Papers

Memory encoded in the environment mediates interactions between active agents, from trail-following organisms to synthetic active matter depositing persistent tracks. Although such memory is known to strongly affect transport, its consequences for collective first-passage phenomena remain largely unexplored. Here we study $N$ one-dimensional random walkers interacting through a shared trail field. We characterize the $k^{\rm th}$ (among $N$) arrival time at a fixed target, and the probability that exactly $k$ walkers in $[0,1]$ reach one boundary before the other. For the broad class of self-interacting walkers with a saturating response to the trail, we derive exact expressions for the corresponding persistence exponents and splitting probabilities. Strikingly, despite the strong history-dependent correlations generated by the common environment, splitting probabilities are exactly identical whether walkers explore simultaneously or one after another. This invariance breaks down for nonsaturating trail interactions. Our results follow from an exact representation of the collective trail field and establish a framework for first-passage phenomena in systems coupled through persistent environmental memory.

Molybdenum disulfide (MoS$_2$) is a semiconductor whose vibrational and excitonic properties are highly sensitive to layer number and structural disorder. We demonstrate the growth of MoS$_2$ monolayers on inert, electronics-compatible SiO$_2$ substrates using room-temperature pulsed laser deposition (PLD). Control of the process parameters enables tuning from monolayer to multilayer films, which we investigate by multiwavelength Raman spectroscopy. The evolution of the Raman-shift difference between the $E_{2g}^{1}$ and $A_{1g}$ modes, combined with an assessment of defect density, tracks film growth as a function of the number of deposition laser pulses. Although excitonic effects strongly influence the optical response of two-dimensional transition-metal dichalcogenides, experimental reports of symmetry-selective exciton-phonon coupling remain limited. We provide experimental evidence of symmetry-dependent exciton-phonon coupling in PLD-grown monolayer MoS$_2$. Specifically, we observe modulation of the resonant behaviour of the out-of-plane $A_{1g}$ and in-plane $E_{2g}^{1}$ modes, related to their different coupling to A excitons, predominantly derived from Mo $d_{z^2}$ orbitals, and C excitons, characterized by mixed orbital contributions from Mo $d_{z^2}$ and S $p_x$ and $p_y$ states. Comparison with mechanically exfoliated monolayers reveals the role of growth-induced defects in modulating these interactions. These findings establish room-temperature PLD as a viable approach for growing two-dimensional MoS$_2$ on inert, electronics-compatible substrates and provide insight into the interplay between excitonic resonances and growth-induced disorder in two-dimensional MoS$_2$.

Elastic parameters are fundamental rock properties for reservoir characterization, but their reliable estimation from angle-stack seismic data remains challenging due to strong nonlinearity and imperfect physical modeling. Conventional deterministic approaches based on linearized Zoeppritz approximations yield a single point estimate and cannot quantify solution uncertainty, while probabilistic methods are computationally expensive. To address these limitations, we present a workflow for elastic parameter inversion from angle-stack seismic data using a guided diffusion model as an implicit prior over the joint distribution of P-wave velocity, S-wave velocity, and density. The diffusion model is trained in an unsupervised manner on benchmark datasets and well-log-derived synthetic models, learning the non-Gaussian statistical coupling among the three elastic parameters. For guidance, we employ Diffusion Posterior Sampling (DPS), which approximates the likelihood function through a forward operator based on the Aki-Richards approximation and injects data-consistency gradient corrections at each reverse diffusion step, sampling from a posterior conditioned on the misfit between observed and modeled angle-stack data. We evaluate the framework on two datasets: the 2D Otway synthetic elastic model and field data from the Poseidon field, NW Shelf, Browse Basin, Australia, comparing it against two baselines: LSQR least-squares inversion and ADMM-based inversion with total variation regularization. Quantitative comparisons confirm that the diffusion-based framework recovers sharper lithological contrasts and geologically more realistic elastic profiles. Uncertainty quantification is achieved by generating multiple independent posterior realizations through repeated reverse diffusion runs, producing spatially resolved uncertainty maps for each elastic parameter.

