We explore the equivalence between neural networks and kernel methods by deriving the first exact representation of any finite-size parametric classification model trained with gradient descent as a kernel machine. We compare our exact representation to the well-known Neural Tangent Kernel (NTK) and discuss approximation error relative to the NTK and other non-exact path kernel formulations. We experimentally demonstrate that the kernel can be computed for realistic networks up to machine precision. We use this exact kernel to show that our theoretical contribution can provide useful insights into the predictions made by neural networks, particularly the way in which they generalize.

The Gamow Explorer will use Gamma Ray Bursts (GRBs) to: 1) probe the high redshift universe (z > 6) when the first stars were born, galaxies formed and Hydrogen was reionized; and 2) enable multi-messenger astrophysics by rapidly identifying Electro-Magnetic (IR/Optical/X-ray) counterparts to Gravitational Wave (GW) events. GRBs have been detected out to z ~ 9 and their afterglows are a bright beacon lasting a few days that can be used to observe the spectral fingerprints of the host galaxy and intergalactic medium to map the period of reionization and early metal enrichment. Gamow Explorer is optimized to quickly identify high-z events to trigger follow-up observations with JWST and large ground-based telescopes. A wide field of view Lobster Eye X-ray Telescope (LEXT) will search for GRBs and locate them with arc-minute precision. When a GRB is detected, the rapidly slewing spacecraft will point the 5 photometric channel Photo-z Infra-Red Telescope (PIRT) to identify high redshift (z > 6) long GRBs within 100s and send an alert within 1000s of the GRB trigger. An L2 orbit provides > 95% observing efficiency with pointing optimized for follow up by the James Webb Space Telescope (JWST) and ground observatories. The predicted Gamow Explorer high-z rate is >10 times that of the Neil Gehrels Swift Observatory. The instrument and mission capabilities also enable rapid identification of short GRBs and their afterglows associated with GW events. The Gamow Explorer will be proposed to the 2021 NASA MIDEX call and if approved, launched in 2028.

Error-bounded lossy compression is one of the most efficient solutions to reduce the volume of scientific data. For lossy compression, progressive decompression and random-access decompression are critical features that enable on-demand data access and flexible analysis workflows. However, these features can severely degrade compression quality and speed. To address these limitations, we propose a novel streaming compression framework that supports both progressive decompression and random-access decompression while maintaining high compression quality and speed. Our contributions are three-fold: (1) we design the first compression framework that simultaneously enables both progressive decompression and random-access decompression; (2) we introduce a hierarchical partitioning strategy to enable both streaming features, along with a hierarchical prediction mechanism that mitigates the impact of partitioning and achieves high compression quality -- even comparable to state-of-the-art (SOTA) non-streaming compressor SZ3; and (3) our framework delivers high compression and decompression speed, up to 6.7$\times$ faster than SZ3.

Gravitational waves (GWs) generated by axisymmetric rotating collapse, bounce, and early postbounce phases of a galactic core-collapse supernova will be detectable by current-generation gravitational wave observatories. Since these GWs are emitted from the quadrupole-deformed nuclear-density core, they may encode information on the uncertain nuclear equation of state (EOS). We examine the effects of the nuclear EOS on GWs from rotating core collapse and carry out 1824 axisymmetric general-relativistic hydrodynamic simulations that cover a parameter space of 98 different rotation profiles and 18 different EOS. We show that the bounce GW signal is largely independent of the EOS and sensitive primarily to the ratio of rotational to gravitational energy, and at high rotation rates, to the degree of differential rotation. The GW frequency of postbounce core oscillations shows stronger EOS dependence that can be parameterized by the core's EOS-dependent dynamical frequency $\sqrt{G\bar{\rho}_c}$. We find that the ratio of the peak frequency to the dynamical frequency follows a universal trend that is obeyed by all EOS and rotation profiles and that indicates that the nature of the core oscillations changes when the rotation rate exceeds the dynamical frequency. We find that differences in the treatments of low-density nonuniform nuclear matter, of the transition from nonuniform to uniform nuclear matter, and in the description of nuclear matter up to around twice saturation density can mildly affect the GW signal. We find that approximations and uncertainties in electron capture rates can lead to variations in the GW signal that are of comparable magnitude to those due to different nuclear EOS. This emphasizes the need for reliable nuclear electron capture rates and for self-consistent multi-dimensional neutrino radiation-hydrodynamic simulations of rotating core collapse.

