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We measure the absolute proper motion of Andromeda III using ACS/WFC and WFPC2 exposures spanning an unprecedented 22-year time baseline. The WFPC2 exposures have been processed using a deep-learning centering procedure recently developed as well as an improved astrometric calibration of the camera. The absolute proper motion zero point is given by 98 galaxies and 16 Gaia EDR3 stars. The resulting proper motion is $(\mu_{\alpha} , \mu_{\delta}) = (-10.5\pm12.5, 47.5\pm12.5)~\mu$as yr$^{-1}$. We perform an orbit analysis of And III using two estimates of M31's mass and proper motion. We find that And III's orbit is consistent with dynamical membership to the Great Plane of Andromeda system of satellites although with some looser alignment compared to the previous two satellites NGC 147 and NGC 185. And III is bound to M31 if M31's mass is $M_{\mathrm{vir}}\geq 1.5\times10^{12}\,M_{\odot}$., Comment: Accepted for publication in the Astrophysical Journal

A basic datum of a rank-$r$ $\mathcal{N}{=}2$ superconformal field theory (SCFT) is the $r$-tuple of its Coulomb branch scaling dimensions, i.e., the scaling dimensions of a set of special protected scalar operators whose vevs generate the coordinate ring of the Coulomb branch of the theory. It is well known that when the coordinate ring is freely generated these scaling dimensions can only take values in a small set of rational numbers. But there are further constraints on which $r$-tuples of these numbers can appear. The main aim of this work is to clarify what these are. Along the way we also compute explicitly the $r$-tuples of allowed scaling dimensions for theories of ranks $r = 2, 3, 4$., Comment: 32 pages

This paper explores the potential benefits and challenges of integrating Electric Vehicles (EVs) and Autonomous Ground Vehicles (AGVs) in industrial settings to improve sustainability and operational efficiency. While EVs offer environmental advantages, barriers like high costs and limited range hinder their widespread use. Similarly, AGVs, despite their autonomous capabilities, face challenges in technology integration and reliability. To address these issues, the paper develops a fleet management tool tailored for coordinating electric AGVs in industrial environments. The study focuses on simulating electric AGV performance in a primary aluminum plant to provide insights into their effectiveness and offer recommendations for optimizing fleet performance., Comment: accepted for presentation as WiP at the 2024 IEEE International Conference on Emerging Technologies and Factory Automation (ETFA2024)

A supersolid is a counter-intuitive phase of matter where its constituent particles are arranged into a crystalline structure, yet they are free to flow without friction. This requires the particles to share a global macroscopic phase while being able to reduce their total energy by spontaneous, spatial self-organisation. This exotic state of matter has been achieved in different systems using Bose-Einstein condensates coupled to cavities, possessing spin-orbit coupling, or dipolar interactions. Here we provide experimental evidence of a new implementation of the supersolid phase in a novel non-equilibrium context based on exciton-polaritons condensed in a topologically non-trivial, bound-in-the-continuum state with exceptionally low losses. We measure the density modulation of the polaritonic state indicating the breaking of translational symmetry with a remarkable precision of a few parts in a thousand. Direct access to the phase of the wavefunction allows us to additionally measure the local coherence of the superfluid component. We demonstrate the potential of our synthetic photonic material to host phonon dynamics and a multimode excitation spectrum.

In this paper, we construct the associated vertex operator algebras for all $\mathcal{N}=2$ superconformal field theories of rank one. We give a uniform presentation through free-field realizations, which turns out to be a particularly suitable framework for this task. The elementary building blocks of the construction are dictated by the low energy degrees of freedom on the Higgs branch, which are well understood for rank-one theories. We further analyze the interplay between Higgs and Coulomb data on the moduli space of vacua, which tightly constrain the overall structure of the free field realizations. Our results suggest a plausible bottom-up classification scheme for low-rank SCFTs incorporating vertex algebra techniques., Comment: 45 pages, 2 figures, 11 tables

