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We propose a general approach for deriving transparent boundary conditions for the stationary Schroedinger equation with arbitrary potential. It is proven that the transparent boundary conditions can be written in terms of the Weyl-Titchmarsh coefficients. As examples for the application of the proposed approach, two special cases for the stationary Schroedinger equation with the harmonic potential and the Poeschl-Teller potential are considered.

We present an updated global analysis of beauty decays sensitive to the angle $\gamma$ of the Cabibbo-Kobayashi-Maskawa matrix and of $D$-meson mixing data in the framework of approximate universality for charm mixing. We extract the fundamental theoretical parameters determining absorptive and dispersive contributions to $D$ meson mixing and CP violation, together with the angle $\gamma$. The results for the charm mixing parameters are $x_{12} \simeq x = (0.402 \pm 0.034)\%$ and $y_{12} \simeq y = (0.629 \pm 0.021)\%$, while the two CP-violating phases are given by $\varphi_2^M = (2.4 \pm 1.6)^{\Large \circ}$ and $\varphi_2^{\Gamma} = (2.6 \pm 1.2)^{\Large \circ}$. The angle $\gamma$ is found to be $\gamma = (68.4 \pm 2.6)^{\Large \circ}$.

Currently, large language models are gaining popularity, their achievements are used in many areas, ranging from text translation to generating answers to queries. However, the main problem with these new machine learning algorithms is that training such models requires large computing resources that only large IT companies have. To avoid this problem, a number of methods (LoRA, quantization) have been proposed so that existing models can be effectively fine-tuned for specific tasks. In this paper, we propose an E2E (end to end) audio summarization model using these techniques. In addition, this paper examines the effectiveness of these approaches to the problem under consideration and draws conclusions about the applicability of these methods., Comment: 36 pages, Master's thesis, 14 figures

We propose a new uncertainty estimator for gradient-free optimisation of black-box simulators using deep generative surrogate models. Optimisation of these simulators is especially challenging for stochastic simulators and higher dimensions. To address these issues, we utilise a deep generative surrogate approach to model the black box response for the entire parameter space. We then leverage this knowledge to estimate the proposed uncertainty based on the Wasserstein distance - the Wasserstein uncertainty. This approach is employed in a posterior agnostic gradient-free optimisation algorithm that minimises regret over the entire parameter space. A series of tests were conducted to demonstrate that our method is more robust to the shape of both the black box function and the stochastic response of the black box than state-of-the-art methods, such as efficient global optimisation with a deep Gaussian process surrogate., Comment: European Conference on Artificial Intelligence 2024 Main Track

Identifying multi-exciton states generated from singlet fission is key to understanding the carrier multiplication process, which presents a strategy for improving the efficiency of photovoltaics and bio-imaging. Broadband optically detected magnetic resonance is a sensitive technique to detect multi-exciton states. Here we report a dominant species emerging under intense light excitation corresponding to a weakly exchange coupled triplet pair located on adjacent molecules oriented by nearly 90 degrees, contrasting to the pi-stacked triplet pair under low excitation intensity. The weakly coupled species model precisely reproduces the intricate spin transitions in the Hilbert space of the triplet pair. Combining the magneto photoluminescence and high-magnetic field ODMR, we also identify a strongly exchange-coupled state of three triplet excitons formed by photoexcited V2, which manifests through the magnetic field induced level crossings between its quintet and triplet manifolds. The excellent agreement between the experimental Zeeman fan and the two-triplet spin Hamiltonian highlights the potential of multi-exciton states for quantum information processing.

Optical control and read-out of the ground state spin structure has been demonstrated for defect states in crystalline semiconductors, including the diamond NV- center, and these are promising systems for quantum technologies. Molecular organic semiconductors offer synthetic control of spin placement, in contrast to current limitations in these crystalline systems. Here we report the discovery of spin-optical addressability in a diradical molecule that comprises two trityl radical groups coupled via a fluorene bridge. We demonstrate the three important properties that enable operation as a spin-photon interface: (i) triplet and singlet spin states show photoluminescence peaked at 640 and 700 nm respectively; this allows easy optical measurement of ground state spin. (ii) the ground state spin exchange is small (~60 {\mu}eV) that allows preparation of ground state spin population. This can be achieved by spin-selective excited state intersystem crossing, and we report up to 8% microwave-driven contrast in photoluminescence. (iii) both singlet and triplet manifolds have near-unity photoluminescence quantum yield, which is in contrast to the near-zero quantum yields in prior reports of molecular diradicals. Our results establish these tuneable open-shell organic molecules as a platform to engineer tailor-made spin-optical interfaces.

