Search

We report on the first measurement of $J/\psi$ photoproduction from nuclei in the photon energy range of $7$ to $10.8$ GeV, extending above and below the photoproduction threshold in the free proton of $\sim8.2$ GeV. The experiment used a tagged photon beam incident on deuterium, helium, and carbon, and the GlueX detector at Jefferson Lab to measure the semi-inclusive $A(\gamma,e^+e^-p)$ reaction with a dilepton invariant mass $M(e^+e^-)\sim m_{J/\psi}=3.1$ GeV. The incoherent $J/\psi$ photoproduction cross sections in the measured nuclei are extracted as a function of the incident photon energy, momentum transfer, and proton reconstructed missing light-cone momentum fraction. Comparisons with theoretical predictions suggest an excess of the measured cross section for sub-threshold production and for interactions with high missing light-cone momentum fraction protons. The measured enhancement can be explained by modified gluon structure for high-virtuality bound-protons.

We suggest a novel scheme for generating multimode squeezed states for the boson sampling implementation. The idea is to replace a commonly used linear interferometer by a multimode resonator containing a passive optical element consisting of two-level atoms dispersively interacting with photons and self-generating a squeezed compound state of both bosons -- photons and atoms. The suggested scheme does not need (a) on-demand external sources of photons in squeezed or Fock quantum states and (b) numerous interchannel couplers which introduce phase noise and losses that prevent scaling up the system and achieving quantum advantage. The idea is illustrated by a setup based on a Bose-Einstein-condensed gas confined in a multimode resonator, one of whose optical modes is in the classical coherent regime. The joint probability distribution of photon and/or noncondensed atom numbers is calculated via a matrix hafnian that, for certain parameters of the system, is hardly to be effectively calculated by classical computers. Such experiments are at reach via existing cavity-QED and cold-gas technology.

Deep learning regularization techniques, such as dropout, layer normalization, or weight decay, are widely adopted in the construction of modern artificial neural networks, often resulting in more robust training processes and improved generalization capabilities. However, in the domain of Reinforcement Learning (RL), the application of these techniques has been limited, usually applied to value function estimators, and may result in detrimental effects. This issue is even more pronounced in offline RL settings, which bear greater similarity to supervised learning but have received less attention. Recent work in continuous offline RL has demonstrated that while we can build sufficiently powerful critic networks, the generalization of actor networks remains a bottleneck. In this study, we empirically show that applying standard regularization techniques to actor networks in offline RL actor-critic algorithms yields improvements of 6% on average across two algorithms and three different continuous D4RL domains., Comment: https://github.com/DT6A/ActoReg

QCD factorization takes different forms in the large-x and small-x regimes. At large-x, collinear factorization leads to the DGLAP evolution equation, while at small-x, rapidity factorization results in the BFKL equation. To unify these different regimes, a new TMD factorization based on the background field method is proposed. This factorization not only reduces to CSS and DGLAP in the large-x limit and BFKL in the small-x limit, but also defines a general evolution away from these regimes., Comment: 6 pages, 1 figure, submitted to Proceedings, 31st workshop on Deep inelastic scattering: Grenoble, April 8 to 12 2024. arXiv admin note: text overlap with arXiv:2311.16402

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings., Comment: Corresponding Authors: Talia Gershon, Seetharami Seelam,Brian Belgodere, Milton Bonilla

The spin-exotic hybrid meson $\pi_{1}(1600)$ is predicted to have a large decay rate to the $\omega\pi\pi$ final state. Using 76.6~pb$^{-1}$ of data collected with the GlueX detector, we measure the cross sections for the reactions $\gamma p \to \omega \pi^+ \pi^- p$, $\gamma p \to \omega \pi^0 \pi^0 p$, and $\gamma p\to\omega\pi^-\pi^0\Delta^{++}$ in the range $E_\gamma =$ 8-10 GeV. Using isospin conservation, we set the first upper limits on the photoproduction cross sections of the $\pi^{0}_{1}(1600)$ and $\pi^{-}_{1}(1600)$. We combine these limits with lattice calculations of decay widths and find that photoproduction of $\eta'\pi$ is the most sensitive two-body system to search for the $\pi_1(1600)$., Comment: 6 pages, 3 figures plus supplemental materials

