Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust.

Mapping the local and distant Universe is key to our understanding of it. For decades, the Sloan Digital Sky Survey (SDSS) has made a concerted effort to map millions of celestial objects to constrain the physical processes that govern our Universe. The most recent and fifth generation of SDSS (SDSS-V) is organized into three scientific ``mappers". Milky Way Mapper (MWM) that aims to chart the various components of the Milky Way and constrain its formation and assembly, Black Hole Mapper (BHM), which focuses on understanding supermassive black holes in distant galaxies across the Universe, and Local Volume Mapper (LVM), which uses integral field spectroscopy to map the ionized interstellar medium in the local group. This paper describes and outlines the scope and content for the nineteenth data release (DR19) of SDSS and the most substantial to date in SDSS-V. DR19 is the first to contain data from all three mappers. Additionally, we also describe nine value added catalogs (VACs) that enhance the science that can be conducted with the SDSS-V data. Finally, we discuss how to access SDSS DR19 and provide illustrative examples and tutorials.

The measurement of the structure of stellar populations in the Milky Way disk places fundamental constraints on models of galaxy formation and evolution. Previously, the disk's structure has been studied in terms of populations defined geometrically and/or chemically, but a decomposition based on stellar ages provides a more direct connection to the history of the disk, and stronger constraint on theory. Here, we use positions, abundances and ages for 31,244 red giant branch stars from the SDSS-APOGEE survey, spanning 3 < R_{\mathrm{gc}} < 15 kpc, to dissect the disk into mono-age and mono-[Fe/H] populations at low and high [$\alpha$/Fe]. For each population, with \Delta \mathrm{age} < 2 Gyr and \Delta \mathrm{[Fe/H]} < 0.1 dex, we measure the structure and surface-mass density contribution. We find that low [$\alpha$/Fe] mono-age populations are fit well by a broken exponential, which increases to a peak radius and decreases thereafter. We show that this profile becomes broader with age, interpreted here as a new signal of disk heating and radial migration. High [$\alpha$/Fe] populations are well fit as single exponentials within the radial range considered, with an average scale length of $1.9\pm 0.1$ kpc. We find that the relative contribution of high to low [$\alpha$/Fe] populations at $R_0$ is $f_\Sigma = 18\% \pm 5\%$; high [$\alpha$/Fe] contributes most of the mass at old ages, and low [$\alpha$/Fe] at young ages. The low and high [$\alpha$/Fe] populations overlap in age at intermediate [Fe/H], although both contribute mass at $R_{0}$ across the full range of [Fe/H]. The mass weighted scale height $h_Z$ distribution is a smoothly declining exponential function. High [$\alpha$/Fe] populations are thicker than low [$\alpha$/Fe], and the average $h_Z$ increases steadily with age, between 200 and 600 pc.

We create the first large-scale mock spectroscopic survey of gas absorption sightlines traversing the interstellar medium (ISM), circumgalactic medium (CGM), and intergalactic medium (IGM) surrounding galaxies of virtual Universes. That is, we create mock, or synthetic, absorption spectra by drawing lines-of-sight through cosmological hydrodynamical simulations, using a new mesh-free Voronoi ray-tracing algorithm. The result is the Synthetic Absorption Line Spectral Almanac (SALSA), which is publicly released on a feature-rich online science platform (this http URL). It spans a range of ions, transitions, instruments, observational characteristics, assumptions, redshifts, and simulations. These include, but are not limited to: (ions) HI, OI, CI, MgI, MgII, FeII, SiII, CaII, ZnII, SiIII, SiIV, NV, CII, CIV, OVI; (instruments) SDSS-BOSS, KECK-HIRES, UVES, COS, DESI, 4MOST, WEAVE, XSHOOTER; (model choices) with/without dust depletion, noise, quasar continua, foregrounds; (redshift) from z=0 to z~6; (ancillary data) integrated equivalent widths, column densities, distances and properties of nearby galaxies; (simulations) IllustrisTNG including TNG50, TNG-Cluster, EAGLE, and SIMBA. This scope is not fixed, and will grow and evolve with community interest and requests over time -- suggestions are welcome. The resulting dataset is generic and broadly applicable, enabling diverse science goals such as: (i) studies of the underlying physical gas structures giving rise to particular absorption signatures, (ii) galaxy-absorber and halo-absorber correlations, (iii) virtual surveys and survey strategy optimization, (iv) stacking experiments and the identification of faint absorption features, (v) assessment of data reduction methods and completeness calculations, (vi) inference of physical properties from observables, and (vii) apples-to-apples comparisons between simulations and data.