The Spin Physics Detector collaboration proposes to install a universal detector in the second interaction point of the NICA collider under construction (JINR, Dubna) to study the spin structure of the proton and deuteron and other spin-related phenomena using a unique possibility to operate with polarized proton and deuteron beams at a collision energy up to 27 GeV and a luminosity up to $10^{32}$ cm$^{-2}$ s$^{-1}$. As the main goal, the experiment aims to provide access to the gluon TMD PDFs in the proton and deuteron, as well as the gluon transversity distribution and tensor PDFs in the deuteron, via the measurement of specific single and double spin asymmetries using different complementary probes such as charmonia, open charm, and prompt photon production processes. Other polarized and unpolarized physics is possible, especially at the first stage of NICA operation with reduced luminosity and collision energy of the proton and ion beams. This document is dedicated exclusively to technical issues of the SPD setup construction.

We address the role of noisy squeezing in security and performance of continuous-variable (CV) quantum key distribution (QKD) protocols. Squeezing has long been recognized for its numerous advantages in CV QKD, such as enhanced robustness against channel noise and loss, and improved secret key rates. However, the noise of the squeezed states, that unavoidably originates already from optical loss in the source, raises concerns about its potential exploitation by an eavesdropper. This is particularly relevant if this noise is pessimistically assumed untrusted. We address the allocation of untrusted noise within a squeezed state and show that anti-squeezing noise is typically more harmful for security of the protocols, as it potentially provides more information to an eavesdropper. Although the anti-squeezing noise may not directly contribute to the generated key data, it is involved in parameter estimation and can in fact be harmful even if considered trusted. Our study covers the effects of anti-squeezing noise in both the asymptotic and finite-size regimes. We highlight the positive effects and limitations of imposing trust assumption on anti-squeezing noise. Additionally, we emphasize the detrimental impact of untrusted noise in both fiber and free-space fading links. Our findings offer essential insights for practical implementations and optimization of squeezed-state CV QKD protocols in realistic scenarios.

Building scalable and secure quantum networks with many users has a high application potential but also holds many practical challenges. A significant stride in this pursuit involves extending quantum key distribution, an information-theoretically secure method for establishing cryptographic keys between two distant users, from a point-to-point protocol implemented on direct optical connections to a quantum access network. Yet, realizations of quantum access networks have, so far, relied on probabilistic or time-sharing strategies. Here, we show theoretically and experimentally that a solution without these constraints can come from the exclusive features of continuous-variable systems. Based on coherent states, we propose continuous-variable quantum passive-optical-network (CV-QPON) protocols, enabling deterministic and simultaneous secret key generation among all network users. We achieve this by leveraging the inherent wave-like property of coherent states split at a beam splitter and electric-field quadrature measurements. We show two protocols with different trust levels assigned to the network users and experimentally demonstrate key generation in a quantum access network with 8 users, each with an 11 km span of access link. Depending on the trust assumptions about users, we reach 1.5 Mbits/s and 2.1 Mbits/s of total network key generation. Demonstrating the potential to expand the network's capacity to accommodate tens of users at a high rate, our CV-QPON protocols offer a pathway toward establishing low-cost, high-rate, and scalable quantum access networks using standard telecom technologies and directly exploiting the existing access network infrastructure.