Medical imaging systems such as CT, MRI, PET, and SPECT do not directly acquire images. Instead, they measure physical signals that encode anatomical or physiological information, and image reconstruction recovers the underlying image by solving an inverse problem. Although these imaging modalities are governed by different imaging physics, they share a common computational framework that naturally connects medical physics, linear algebra, probability, numerical optimization, and efficient computing. As medical imaging systems acquire increasingly large and higher-dimensional datasets, image reconstruction has become one of the primary computational bottlenecks in modern medical imaging. Advanced reconstruction methods, including analytical reconstruction, iterative optimization, and statistical model-based reconstruction, substantially improve image quality while reducing radiation dose or scan time, but at significantly increased computational cost. Efficient computing has therefore become essential for achieving clinically practical reconstruction times. This chapter presents a unified computational perspective on medical image acquisition and reconstruction across CT, MRI, PET, and SPECT. It first reviews the imaging physics and data acquisition process for each modality and derives a generalized mathematical framework for image reconstruction. Building on this framework, the chapter discusses analytical, iterative, and statistical reconstruction methods together with their computational characteristics. Finally, it examines efficient computing considerations, including optimization algorithms, physics-aware forward operators, memory-efficient implementations, and parallel computing strategies. Together, these topics demonstrate how the integration of imaging physics, mathematical modeling, and efficient computing enables accurate and scalable medical image reconstruction.

We present high-resolution (R~45,000), high signal-to-noise (S/N>200) H and K-band spectra of BD+44 493, obtained with the newly commissioned IGRINS-2 instrument on the Gemini North Telescope. BD+44 493 is a well-known carbon-enhanced (CEMP; [C/Fe]=+1.40) ultra metal-poor (UMP; [Fe/H]~-4.0) star that has been extensively studied with high-resolution spectroscopy from the ultraviolet to the infrared. From the IGRINS-2 data, we derive new estimates of the abundances of C, Mg, Si, and Ca by measuring the equivalent widths of several atomic absorption features and comparing the values and line profiles with synthetic models. We measure, for the first time, a weak atomic carbon absorption feature (C I) in a UMP star in the H-band. Our estimated abundance agrees well with other values derived from CH molecules in the optical. The abundance values for the other elements are also in good agreement with previous estimates derived from ultraviolet, optical, and near-infrared observations, confirming the speculations that BD+44 493 is a bona fide second-generation star. With these data, we demonstrate that IGRINS-2 is a powerful new resource for studying and characterizing low-metallicity stars in the Milky Way and beyond.

We present a two-stage inverse design procedure for producing disordered stealthy hyperuniform trivalent photonic networks in two dimensions with isotropic complete photonic band gaps (PBGs) blocking light regardless of direction or polarization (TE or TM) over a wide frequency range. Most ordinary disordered systems fail to maintain complete PBGs as system size increases. The only known exceptions that remain open in the largest simulations have been generated by mapping stealthy hyperuniform point patterns into trivalent networks. However, the resulting networks are not truly stealthy hyperuniform two-phase media. Although their PBGs remain open, they are relatively narrow due to limited overlap between the TE and TM band gaps and broad band tails caused by localized defect states. By contrast, our two-stage inverse design aims to make the final network itself stealthy hyperuniform, achieving unprecedented near-optimal overlap between the TE and TM band gaps and a small defect state density at the band edges. We obtain not only single realizations with large PBGs, but a striking homogeneity across a large ensemble, effectively probing a network with 100,000 vertices. This ensemble-based band gap is comparable in width to the complete PBG of an anisotropic honeycomb photonic crystal with the same network parameters and nearly an order of magnitude wider than the previously widest known isotropic complete PBGs. Our designs can be fabricated using additive manufacturing, offering new pathways to manipulate electromagnetic waves for photonic technologies.

A response-register quantum oracle can reveal a function value or encode it as a phase through kickback. We quantify how much value information can be recovered from one query at a fixed fidelity to a target phase character, and how noise and implementation errors affect readout performance. For pure response states over finite Abelian groups, the known covariant discrimination solution fixes the optimal readout probability exactly in terms of the $\ell_1$-norm coherence of the response state in the character basis. Using a known coherence bound, we determine the complete pure-state readout interval at fixed target-character fidelity. Discarding the response leaves a mixture of phase-oracle channels whose distance from the target, measured by half the diamond norm, is at most the target-character infidelity, with equality for surjective functions. We then identify when coherence alone determines performance and when apparatus correlations or loss profiles are also needed. For coherent analyzer errors, we derive the full quadratic loss and an exact scalar reduction of the profile model on the upper phase–value boundary. For mode-dependent loss, no-detection events can carry value information, and a universal bound is attained under a phase–loss compatibility condition. Photonic-mesh simulations test the analyzer-error predictions, with the controlled oracle itself assumed. Selected results extend to finite non-Abelian groups, where scalar phase kickback is restricted to one-dimensional representations.