Using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT), an experimental and theoretical study of changes in the electronic structure (dispersion dependencies) and corresponding modification of the energy band gap at the Dirac point (DP) for topological insulator (TI) $\mathrm{Mn}_{1-x} \mathrm{Ge}_x \mathrm{Bi}_2 \mathrm{Te}_4$ have been carried out with gradual replacement of magnetic Mn atoms by non-magnetic Ge atoms when concentration of the latter was varied from 10$\%$ to 75$\%$. It was shown that when Ge concentration increases then the bulk band gap decreases and reaches zero plateau in the concentration range of 45$\%$-60$\%$ while non-topological surface states (TSS) are present and exhibit an energy splitting of 100 and 70 meV in different types of measurements. It was also shown that TSS disappear from the measured band dispersions at a Ge concentration of about 40$\%$. DFT calculations of $\mathrm{Mn}_{1-x} \mathrm{Ge}_x \mathrm{Bi}_2 \mathrm{Te}_4$ band structure were carried out to identify the nature of observed band dispersion features and to analyze a possibility of magnetic Weyl semimetal state formation in this system. These calculations were performed for both antiferromagnetic (AFM) and ferromagnetic (FM) ordering types while the spin-orbit coupling (SOC) strength was varied or a strain (compression or tension) along the $c$-axis was applied. Calculations show that two different series of topological phase transitions (TPTs) may be implemented in this system depending on the magnetic ordering. At AFM ordering transition between TI and trivial insulator phase goes through the Dirac semimetal state, whereas for FM phase such route admits three intermediate states instead of one (TI - Dirac semimetal - Weyl semimetal - Dirac semimetal - trivial insulator).

We measure the spin-density matrix elements (SDMEs) of the $\Delta^{++}(1232)$ in the photoproduction reaction $\gamma p \to \pi^-\Delta^{++}(1232)$ with the GlueX experiment in Hall D at Jefferson Lab. The measurement uses a linearly--polarized photon beam with energies from $8.2$ to $8.8$~GeV and the statistical precision of the SDMEs exceeds the previous measurement by three orders of magnitude for the momentum transfer squared region below $1.4$ GeV$^2$. The data are sensitive to the previously undetermined relative sign between couplings in existing Regge-exchange models. Linear combinations of the extracted SDMEs allow for a decomposition into natural and unnatural--exchange amplitudes. We find that the unnatural exchange plays an important role in the low momentum transfer region.

In deep Reinforcement Learning (RL), value functions are typically approximated using deep neural networks and trained via mean squared error regression objectives to fit the true value functions. Recent research has proposed an alternative approach, utilizing the cross-entropy classification objective, which has demonstrated improved performance and scalability of RL algorithms. However, existing study have not extensively benchmarked the effects of this replacement across various domains, as the primary objective was to demonstrate the efficacy of the concept across a broad spectrum of tasks, without delving into in-depth analysis. Our work seeks to empirically investigate the impact of such a replacement in an offline RL setup and analyze the effects of different aspects on performance. Through large-scale experiments conducted across a diverse range of tasks using different algorithms, we aim to gain deeper insights into the implications of this approach. Our results reveal that incorporating this change can lead to superior performance over state-of-the-art solutions for some algorithms in certain tasks, while maintaining comparable performance levels in other tasks, however for other algorithms this modification might lead to the dramatic performance drop. This findings are crucial for further application of classification approach in research and practical tasks., Comment: https://github.com/DT6A/ClORL

