ID: 2204.08073

Intelligent Explorations of the String Theory Landscape

April 17, 2022

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An autoencoder for heterotic orbifolds with arbitrary geometry

December 1, 2022

85% Match
Enrique Escalante-Notario, Ignacio Portillo-Castillo, Saul Ramos-Sanchez
High Energy Physics - Theory

Artificial neural networks have become important to improve the search for admissible string compactifications and characterize them. In this paper we construct the heterotic orbiencoder, a general deep autoencoder to study heterotic orbifold models arising from various Abelian orbifold geometries. Our neural network can be easily trained to successfully encode the large parameter space of many orbifold geometries simultaneously, independently of the statistical dissimilariti...

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The Standard Model from String Theory: What Have We Learned?

January 3, 2024

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Fernando Marchesano, Gary Shiu, Timo Weigand
High Energy Physics - Theory
High Energy Physics - Phenom...

Amidst all candidates of physics beyond the Standard Model, string theory provides a unique proposal for incorporating gauge and gravitational interactions. In string theory, a four-dimensional theory that unifies quantum mechanics and gravity is obtained automatically if one posits that the additional dimensions predicted by the theory are small and curled up, a concept known as compactification. The gauge sector of the theory is specified by the topology and geometry of the...

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Brain Webs for Brane Webs

February 11, 2022

85% Match
Guillermo Arias-Tamargo, Yang-Hui He, Elli Heyes, ... , Rodriguez-Gomez Diego
High Energy Physics - Theory

We propose a new technique for classifying 5d Superconformal Field Theories arising from brane webs in Type IIB String Theory, using technology from Machine Learning to identify different webs giving rise to the same theory. We concentrate on webs with three external legs, for which the problem is analogous to that of classifying sets of 7-branes. Training a Siamese Neural Network to determine equivalence between any two brane webs shows an improved performance when webs are ...

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Probing intractable beyond-standard-model parameter spaces armed with Machine Learning

April 3, 2024

84% Match
Rajneil Baruah, Subhadeep Mondal, ... , Roy Satyajit
High Energy Physics - Phenom...

This article attempts to summarize the effort by the particle physics community in addressing the tedious work of determining the parameter spaces of beyond-the-standard-model (BSM) scenarios, allowed by data. These spaces, typically associated with a large number of dimensions, especially in the presence of nuisance parameters, suffer from the curse of dimensionality and thus render naive sampling of any kind -- even the computationally inexpensive ones -- ineffective. Over ...

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Machine Learning Algebraic Geometry for Physics

April 21, 2022

84% Match
Jiakang Bao, Yang-Hui He, ... , Hirst Edward
Algebraic Geometry
Machine Learning

We review some recent applications of machine learning to algebraic geometry and physics. Since problems in algebraic geometry can typically be reformulated as mappings between tensors, this makes them particularly amenable to supervised learning. Additionally, unsupervised methods can provide insight into the structure of such geometrical data. At the heart of this programme is the question of how geometry can be machine learned, and indeed how AI helps one to do mathematics...

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Searching for the Standard Model in the String Landscape : SUSY GUTs

January 12, 2011

84% Match
Stuart Raby
High Energy Physics - Phenom...
High Energy Physics - Theory

The goal of the present review article is to describe the ingredients necessary to find the Standard Model in the string landscape.

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Desperately Seeking the Standard Model

December 5, 2003

84% Match
Carlos Munoz
High Energy Physics - Phenom...
High Energy Physics - Theory

In the mid eighties string phenomenology started. Since then, its main objective, the search of the standard model, has not been accomplished yet. In this talk, on the ocassion of the 2nd International Conference on String Phenomenology in 2003, I will review this crucial issue.

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Exploring QCD matter in extreme conditions with Machine Learning

March 27, 2023

84% Match
Kai Zhou, Lingxiao Wang, ... , Shi Shuzhe
High Energy Astrophysical Ph...

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ...

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A Living Review of Machine Learning for Particle Physics

February 2, 2021

84% Match
Matthew Feickert, Benjamin Nachman
Machine Learning
Data Analysis, Statistics an...
Machine Learning

Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a nearly comprehensive list of citations for those developing and applying these approaches to experimental, phenomenological, or theoretical analyses. As a living document, it will be updated as often as possible to incorporate the latest develop...

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SO(8) Supergravity and the Magic of Machine Learning

June 1, 2019

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Iulia M. Comsa, Moritz Firsching, Thomas Fischbacher
Machine Learning

Using de Wit-Nicolai $D=4\;\mathcal{N}=8\;SO(8)$ supergravity as an example, we show how modern Machine Learning software libraries such as Google's TensorFlow can be employed to greatly simplify the analysis of high-dimensional scalar sectors of some M-Theory compactifications. We provide detailed information on the location, symmetries, and particle spectra and charges of 192 critical points on the scalar manifold of SO(8) supergravity, including one newly discovered $\ma...

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