We present a cosmic void catalog using the large-scale structure galaxy catalog from the Baryon Oscillation Spectroscopic Survey (BOSS). This galaxy catalog is part of the Sloan Digital Sky Survey (SDSS) Data Release 12 and is the final catalog of SDSS-III. We take into account the survey boundaries, masks, and angular and radial selection functions, and apply the ZOBOV void finding algorithm to the galaxy catalog. We identify a total of 10,643 voids. After making quality cuts to ensure that the voids represent real underdense regions, we obtain 1,228 voids with effective radii spanning the range 20-100Mpc/h and with central densities that are, on average, 30% of the mean sample density. We release versions of the catalogs both with and without quality cuts. We discuss the basic statistics of voids, such as their size and redshift distributions, and measure the radial density profile of the voids via a stacking technique. In addition, we construct mock void catalogs from 1000 mock galaxy catalogs, and find that the properties of BOSS voids are in good agreement with those in the mock catalogs. We compare the stellar mass distribution of galaxies living inside and outside of the voids, and find no significant difference. These BOSS and mock void catalogs are useful for a number of cosmological and galaxy environment studies.

We present new VLTI/GRAVITY astrometry and updated orbit fits for the directly imaged companions YSES 1 b and HR 2562 B, substellar objects straddling the planet-brown dwarf boundary. Using high-precision astrometry, radial velocity (RV) data, and proper motions, we derive revised orbital parameters with orbitize! arXiv:1910.01756. For YSES 1 b, the inclusion of GRAVITY astrometry and a relative radial velocity measurement from arXiv:2409.16660 overcomes the traditional challenge of constraining eccentricities for distant companions, enabling the first orbit fit and yielding a constrained eccentricity of 0.44 (0.20). This represents the first full orbit fit for the system. Additionally, we calculate a median line-of-sight stellar obliquity of 12 (+11, -8) degrees, providing further insight into the system's dynamical architecture. For HR 2562 B, our analysis agrees with arXiv:2302.04893, confirming a low-eccentricity orbit (0.34 (0.20)) and an inclination of 87 (1) degrees. We find HR 2562 B's orbit to be nearly coplanar with the debris disk, with a mutual inclination of 3.7 (0.3) degrees. For both YSES 1 b and HR 2562 B the lower eccentricities favor an in situ formation scenario over extreme scattering or cloud fragmentation.

Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular techniques for finding approximate nearest neighbor searches in high-dimensional spaces. The main benefits of LSH are its sub-linear query performance and theoretical guarantees on the query accuracy. In this survey paper, we provide a review of state-of-the-art LSH and Distributed LSH techniques. Most importantly, unlike any other prior survey, we present how Locality Sensitive Hashing is utilized in different application domains.