Gamma-ray bursts (GRBs) are ideal probes of the Universe at high redshift (z > 5), pinpointing the locations of the earliest star-forming galaxies and providing bright backlights that can be used to spectrally fingerprint the intergalactic medium and host galaxy during the period of reionization. Future missions such as Gamow Explorer are being proposed to unlock this potential by increasing the rate of identification of high-z GRBs to rapidly trigger observations from 6-10 m ground telescopes, JWST, and the Extremely Large Telescopes. Gamow was proposed to the NASA 2021 Medium-Class Explorer (MIDEX) program as a fast-slewing satellite featuring a wide-field lobster-eye X-ray telescope (LEXT) to detect and localize GRBs, and a 30 cm narrow-field multi-channel photo-z infrared telescope (PIRT) to measure their photometric redshifts using the Lyman-alpha dropout technique. To derive the PIRT sensitivity requirement we compiled a complete sample of GRB optical-near-infrared afterglows from 2008 to 2021, adding a total of 66 new afterglows to our earlier sample, including all known high-z GRB afterglows. We performed full light-curve and spectral-energy-distribution analyses of these afterglows to derive their true luminosity at very early times. For all the light curves, where possible, we determined the brightness at the time of the initial finding chart of Gamow, at different high redshifts and in different NIR bands. We then followed the evolution of the luminosity to predict requirements for ground and space-based follow-up. We find that a PIRT sensitivity of 15 micro-Jy (21 mag AB) in a 500 s exposure simultaneously in five NIR bands within 1000s of the GRB trigger will meet the Gamow mission requirement to recover > 80% of all redshifts at z > 5., Comment: 44 pages, 10 figures, 1 table. Accepted for publication in Astronomy and Astrophysics 15 Feb 2024. Abstract abridged for arXiv

Scientists are increasingly leveraging advances in instruments, automation, and collaborative tools to scale up their experiments and research goals, leading to new bursts of discovery. Various scientific disciplines, including neuroscience, have adopted key technologies to enhance collaboration, reproducibility, and automation. Drawing inspiration from advancements in the software industry, we present a roadmap to enhance the reliability and scalability of scientific operations for diverse research teams tackling large and complex projects. We introduce a five-level Capability Maturity Model describing the principles of rigorous scientific operations in projects ranging from small-scale exploratory studies to large-scale, multi-disciplinary research endeavors. Achieving higher levels of operational maturity necessitates the adoption of new, technology-enabled methodologies, which we refer to as SciOps. This concept is derived from the DevOps methodologies that have revolutionized the software industry. SciOps involves digital research environments that seamlessly integrate computational, automation, and AI-driven efforts throughout the research cycle-from experimental design and data collection to analysis and dissemination, ultimately leading to closed-loop discovery. This maturity model offers a framework for assessing and improving operational practices in multidisciplinary research teams, guiding them towards greater efficiency and effectiveness in scientific inquiry., Comment: 16 pages, two figures

We determine new genus 2 Seiberg-Witten curves for four dimensional rank 2 absolute N=4 superYang-Mills theories using the automorphism twist approach. The conformal manifolds of these curves agree with those predicted by S-duality orbits of global structures, and we use this to identify which of the two S-duality orbits of the $so(5) \simeq sp(4)$ superYang-Mills theory the genus-2 curve corresponds to. We also compare the curves to earlier constructions of Seiberg-Witten curves for these theories as spectral curves of integrable systems. These spectral curves have genus greater than the rank, and so only give a Coulomb branch geometry upon projection to a sublattice of the homology lattice of the curves. We show how to determine the correct sublattice projection, and find that the integrable system curves do not apply to our theories., Comment: 41 pages

