficient Learning via Minimizing Hyperspherical Energy [48.47217827782576]
This paper considers the problem of data-efficient learning from scratch using a small amount of representative data.
We propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL.
arXiv Detail & Related papers (2022-06-30T11:39:12Z) - INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL [90.06845886194235]
We propose a modified objective for model-based reinforcement learning (RL)
We integrate a term inspired by variational empowerment into a state-space model based on mutual information.
We evaluate the approach on a suite of vision-based robot control tasks with natural video backgrounds.
arXiv Detail & Related papers (2022-04-18T23:09:23Z) - Leveraging Quantum Annealer to identify an Event-topology at High Energy
Colliders [3.39322931607753]
We propose a simple and well motivated method with a quantum annealer to identify an event-topology.
We show that a computing complexity can be reduced significantly to the order of the order of particles.
arXiv Detail & Related papers (2021-11-15T14:42:05Z) - Nuclear two point correlation functions on a quantum-computer [105.89228861548395]
We use current quantum hardware and error mitigation protocols to calculate response functions for a highly simplified nuclear model.
In this work we use current quantum hardware and error mitigation protocols to calculate response functions for a modified Fermi-Hubbard model in two dimensions with three distinguishable nucleons on four lattice sites.
arXiv Detail & Related papers (2021-11-04T16:25:33Z) - Integration of Data and Theory for Accelerated Derivable Symbolic
Discovery [3.7521856498259627]
We develop a methodology combining automated theorem proving with symbolic regression, enabling principled derivations of laws of nature.
We demonstrate this for Kepler's third law, Einstein's relativistic time dilation, and Langmuir's theory of adsorbing.
The combination of logical reasoning with machine learning provides generalizable insights into key aspects of the natural phenomena.
arXiv Detail & Related papers (2021-09-03T17:19:17Z) - Physics-constrained Bayesian inference of state functions in classical
density-functional theory [0.6445605125467573]
We develop a novel data-driven approach to the inverse problem of classical statistical mechanics.
We develop an efficient learning algorithm which characterises the construction of approximate free energy functionals.
We consider excluded volume particle interactions, which are ubiquitous in nature, whilst being highly challenging for modelling in terms of free energy.
arXiv Detail & Related papers (2020-10-07T12:43:42Z) - Focus of Attention Improves Information Transfer in Visual Features [80.22965663534556]
This paper focuses on unsupervised learning for transferring visual information in a truly online setting.
The computation of the entropy terms is carried out by a temporal process which yields online estimation of the entropy terms.
In order to better structure the input probability distribution, we use a human-like focus of attention model.
arXiv Detail & Related papers (2020-06-16T15:07:25Z) - On Geometry of Information Flow for Causal Inference [0.0]
This paper takes the perspective of information flow, which includes the Nobel prize winning work on Granger-causality.
Our main contribution will be to develop analysis tools that will allow a geometric interpretation of information flow as a causal inference indicated by transfer entropy.
arXiv Detail & Related papers (2020-02-06T02:46:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.