A quasi-exponential is an entire function of the form $e^{cu}p(u)$, where $p(u)$ is a polynomial and $c \in \mathbb{C}$. Let $V = \langle e^{h_1u}p_1(u), \dots, e^{h_Nu}p_N(u) \rangle$ be a vector space with a basis of quasi-exponentials. We show that if $h_1, \dots, h_N$ are nonnegative and all of the complex zeros of the Wronskian $\operatorname{Wr}(V)$ are real, then $V$ is totally nonnegative in the sense that all of its Grassmann-Pl\"{u}cker coordinates defined by the Taylor expansion about $u=t$ are nonnegative, for any real $t$ greater than all of the zeros of $\operatorname{Wr}(V)$. Our proof proceeds by showing that the higher Gaudin Hamiltonians $T_\lambda^G(t)$ introduced in [ALTZ14] are universal Pl\"ucker coordinates about $u=t$ for the Wronski map on spaces of quasi-exponentials. The result that $V$ is totally nonnegative follows from the fact that $T_\lambda^G(t)$ is positive semidefinite, which we establish using partial traces. We also show that if $h_1 = \cdots = h_N = 0$ then $T_\lambda^G(t)$ equals $\beta^\lambda(t)$, which is the universal Pl\"ucker coordinate for the Wronski map on spaces of polynomials introduced in [KP23]., Comment: 24 pages

Identification of crystal structures is a crucial stage in the exploration of novel functional materials. This procedure is usually time-consuming and can be false-positive or false-negative. This necessitates a significant level of expert proficiency in the field of crystallography and, especially, requires deep experience in perovskite - related structures of hybrid perovskites. Our work is devoted to the machine learning classification of structure types of hybrid lead halides based on available X-ray diffraction data. Here, we proposed a simple approach to quickly identify of dimensionality of inorganic substructures, types of lead halide polyhedra connectivity and structure types using common powder XRD data and ML - decision tree classification model. The average accuracy of our ML algorithm in predicting the dimensionality of inorganic substructure, type of connection of lead halide and inorganic substructure topology by theoretically calculated XRD pattern among 14 most common structure types reaches 0.86+-0.05, 0.827+-0.028 and 0.71+-0.05, respectively. The validation of our decision tree classification ML model on experimental XRD data shows the accuracies of 1.0 and 0.82 for the dimension and structure type prediction. Thus, our approach can significantly simplify and accelerate the interpretation of highly complicated XRD data for hybrid lead halides.

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.

The mechanism of the peculiar transport properties around the magnetic ordering temperature of semiconducting antiferromagnetic EuCd$_2$P$_2$ is not yet understood. With a huge peak in the resistivity observed above the N\'eel temperature, $T_{\rm N}=10.6\,\rm K$, it exhibits a colossal magnetoresistance effect. Recent reports on observations of ferromagnetic contributions above $T_{\rm N}$ as well as metallic behavior below this temperature have motivated us to perform a comprehensive characterization of this material, including its resistivity, heat capacity, magnetic properties and electronic structure. Our transport measurements revealed quite different temperature dependence of resistivity with the maximum at $14\,\rm K$ instead of previously reported $18\,\rm K$. Low-field susceptibility data support the presence of static ferromagnetism above $T_{\rm N}$ and show a complex behavior of the material at small applied magnetic fields. Namely, signatures of reorientation of magnetic domains are observed up to $T=16\,\rm K$. Our magnetization measurements indicate a magnetocrystalline anisotropy which also leads to a preferred alignment of the magnetic clusters above $T_{\rm N}$. The momentum-resolved photoemission experiments at temperatures from $24\,\rm K$ down to $2.5\,\rm K$ indicate the permanent presence of a fundamental band gap without change of the electronic structure when going through $T_N$ that is in contradiction with previous results. We performed \textit{ab initio} band structure calculations which are in good agreement with the measured photoemission data when assuming an antiferromagnetic ground state. Calculations for the ferromagnetic phase show a much smaller bandgap, indicating the importance of possible ferromagnetic contributions for the explanation of the colossal magnetoresistance effect in the related EuZn$_2$P$_2$., Comment: 13 pages, 9 figures

We give new combinatorial formulae for vector-valued weight functions (off-shell nested Bethe vectors) for the evaluation modules over the Yangian Y(gl_n). This paper extends the result for the Yangian Y(gl_4) established earlier in arXiv:2312.00980., Comment: LaTeX, 16 pages