Belief Propagation (BP) is a popular, distributed heuristic for performing MAP computations in Graphical Models. BP can be interpreted, from a variational perspective, as minimizing the Bethe Free Energy (BFE). BP can also be used to solve a special class of Linear Programming (LP) problems. For this class of problems, MAP inference can be stated as an integer LP with an LP relaxation that coincides with minimization of the BFE at ``zero temperature". We generalize these prior results and establish a tight characterization of the LP problems that can be formulated as an equivalent LP relaxation of MAP inference. Moreover, we suggest an efficient, iterative annealing BP algorithm for solving this broader class of LP problems. We demonstrate the algorithm's performance on a set of weighted matching problems by using it as a cutting plane method to solve a sequence of LPs tightened by adding ``blossom'' inequalities.

Work in the Open Archives Initiative - Object Reuse and Exchange (OAI-ORE) focuses on an important aspect of infrastructure for eScience: the specification of the data model and a suite of implementation standards to identify and describe compound objects. These are objects that aggregate multiple sources of content including text, images, data, visualization tools, and the like. These aggregations are an essential product of eScience, and will become increasingly common in the age of data-driven scholarship. The OAI-ORE specifications conform to the core concepts of the Web architecture and the semantic Web, ensuring that applications that use them will integrate well into the general Web environment.

The Event Horizon Telescope is preparing to produce time sequences of black hole images, or movies. In anticipation, we developed an autocorrelation technique to measure apparent rotational motion using the image-domain pattern speed $\Omega_p$. Here, we extend this technique to the visibility domain and introduce the visibility amplitude pattern speed $\Omega_{\mathrm{VA}}$. We show that in the Illinois v3 library of EHT source models, $\Omega_{\mathrm{VA}}$ depends on the source inclination, black hole mass, black hole spin, accretion state (MAD or SANE), and baseline length, and then provide approximate fits for this dependence. We show that $\Omega_{\mathrm{VA}}$ is particularly sensitive to baseline length for MAD (strongly magnetized) models, and that the slope of this dependence can be used to constrain black hole spin. As with $\Omega_p$, models predict that $\Omega_{\mathrm{VA}}$ is well below the Keplerian frequency in the emission region for all model parameters. This is consistent with the idea that $\Omega_{\mathrm{VA}}$ measures an angular phase speed for waves propagating through the emission region. Finally, we identify the information that would be provided by space-based millimeter VLBI such as the proposed BHEX mission.

Complex spin configurations in magnetic materials, ranging from collinear single-Q to noncoplanar multi-Q states, exhibit rich symmetry and chiral properties. However, their detailed characterization is often hindered by the limited spatial resolution of neutron diffraction techniques. Here we employ magnetic circular dichroism (MCD) and magnetic linear dichroism (MLD) to investigate the triangular lattice antiferromagnet Co$_{1/3}$TaS$_2$, revealing three-state (Z3) nematicity and also spin chirality across its multi-Q magnetic phases. At intermediate temperatures, the presence of MLD identifies nematicity arising from a single-Q stripe phase, while at high magnetic fields and low temperatures, a phase characterized solely by MCD emerges, signifying a purely chiral non-coplanar triple-Q state. Notably, at low temperatures and small fields, we discover a unique phase where both chirality and nematicity coexist. A theoretical analysis based on a continuous multi-Q manifold captures the emergence of these distinct magnetic phases, as a result of the interplay between four-spin interactions and weak magnetic anisotropy. Additionally, MCD and MLD microscopy spatially resolves the chiral and nematic domains. Our findings establish Co$_{1/3}$TaS$_2$ as a rare platform hosting diverse multi-Q states with distinct combinations of spin chirality and nematicity while demonstrating the effectiveness of polarized optical techniques in characterizing complex magnetic textures.

Chemically complex materials (CCMs) exhibit extraordinary functional properties but pose significant challenges for atomistic modeling due to their vast configurational heterogeneity. We introduce Hop-Decorate (HopDec), a high-throughput, Python-based atomistic workflow that automates the generation of defect transport data in CCMs. HopDec integrates accelerated molecular dynamics with a novel redecoration algorithm to efficiently sample migration pathways across chemically diverse local environments. The method constructs a defect-state graph in which transitions are associated with distributions of kinetic and thermodynamic parameters, enabling direct input into kinetic Monte Carlo and other mesoscale models. We demonstrate HopDec's capabilities through applications to a Cu-Ni alloy and the spinel oxide (Fe,Ni)Cr2O4, revealing simple predictive relationships in the former and complex migration behaviors driven by cation disorder in the latter. These results highlight HopDec's ability to extract physically meaningful trends and support reduced-order or machine-learned models of defect kinetics, bridging atomic-scale simulations and mesoscale predictions in complex material systems.