Cognitive Radar Networks, which were popularized by Simon Haykin in 2006, have been proposed to address limitations with legacy radar installations. These limitations include large physical size, power consumption, fixed operating parameters, and single point vulnerabilities. Cognitive radar solves part of this problem through adaptability, using biologically inspired techniques to observe the environment and adjust operation accordingly. Cognitive radar networks (CRNs) extend the capabilities of cognitive radar spatially, providing the opportunity to observe targets from multiple angles to mitigate stealth effects; distribute resources over space and in time; obtain better tracking performance; and gain more information from a scene. Often, problems of cognition in CRNs are viewed through the lens of iterative learning problems - one or multiple cognitive processes are implemented in the network, where each process first observes the environment, then selects operating parameters (from discrete or continuous options) using the history of observations and previous rewards, then repeats the cycle. Further, cognitive radar networks often are modeled with a flexible architecture and wide-bandwidth front-ends, enabling the addition of electronic support measures such as passive signal estimation. In this work we consider questions of the form "How should a cognitive radar network choose when to observe targets?" and "How can a cognitive radar network reduce the amount of energy it uses?". We implement tools from the multi-armed bandit and age of information literature to select modes for the network, choosing either an active radar mode or a passive signal estimation mode. We show that through the use of target classes, the network can determine how often each target should be observed to optimize tracking performance., Comment: 13 pages, 11 figures

A well-established organisational principle for Argyres--Douglas-type $\mathcal{N}=2$ superconformal field theories in four dimensions is to characterise such theories by the data defining a(n irregular) Hitchin system on $\mathbb{CP}^1$. The dictionary between Hitchin system data and various features of the corresponding SCFT has been studied extensively, but the overall structure of the resulting space of SCFTs still appears quite complicated. In this work, we systematically delineate a variety of simplifications that arise within this class of constructions due to several large classes of isomorphisms between SCFTs associated with inequivalent Hitchin system data (and their exactly marginal gaugings). We restrict to the most studied class of theories, namely the type $A$ theories without outer automorphism twists., Comment: 65 pages

The term Research Software Engineer, or RSE, emerged a little over 10 years ago as a way to represent individuals working in the research community but focusing on software development. The term has been widely adopted and there are a number of high-level definitions of what an RSE is. However, the roles of RSEs vary depending on the institutional context they work in. At one end of the spectrum, RSE roles may look similar to a traditional research role. At the other extreme, they resemble that of a software engineer in industry. Most RSE roles inhabit the space between these two extremes. Therefore, providing a straightforward, comprehensive definition of what an RSE does and what experience, skills and competencies are required to become one is challenging. In this community paper we define the broad notion of what an RSE is, explore the different types of work they undertake, and define a list of fundamental competencies as well as values that define the general profile of an RSE. On this basis, we elaborate on the progression of these skills along different dimensions, looking at specific types of RSE roles, proposing recommendations for organisations, and giving examples of future specialisations. An appendix details how existing curricula fit into this framework., Comment: 34 pages, public repository for feedback here: https://github.com/the-teachingRSE-project/competencies

Accurately evaluating the similarity of object vector embeddings is of critical importance for natural language processing, information retrieval and classification tasks. Popular similarity scores (e.g cosine similarity) are based on pairs of embedding vectors and disregard the distribution of the ensemble from which objects are drawn. Human perception of object similarity significantly depends on the context in which the objects appear. In this work we propose the $\textit{surprise score}$, an ensemble-normalized similarity metric that encapsulates the contrast effect of human perception and significantly improves the classification performance on zero- and few-shot document classification tasks. This score quantifies the surprise to find a given similarity between two elements relative to the pairwise ensemble similarities. We evaluate this metric on zero/few shot classification and clustering tasks and typically find 10-15 % better performance compared to raw cosine similarity. Our code is available at https://github.com/MeetElise/surprise-similarity., Comment: 9 pages, 2 figures and 4 tables

High-temperature ($q\to1$) asymptotics of 4d superconformal indices of Lagrangian theories have been recently analyzed up to exponentially suppressed corrections. Here we use RG-inspired tools to extend the analysis to the exponentially suppressed terms in the context of Schur indices of $N=2$ SCFTs. In particular, our approach explains the curious patterns of logarithms (polynomials in $1/\log q$) found by Dedushenko and Fluder in their numerical study of the high-temperature expansion of rank-$1$ theories. We also demonstrate compatibility of our results with the conjecture of Beem and Rastelli that Schur indices satisfy finite-order, possibly twisted, modular linear differential equations (MLDEs), and discuss the interplay between our approach and the MLDE approach to the high-temperature expansion. The expansions for $q$ near roots of unity are also treated. A byproduct of our analysis is a proof (for Lagrangian theories) of rationality of the conformal dimensions of all characters of the associated VOA, that mix with the Schur index under modular transformations., Comment: 39 pages plus two appendices