The streaming instability (SI) is a leading mechanism for planetesimal formation, driving the aerodynamic concentration of solids in protoplanetary disks. The SI triggers strong clumping (i.e., strong enough for clumps to collapse) when the solid-to-gas column density ratio, $Z$, exceeds a threshold, $\Zcrit$. This threshold depends on the dimensionless stopping time, $\tau_s$. Although the strong-clumping threshold has been explored over the last decade, it has been determined largely through 2D axisymmetric simulations. In this work, we perform a suite of 3D, vertically stratified simulations to establish a clumping threshold across $10^{-3} \leq \tau_s \leq 1.0$. Additionally, we study SI-driven concentration that is unique to 3D. We find that $\Zcrit$ is as low as $\approx 0.002$ at $\tau_s=0.1$ and exceeds $\approx 0.03$ at $\tau_s=10^{-3}$. Compared to 2D, our 3D results yield lower $\Zcrit$ for \tau_s > 0.02, but higher for $\tau_s \leq 0.02$, with a sharp transition between $\tau_s = 0.02$ and 0.03. This transition correlates with midplane density ratio ($\epsilon$): \epsilon < 1 where 3D gives lower thresholds, and \epsilon > 1 where 3D gives higher thresholds. We also find a filaments-in-filaments structure when \epsilon < 1, which enhances clumping compared to 2D. By contrast, when \epsilon > 1 and $\tau_s \leq 0.03$, dust filaments in 3D do not drift inward, suppressing filament mergers and strong clumping. In 2D, filaments drift inward regardless of $\epsilon$, triggering strong clumping easier in this regime. Our results underscore the necessity of 3D simulations for accurately capturing SI-driven concentration and building the strong-clumping threshold.

Joint entity-relation extraction (JERE) identifies both entities and their relationships simultaneously. Traditional machine-learning based approaches to performing this task require a large corpus of annotated data and lack the ability to easily incorporate domain specific information in the construction of the model. Therefore, creating a model for JERE is often labor intensive, time consuming, and elaboration intolerant. In this paper, we propose harnessing the capabilities of generative pretrained large language models (LLMs) and the knowledge representation and reasoning capabilities of Answer Set Programming (ASP) to perform JERE. We present a generic workflow for JERE using LLMs and ASP. The workflow is generic in the sense that it can be applied for JERE in any domain. It takes advantage of LLM's capability in natural language understanding in that it works directly with unannotated text. It exploits the elaboration tolerant feature of ASP in that no modification of its core program is required when additional domain specific knowledge, in the form of type specifications, is found and needs to be used. We demonstrate the usefulness of the proposed workflow through experiments with limited training data on three well-known benchmarks for JERE. The results of our experiments show that the LLM + ASP workflow is better than state-of-the-art JERE systems in several categories with only 10\% of training data. It is able to achieve a 2.5 times (35\% over 15\%) improvement in the Relation Extraction task for the SciERC corpus, one of the most difficult benchmarks.

AI/ML informed Symbolic Regression is the next stage of scientific modeling. We utilize a highly customizable symbolic regression package ``PySR" to model the $x$ and $t$ dependence of the flavor isovector combination $H_{u-d}(x,t,\xi)$ at $\xi=0$. These PySR models were trained on GPD results provided by both Lattice QCD and phenomenological sources GGL, GK, and VGG. We demonstrate, for the first time, the consistency and systematic convergence of Symbolic Regression by quantifying the disparate models through their Taylor expansion coefficients. In addition to PySR penalizing models with higher complexity and mean-squared error, we implement schemes that test specific physics hypotheses, including force-factorized $x$ and $t$ dependence and Regge behavior in PySR GPDs. We show that PySR can identify factorizing GPD sources based on their response to the Force-Factorized model. Knowing the precise behavior of the GPDs, and their uncertainties in a wide range in $x$ and $t$, crucially impacts our ability to concretely and quantitatively predict hadronic spatial distributions and their derived quantities.

