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Linear optics parameters are often estimated using readily available turn-by-turn (TbT) data. In-plane beta functions, which are the most common measurement objectives, can be estimated from the amplitudes or phases of TbT data, providing an overall characterization of the linear lattice. In addition to estimating uncoupled Twiss parameters, TbT data can also be utilized for characterizing coupled linear motion. We investigate several methods for constructing a full normalization matrix at each beam position monitor (BPM). BPMs provide information on beam centroid transverse coordinates, but direct observation of transverse momenta is not available. To estimate momenta, one can use a pair of BPMs or fit data obtained from several BPMs. By using both coordinates and momenta, it is possible to fit the one-turn matrix or its power at each BPM, allowing for the computation of coupled Twiss parameters. Another approach involves the minimization of peak coupling amplitudes in the spectrum of normalized complex coordinates. Similarly, the normalization matrix can be estimated by fitting linear coupled invariants. In this study, we derive and utilize a special form of the normalization matrix, which remains non-singular in the zero coupling limit. Coupled Twiss parameters can be obtained from the normalization matrix. The paper presents the results of applying and comparing these methods to both modeled and measured VEPP-4M TbT data, demonstrating effective estimation of coupled Twiss parameters., Comment: 12 pages, 11 figures, improve figures, text corrections, new experimental data

We present a review of the relations between various equations for maximal cut banana Feynman diagrams, i.e. amplitudes with propagators substituted with $\delta$-functions. We consider both equal and generic masses. There are three types of equation to consider: those in coordinate space, their Fourier transform and Picard-Fuchs equations originating from the parametric representation. First, we review the properties of the corresponding differential operators themselves, mainly their factorization properties at the equal mass locus and their form at special values of the dimension. Then we study the relation between the Fourier transform of the coordinate space equations and the Picard-Fuchs equations and show that they are related by factorization as well. The equations in question are the counterparts of the Virasoro constraints in the much-better studied theory of eigenvalue matrix models and are the first step towards building a full-fledged theory of Feynman integrals, which will reveal their hidden integrable structure., Comment: 13 pages

The primary objective of accelerator tuning is to correct its linear optics. This involves adjusting a large set of parameters, such as quadrupole lens gradients and alignment errors, as well as addressing various calibration errors in beam position monitors (BPMs) and other components. These errors can significantly impact the accelerator performance. Using the full set of parameters might reduce the correction efficiency. Typically, it is advisable to prioritize the correction of large errors, as their localization can enhance the overall efficiency of the correction process. The task of error localization involves identifying the locations of potential error sources, along with their type. This paper explores and compares several commonly used error localization methods, using the SKIF model to test and evaluate their efficiency. Employing multiple methods enables cross-validation of the results obtained from the error localization process. The combined results from several localization methods improve the overall accuracy of error localization., Comment: 10 pages, 9 figures, fix typos, fix refs, minor text correction, improve figures

We review the main ideas underlying the emerging theory of Yangians -- the new type of hidden symmetry in string-inspired models. Their classification by quivers is a far-going generalization of simple Lie algebras classification by Dynkin diagrams. However, this is still a kind of project, while a more constructive approach goes through toric Calabi-Yau spaces, related supersymmetric systems and the Duistermaat-Heckmann/equivariant integrals between the fixed points in the ADHM-like moduli spaces. These fixed points are classified by crystals (Young-type diagrams) and Yangian generators describe ``instanton'' transitions between them. Detailed examples will be presented elsewhere.

Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disasters. Despite the widespread acceptance of climate models produced by the Coupled Model Intercomparison Project (CMIP), they still face challenges in accurately predicting climate extremes, which pose most significant threats to both people and the environment. To address this limitation and improve predictions of natural disaster risks, we introduce Extreme Model Output Statistics (X-MOS). This approach utilizes deep regression techniques to precisely map CMIP model outputs to real measurements obtained from weather stations, which results in a more accurate analysis of the XXI climate extremes. In contrast to previous research, our study places a strong emphasis on enhancing the estimation of the tails of future climate parameter distributions. The latter supports decision-makers, enabling them to better assess climate-related risks across the globe.

For the search of the chiral magnetic effect (CME), STAR previously presented the results from isobar collisions (${^{96}_{44}\text{Ru}}+{^{96}_{44}\text{Ru}}$, ${^{96}_{40}\text{Zr}}+{^{96}_{40}\text{Zr}}$) obtained through a blind analysis. The ratio of results in Ru+Ru to Zr+Zr collisions for the CME-sensitive charge-dependent azimuthal correlator ($\Delta\gamma$), normalized by elliptic anisotropy ($v_{2}$), was observed to be close to but systematically larger than the inverse multiplicity ratio. The background baseline for the isobar ratio, $Y = \frac{(\Delta\gamma/v_{2})^{\text{Ru}}}{(\Delta\gamma/v_{2})^{\text{Zr}}}$, is naively expected to be $\frac{(1/N)^{\text{Ru}}}{(1/N)^{\text{Zr}}}$; however, genuine two- and three-particle correlations are expected to alter it. We estimate the contributions to $Y$ from those correlations, utilizing both the isobar data and HIJING simulations. After including those contributions, we arrive at a final background baseline for $Y$, which is consistent with the isobar data. We extract an upper limit for the CME fraction in the $\Delta\gamma$ measurement of approximately $10\%$ at a $95\%$ confidence level on in isobar collisions at $\sqrt{s_{\text{NN}}} = 200$ GeV, with an expected $15\%$ difference in their squared magnetic fields., Comment: 19 pages, 14 figures