Cognitive radar networks (CRNs) are capable of optimizing operating parameters in order to provide actionable information to an operator or secondary system. CRNs have been proposed to answer the need for low-cost devices tracking potentially large numbers of targets in geographically diverse regions. Networks of small-scale devices have also been shown to outperform legacy, large scale, high price, single-device installations. In this work, we consider a CRN tracking multiple targets with a goal of providing information which is both fresh and accurate to a measurement fusion center (FC). We show that under a constraint on the update rate of each radar node, the network is able to utilize Age of Information (AoI) metrics to maximize the resource utilization and minimize error per track. Since information freshness is critical to decision-making, this structure enables a CRN to provide the highest-quality information possible to a downstream system or operator. We discuss centralized and distributed approaches to solving this problem, taking into account the quality of node observations, the maneuverability of each target, and a limit on the rate at which any node may provide updates to the FC. We present a centralized AoI-inspired node selection metric, where a FC requests updates from specific nodes. We compare this against several alternative techniques. Further, we provide a distributed approach which utilizes the Age of Incorrect Information (AoII) metric, allowing each independent node to provide updates according to the targets it can observe. We provide mathematical analysis of the rate limits defined for the centralized and distributed approaches, showing that they are equivalent. We conclude with numerical simulations demonstrating that the performance of the algorithms exceeds that of alternative approaches, both in resource utilization and in tracking performance., Comment: 15 pages, double column, 14 figures

In this article, we single out representations of surface groups into $\mathsf{PSL}_d(\mathbb{C})$ which generalize the well-studied family of pleated surfaces into $\mathsf{PSL}_2(\mathbb{C})$. Our representations arise as sufficiently generic $\lambda$-Borel Anosov representations, which are representations that are Borel Anosov with respect to a maximal geodesic lamination $\lambda$. For fixed $\lambda$ and $d$, we provide a holomorphic parametrization of the space $\mathcal{R}(\lambda,d)$ of $(\lambda,d)$-pleated surfaces which extends both work of Bonahon for pleated surfaces and Bonahon and Dreyer for Hitchin representations., Comment: 125 pages, 14 figures. Comments are welcome!

A Deep Learning (DL) algorithm is built and tested for its ability to determine centers of star images on HST/WFPC2 exposures, in filters F555W and F814W. These archival observations hold great potential for proper-motion studies, but the undersampling in the camera's detectors presents challenges for conventional centering algorithms. Two exquisite data sets of over 600 exposures of the cluster NGC 104 in these filters are used as a testbed for training and evaluation of the DL code. Results indicate a single-measurement standard error of from 8.5 to 11 mpix, depending on detector and filter.This compares favorably to the $\sim20$ mpix achieved with the customary ``effective PSF'' centering procedure for WFPC2 images. Importantly, pixel-phase error is largely eliminated when using the DL method. The current tests are limited to the central portion of each detector; in future studies the DL code will be modified to allow for the known variation of the PSF across the detectors., Comment: accepted for publication by PASP

Whitin Istituto Nazionale di Fisica Nucleare (INFN) outreach activities, the Lab2Go project is of great significance. Its goal is involving high school teachers and students in several laboratory activities, aiming at increasing the weight of experimental contents in teaching and learning. In this article we present the measurement, carried out in the framework of the Lab2Go project, of the cosmic muon flux made by an ArduSiPM-based muon telescope.