Large Language Models (LLMs) have demonstrated proficiency in their reasoning abilities, yet their large size presents scalability challenges and limits any further customization. In contrast, compact models offer customized training but often fall short in solving complex reasoning tasks. This study focuses on distilling the LLMs' decomposition skills into compact models using offline reinforcement learning. We leverage the advancements in the LLM`s capabilities to provide feedback and generate a specialized task-specific dataset for training compact models. The development of an AI-generated dataset and the establishment of baselines constitute the primary contributions of our work, underscoring the potential of compact models in replicating complex problem-solving skills., Comment: https://github.com/DT6A/GSM8K-AI-SubQ

BM@N (Baryonic Matter at Nuclotron) is the first experiment operating and taking data at the Nuclotron/NICA ion-accelerating complex.The aim of the BM@N experiment is to study interactions of relativistic heavy-ion beams with fixed targets. We present a technical description of the BM@N spectrometer including all its subsystems., Comment: 34 pages, 47 figures, 6 tables

The availability of high-intensity, heavy-ion beams coupled to sensitive, large solid-angleacceptance spectrometers has enabled a detailed examination of the fission fragments produced in induced-fission reactions. The abrasion-fission process involves the formation of projectile-like prefragments in violent nuclear collisions at relative energies in excess of 100 MeV/u. At intermediate energies below this threshold, experiments suggest a change in the prefragment kinematic qualities. Information regarding the influence of this transitional phase upon the evolution of nuclei approaching the point of scission is scarce. In this article, data are presented for over 200 nuclei from nickel to palladium produced in abrasion-fission reactions of a 80 MeV/u 238U beam. Cross sections were obtained following yield measurements performed for the principal charge states of the identified fission fragments and a detailed analysis of the ion transmission. A full kinematic analysis of the fission fragments has been performed using the LISE++ software package, where the trajectory of an ion passing through a spectrometer can be reconstructed based upon measurements at the focal plane. The results obtained at the S800 spectrograph are compared with predictions obtained with a three-fission progenitor (3EER) model. Systematic studies of fission-fragment properties continue to provide a valuable experimental benchmark for theoretical efforts directed toward describing this complex decay channel, that is important in the context of planning experiments to explore the neutron-rich region of the nuclear chart at rare-isotope beam facilities., Comment: 14 pages, 18 figures

We give new combinatorial formulae for vector-valued weight functions (off-shell nested Bethe vectors) for the evaluation modules over the Yangian Y(gl_4)., Comment: LaTeX, 27 pages

This paper introduces a transverse-momentum dependent (TMD) factorization scheme designed to unify both large and small Bjorken-x regimes. We compute the next-to-leading order (NLO) quantum chromodynamics (QCD) corrections to the gluon TMD operator for an unpolarized hadron within this proposed scheme. This leads to the emergence of a new TMD evolution, incorporating those in transverse momentum, rapidity, and Bjorken-x. When matched to the collinear factorization scheme, our factorization scheme faithfully reproduces the well-established Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) and Collins-Soper-Sterman (CSS) evolutions. Conversely, matching with high-energy factorization not only yields the Balitsky-Fadin-Kuraev-Lipatov (BFKL) evolution but also reveals distinctive signatures of CSS logarithms. The development of this novel TMD factorization scheme, capable of seamlessly reconciling disparate Bjorken-x regimes and faithfully reproducing established QCD evolution equations, has the potential to significantly advance our comprehension of high-energy processes and three-dimensional parton structures of hadrons., Comment: 38 pages, 7 figures; v2: Published version

The practical applications of surface plasmon polaritons (SPPs) require the deep understanding of the impact of electrical characteristics variability and geometrical irregularity of the metal-dielectric interface. Traditional methods in the theory of wave scattering at rough interfaces fail to treat simultaneously the interference (Anderson) localization of the SPP, which may arise due to its multiple back-scattering by random distortions of surface relief, and the leakage into the uniform dielectric half-space. In our previous works [Low Temp. Phys. \textbf{42}, 685 (2016); Ann.~Phys.~\textbf{455}, 169378 (2023)], by representing the perturbation of surface impedance as an effective potential in the Schr\"odinger-like equation, we suggested the way to describe the interplay between Anderson localization and the leakage of the SPP. In the present study we show that the problem of SPP scattering from finite geometrically rough region of the interface can be reduced to the problem of its scattering from the same region but with effective random impedance. We calculate the radiation pattern and demonstrate its pronounced anisotropy that arises due to the interplay between different geometrical parameters of the interface roughness.