Before they mature, all known proteins translocate through the ribosomal exit tunnel in a form of extended or partially folded nascent polypeptide. This translocation occurs in the natural direction from the peptidyl transferase centre buried deep in the large ribosomal subunit through a tunnel to the ribosomal surface. Some proteins, however, enter the ribosomal tunnel from outside and translocate in the reverse direction. In this work, we address a simple question: to what extent is the ribosomal tunnel intrinsically directional. To compare the forward and reverse peptide translocations, we performed biased molecular dynamics simulations and assessed the directionality of tunnels from four different organisms using a model poly-alanine decapeptide. Our simulations reveal that the tunnel architecture intrinsically favours the natural direction translocation from peptidyl transferase centre to the ribosomal surface. Consequently, proteins that occupy the tunnel in the reverse direction likely require dedicated structural machinery to overcome this intrinsic bias.

Fermion bound states in the core of a line-shaped vortex of a two-dimensional topological superconductor are investigated. The superconducting pairing potential, described in terms of elliptical coordinates, vanishes along a line defect with the two foci at the endpoints. The superconductivity is induced into a topological insulator via proximity effect with a type II s-wave superconductor. The spin and the momentum are perpendicularly locked by the strong spin-orbit coupling via Rashba interaction. A zero-energy Majorana state arises from the Berry phase together with a sequence of equally spaced fermion exitations. By solving the Bogoliubov-de Gennes equations using the method employed by Caroli, de Gennes and Matricon we calculate the energies, the wave-functions and spin-polarization of the bound states. An analytic expression for the local density of states within the vortex is obtained.

In this study, a conditional diffusion model – a class of generative machine learning models – is developed to generate synthetic, experimental data-based trajectories of turbulent flames. Generated experimental data corresponds to simultaneous field measurements, namely OH planar laser-induced fluorescence (OH-PLIF) fields and multi-component particle image velocimetry (PIV) fields, for attached and detached flame states in a swirl combustor configuration. This is done using an x-prediction flow matching framework combined with a pixel-based spatiotemporal transformer, which is capable of generating entire spatiotemporal slabs containing synthetic flame evolution at inference time, conditioned on the flame regime. Using this framework, synthetic flames were found to preserve key flame features and statistical consistency across space and time, particularly at the large scales – deviations at high temporal frequencies and small spatial length scales were found to depend on the time-span of the generated space-time slabs. An extrapolation task of transition synthesis is also conducted, in which the conditional diffusion model is used to synthesize spatiotemporally coherent flame transitions (flame liftoff and reattachment) unseen by the model during training. This was accomplished using a model for the denoising transition velocity that relies on time-varying linear combinations of attached and detached denoising velocities, leading to an approach that (a) allows for control of the generated transition directions and timescales, and (b) retains sample-to-sample variability in the generated transitions in the process. Overall, this study provides a promising pathway for the utilization of experimental data-based generative models as a new means of data exploration in data-sparse environments, complementing both experiments and computational fluid dynamics-based approaches.

Matrix chain multiplication – computing $\mathcal{W} = M^{(0)}\cdots M^{(K-1)}$ where $M^{(k)} \in \mathbb{R}^{P_k \times P_{k+1}}$ – arises in scientific computing, machine learning, and graph analysis. Despite the importance of this problem, for chains of distinct matrices, the classical number of operations grows linearly with the chain length $K$ and polynomially in the matrix dimensions. We present Two-Tower Matrix Multiplication, a quantum subroutine that encodes the product $\mathcal{W}$ of the $K$ matrices into a quantum state in circuit depth $\mathcal{O}(\max_{k} \mathrm{polylog} (P_k P_{k+1}))$, which is independent of~$K$ within the QRAM-based state-preparation model, whereas the qubit count is $\mathcal{O}\bigl(\sum_{k} \log P_k \bigr)$; the total gate count remains linear in $K$, so the gain is in the circuit depth. The construction interleaves state-preparation operators across two layers; within each layer, all operators act on disjoint registers and execute in parallel. This subroutine can be specialized for the chain-vector case, which computes the product of $K-1$ matrices applied to a vector. We prove the correctness of the subroutine for all $K$ and provide two implementations using the Qiskit and QCLAB frameworks. The subroutine is applicable to any downstream quantum algorithm that operates on a matrix encoded in the statevector, including norm estimation, graph-matrix powers, linear system solving, and quantum machine learning kernels.