The rapid neutron-capture ($r$-process) element europium (Eu) is a valuable tracer of neutron star mergers and other rare nucleosynthetic events. The stellar spectroscopic survey GALAH's unique wavelength range and setup include the Eu absorption feature at $\sim6645$ Å for almost a million stars in the most recent Data Release 4 (DR4). However, DR4 also saw a decreased precision in reported Eu measurements compared to previous data releases. In this work, we use a convolutional neural network (CNN) to perform label transfer, wherein we use the GALAH DR4 spectra and stellar parameters to infer DR3 [Eu/H] abundances. This CNN is then applied to DR4 spectra without corresponding DR3 Eu abundances to develop a new, publicly available catalogue of [Eu/H] values for high signal-to-noise targets. We include [Eu/H] predictions for $118\,946$ stars, out of which $54\,068$ giants constitute our "golden sample" of high-confidence predictions, which pass stricter quality cuts and have a reported precision $\lesssim0.1$. To overcome the scarcity of training data in the low metallicity regime, we provide an additional catalogue of [Eu/H] abundances for metal poor (\mathrm{[Fe/H]}<-1) stars derived from synthesis of the Eu feature. Our "golden sample" can be combined with [Eu/H] values from GALAH DR3 to create a catalogue of over $100\,000$ vetted, high-quality abundances on a homogeneous scale. Moreover, we are able to reproduce known science results, including the elevated Eu abundances of accreted stars and previously observed Galactic chemical evolution trends. This catalogue represents one of the largest available samples of [Eu/H] abundances for high signal-to-noise targets.

We introduce a novel approach to detecting microlensing events and other transients in light curves, utilising the isolation forest (iForest) algorithm for anomaly detection. Focusing on the Legacy Survey of Space and Time by the Vera C. Rubin Observatory, we show that an iForest trained on signal-less light curves can efficiently identify microlensing events by different types of dark objects and binaries, as well as variable stars. We further show that the iForest has real-time applicability through a drip-feed analysis, demonstrating its potential as a valuable tool for LSST alert brokers to efficiently prioritise and classify transient candidates for follow-up observations.

Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing frameworks for two distinct tasks: capturing chaotic dynamics of the Hénon map and forecasting the Mackey-Glass time series. Integrate-and-fire neurons can be implemented in low-power neuromorphic architectures such as Intel Loihi. We explore the impact of network topologies created through random interactions on the reservoir's performance. Our study reveals task-specific variations in network effectiveness, highlighting the importance of tailored architectures for distinct computational tasks. To identify optimal network configurations, we employ a meta-learning approach combined with simulated annealing. This method efficiently explores the space of possible network structures, identifying architectures that excel in different scenarios. The resulting networks demonstrate a range of behaviors, showcasing how inherent architectural features influence task-specific capabilities. We study the reservoir computing performance using a custom integrate-and-fire code, Intel's Lava neuromorphic computing software framework, and via an on-chip implementation in Loihi. We conclude with an analysis of the energy performance of the Loihi architecture.

We present suppression predictions from our pQCD-based energy loss model, which receives small system size corrections, for high-$p_T$ $\pi$, $D$ and $B$ meson $R_{AB}$ as a function of centrality, flavor, $\sqrt{s_{NN}}$, and $p_T$ from large to small collision systems at RHIC and LHC. A statistical analysis is used to constrain the effective strong coupling in our model to available high-$p_T$ suppression data from central heavy-ion collisions at RHIC and LHC, yielding good agreement with all available data. We estimate two important theoretical uncertainties in our model, stemming from: the transition between vacuum and hard thermal loop propagators in the collisional energy loss, and from the angular cutoff on the radiated gluon momentum. We find, consistently, that the extracted $\alpha_s$ remains relatively unchanged across heavy- and light-flavor final states and across central, semi-central, and peripheral collisions. We make predictions from our large-system-constrained model for small systems and find good agreement with photon-normalized $R^{\pi^0}_{d \text{Au}} \simeq 0.75 $in$0-5\%$centrality$d$ + Au collisions by PHENIX. However, we find strong disagreement with the measured $R^{h^{\pm}}_{p \text{Pb}} \gtrsim 1$in$0-5\%$centrality$p$ + Pb collisions by ALICE and ATLAS; we argue that this disagreement is due, in large part, to centrality bias. We make predictions for the ratio of suppression in ${}^3$He + Au and $p$ + Au collisions, which may in the future be used to disentangle final- from initial-state suppression in small systems. We then compare our results to various subsets of data, which allows us to estimate the preferred: low-$p_T$ scale at which non-perturbative processes become important, scales at which the strong coupling runs, and scale at which vacuum propagators transition to thermally modified propagators in collisional energy loss.