Magnetic reconnection is a ubiquitous plasma process in which oppositely directed magnetic field lines break and rejoin, resulting in a change of the magnetic field topology. Reconnection generates magnetic islands: regions enclosed by magnetic field lines and separated by reconnection points. Proper identification of these features is important to understand particle acceleration and overall behavior of plasma. We present a contour-tree based visualization for robust and objective identification of islands and reconnection points in two-dimensional (2D) magnetic reconnection simulations. The application of this visualization to a simple simulation has revealed a physical phenomenon previously not reported, resulting in a more comprehensive understanding of magnetic reconnection.

The Gamow Explorer will use Gamma Ray Bursts (GRBs) to: 1) probe the high redshift universe (z > 6) when the first stars were born, galaxies formed and Hydrogen was reionized; and 2) enable multi-messenger astrophysics by rapidly identifying Electro-Magnetic (IR/Optical/X-ray) counterparts to Gravitational Wave (GW) events. GRBs have been detected out to z ~ 9 and their afterglows are a bright beacon lasting a few days that can be used to observe the spectral fingerprints of the host galaxy and intergalactic medium to map the period of reionization and early metal enrichment. Gamow Explorer is optimized to quickly identify high-z events to trigger follow-up observations with JWST and large ground-based telescopes. A wide field of view Lobster Eye X-ray Telescope (LEXT) will search for GRBs and locate them with arc-minute precision. When a GRB is detected, the rapidly slewing spacecraft will point the 5 photometric channel Photo-z Infra-Red Telescope (PIRT) to identify high redshift (z > 6) long GRBs within 100s and send an alert within 1000s of the GRB trigger. An L2 orbit provides > 95% observing efficiency with pointing optimized for follow up by the James Webb Space Telescope (JWST) and ground observatories. The predicted Gamow Explorer high-z rate is >10 times that of the Neil Gehrels Swift Observatory. The instrument and mission capabilities also enable rapid identification of short GRBs and their afterglows associated with GW events. The Gamow Explorer will be proposed to the 2021 NASA MIDEX call and if approved, launched in 2028.

Double-degenerate (DD) mergers of carbon-oxygen white dwarfs have recently emerged as a leading candidate for normal Type Ia supernovae (SNe Ia). However, many outstanding questions surround DD mergers, including the characteristics of their light curves and spectra. We have recently identified a spiral instability in the post-merger phase of DD mergers and demonstrated that this instability self-consistently leads to detonation in some cases. We call this the spiral merger SN Ia model. Here, we utilize the SuperNu radiative transfer software to calculate three-dimensional synthetic light curves and spectra of the spiral merger simulation with a system mass of 2.1 $M_\odot$ from Kashyap et al. Because of their large system masses, both violent and spiral merger light curves are slowly declining. The spiral merger resembles very slowly declining SNe Ia, including SN 2001ay, and provides a more natural explanation for its observed properties than other SN Ia explosion models. Previous synthetic light curves and spectra of violent DD mergers demonstrate a strong dependence on viewing angle, which is in conflict with observations. Here, we demonstrate that the light curves and spectra of the spiral merger are less sensitive to the viewing angle than violent mergers, in closer agreement with observation. We find that the spatial distribution of 56Ni and IMEs follows a characteristic hourglass shape. We discuss the implications of the asymmetric distribution of 56Ni for the early-time gamma-ray observations of 56Ni from SN 2014J. We suggest that DD mergers that agree with the light curves and spectra of normal SNe Ia will likely require a lower system mass.

We analyze Linear Programming (LP) decoding of graphical binary codes operating over soft-output, symmetric and log-concave channels. We show that the error-surface, separating domain of the correct decoding from domain of the erroneous decoding, is a polytope. We formulate the problem of finding the lowest-weight pseudo-codeword as a non-convex optimization (maximization of a convex function) over a polytope, with the cost function defined by the channel and the polytope defined by the structure of the code. This formulation suggests new provably convergent heuristics for finding the lowest weight pseudo-codewords improving in quality upon previously discussed. The algorithm performance is tested on the example of the Tanner [155, 64, 20] code over the Additive White Gaussian Noise (AWGN) channel.

