Letters in High Energy Physics http://journals.andromedapublisher.com/index.php/LHEP <p>Letters in High Energy Physics (LHEP) is a quarterly, peer-reviewed, hybird open access journal which specializes in theory, phenomenological, and experimental aspects of particle physics. Additional topics covered by LHEP include astrophysics, gravity, and cosmology.&nbsp; LHEP publishes articles in the letter format.</p> <p>The goal is to provide the high energy physics community with a medium through which researchers are able to publish informative summaries of important findings in the field.</p> <p style="text-align: justify;"><span style="font-family: 'Minion W08 Regular_1167271',Times; font-size: 17px; font-variant-ligatures: normal; background-color: #ffffff;">&nbsp;</span></p> Andromeda Publishing And Academic Services LTD en-US Letters in High Energy Physics 2632-2714 <p align="justify">Letters in High Energy Physics (LHEP) is an open access journal published by Andromeda Publishing and Education Services. The articles in LHEP are distributed according to the terms of<a href="http://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener"> the creative commons license CC-BY 4.0</a>. Under the terms of this license, copyright is retained by the author while use, distribution and reproduction in any medium are permitted provided proper credit is given to original authors and sources.</p> <h2 style="margin-bottom: 0px; margin-top: 0px;">Terms of Submission</h2> <p style="margin-top: 0px; margin-bottom: 0px;" align="justify">By submitting an article for publication in LHEP, the submitting author asserts that:.</p> <ol style="margin-top: 0px; margin-bottom: 0px;"> <li>The article presents original contributions by the author(s) which have not been published previously in a peer-reviewed medium and are not subject to copyright protection.</li> <li>The co-authors of the article, if any, as well as any institution whose approval is required, agree to the publication of the article in LHEP.</li> </ol> A new perspective on the Ermakov-Pinney and scalar wave equations http://journals.andromedapublisher.com/index.php/LHEP/article/view/134 <p>The first part of the paper proves that a subset of the general set of Ermakov-Pinney equations<br>can be obtained by differentiation of a first-order non-linear differential equation. The second part<br>of the paper proves that, similarly, the equation for the amplitude function for the parametrix of<br>the scalar wave equation can be obtained by covariant differentiation of a first-order non-linear<br>equation. The construction of such a first-order non-linear equation relies upon a pair of auxiliary<br>1-forms (psi,rho). The 1-form psi satisfies the divergenceless condition div(psi) = 0, whereas the 1-form rho <br>fulfills the non-linear equation div(rho)+rho**2 = 0. The auxiliary 1-forms (psi,rho) are evaluated explicitly<br>in Kasner space-time, and hence also amplitude and phase function in the parametrix are obtained.<br>Thus, the novel method developed in this paper can be used with profit in physical applications.</p> Giampiero Esposito Marica Minucci, Dr. ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2019-08-06 2019-08-06 2 3 5 5 10.31526/lhep.3.2019.134 Asymmetric nonsingular bounce from a dynamic scalar field http://journals.andromedapublisher.com/index.php/LHEP/article/view/132 <p>We present a dynamical model for a time-asymmetric nonsingular bounce<br>with a post-bounce change of the effective equation-of-state parameter.<br>Specifically, we consider a scalar-field model with a<br>time-reversal-noninvariant effective potential.</p> Frans R. Klinkhamer Ziliang L. Wang ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2019-09-03 2019-09-03 2 3 9 9 10.31526/lhep.3.2019.132 Connections between physics, mathematics, and deep learning http://journals.andromedapublisher.com/index.php/LHEP/article/view/110 <p>Starting from Fermat’s principle of least action, which governs classical and quantum mechanics and from<br>the theory of exterior differential forms, which governs the geometry of curved manifolds, we show how<br>to derive the equations governing neural networks in an intrinsic, coordinate-invariant way, where the loss<br>function plays the role of the Hamiltonian. To be covariant, these equations imply a layer metric which is<br>instrumental in pretraining and explains the role of conjugation when using complex numbers. The differential<br>formalism clarifies the relation of the gradient descent optimizer with Aristotelian and Newtonian<br>mechanics. The Bayesian paradigm is then analyzed as a renormalizable theory yielding a new derivation<br>of the Bayesian information criterion. We hope that this formal presentation of the differential geometry<br>of neural networks will encourage some physicists to dive into deep learning and, reciprocally, that the<br>specialists of deep learning will better appreciate the close interconnection of their subject with the foundations<br>of classical and quantum field theory.</p> Jean Thierry-Mieg ##submission.copyrightStatement## http://creativecommons.org/licenses/by/4.0 2019-09-17 2019-09-17 2 3 8 8 10.31526/lhep.3.2019.110