The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to a general case. We demonstrate how the task of learning a generative flow network can be efficiently redefined as an entropy-regularized RL problem with a specific reward and regularizer structure. Furthermore, we illustrate the practical efficiency of this reformulation by applying standard soft RL algorithms to GFlowNet training across several probabilistic modeling tasks. Contrary to previously reported results, we show that entropic RL approaches can be competitive against established GFlowNet training methods. This perspective opens a direct path for integrating RL principles into the realm of generative flow networks., Comment: AISTATS 2024 (Oral)

State-space models (SSMs) have recently emerged as a framework for learning long-range sequence tasks. An example is the structured state-space sequence (S4) layer, which uses the diagonal-plus-low-rank structure of the HiPPO initialization framework. However, the complicated structure of the S4 layer poses challenges; and, in an effort to address these challenges, models such as S4D and S5 have considered a purely diagonal structure. This choice simplifies the implementation, improves computational efficiency, and allows channel communication. However, diagonalizing the HiPPO framework is itself an ill-posed problem. In this paper, we propose a general solution for this and related ill-posed diagonalization problems in machine learning. We introduce a generic, backward-stable "perturb-then-diagonalize" (PTD) methodology, which is based on the pseudospectral theory of non-normal operators, and which may be interpreted as the approximate diagonalization of the non-normal matrices defining SSMs. Based on this, we introduce the S4-PTD and S5-PTD models. Through theoretical analysis of the transfer functions of different initialization schemes, we demonstrate that the S4-PTD/S5-PTD initialization strongly converges to the HiPPO framework, while the S4D/S5 initialization only achieves weak convergences. As a result, our new models show resilience to Fourier-mode noise-perturbed inputs, a crucial property not achieved by the S4D/S5 models. In addition to improved robustness, our S5-PTD model averages 87.6% accuracy on the Long-Range Arena benchmark, demonstrating that the PTD methodology helps to improve the accuracy of deep learning models.

The prototype of a TDC board has been developed for the new high granular time-of-flight neutron detector (HGND). The board is based on the standard LVDS 4x asynchronous oversampling using the xc7k160 FPGA with a 100 ps bin width. The HGND is being developed for the BM@N (Baryonic Matter at Nuclotron) experiment to identify neutrons and to measure their energies in heavy-ion collisions at ion beam energies up to 4 A GeV. The HGND consists of about 2000 scintillator detectors (cells) with a size of $40 \times 40 \times 25 mm^3$ and light readout with EQR15 11-6060D-S photodetectors. To measure the time resolution of the scintillator cells, the two-channel FPGA TDC board prototype with two scintillator cells was tested with an electron beam of "Pakhra" synchrotron at the LPI institute (Moscow, Russia). The measured cell time resolution is 146 ps, which is in a good agreement with the 142 ps time resolution measured with a 12-bit @ 5 GS/s CAEN DT5742 digitizer. For the full HGND, the TDC readout board with three such FPGAs will read 250 channels. In total, eight such TDC boards will be used for the full HGND at the BM@N experiment.

Knowledge distillation (KD) is a powerful model compression technique broadly used in practical deep learning applications. It is focused on training a small student network to mimic a larger teacher network. While it is widely known that KD can offer an improvement to student generalization in i.i.d setting, its performance under domain shift, i.e. the performance of student networks on data from domains unseen during training, has received little attention in the literature. In this paper we make a step towards bridging the research fields of knowledge distillation and domain generalization. We show that weight averaging techniques proposed in domain generalization literature, such as SWAD and SMA, also improve the performance of knowledge distillation under domain shift. In addition, we propose a simplistic weight averaging strategy that does not require evaluation on validation data during training and show that it performs on par with SWAD and SMA when applied to KD. We name our final distillation approach Weight-Averaged Knowledge Distillation (WAKD)., Comment: ICCV 2023 Workshop on Out-of-Distribution Generalization in Computer Vision (OOD-CV)