The Divertor Tokamak Test (DTT) facility, construction starting at Frascati, Italy, is designed to test different solutions for divertor in view of DEMO. A preliminary analysis of the error fields (EFs) assumed a simplified model of rigid and independent displacements and rotations. A methodology based on the first order truncated Taylor expansion has been applied, linking the displacement parameters and the EFs within the required accuracies. A system of in-vessel copper coils has been designed to counteract EFs and the ampere-turns necessary to force them back within the request limits has been calculated. Here, the details of the analysis have been provided.

Gamma-ray bursts (GRBs) are bright extragalactic flashes of gamma-ray radiation and briefly the most energetic explosions in the Universe. Their catastrophic origin (the merger of compact objects or the collapse of massive stars) drives the formation of a newborn compact remnant (black hole or magnetar) that powers two highly relativistic jets. To distinguish between magnetized and baryonic jet models and ultimately determine the power source for these energetic explosions, our team studies the polarization of the light during the first minutes after the explosion (using novel instruments on fully autonomous telescopes around the globe) to directly probe the magnetic field properties in these extragalactic jets. This technology allowed the detection of highly polarized optical light in GRB 120308A and confirmed the presence of mildly magnetized jets with large-scale primordial magnetic fields in a reduced sample of GRBs (e.g. GRB 090102, GRB 110205A, GRB 101112A, GRB 160625B). Here we discuss the observations of the most energetic and first GRB detected at very high TeV energies, GRB 190114C, which opens a new frontier in GRB magnetic field studies suggesting that some jets can be launched highly magnetized and that the collapse and destruction of these magnetic fields at very early times may have powered the explosion itself. Additionally, our most recent polarimetric observations of the jet of GRB 141220A indicate that, when the jetted ejected material is decelerated by the surrounding environment, the magnetic field amplification mechanisms at the front shock (needed to generate the observed synchrotron emission) produce small magnetic domains. These measurements validate theoretical expectations and contrast with previous observations that suggest large magnetic domains in collisionless shocks (i.e. GRB 091208B)., Comment: To appear in the Proceedings of the 16th Marcel Grossmann Meeting (July 5-10, 2021)

Despite ground-breaking observations of supersolidity in spin-orbit-coupled Bose-Einstein condensates, until now the dynamics of the emerging spatially periodic density modulations has been vastly unexplored. Here, we demonstrate the nonrigidity of the density stripes in such a supersolid condensate and explore their dynamic behavior subject to spin perturbations. We show both analytically in infinite systems and numerically in the presence of a harmonic trap how spin waves affect the supersolid's density profile in the form of crystal waves, inducing oscillations of the periodicity as well as the orientation of the fringes. Both these features are well within reach of present-day experiments. Our results show that this system is a paradigmatic supersolid, featuring superfluidity in conjunction with a fully dynamic crystalline structure., Comment: 7+5 pages, 2+3 figures. Minor changes, version as published in PRL

This is the third in a series of papers which outlines an approach to the classification of $\mathcal{N}{=}2$ superconformal field theories at rank 2 via the study of their Coulomb branch geometries. Here we use the fact that the encoding of a Coulomb branch geometry as a Seiberg-Witten curve and 1-form enjoys a large reparametrisation invariance. While there is always a unique way to fix this invariance such that the curve and 1-form are single-valued over the Coulomb branch -- the "canonical frame" of the curve used in the first two papers in this series -- there are other useful frames in which the curve is single-valued but the 1-form is allowed to be multi-valued. In these frames, which we call "automorphism frames", the 1-form is periodic up to an automorphism twist. We argue that the multi-valuedness of the automorphism frame can simplify the computational complexity of finding new consistent scale invariant solutions. We demonstrate this in an example by using the automorphism frame to construct for the first time a genus 2 Seiberg-Witten curve for the $\mathcal{N}{=}4$ SU(3) superYang-Mills theory, a solution that is hard to find by other approaches., Comment: 34 pages and 3 appendices