Detailed detector simulation is the major consumer of CPU resources at LHCb, having used more than 90% of the total computing budget during Run 2 of the Large Hadron Collider at CERN. As data is collected by the upgraded LHCb detector during Run 3 of the LHC, larger requests for simulated data samples are necessary, and will far exceed the pledged resources of the experiment, even with existing fast simulation options. An evolution of technologies and techniques to produce simulated samples is mandatory to meet the upcoming needs of analysis to interpret signal versus background and measure efficiencies. In this context, we propose Lamarr, a Gaudi-based framework designed to offer the fastest solution for the simulation of the LHCb detector. Lamarr consists of a pipeline of modules parameterizing both the detector response and the reconstruction algorithms of the LHCb experiment. Most of the parameterizations are made of Deep Generative Models and Gradient Boosted Decision Trees trained on simulated samples or alternatively, where possible, on real data. Embedding Lamarr in the general LHCb Gauss Simulation framework allows combining its execution with any of the available generators in a seamless way. Lamarr has been validated by comparing key reconstructed quantities with Detailed Simulation. Good agreement of the simulated distributions is obtained with two-order-of-magnitude speed-up of the simulation phase., Comment: Under review in EPJ Web of Conferences (CHEP 2023)

Quantum key distribution (QKD) is a well-known application of quantum information theory that guarantees information-theoretically secure key exchange. As QKD becomes more and more commercially viable, challenges such as scalability, network integration, and high production costs need to be addressed. Photonic and electronic integrated circuits that can be produced in large volumes at low cost hold the key to large-scale deployment of next-generation QKD systems. Here, we present a continuous-variable (CV) QKD system using an integrated photonic-electronic receiver that combines a silicon photonic integrated circuit implementing a phase-diverse receiver with custom-designed GaAs pHEMT transimpedance amplifiers. The QKD system operates at a classical telecom symbol rate of 10 GBaud, generating high secret key rates exceeding 0.7 Gb/s over a distance of 5 km and 0.3 Gb/s over a distance of 10 km. The secret keys are secure against collective attacks with finite-size effects taken into account. Well-designed digital signal processing enabled the high-speed operation. Our experiment sets a new record for secure quantum communication and paves the way for the next generation of CV-QKD systems.

Quantum key distribution (QKD) enables two remote parties to share encryption keys with security based on the laws of physics. Continuous variable (CV) QKD with coherent states and coherent detection integrates well with existing telecommunication networks. However, thus far, long-distance CV-QKD has only been demonstrated using a highly complex scheme where the local oscillator is transmitted, opening security loopholes for eavesdroppers and limiting its potential applications. Here, we report a long-distance CV-QKD experiment with a locally generated local oscillator over a 100 km fiber channel with a total loss of 15.4 dB. This record-breaking distance is achieved by controlling the phase-noise-induced excess noise through a machine-learning framework for carrier recovery and optimizing the modulation variance. We implement the full CV-QKD protocol and demonstrate the generation of keys secure against collective attacks in the finite-size regime. Our results mark a significant milestone for realizing CV quantum access networks with a high loss budget, and pave the way for large-scale deployment of secure QKD.

We investigate the physical mechanisms for spin-dependent recombination of a strongly bound pair of triplet excitons generated by singlet fission and forming a spin quintet (total spin of two) bi-exciton. For triplet excitons the spin-dependent recombination pathways can involve intersystem crossing or triplet-triplet annihilation back to the singlet ground state. However the modeling of spin-dependent recombination for quintets is still an open question. Here we introduce two theoretical models and compare their predictions with the broadband optically detected magnetic resonance spectrum of a long lived quintet bi-exciton with known molecular structure. This spectrum measures the change in the fluorescence signal induced by microwave excitation of each of the ten possible spin transitions within the quintet manifold as function of the magnetic field. While most of the experimental features can be reproduced for both models, the behavior of some of the transitions is only consistent with the quintet spin-recombination model inspired by triplet intersystem crossing which can reproduce accurately the experimental two-dimensional spectrum with a small number of kinetic parameters. Thus quantitative analysis of the broadband optically detected magnetic resonance signal enables quantitative understanding of the dominant spin-recombination processes and estimation of the out-of equilibrium spin populations., Comment: optimization code available at https://github.com/yneter/ampodmr

There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. A widespread Deep Neural Networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a clear relation between observations and the target variable. However, at the moment, there is no dominant solution for the symbolic regression task, and we aim to reduce this gap with our algorithm. In this work, we propose a novel deep learning framework for symbolic expression generation via variational autoencoder (VAE). In a nutshell, we suggest using a VAE to generate mathematical expressions, and our training strategy forces generated formulas to fit a given dataset. Our framework allows encoding apriori knowledge of the formulas into fast-check predicates that speed up the optimization process. We compare our method to modern symbolic regression benchmarks and show that our method outperforms the competitors under noisy conditions. The recovery rate of SEGVAE is 65% on the Ngyuen dataset with a noise level of 10%, which is better than the previously reported SOTA by 20%. We demonstrate that this value depends on the dataset and can be even higher.