This study presents an AI-powered system for generating isomorphic physics problems using large language model (LLM)-based agent workflows. The system is designed around three practical goals: preserving the same conceptual and problem-solving structure as the original problems, varying construct-irrelevant features such as scenarios and numerical values, and producing questions that are directly usable without expert revision. The workflow combines prompt chaining, agent-based verification, and automated LaTeX compilation within a publicly accessible website hosted on a Raspberry Pi. To evaluate the system, we developed an eight-item rubric and tested the system using 13 multiple-choice questions from a calculus-based introductory Newtonian mechanics course. The evaluation results showed that 89% of the questions generated were rated as fully specified and directly usable. However, the system also showed limitations. The results suggest that LLM-based systems have significant potential for reliable instructional problem generation while also highlighting important challenges for future development.

Three-dimensional (3D) iodide double-perovskite (elpasolite) semiconductors have attracted interest as potential lead-free metal-halide absorber layers for solar applications. Although studied extensively by computational methods, they have remained largely inaccessible synthetically, consistent with their predicted thermodynamic instability. Here, we report the first synthesis of bulk Cs2AgBiI6, demonstrating both microcrystalline and thin-film forms. Microcrystalline powders of Cs2AgBiI6 were prepared via anion exchange from phase-pure Cs2AgBiBr6 microcrystals. The resulting iodide elpasolite shows broad absorption throughout the visible with a 1.70 +/- 0.05 eV optical bandgap and near-infrared photoluminescence centered at 1.03 eV. We identify the elimination of trace moisture in bulk Cs2AgBiBr6 as the critical factor enabling complete halide exchange and isolation of bulk Cs2AgBiI6 with phase purity. In inert atmosphere, microcrystalline Cs2AgBiI6 shows no decomposition when stored for months at room temperature or heated to ~70 {\deg}C, and it appears equally stable in dry air. Building upon these insights, we then demonstrate the preparation of phase-pure Cs2AgBiI6 films by flash thermal evaporation of Cs2AgBiBr6 followed by anion exchange. Photoconductivity measurements on such Cs2AgBiI6 films demonstrate photocarrier generation and transport, marking the first optoelectronic measurement on this elusive 3D iodide double perovskite.

Yielding in disordered soft solids originates from the progressive redistribution of load-bearing capacity from recoverable elastic networks to frictional interactions through deformation-induced structural evolution. We present a unified, non-singular constitutive framework demonstrating that this mechanism naturally generates a finite yield stress and a smooth solid-to-fluid transition without prescribed yield criteria, constitutive switching, or divergent viscosities. The framework captures diverse transient and steady rheological phenomena, including Herschel-Bulkley behavior, stress overshoots, hysteresis, viscosity bifurcation, plug-flow formation, and thixotropic steady-state shear banding.

Multimaterial 3D printing enables the fabrication of functionally graded components, but optimizing their spatial material distribution alongside structural topology remains a formidable challenge due to high-dimensional design spaces and complex constitutive modeling. This paper presents an end-to-end computational framework integrating sparsified physics-augmented neural networks with finite-element-based topology optimization. By extracting closed-form, composition-aware hyperelastic constitutive laws from experimental data, this approach facilitates exact symbolic differentiation via the adjoint state method implemented with FEniCSx, efficiently circumventing the bottlenecks of applying neural network constitutive models. This pipeline is deployed on soft robotic gripper applications, demonstrating continuous composition optimization for highly anisotropic contact responses, and the concurrent optimization of macroscopic topology and material distribution under non-failure stretch constraints. This methodology could replace laborious empirical prototyping, establishing interpretable machine-learning models as practical, robust design primitives for advanced multimaterial additive manufacturing.

