Heterotic flux vacua with a small superpotential
Journal of High Energy Physics Springer Nature 2026:10 (2026) 85
Abstract:
We study heterotic Calabi-Yau compactifications with NSNS three-form flux in view of moduli stabilisation and investigate whether the value |W0| of the flux superpotential evaluated at supersymmetric minima can be small. Unlike in type IIB string theory, heterotic compactifications lack a no-scale structure, so that a non-vanishing flux superpotential generically induces a tree-level scalar potential for all moduli. Controlled moduli stabilisation therefore requires the flux superpotential to be sufficiently small in order to compete with non-perturbative effects. Working within a four-dimensional effective field theory and exploiting the special geometry of Calabi-Yau complex structure moduli spaces, we analyse the complex structure F-term equations and derive two no-go theorems: (1) supersymmetric vacua with vanishing |W0|, which would lead to a vanishing tree-level scalar potential as in type IIB, occur only at singular loci in moduli space, and (2) no supersymmetric vacua with small |W0| exist in the large complex structure limit. Motivated by these results, we analyse explicit models away from the large complex structure regime using exact period expressions and identify one- and two-parameter examples in which suitable flux choices allow for values of |W0| of order unity, with some one-parameter models admitting values moderately below unity. Our findings show that small heterotic flux superpotentials are highly constrained, with the values of |W0| found in the examples studied being only marginally compatible with moduli stabilisation.Calabi–Yau metrics with Kähler moduli dependence
Machine Learning: Science and Technology IOP Publishing 7:4 (2026) 045020-045020
Abstract:
Abstract We present a method to construct approximate analytic expressions for Ricci-flat K"ahler metrics on Calabi–-Yau threefolds with explicit dependence on the K"ahler moduli. Our strategy combines numerical data obtained from machine learning with an explicit analytic Ansatz for the K"ahler potential and symbolic regression methods. Specifically, we use neural networks to learn the K"ahler potential at selected points in K"ahler moduli space, fit this data to analytic expressions with K"ahler moduli-dependent parameters, and determine an analytic form of these coefficients as functions of the K"ahler moduli using symbolic regression. In this way, we reconstruct closed-form approximations to the Ricci-flat metric that retain explicit K"ahler-moduli dependence. We apply this method to two Calabi–-Yau threefolds with $h^{1,1}=2$, namely a bicubic hypersurface in $\mathbb{P}^2 \times \mathbb{P}^2$ and a bi-degree $(2,4)$ hypersurface in $\mathbb{P}^1 \times \mathbb{P}^3$, both of which admit nontrivial discrete symmetry groups that simplify the structure of the metric. In both cases, the resulting analytic expressions reproduce the numerically learned K"ahler potentials with percent-level accuracy and yield a Ricci-flatness measure that remains sufficiently small across the sampled region. Our results represent a concrete bridge between purely numerical results for Calabi–-Yau metrics and analytic constructions, opening the door to a systematic study of their dependence on K"ahler moduli.Quantum annealing: optimisation, sampling, and many-body dynamics
Contemporary Physics Taylor & Francis ahead-of-print:ahead-of-print (2026) 1-29
Abstract:
Quantum annealing is a computational paradigm in which optimisation problems are encoded in the energy landscape of an interacting quantum system and explored through its dynamical evolution. By continuously transforming an initial Hamiltonian into one whose ground state represents the solution, the system navigates complex energy landscapes through a combination of quantum fluctuations, tunnelling processes, and dissipative dynamics. Quantum annealing is primarily designed for discrete optimisation and sampling tasks and provides a physically motivated heuristic for exploring rugged landscapes that arise across science and engineering. Modern quantum annealers realise programmable spin systems with thousands of qubits, making them among the largest controllable quantum devices currently available. Beyond optimisation, they also serve as experimental platforms for studying non-equilibrium many-body quantum dynamics in regimes that are challenging to access classically. In this review we introduce the principles of quantum annealing, describe the main hardware platforms and algorithmic techniques, and analyse the roles of tunnelling, spectral gaps, and open-system effects in determining performance. We survey applications ranging from optimisation and machine learning to quantum simulation and many-body physics, and discuss the central challenges of benchmarking, scaling, and control. These developments position quantum annealing at the interface of optimisation, stochastic sampling, and programmable quantum dynamics.Statistical patterns in the equations of physics and the emergence of a meta-law of nature
Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences The Royal Society 384:2317 (2026) 20250091
Abstract:
Physics seeks to uncover the laws of Nature and express them through mathematical equations . Despite the vast diversity of natural phenomena, physical equations exhibit structural regularities that set them apart from arbitrary mathematical expressions. While principles such as dimensional analysis have long guided the formulation of physical models, the exploration of more subtle statistical patterns within the equations of physics remains an open question. Here, by analysing four corpora of physics equations and applying advanced implicit-likelihood techniques, we find that the frequency of mathematical operators follows an exponential decay law, in contrast to Zipf's power law for word frequencies in natural languages. This reveals a statistical meta-law of physics, possibly reflecting a combination of communication efficiency and constraints imposed by Nature itself. The meta-law offers practical benefits for symbolic regression by drastically narrowing down the space of physically plausible expressions. More broadly, it may inform the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.Reproducing Standard Model fermion masses and mixing in string theory: A heterotic line bundle study
Physical Review D American Physical Society (APS) 113:4 (2026) 046005