We present an analysis of gamma-ray burst (GRB) progenitor classification, through their positions on a Uniform Manifold Approximation and Projection (UMAP) plot, constructed by Negro et al. 2024, from Fermi-GBM waterfall plots. The embedding plot has a head-tail morphology, in which GRBs with confirmed progenitors (e.g. collapsars vs. binary neutron star mergers) fall in distinct regions. We investigate the positions of various proposed sub-populations of GRBs, including those with and without radio afterglow emission, those with the lowest intrinsic luminosity, and those with the longest lasting prompt gamma-ray duration. The radio-bright and radio-dark GRBs fall in the head region of the embedding plot with no distinctive clustering, although the sample size is small. Our low luminosity GRBs fall in the head/collapsar region. A continuous duration gradient reveals an interesting cluster of the longest GRBs ($T_{90} > 100s$) in a distinct region of the plot, possibly warranting further investigation.

There is a strong interest in finding challenging instances of NP-hard problems, from the perspective of showing quantum advantage. Due to the limits of near-term NISQ devices, it is moreover useful if these instances are small. In this work, we identify two graph families (|V|<1000) on which the Goemans-Williamson algorithm for approximating the Max-Cut achieves at most a 0.912-approximation. We further show that, in comparison, a recent quantum algorithm, Quantum Approximate Optimization Algorithm (depth $p=1$), is a 0.592-approximation on Karloff instances in the limit ($n \to \infty$), and is at best a $0.894$-approximation on a family of strongly-regular graphs. We further explore construction of challenging instances computationally by perturbing edge weights, which may be of independent interest, and include these in the CI-QuBe github repository.

In the Galaxy there are 64 Be X-ray binaries known to-date. Out of those, 42 host a neutron star, and for the reminder the nature of a companion is not known. None, so far, is known to host a black hole. There seems to be no apparent mechanism that would prevent formation or detection of Be stars with black holes. This disparity is referred to as a missing Be -- black hole X-ray binary problem. We point out that current evolutionary scenarios that lead to the formation of Be X-ray binaries predict that the ratio of these binaries with neutron stars to the ones with black holes is rather high F_NStoBH=10-50, with the more likely formation models providing the values at the high end. The ratio is a natural outcome of (i) the stellar initial mass function that produces more neutron stars than black holes and (ii) common envelope evolution (i.e. a major mechanism involved in the formation of interacting binaries) that naturally selects progenitors of Be X-ray binaries with neutron stars (binaries with comparable mass components have more likely survival probabilities) over ones with black holes (which are much more likely to be common envelope mergers). A comparison of this ratio (i.e. F_NStoBH=30) with the number of confirmed Be -- neutron star X-ray binaries (42) indicates that the expected number of Be -- black hole X-ray binaries is of the order of only 0-2. This is entirely consistent with the observed Galactic sample.

We present particle-in-cell simulations of one dimensional relativistic electromagnetic shocks in a uniform magnetic field, for a range of magnetic field strengths, plasma temperatures and numerical initial conditions. We show that the particle energy distributions of these shocks can develop a state of population inversion in the precursor and shock regions, which may allow for synchrotron maser (or maser-like, coherent) emission. Our set-up is applicable to conditions expected in models of fast radio bursts and therefore lends credence to the synchrotron maser model for these transients. We also show, for the first time, how a newly developed ``analytic particle pusher'' for kinetic simulations gives similar results to the commonly-used Boris pusher, but for larger timesteps and without the need to resolve the gyro-radius and gyro-period of the system. This has important implications for modeling astrophysical plasmas in extreme magnetic fields as well as for bridging scales between kinetic and fluid regimes.

Quantum kernel methods are a candidate for quantum speed-ups in supervised machine learning. The number of quantum measurements N required for a reasonable kernel estimate is a critical resource, both from complexity considerations and because of the constraints of near-term quantum hardware. We emphasize that for classification tasks, the aim is reliable classification and not precise kernel evaluation, and demonstrate that the former is far more resource efficient. Furthermore, it is shown that the accuracy of classification is not a suitable performance metric in the presence of noise and we motivate a new metric that characterizes the reliability of classification. We then obtain a bound for N which ensures, with high probability, that classification errors over a dataset are bounded by the margin errors of an idealized quantum kernel classifier. Using chance constraint programming and the subgaussian bounds of quantum kernel distributions, we derive several Shot-frugal and Robust (ShofaR) programs starting from the primal formulation of the Support Vector Machine. This significantly reduces the number of quantum measurements needed and is robust to noise by construction. Our strategy is applicable to uncertainty in quantum kernels arising from any source of unbiased noise.