This article focuses on numerical efficiency of projection algorithms for solving linear optimization problems. The theoretical foundation for this approach is provided by the basic result that bounded finite dimensional linear optimization problem can be solved by single projection operation on the feasible polyhedron. The further simplification transforms this problem into projection of a special point onto a convex polyhedral cone generated basically by inequalities of the original linear optimization problem., Comment: 11 pages, 6 figures

We analyze the world polarized deep-inelastic scattering (DIS) and semi-inclusive DIS (SIDIS) data at low values of $x < 0.1$, using small-$x$ evolution equations for the flavor singlet and nonsinglet helicity parton distribution functions (hPDFs). The hPDFs for quarks, antiquarks, and gluons are extracted and evolved to lower values of $x$ to make predictions for the future Electron-Ion Collider (EIC). We improve on our earlier work by employing the more realistic large-$N_c\, \& N_f$ limit of the revised small-$x$ helicity evolution, and incorporating running coupling corrections along with SIDIS data into the fit. We find an anti-correlation between the signs of the gluon and $C$-even quark hPDFs as well as the $g_1$ structure function. While the existing low-$x$ polarized DIS and SIDIS data are insufficient to constrain the initial conditions for the polarized dipole amplitudes in the helicity evolution equations, future EIC data will allow more precise predictions for hPDFs and the $g_1$ structure function for $x$ values beyond those probed at the EIC. Using the obtained hPDFs, we discuss the contributions to the proton spin from quark and gluon spins at small $x$., Comment: 42 Pages, 21 Figures

We determine the coefficient of proportionality between two multidimensional hypergeometric integrals. One of them is a solution of the dynamical difference equations associated with a Young diagram and the other is the vertex integral associated with the Young diagram. The coefficient of proportionality is the inverse of the product of weighted hooks of the Young diagram. It turns out that this problem is closely related to the question of describing the action of the center of the universal enveloping algebra of $\mathfrak{gl}_n$ on the space of Whittaker vectors in the tensor product of dual Verma modules with fundamental modules, for which we give an explicit basis of simultaneous eigenvectors., Comment: Latex, 15 pages

First physics results of the BM@N experiment at the Nuclotron/NICA complex are presented on {\pi}+ and K+ meson production in interactions of an argon beam with fixed targets of C, Al, Cu, Sn and Pb at 3.2 AGeV. Transverse momentum distributions, rapidity spectra and multiplicities of $\pi^+$ and $K^+$ mesons are measured. The results are compared with predictions of theoretical models and with other measurements at lower energies., Comment: 29 pages, 20 figures

For quantum electronics, the possibility to finely tune the properties of magnetic topological insulators (TIs) is a key issue. We studied solid solutions between two isostructural Z$_2$ TIs, magnetic MnBi$_2$Te$_4$ and nonmagnetic GeBi$_2$Te$_4$, with Z$_2$ invariants of 1;000 and 1;001, respectively. For high-quality, large mixed crystals of Ge$_x$Mn$_{1-x}$Bi$_2$Te$_4$, we observed linear x-dependent magnetic properties, composition-independent pairwise exchange interactions along with an easy magnetization axis. The bulk band gap gradually decreases to zero for $x$ from 0 to 0.4, before reopening for $x>0.6$, evidencing topological phase transitions (TPTs) between topologically nontrivial phases and the semimetal state. The TPTs are driven purely by the variation of orbital contributions. By tracing the x-dependent $6p$ contribution to the states near the fundamental gap, the effective spin-orbit coupling variation is extracted. As $x$ varies, the maximum of this contribution switches from the valence to the conduction band, thereby driving two TPTs. The gapless state observed at $x=0.42$ closely resembles a Dirac semimetal above the Neel temperature and shows a magnetic gap below, which is clearly visible in raw photoemission data. The observed behavior of the Ge$_x$Mn$_{1-x}$Bi$_2$Te$_4$ system thereby demonstrates an ability to precisely control topological and magnetic properties of TIs.

