Particle theory

Abstract:

The IceCube Neutrino Observatory has observed a diffuse flux of high-energy astrophysical neutrinos for more than a decade. A relevant background to the astrophysical flux is prompt atmospheric neutrinos, originating from the decay of charmed mesons produced in cosmic-ray-induced air showers. The production rate of charmed mesons in the very forward phase space of hadronic interactions, and consequently, the prompt neutrino flux, remains uncertain and has not yet been observed by neutrino detectors. An accurate measurement of this flux would enhance our understanding of fundamental particle physics such as hadronic interactions in high-energy cosmic-ray-induced air showers and the nucleon structure. Furthermore, an experimental characterization of this background flux will improve the precision of astrophysical neutrino flux spectral measurements. In this work, we perform a combined fit of cascade-like and track-like neutrino events in IceCube to constrain the prompt atmospheric neutrino flux. Given that the prompt flux is a sub-dominant contribution, treating systematic uncertainties arising from the potential mis-modeling of the conventional and astrophysical neutrino fluxes is critical for its measurement. Our analysis yields a non-zero best-fit result, which is, however, consistent with the null hypothesis of no prompt flux within one standard deviation. Consequently, we establish an upper bound on the flux at 4×10-16$$4\times 10^{-16}$$ (GeV m2$$^2$$ s sr)-1$$^{-1}$$ at 10TeV.

Abstract:

We outline a method to determine analytic Kähler potentials with associated approximately Ricci‐flat Kähler metrics on Calabi–Yau manifolds. Key ingredients are numerically calculating Ricci‐flat Kähler potentials via machine learning techniques and fitting the numerical results to Donaldson's ansatz. We apply this method to the Dwork family of quintic hypersurfaces in P 4 $\mathbb {P}^4$ and an analogous one‐parameter family of bi‐cubic CY hypersurfaces in P 2 × P 2 $\mathbb {P}^2\times \mathbb {P}^2$ . In each case, a relatively simple analytic expression is obtained for the approximately Ricci‐flat Kähler potentials, including the explicit dependence on the complex structure parameter. We find that these Kähler potentials only depend on the modulus of the complex structure parameter.

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.

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.