Existing Hardware Trojans (HT) detection methods face several critical limitations: logic testing struggles with scalability and coverage for large designs, side-channel analysis requires golden reference chips, and formal verification methods suffer from state-space explosion. The emergence of Large Language Models (LLMs) offers a promising new direction for HT detection by leveraging their natural language understanding and reasoning capabilities. For the first time, this paper explores the potential of general-purpose LLMs in detecting various HTs inserted in Register Transfer Level (RTL) designs, including SRAM, AES, and UART modules. We propose a novel tool for this goal that systematically assesses state-of-the-art LLMs (GPT-4o, Gemini 1.5 pro, and Llama 3.1) in detecting HTs without prior fine-tuning. To address potential training data bias, the tool implements perturbation techniques, i.e., variable name obfuscation, and design restructuring, that make the cases more sophisticated for the used LLMs. Our experimental evaluation demonstrates perfect detection rates by GPT-4o and Gemini 1.5 pro in baseline scenarios (100%/100% precision/recall), with both models achieving better trigger line coverage (TLC: 0.82-0.98) than payload line coverage (PLC: 0.32-0.46). Under code perturbation, while Gemini 1.5 pro maintains perfect detection performance (100%/100%), GPT-4o (100%/85.7%) and Llama 3.1 (66.7%/85.7%) show some degradation in detection rates, and all models experience decreased accuracy in localizing both triggers and payloads. This paper validates the potential of LLM approaches for hardware security applications, highlighting areas for future improvement.

The high dimensional and semantically complex nature of textual Big data presents significant challenges for text clustering, which frequently lead to suboptimal groupings when using conventional techniques like k-means or hierarchical clustering. This work presents Semantic Deep Embedded Clustering (SDEC), an unsupervised text clustering framework that combines an improved autoencoder with transformer-based embeddings to overcome these challenges. This novel method preserves semantic relationships during data reconstruction by combining Mean Squared Error (MSE) and Cosine Similarity Loss (CSL) within an autoencoder. Furthermore, a semantic refinement stage that takes advantage of the contextual richness of transformer embeddings is used by SDEC to further improve a clustering layer with soft cluster assignments and distributional loss. The capabilities of SDEC are demonstrated by extensive testing on five benchmark datasets: AG News, Yahoo! Answers, DBPedia, Reuters 2, and Reuters 5. The framework not only outperformed existing methods with a clustering accuracy of 85.7% on AG News and set a new benchmark of 53.63% on Yahoo! Answers, but also showed robust performance across other diverse text corpora. These findings highlight the significant improvements in accuracy and semantic comprehension of text data provided by SDEC's advances in unsupervised text clustering.

We have simulated the formation of an X-ray cluster in a cold dark matter universe using 12 different codes. The codes span the range of numerical techniques and implementations currently in use, including SPH and grid methods with fixed, deformable or multilevel meshes. The goal of this comparison is to assess the reliability of cosmological gas dynamical simulations of clusters in the simplest astrophysically relevant case, that in which the gas is assumed to be non-radiative. We compare images of the cluster at different epochs, global properties such as mass, temperature and X-ray luminosity, and radial profiles of various dynamical and thermodynamical quantities. On the whole, the agreement among the various simulations is gratifying although a number of discrepancies exist. Agreement is best for properties of the dark matter and worst for the total X-ray luminosity. Even in this case, simulations that adequately resolve the core radius of the gas distribution predict total X-ray luminosities that agree to within a factor of two. Other quantities are reproduced to much higher accuracy. For example, the temperature and gas mass fraction within the virial radius agree to about 10%, and the ratio of specific kinetic to thermal energies of the gas agree to about 5%. Various factors contribute to the spread in calculated cluster properties, including differences in the internal timing of the simulations. Based on the overall consistency of results, we discuss a number of general properties of the cluster we have modelled.