This is the second of a series of papers outlining an approach to the classification of $\mathcal{N}{=}2$ superconformal field theories at rank 2 via a systematic analysis of their Coulomb branches, mathematically described by special K\"ahler scale invariant geometries. Here we describe how to make the translation between geometry and field theory data. We apply this strategy to the special K\"ahler geometries found in the first paper of the series where we made strong simplifying assumptions on the form of the solutions. Remarkably, we find that our bottom-up classification strategy pays off even in this simplified setup. All scale invariant solutions in the first paper of the series have an interpretation as the Coulomb branch of at least one $\mathcal{N}{=}2$ superconformal field theory, many matching what is already known. But we also predict the existence of six new rank 2 theories, including an entire new set closed under renormalization group flow. We characterise and discuss in detail each one of these new $\mathcal{N}{=}2$ superconformal field theories. To our knowledge none of them have known string theoretic or higher dimensional realisations., Comment: Section 3, tables, and references updated

In this first of a series of three papers we outline an approach to classifying 4d $\mathcal{N}{=}2$ superconformal field theories at rank 2. The classification of allowed scale invariant $\mathcal{N}=2$ Coulomb branch geometries of dimension (or rank) greater than one is a famous open problem whose solution will greatly constrain the space of $\mathcal{N}{=}2$ superconformal field theories. At rank 2 the problem is equivalent to finding all possible genus 2 Seiberg-Witten curves and 1-forms satisfying a special K\"ahler condition. This is tractable because regular genus 2 Riemann surfaces can be uniformly described as binary-sextic plane curves, and the Seiberg-Witten curves are families of such curves varying meromorphically over the two-dimensional base. There are also solutions consisting of families of degenerate genus-2 Riemann surfaces given by a bouquet of two elliptic curves which are described by a different set of curves. In this paper we set up and carry out the analysis of the generic case, i.e., those whose typical fiber is a regular genus-2 Riemann surface with no extended automorphism, and find the complete answer for polynomial coefficients., Comment: Tables and references updated

A key component of cognitive radar is the ability to generalize, or achieve consistent performance across a range of sensing environments, since aspects of the physical scene may vary over time. This presents a challenge for learning-based waveform selection approaches, since transmission policies which are effective in one scene may be highly suboptimal in another. We address this problem by strategically biasing a learning algorithm by exploiting high-level structure across tracking instances, referred to as meta-learning. In this work, we develop an online meta-learning approach for waveform-agile tracking. This approach uses information gained from previous target tracks to speed up and enhance learning in new tracking instances. This results in sample-efficient learning across a class of finite state target channels by exploiting inherent similarity across tracking scenes, attributed to common physical elements such as target type or clutter statistics. We formulate the online waveform selection problem within the framework of Bayesian learning, and provide prior-dependent performance bounds for the meta-learning problem using Probability Approximately Correct (PAC)-Bayes theory. We present a computationally feasible meta-posterior sampling algorithm and study the performance in a simulation study consisting of diverse scenes. Finally, we examine the potential performance benefits and practical challenges associated with online meta-learning for waveform-agile tracking., Comment: 14 pages, 5 figures

Radar must adapt to changing environments, and we propose changepoint detection as a method to do so. In the world of increasingly congested radio frequencies, radars must adapt to avoid interference. Many radar systems employ the prediction action cycle to proactively determine transmission mode while spectrum sharing. This method constructs and implements a model of the environment to predict unused frequencies, and then transmits in this predicted availability. For these selection strategies, performance is directly reliant on the quality of the underlying environmental models. In order to keep up with a changing environment, these models can employ changepoint detection. Changepoint detection is the identification of sudden changes, or changepoints, in the distribution from which data is drawn. This information allows the models to discard "garbage" data from a previous distribution, which has no relation to the current state of the environment. In this work, bayesian online changepoint detection (BOCD) is applied to the sense and predict algorithm to increase the accuracy of its models and improve its performance. In the context of spectrum sharing, these changepoints represent interferers leaving and entering the spectral environment. The addition of changepoint detection allows for dynamic and robust spectrum sharing even as interference patterns change dramatically. BOCD is especially advantageous because it enables online changepoint detection, allowing models to be updated continuously as data are collected. This strategy can also be applied to many other predictive algorithms that create models in a changing environment., Comment: 8 pages, 7 figures, 1 table