The HGND (High Granular Neutron Detector) is developed for the BM@N (Baryonic Matter at Nuclotron) experiment on the extracted beam of the Nuclotron at JINR, Dubna. The HGND will be used to measure the azimuthal flow of neutrons produced with energies ranging from 300 to 4000 MeV in heavy-ion collisions at beam energies of 2--4 AGeV. The azimuthal flow of charged particles will be measured using the BM@N magnet spectrometer. The data on the azimuthal flow of neutrons will shed light on the study of the high-density Equation of State (EoS) of isospin-symmetric nuclear matter, which is crucial for studying astrophysical phenomena such as neutron stars and their mergers. The HGND has a highly granular structure with approximately 2000 plastic scintillation detectors (cells), each measuring 4$\times$4$\times$2.5 cm$^3$. These detectors are arranged in 16 layers, with 121 detectors in each layer, and are subdivided by copper absorber plates with a thickness of 3 cm. The light from each cell is detected with SiPM (Silicon Photomultiplier) with an active area of 6$\times$6 mm$^2$. Developed multi-channel TDC board based on the Kintex FPGA chip with a bin width of 100 ps will be used to perform precise timestamp and amplitude measurement using Time-over-Threshold (ToT) method. Good spatial resolution due to the high granularity together with a cell's time resolution of 100-150 ps ensures neutron reconstruction with good energy resolution. The design of the detector as well as the results from test measurements and simulations have been presented.

A detailed review of the $p,q$-duality for Calogero system and its generalizations is given. For the first time, we present some of elliptic-trigonometric Hamiltonians dual to the elliptic Ruijsenaars Hamiltonians (i.e. trigonometric-elliptic ones), and explain their relations to the bi-elliptic Koroteev-Shakirov (KS) model. The most interesting self-dual double-elliptic (DELL) system remains a mystery, but we provide a clearer formulation of the problem and describe the steps that are still to be done., Comment: LaTeX, 29 pages

The Snowmass report on rare processes and precision measurements recommended Mu2e-II and a next generation muon facility at Fermilab (Advanced Muon Facility) as priorities for the frontier. The Workshop on a future muon program at FNAL was held in March 2023 to discuss design studies for Mu2e-II, organizing efforts for the next generation muon facility, and identify synergies with other efforts (e.g., muon collider). Topics included high-power targetry, status of R&D for Mu2e-II, development of compressor rings, FFA and concepts for muon experiments (conversion, decays, muonium and other opportunities) at AMF. This document summarizes the workshop discussions with a focus on future R&D tasks needed to realize these concepts., Comment: 68 pages, 36 figures

Many problems in science and technology require finding global minima or maxima of various objective functions. The functions are typically high-dimensional; each function evaluation may entail a significant computational cost. The importance of global optimization has inspired development of numerous heuristic algorithms based on analogies with physical, chemical or biological systems. Here we present a novel algorithm, SmartRunner, which employs a Bayesian probabilistic model informed by the history of accepted and rejected moves to make a decision about the next random trial. Thus, SmartRunner intelligently adapts its search strategy to a given objective function and moveset, with the goal of maximizing fitness gain (or energy loss) per function evaluation. Our approach can be viewed as adding a simple adaptive penalty to the original objective function, with SmartRunner performing hill ascent or descent on the modified landscape. This penalty can be added to many other global optimization algorithms. We explored SmartRunner's performance on a standard set of test functions, finding that it compares favorably against several widely-used alternatives: simulated annealing, stochastic hill climbing, evolutionary algorithm, and taboo search. Interestingly, adding the adaptive penalty to the first three of these algorithms considerably enhances their performance. We have also employed SmartRunner to study the Sherrington-Kirkpatrick (SK) spin glass model and Kauffman's NK fitness model - two NP-hard problems characterized by numerous local optima. In systems with quenched disorder, SmartRunner performs well compared to the other global optimizers. Moreover, in finite SK systems it finds close-to-optimal ground-state energies averaged over disorder., Comment: 25 pages, 7 figures, 2 tables in the main text; 22 pages, 18 figures in the supplement

To study the dependence of the equation of state of high density nuclear matter on the term characterizing the isospin (proton-neutron) asymmetry of nuclear matter, it is necessary to measure azimuthal flow of neutrons as well as azimuthal flow of charged particles from a dense nuclear matter in the nuclear-nuclear collisions. For this purpose INR RAS is developing a new high-granular neutron detector which will be used in the BM@N experiment at the extracted beam of the Nuclotron accelerator at JINR (Dubna). This detector will identify neutrons and measure their energies in the heavy-ion collisions up to 4 GeV per nucleon. This article presents the results of measurements of the time resolution and light yields of samples of scintillation detectors with sizes 40$\times$40$\times$25 mm$^3$ that will be used in a neutron detector based on the currently available fast plastic scintillator manufactured by JINR using an EQR15 11-6060D-S photodetector for light readout. For comparison, the results of measurements for a detector of the same size with a fast scintillator EJ-230 and with the same type of photodetector are given. The measurements were made on cosmic muons as well as on the electron synchrotron "Pakhra" of the Lebedev Physical Institute of the Russian Academy of Sciences located in Troitsk, Moscow.

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