Many open problems across physical, biological, and engineered systems involve non-equilibrium transport processes where the governing conservation laws are known, but the underlying constitutive relations remain latent and time-varying. Conventional data-driven approaches like deep neural networks capture statistical patterns but routinely violate fundamental mass conservation. Here, we introduce a unified Bayesian inverse transport framework that resolves this by embedding Bayesian Neural Networks (BNNs) directly within exact partial differential equations in age-time space. By evaluating this framework on complex, real-world human cohort advection across China, Japan, and South Korea, we demonstrate that this physical constraint enables consistent uncertainty propagation and missing-data reconstruction from sparse observations. Beyond demography, this framework provides a generalizable foundation for observing and forecasting non-equilibrium boundary dynamics across various fields.

In an ideal world, the measurement noise in gravitational wave data would be stationary and Gaussian. In reality, neither of these conditions holds. Here a general framework is introduced that can be used to model non-stationary noise in an easily interpretable way, using a dynamic power spectrum $S(f,t)$. The construction is a Gram-factor model for the noise covariance matrix that is positive semi-definite by construction. This construction generalizes the familiar stationary power spectrum $S(f)$. The dynamic spectrum encodes the properties of the noise covariance matrix in any basis, including the frequency domain, time domain, and time-frequency wavelet domain. Closed form expressions are given for discrete Fourier representations of the data, and for discrete Wilson-Daubechies wavelet representations of the data. Both take the form of Gramian matrices. Examples are provided, including the non-stationarity caused by window functions, the modulated response to galactic binary signals for space-based detectors, and other, more general types of non-stationarity.

Inner disk chemistry offers a valuable window onto disk evolution and planet formation. Key planet formation processes, including pebble drift, planetesimal and planet formation, dust traps, and snow lines, determine the delivery rates of oxygen and carbon to the inner disk. As a result, measurements of molecules sensitive to the gas-phase C/O ratio, in disks over a range of ages, can constrain the relative importance and time evolution of these processes. Here we report JWST/MIRI observations of T Tauri disks in the young cluster IC 348, extending to older ages previous studies of disks. Like younger disks, IC 348 sources show a broad range of molecular ratios, indicating diverse planet formation histories. However, the similar HCN/H$_2$O and C$_2$H$_2$/H$_2$O flux ratio distributions of IC 348 (2–5 Myr old) and Taurus (1–2 Myr old) sources imply little to no evolution in the inner disk C/O ratio over this time interval, even as disk masses decline by a factor of 5 on average. This result contradicts the general prediction of increasing C/O ratio with time in models of disk chemical evolution, indicating the need to better understand the interplay between (and efficiencies of) planet formation and disk evolution processes. We also find that the cold-to-warm water flux ratios show no evolution and do not correlate with the hydrocarbon-to-water ratios. These results suggest that most inner T Tauri disks are not dominated by rapid icy pebble drift and avoid the ”meter-size barrier problem” once thought to be an obstacle to planet formation.

Entangling gates of trapped ions are typically mediated by collective motional degrees of freedom. Weak coupling between qubit and motional degrees of freedom and the resulting harmonic dynamics give access to a broad range of gate schemes, but also impose strict limitations on achievable gate times. In this paper, we devise optimally designed driving schemes for the realization of fast, high-fidelity entangling gates mediated by anharmonic dynamics. The driving can also be optimized to achieve resilience to multiple system imperfections, and the anharmonicity in the motional dynamics can be used to enhance such resilience.

While traditional evaluations of optical information transfer rely on disjointed abstractions to bridge electromagnetic propagation, coherence, and communication theory, we introduce an end-to-end framework that directly connects rigorous subwavelength wave physics to Shannon mutual information. By lifting the Maxwell current-to-field Green's function to propagate second-order field correlations (the mutual intensity), we establish a unified linear channel model that encapsulates coherent communication, phase retrieval, and incoherent imaging. Applying this framework, we demonstrate that the mutual-information-optimized photonic front end is dictated jointly by available spatial degrees of freedom, source statistics, and detection laws. For coherent sources measured by square-law detectors, we identify a structural transition: when detectors outnumber sources, topology-optimized front ends shift from point-focusing to interferometric mixing. This mixing leverages interference cross terms to make relative source phases information-bearing, yielding mutual information that surpasses the point-focusing amplitude-only baseline. Conversely, for spatially incoherent sources, the channel reduces to the Hadamard square of the Green's function. In this regime, under an isotropic source covariance, we prove that point-focusing uniquely maximizes the mutual information at fixed Frobenius norm. Under source correlations, the optimized front ends instead favor optical mixing. Finally, we derive closed-form upper bounds on achievable incoherent mutual information, governed entirely by the coherent singular values of the underlying electromagnetic operator. Potential applications include near-field microscopy, direct-detection optical datalinks, reference-free phase retrieval, fluorescence and thermal imaging, and structure-agnostic benchmarks for end-to-end-designed computational imagers.