Semantic image interpretation can vastly benefit from approaches that combine sub-symbolic distributed representation learning with the capability to reason at a higher level of abstraction. Logic Tensor Networks (LTNs) are a class of neuro-symbolic systems based on a differentiable, first-order logic grounded into a deep neural network. LTNs replace the classical concept of training set with a knowledge base of fuzzy logical axioms. By defining a set of differentiable operators to approximate the role of connectives, predicates, functions and quantifiers, a loss function is automatically specified so that LTNs can learn to satisfy the knowledge base. We focus here on the subsumption or \texttt{isOfClass} predicate, which is fundamental to encode most semantic image interpretation tasks. Unlike conventional LTNs, which rely on a separate predicate for each class (e.g., dog, cat), each with its own set of learnable weights, we propose a common \texttt{isOfClass} predicate, whose level of truth is a function of the distance between an object embedding and the corresponding class prototype. The PROTOtypical Logic Tensor Networks (PROTO-LTN) extend the current formulation by grounding abstract concepts as parametrized class prototypes in a high-dimensional embedding space, while reducing the number of parameters required to ground the knowledge base. We show how this architecture can be effectively trained in the few and zero-shot learning scenarios. Experiments on Generalized Zero Shot Learning benchmarks validate the proposed implementation as a competitive alternative to traditional embedding-based approaches. The proposed formulation opens up new opportunities in zero shot learning settings, as the LTN formalism allows to integrate background knowledge in the form of logical axioms to compensate for the lack of labelled examples.

We study the interplay between intrinsic spin-orbit coupling and nonlinear photon-photon interactions in a nonparaxial, elliptically polarized fluid of light propagating in a bulk Kerr medium. We find that in situations where the nonlinear interactions induce birefringence, i.e., a polarization-dependent nonlinear refractive index, their interplay with spin-orbit coupling results in an interference between the two polarization components of the fluid traveling at different wave vectors, which entails the breaking of translation symmetry along the propagation direction. This phenomenon leads to a Floquet band structure in the Bogoliubov spectrum of the fluid, and to characteristic oscillations of its intensity correlations. We characterize these oscillations in detail and point out their exponential growth at large propagation distances, revealing the presence of parametric resonances., Comment: 7 pages, 4 figures + Supplemental Material

The Coulomb phase of a quantum field theory, when present, illuminates the analysis of its line operators and one-form symmetries. For 4d $\mathcal{N}=2$ field theories the low energy physics of this phase is encoded in the special K\"ahler geometry of the moduli space of Coulomb vacua. We clarify how the information on the allowed line operator charges and one-form symmetries is encoded in the special K\"ahler structure. We point out the important difference between the lattice of charged states and the homology lattice of the abelian variety fibered over the moduli space, which, when principally polarized, is naturally identified with a choice of the lattice of mutually local line operators. This observation illuminates how the distinct S-duality orbits of global forms of $\mathcal{N}=4$ theories are encoded geometrically., Comment: v3 - with minor corrections

It is expected on general grounds that the moduli space of 4d $\mathcal{N}=3$ theories is of the form $\mathbb{C}^{3r}/\Gamma$, with $r$ the rank and $\Gamma$ a crystallographic complex reflection group (CCRG). As in the case of Lie algebras, the space of CCRGs consists of several infinite families, together with some exceptionals. To date, no 4d $\mathcal{N}=3$ theory with moduli space labelled by an exceptional CCRG (excluding Weyl groups) has been identified. In this work we show that the 4d $\mathcal{N}=3$ theories proposed in \cite{Garcia-Etxebarria:2016erx}, constructed via non-geometric quotients of type-$\mathfrak{e}$ 6d (2,0) theories, realize nearly all such exceptional moduli spaces. In addition, we introduce an extension of this construction to allow for twists and quotients by outer automorphism symmetries. This gives new examples of 4d $\mathcal{N}=3$ theories going beyond simple S-folds., Comment: 33+25 pages; v2: references added

