Note on large-pp limit of (2,2p+1)(2,2p+1) minimal Liouville gravity and moduli space volumes

In this note we report on some properties of correlation numbers for 2-dimensional Liouville gravity coupled with $(2,2p+1)$ minimal model at large $p$. In the limit $p \to \infty$, for some explicitly known examples in a particular region of parameter space correlation numbers are shown to reduce to Weil-Petersson volumes, analytically continued to imaginary geodesic lengths. This marks another connection of this limit with JT-gravity. We also comment on supposed geometric meaning of the obtained answers outside of this region, in particular, the meaning of the minimal model fusion rules. Another observation is the proportionality of correlation number to the number of conformal blocks when $p$ is big enough compared to parameters of the correlator. This proportionality is valid even without taking the limit.Comment: 13 pages; added comments, updated reference

Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks

The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced. Such large scale productions are extremely demanding in terms of computing resources. Thus new approaches to event generation and simulation of detector responses are needed. In LHCb, the accurate simulation of Cherenkov detectors takes a sizeable fraction of CPU time. An alternative approach is described here, when one generates high-level reconstructed observables using a generative neural network to bypass low level details. This network is trained to reproduce the particle species likelihood function values based on the track kinematic parameters and detector occupancy. The fast simulation is trained using real data samples collected by LHCb during run 2. We demonstrate that this approach provides high-fidelity results.Comment: Proceedings for 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research. (Fixed typos and added one missing reference in the revised version.

A Novel Approach to a Piezoelectric Sensing Element

Piezoelectric materials have commonly been used in pressure and stress sensors; however, many designs consist of thin plate structures that produce small voltage signals when they are compressed or extended under a pressure field. This study used finite element methods to design a novel piezoelectric pressure sensor with a C-shaped piezoelectric element and determine if the voltage signal obtained during hydrostatic pressure application was enhanced compared to a standard thin plate piezoelectric element. The results of this study demonstrated how small deformations of this C-shaped sensor produced a large electrical signal output. It was also shown that the location of the electrodes for this sensor needs to be carefully chosen and that the electric potential distribution varies depending on the poling of the piezoelectric element. This study indicated that the utilization of piezoelectric materials of different shapes and geometries embedded in a polymer matrix for sensing applications has several advantages over thin plate solid piezoelectric structures

Generative deep Gaussian processes

In this paper, the possibilities of using combinations of Gaussian models for the problems of describing two-dimensional models are investigated. It is proposed to use multidimensional deep Gaussian models as the basis for such a description. The tasks are formalized, the solution of which is necessary for the correct training of these models from single images. In the framework of solving these problems, a consistent Bayesian derivation of the parameters of the corresponding deep Gaussian models was performed. In the framework of experiments to simulate images of different types, the consistency of the found relations is shown