The nuclear stellar clusters surrounding supermassive black holes (SMBHs) host millions of stars and stellar remnants. We study how stellar collisions and binary disruptions, alongside two-body scattering and gravitational-wave (GW) emission, shape the stellar distribution and regulate the abundance of stars on tightly bound orbits. We show the following. (a) Stars in the inner region of the cluster follow a steady-state density profile scaling as $n(r)\propto r^{-5/4}$, set by the balance between collisional depletion and binary replenishment. This profile is largely independent of whether two-body scattering or GW emission drives the orbital evolution prior to the collisions. (b) For SMBHs with $M\lesssim2 \times 10^7 M_\odot$, roughly half of the stars injected by the Hills mechanism eventually collide. The rest are tidally disrupted while on orbits with periods of order months to years. (c) For more massive SMBHs, these short-orbital-period tidal disruption events are suppressed, and most injected stars are ultimately destroyed by collisions. (d) Stellar extreme-mass-ratio inspirals (sEMRIs) can form around SMBHs with $M\gtrsim2\times10^6 M_\odot$, but are typically terminated by collisions before circularizing. Our model highlights the dynamical challenge stellar collisions pose for the formation of sEMRIs and, consequently, for stellar models of quasiperiodic eruptions. Applied to the Galactic Center, the collision-regulated density profile is consistent with the observed stellar distribution slope. Based on this profile, we estimate the stellar mass within the orbit of S2, finding it consistent with the observational upper limit, account for the recently discovered star S301, predict that most stars near Sgr$~{\rm A}^*$ follow eccentric orbits, and determine their typical eccentricities.

In this study we introduce a new simulation campaign designed to understand how parameters that control star-formation and AGN feedback processes in cosmological hydrodynamical simulations impact observables such as the galaxy stellar-mass function (GSMF) and the gas fractions in large dark matter halos. These simulations are zoom-ins to halos selected from the MillenniumTNG (MTNG) simulation, and are run employing a novel multi-zoom approach which simultaneously re-simulates several sub-regions of a given large volume at a higher resolution than the background, thus reducing computational cost and imbalances in parallelization. We measure the GSMF and gas-fractions in halos for each of the re-simulations, and train Gaussian-process emulators on these quantities. The resulting emulators predict the GSMF and gas-fractions in halos with $\sim0.1\,\mathrm{dex}$ and $\sim 10\%$ precision respectively. Using the emulators we can simultaneously fit recent measurements of both quantities, in particular the lower gas fractions now observed even for comparatively massive clusters. Interestingly, we find a combination of parameters of the MTNG galaxy formation model that provides a qualitatively good fit to both the measured GSMF and gas fractions. This combination of parameters differs from the fiducial one mainly by requiring that stellar-feedback is significantly less energetic, and that kinetic AGN feedback events are significantly more energetic and rare. This finding implies that the MTNG model can be consistent with scenarios of strong feedback that remove large amounts of gas from groups and clusters, albeit we caution that we have not extensively examined the effect of these new parameters on many quantities for which MTNG made successful predictions.

Fully-connected transverse interactions have been considered as catalysts for quantum annealing that could mitigate exponentially small gaps and circumvent first-order phase transitions, but their experimental implementation remains challenging. In this work, we introduce a procedure for emulating their effects via a self-consistent transverse field. All that is required beyond conventional transverse-field annealing is the ability to make measurements in the transverse ($\hat{\sigma}^x$) basis. We show that this protocol yields identical dynamics in the large-system limit, and study the approach to that limit in numerical simulations of the (uniform) $p$-spin model. However, realizing the protocol in practice requires us to consider a series of approximate variants, each of whose errors we quantify and demonstrate can be made sufficiently small. Lastly, we show how to map the protocol onto annealing platforms that vary only a single control parameter. Even after the multiple stages of approximation, our procedure can generate dynamics that agree well with the original transverse-interaction catalyst, establishing self-consistent transverse fields as a viable alternative on near-term quantum annealers.