This work addresses the coexistence problem for radar networks. Specifically, we model a network of cooperative, independent, and non-communicating radar nodes which must share resources within the network as well as with non-cooperative nearby emitters. We approach this problem using online Machine Learning (ML) techniques. Online learning approaches are specifically preferred due to the fact that each radar node has no prior knowledge of the environment nor of the positions of the other radar nodes, and due to the sequential nature of the problem. For this task we specifically select the multi-player multi-armed bandit (MMAB) model, which poses the problem as a sequential game, where each radar node in a network makes independent selections of center frequency and waveform with the same goal of improving tracking performance for the network as a whole. For accurate tracking, each radar node communicates observations to a fusion center on set intervals. The fusion center has knowledge of the radar node placement, but cannot communicate to the individual nodes fast enough for waveform control. Every radar node in the network must learn the behavior of the environment, which includes rewards, interferer behavior, and target behavior. Our contributions include a mathematical description of the MMAB framework adapted to the radar network scenario. We conclude with a simulation study of several different network configurations. Experimental results show that iterative, online learning using MMAB outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation approaches., Comment: 14 pages, 10 figures

Superconformal field theories (SCFTs) occupy a central role in the study of many aspects of quantum field theory. In this white paper for the Snowmass process we give a brief overview of aspects of SCFTs in $3\leq D \leq 6$ space-time dimensions, including classification efforts and some of the vast current research trends on the physical and mathematical structures generated by this rich class of physical theories., Comment: References updated

There is a well-known map from 4d $\mathcal{N}=2$ superconformal field theories (SCFTs) to 2d vertex operator algebras (VOAs). The 4d Schur index corresponds to the VOA vacuum character, and must be a solution with integral coefficients of a modular differential equation. This suggests a classification program for 4d $\mathcal{N}=2$ SCFTs that starts with modular differential equations and proceeds by imposing all known constraints that follow from the 4d $\to$ 2d map. This program becomes fully algorithmic once one specifies the $\mathrm{\textit{order}}$ of the modular differential equation and the $\mathrm{\textit{rank}}$ (complex dimension of the Coulomb branch) of the $\mathcal{N}=2$ theory. As a proof of concept, we apply the algorithm to the study of rank-two $\mathcal{N}=2$ SCFTs whose Schur indices satisfy a fourth-order untwisted modular differential equation. Scanning over a large number of putative cases, only 15 satisfy all of the constraints imposed by our algorithm, six of which correspond to known 4d SCFTs. More sophisticated constraints can be used to argue against the existence of the remaining nine cases. Altogether, this indicates that our knowledge of such rank-two SCFTs is surprisingly complete., Comment: 32+21 pages; v2: references added

Online selection of optimal waveforms for target tracking with active sensors has long been a problem of interest. Many conventional solutions utilize an estimation-theoretic interpretation, in which a waveform-specific Cram\'{e}r-Rao lower bound on measurement error is used to select the optimal waveform for each tracking step. However, this approach is only valid in the high SNR regime, and requires a rather restrictive set of assumptions regarding the target motion and measurement models. Further, due to computational concerns, many traditional approaches are limited to near-term, or myopic, optimization, even though radar scenes exhibit strong temporal correlation. More recently, reinforcement learning has been proposed for waveform selection, in which the problem is framed as a Markov decision process (MDP), allowing for long-term planning. However, a major limitation of reinforcement learning is that the memory length of the underlying Markov process is often unknown for realistic target and channel dynamics, and a more general framework is desirable. This work develops a universal sequential waveform selection scheme which asymptotically achieves Bellman optimality in any radar scene which can be modeled as a $U^{\text{th}}$ order Markov process for a finite, but unknown, integer $U$. Our approach is based on well-established tools from the field of universal source coding, where a stationary source is parsed into variable length phrases in order to build a context-tree, which is used as a probabalistic model for the scene's behavior. We show that an algorithm based on a multi-alphabet version of the Context-Tree Weighting (CTW) method can be used to optimally solve a broad class of waveform-agile tracking problems while making minimal assumptions about the environment's behavior., Comment: 23 pages, 5 figures