The volume of data processed by the Large Hadron Collider experiments demands sophisticated selection rules typically based on machine learning algorithms. One of the shortcomings of these approaches is their profound sensitivity to the biases in training samples. In the case of particle identification (PID), this might lead to degradation of the efficiency for some decays not present in the training dataset due to differences in input kinematic distributions. In this talk, we propose a method based on the Common Specific Decomposition that takes into account individual decays and possible misshapes in the training data by disentangling common and decay specific components of the input feature set. We show that the proposed approach reduces the rate of efficiency degradation for the PID algorithms for the decays reconstructed in the LHCb detector., Comment: proceedings of ACAT-2021

Flavour mixing and CP violation as measured in weak decays and mixing of neutral mesons are a fundamental tool to test the Standard Model (SM) and to search for new physics. New analyses performed at the LHC experiment open an unprecedented insight into the Cabibbo-Kobayashi-Maskawa (CKM) metrology and new evidence for rare decays. Important progress has also been achieved in theoretical calculations of several hadronic quantities with a remarkable reduction of the uncertainties. This improvement is essential since previous studies of the Unitarity Triangle did show that possible contributions from new physics, if any, must be tiny and could easily be hidden by theoretical and experimental errors. Thanks to the experimental and theoretical advances, the CKM picture provides very precise SM predictions through global analyses. We present here the results of the latest global SM analysis performed by the UTfit collaboration including all the most updated inputs from experiments, lattice QCD and phenomenological calculations., Comment: 24 pages, 7 figures, 11 tables

Magnetic fields are responsible for a multitude of Solar phenomena, including such destructive events as solar flares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle, in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Sun. The field of quantitative inference of magnetic field vectors and related solar atmospheric parameters from such observations has long been investigated. In recent years, very sophisticated codes for spectropolarimetric observations have been developed. Over the past two decades, neural networks have been shown to be a fast and accurate alternative to classic inversion technique methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, the degeneracies, and the uncertainties of each parameter remain uncovered. In this paper, we provide end-to-end inversion codes based on the simple Milne-Eddington model of the stellar atmosphere and deep neural networks to both parameter estimation and their uncertainty intervals. The proposed framework is designed in such a way that it can be expanded and adapted to other atmospheric models or combinations of them. Additional information can also be incorporated directly into the model. It is demonstrated that the proposed architecture provides high accuracy of results, including a reliable uncertainty estimation, even in the multidimensional case. The models are tested using simulation and real data samples., Comment: 17 pages with 7 figures and 3 tables, submitted to Solar Physics

Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and most of these objects have not been subject to such studies. }{Flux time series are actively used as an affordable alternative for photometric classification and characterization, for instance, peak identifications and luminosity decline estimations. However, the collected time series are multidimensional and irregularly sampled, while also containing outliers and without any well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each passband.}{We examined several light curve approximation methods based on neural networks such as multilayer perceptrons, Bayesian neural networks, and normalizing flows to approximate observations of a single light curve. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients.}{The tests demonstrate that even just a few observations are enough to fit the networks and improve the quality of approximation, compared to state-of-the-art models. The methods described in this work have a low computational complexity and are significantly faster than Gaussian processes. Additionally, we analyzed the performance of the approximation techniques from the perspective of further peak identification and transients classification. The study results have been released in an open and user-friendly Fulu Python library available on GitHub for the scientific community.

Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.

Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. These models learn to classify supernova types using their light curves as inputs. Preprocessing these curves is a crucial step that significantly affects the final quality. In this talk, we study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve. We use these approximations as inputs for supernovae classification models and demonstrate that the proposed methods outperform the state-of-the-art based on Gaussian processes applying to the Zwicky Transient Facility Bright Transient Survey light curves. MLP demonstrates similar quality as Gaussian processes and speed increase. Normalizing Flows exceeds Gaussian processes in terms of approximation quality as well., Comment: 5 pages, 2 figures and 2 tables