Understanding the morphology of fast radio bursts (FRB), and whether all sources repeat, are key challenges that are becoming more tractable given the increase in data from surveys such as the Canadian Hydrogen Intensity Mapping Experiment FRB project (CHIME/FRB). We present a Convolutional Autoencoder unsupervised classifier for separating the CHIME/FRB data into morphological classes. This data-driven approach is more reproducible than visual inspection, since groupings are learned from the data itself and not subject to differences between expert annotations. While most bursts occupy a similar area of morphological parameter space, we identify three classes of bursts separate from the general FRB population. While one class contains bursts with short bandwidth, and downward-drifting sub-burst structure, the characteristic bursts of other classes have very short temporal width, and occupy the entire CHIME observing band. We identify two distinct subgroups of temporally short, simple broadband bursts; one with minimal scattering and the other with higher scattering. As an additional output of our classifier, we provide a binary FRB repeatability classification, and train the classifications on simulations that mimic the first FRB catalogue from CHIME/FRB. We are able to correctly identify 86$\%$ of repeater bursts. We find that our approach is able to independently recover the downward linear drifting burst morphologies previously defined through visually inspection. Overall, we find that although there exists FRB subgroups with higher or lower proportion of repeaters, there is substantial overlap between the morphological properties of repeaters and one-off bursts consistent with previous studies.

With current advances in gravitational wave (GW) detection made by the worldwide LIGO-Virgo-KAGRA (LVK) network of detectors, ever-more sensitive tests of gravity in the strong-field regime are now possible. This enables one to test gauge theories beyond Einstein-Hilbert action, such as Weyl gauge theories of gravity. The only anomaly-free (quantum) gauge theory of a space-time symmetry beyond Poincaré is based on Weyl gauge group (of dilatations and Poincaré symmetry) with Weyl conformal geometry as its natural underlying geometry. This gauge theory has spontaneous breaking of Weyl gauge symmetry to Einstein-Hilbert and Proca actions, plus a positive cosmological constant. We investigate the GW polarisation modes of Weyl (quadratic) gauge theory of gravity in Weyl geometry and compare our findings to the most recent experimental data. We show how the geodesic deviation equation from Riemannian geometry translates to Weyl geometry, and explain why it is crucial to perform the analysis around de Sitter background, which is the correct low-energy limit of Weyl quadratic gravity, to not alter the GW content, and then compute the polarisation modes. In addition to the two transverse-traceless tensor modes predicted by Einstein-Hilbert action, we find two additional vector modes induced by the transverse fluctuations of the Weyl gauge field. If detected, these vectors modes would be important evidence for Weyl gauge symmetry.

Magneto-optical traps (MOTs) are a workhorse technology for neutral-atom quantum sensing, simulation, and computing. Here, we demonstrate a MOT optimized for inertial quantum sensing and precision metrology applications. Our MOT traps $4\times10^8$ cesium (Cs) atoms in a robust, modular system that achieves stable operation through two design features: (1) we use divergent cooling laser beams to reduce unwanted reflections and (2) optical elements are mounted in a fiber-coupled, compact, and modular cage rigidly attached to the vacuum chamber. Following polarization-gradient cooling (PGC), the system produces atom samples with temperatures below 10 ${\mu}$K, similar to those achieved in conventional MOTs that use collimated laser beams. In addition, we observe trapping of $2\times10^7$ Cs atoms in a non-conventional MOT geometry, where the cooling laser beams are diagonal to the principal axis of the quadrupole magnetic field.