NetHack is known as the frontier of reinforcement learning research where learning-based methods still need to catch up to rule-based solutions. One of the promising directions for a breakthrough is using pre-collected datasets similar to recent developments in robotics, recommender systems, and more under the umbrella of offline reinforcement learning (ORL). Recently, a large-scale NetHack dataset was released; while it was a necessary step forward, it has yet to gain wide adoption in the ORL community. In this work, we argue that there are three major obstacles for adoption: resource-wise, implementation-wise, and benchmark-wise. To address them, we develop an open-source library that provides workflow fundamentals familiar to the ORL community: pre-defined D4RL-style tasks, uncluttered baseline implementations, and reliable evaluation tools with accompanying configs and logs synced to the cloud., Comment: Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks. Source code at https://github.com/corl-team/katakomba

This document presents the initial scientific case for upgrading the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab) to 22 GeV. It is the result of a community effort, incorporating insights from a series of workshops conducted between March 2022 and April 2023. With a track record of over 25 years in delivering the world's most intense and precise multi-GeV electron beams, CEBAF's potential for a higher energy upgrade presents a unique opportunity for an innovative nuclear physics program, which seamlessly integrates a rich historical background with a promising future. The proposed physics program encompass a diverse range of investigations centered around the nonperturbative dynamics inherent in hadron structure and the exploration of strongly interacting systems. It builds upon the exceptional capabilities of CEBAF in high-luminosity operations, the availability of existing or planned Hall equipment, and recent advancements in accelerator technology. The proposed program cover various scientific topics, including Hadron Spectroscopy, Partonic Structure and Spin, Hadronization and Transverse Momentum, Spatial Structure, Mechanical Properties, Form Factors and Emergent Hadron Mass, Hadron-Quark Transition, and Nuclear Dynamics at Extreme Conditions, as well as QCD Confinement and Fundamental Symmetries. Each topic highlights the key measurements achievable at a 22 GeV CEBAF accelerator. Furthermore, this document outlines the significant physics outcomes and unique aspects of these programs that distinguish them from other existing or planned facilities. In summary, this document provides an exciting rationale for the energy upgrade of CEBAF to 22 GeV, outlining the transformative scientific potential that lies within reach, and the remarkable opportunities it offers for advancing our understanding of hadron physics and related fundamental phenomena., Comment: Updates to the list of authors; Preprint number changed from theory to experiment; Updates to sections 4 and 6, including additional figures

The discovery of a square magnetic-skyrmion lattice in GdRu$_2$Si$_2$, with the smallest so far found skyrmion diameter and without a geometrically frustrated lattice, has attracted significant attention, particularly for potential applications in memory devices and quantum computing. In this work, we present a comprehensive study of surface and bulk electronic structures of GdRu$_2$Si$_2$ by utilizing momentum-resolved photoemission (ARPES) measurements and first-principles calculations. We show how the electronic structure evolves during the antiferromagnetic transition when a peculiar helical order of 4$f$ magnetic moments within the Gd layers sets in. A nice agreement of the ARPES-derived electronic structure with the calculated one has allowed us to characterize the features of the Fermi surface (FS), unveil the nested region along the $k_z$ at the corner of the 3D FS, and reveal their orbital compositions. Our findings suggest that the Ruderman-Kittel-Kasuya-Yosida interaction plays a decisive role in stabilizing the spiral-like order of Gd 4$f$ moments responsible for the skyrmion physics in GdRu$_2$Si$_2$. Our results provide a deeper understanding of electronic and magnetic properties of this material, which is crucial for predicting and developing novel skyrmion-based devices., Comment: 13 pages, 8 figures

Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity. While these algorithms have led to noteworthy improvements, many incorporate seemingly minor design choices that impact their effectiveness beyond core algorithmic advances. However, the effect of these design choices on established baselines remains understudied. In this work, we aim to bridge this gap by conducting a retrospective analysis of recent works in offline RL and propose ReBRAC, a minimalistic algorithm that integrates such design elements built on top of the TD3+BC method. We evaluate ReBRAC on 51 datasets with both proprioceptive and visual state spaces using D4RL and V-D4RL benchmarks, demonstrating its state-of-the-art performance among ensemble-free methods in both offline and offline-to-online settings. To further illustrate the efficacy of these design choices, we perform a large-scale ablation study and hyperparameter sensitivity analysis on the scale of thousands of experiments., Comment: Source code: https://github.com/DT6A/ReBRAC

The GlueX experiment at Jefferson Lab studies photoproduction of mesons using linearly polarized $8.5\,\text{GeV}$ photons impinging on a hydrogen target which is contained within a detector with near-complete coverage for charged and neutral particles. We present measurements of spin-density matrix elements for the photoproduction of the vector meson $\rho$(770). The statistical precision achieved exceeds that of previous experiments for polarized photoproduction in this energy range by orders of magnitude. We confirm a high degree of $s$-channel helicity conservation at small squared four-momentum transfer $t$ and are able to extract the $t$-dependence of natural and unnatural-parity exchange contributions to the production process in detail. We confirm the dominance of natural-parity exchange over the full $t$ range. We also find that helicity amplitudes in which the helicity of the incident photon and the photoproduced $\rho(770)$ differ by two units are negligible for $-t<0.5\,\text{GeV}^{2}/c^{2}$., Comment: 15 pages, 11 figures

We describe boson sampling of interacting atoms from the noncondensed fraction of Bose-Einstein-condensed (BEC) gas confined in a box trap as a new platform for studying computational #P-hardness and quantum supremacy of many-body systems. We calculate the characteristic function and statistics of atom numbers via newly found hafnian master theorem. Using Bloch-Messiah reduction, we find that interatomic interactions give rise to two equally important entities - eigen-squeeze modes and eigen-energy quasiparticles - whose interplay with sampling atom states determines behavior of the BEC gas. We infer that two necessary ingredients of #P-hardness, squeezing and interference, are self-generated in the gas and, contrary to Gaussian boson sampling in linear interferometers, external sources of squeezed bosons are not required., Comment: 22 pages

Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well. Recent state of the art methods such as HypViT move to more complex embedding spaces that may yield better results but are harder to scale to production environments. In this work, we first construct a simpler model based on triplet loss with hard negatives mining that performs at the state of the art level but does not have these drawbacks. Second, we introduce a novel approach for image retrieval postprocessing called Siamese Transformer for Image Retrieval (STIR) that reranks several top outputs in a single forward pass. Unlike previously proposed Reranking Transformers, STIR does not rely on global/local feature extraction and directly compares a query image and a retrieved candidate on pixel level with the usage of attention mechanism. The resulting approach defines a new state of the art on standard image retrieval datasets: Stanford Online Products and DeepFashion In-shop. We also release the source code at https://github.com/OML-Team/open-metric-learning/tree/main/pipelines/postprocessing/ and an interactive demo of our approach at https://dapladoc-oml-postprocessing-demo-srcappmain-pfh2g0.streamlit.app/, Comment: 14 pages, 3 figures

We report the total and differential cross sections for $J/\psi$ photoproduction with the large acceptance GlueX spectrometer for photon beam energies from the threshold at 8.2~GeV up to 11.44~GeV and over the full kinematic range of momentum transfer squared, $t$. Such coverage facilitates the extrapolation of the differential cross sections to the forward ($t = 0$) point beyond the physical region. The forward cross section is used by many theoretical models and plays an important role in understanding $J/\psi$ photoproduction and its relation to the $J/\psi-$proton interaction. These measurements of $J/\psi$ photoproduction near threshold are also crucial inputs to theoretical models that are used to study important aspects of the gluon structure of the proton, such as the gluon Generalized Parton Distribution (GPD) of the proton, the mass radius of the proton, and the trace anomaly contribution to the proton mass. We observe possible structures in the total cross section energy dependence and find evidence for contributions beyond gluon exchange in the differential cross section close to threshold, both of which are consistent with contributions from open-charm intermediate states., Comment: 15 pages 18 figures