Using a sequence of effective field theories (EFTs), we calculate the rate for the leptonic decay $B^-\to\tau^-\bar{\nu}_\tau(\gamma)$ including real and virtual QED corrections, with a cut $E_{\rm cut}\ll\Lambda_{\rm QCD}$ imposed on electromagnetic radiation in the $B$-meson rest frame. We establish a factorization theorem for the rate and evaluate it at $\mathcal{O}(\alpha)$, resumming the leading logarithmic corrections to all orders in perturbation theory. The large mass $m_\tau$ allows us to treat the tau lepton as a heavy fermion below $\mu\sim m_B\sim m_\tau$, leading to an EFT construction that is structurally different and noticeably simpler than that for the muon case. In particular, hadron-structure dependent QED corrections can be described in terms of QED-induced $B^-\to\tau^-$ form factors, which should be calculable on the lattice. Our analysis includes the leading $\Lambda_{\rm QCD}/m_\tau$ corrections as well as the leading corrections in $E_{\rm cut}/\Lambda_{\rm QCD}$. We show that contributions of the form $\Lambda_{\rm QCD}m_B/m_\tau^2$, which are naively sizable, cancel among each other. Contrary to the muon channel, structure-dependent corrections involving $BB^\ast\gamma$ transitions are numerically negligible for the tau case. The remaining logarithmic dependence on $E_\mathrm{cut}$ is mild. We estimate that the present uncertainty in the calculation of the structure-dependent QED corrections is about 0.5% of the rate. As a byproduct, we present a state-of-the-art prediction for the lepton flavor universality ratio of the tau and muon channels.

We develop a semi-analytical framework to map ultra-high-energy neutrino fluxes onto event rates at neutrino telescopes. The formulation is based on the Boltzmann equation for the distribution of secondary muons produced by neutrino interactions in matter, and naturally accounts for the effective target volume relevant for through-going tracks. This "soft volume" is controlled by muon propagation and stochastic energy losses, and can be significantly larger than the instrumented detector volume. Exploiting the dominance of soft energy losses, we derive a controlled second-order expansion of the collision operator, reducing the transport problem to a drift-diffusion equation in energy space, with rare hard scatterings treated perturbatively. The resulting master formula provides a fast alternative to full Monte Carlo simulations while retaining a direct connection to the microscopic muon energy-loss processes. We apply the formalism to IceCube through-going muon data, marginalizing over theoretical uncertainties in the transport coefficients, and obtain a diffuse-flux fit compatible with the experimental result. We also revisit the interpretation of the ultra-high-energy track event reported by KM3NeT, assessing its consistency with IceCube non-observation in the same energy range. Our results provide a first-principles bridge between neutrino-flux models and track-event observables, with direct applications to precision neutrino astronomy and searches for physics beyond the Standard Model.

We investigate the contributions of Lepton Number Violating (LNV) effective operators to the rare decays $B\to K^{(\ast)}\nu\nu$ and $K\to \pi\nu\nu$. Such operators can modify the kinematic distributions of these processes, providing distinctive probes of physics beyond the Standard Model. Through a renormalization-group analysis, we show that the Standard Model Effective Field Theory (SMEFT) operators responsible for these effects are subject to stringent indirect constraints from neutrino physics. In particular, we find that the mild excess reported by Belle-II in the $B^+\to K^+\nu\nu$ channel cannot be explained by LNV SMEFT operators without introducing significant fine-tuning in neutrino masses. We then show that these constraints can be evaded in the SMEFT minimally extended by a light right-handed neutrino, allowing for sizable effects in rare meson decays. Finally, we explore the implications of this viable scenario for low-energy processes, including neutrinoless double-beta decays.

Self-interacting dark matter turns the structure of galactic halos into a direct requirement on a low-energy scattering amplitude. We show that, for weakly coupled scalar dark matter, this requirement implies a much stronger mass bound on the dark matter particle than partial-wave unitarity alone. Using analyticity, crossing symmetry, locality and partial-wave unitarity, we compute the maximal allowed threshold amplitude with a dispersive primal S-matrix bootstrap, assuming only a weakly coupled EFT below a scale $\Lambda$ and allowing arbitrary UV particle content above $\Lambda$. For the benchmark self-interaction cross section $\sigma_{\rm self}=10^{-24}(M/\mathrm{GeV})\mathrm{cm}^2$, the mass of a generic weakly coupled scalar satisfies $M\lesssim 0.3\,\mathrm{GeV}$ in the controlled EFT regime. If dark matter is a derivative-dominated pseudo-Nambu-Goldstone boson, the mass bound is lowered to the MeV scale or below, depending on the hierarchy $M/\Lambda$.