We construct an encoding of finite strings over a fixed finite alphabet as natural numbers, based on a block partition of the Fibonacci sequence. Each position in the string selects one Fibonacci number from a dedicated block, with unused indices between blocks guaranteeing non-adjacency. The encoded number is the sum of the selected Fibonacci numbers, and Zeckendorf's theorem guarantees that this sum uniquely determines the selection. The encoding is injective, the string length is recoverable from the code, and the worst-case digit count of the encoded number grows as $Θ(m)$ for strings of length $m$, matching the information-theoretic lower bound up to a constant factor. We also prove that the natural right-nested use of Rosko's (2025) binary carryless pairing for sequence encoding has worst-case $Θ(2^m)$ digit growth, an exponential blowup that the block-partition construction avoids entirely.

The ratio between the gravitational waves (GW) and electromagnetic waves (EMW) luminosity distance ratio is a key observable that allows to test the nature of gravity, using gravitational waves emitted from compact binary coalescences. We develop a new non parametric method for constraining the GW-EMW distance ratio, in order to perform model independent analysis of observational data, not based on any specific theoretical of phenomenological assumption. We apply the method to the analysis of binary black hole mergers data from the GWTC-3 catalogue, performing a joint analysis of cosmological and population parameters. The results are consistent with general relativity and with previous analyses based on parametric methods.

Deep Learning (DL) has become a key technology that assists radio frequency (RF) signal classification applications, such as modulation classification. However, the DL models are vulnerable to adversarial machine learning threats, such as data manipulation attacks. We study a physical backdoor (Trojan) attack that targets a DL-based modulation classifier. In contrast to digital backdoor attacks, where digital triggers are injected into the training dataset, we use power amplifier (PA) non-linear distortions to create physical triggers before the dataset is formed. During training, the adversary manipulates amplitudes of RF signals and changes their labels to a target modulation scheme, training a backdoored model. At inference, the adversary aims to keep the backdoor attack inactive such that the backdoored model maintains high accuracy on test signals. However, if they apply the same manipulation used during training on these test signals, the backdoor is activated, and the model misclassifies these signals. We demonstrate that our proposed attack achieves high attack success rates with few manipulated RD signals for different noise levels. Furthermore, we test the resilience of the proposed attack to multiple defense techniques, and the results show that these techniques fail to mitigate the attack.

The Kelly or proportional allocation mechanism is a simple and efficient auction-based scheme that distributes an infinitely divisible resource proportionally to the agents bids. When agents are aware of the allocation rule, their interactions form a game extensively studied in the literature. This paper examines the less explored repeated Kelly game, focusing mainly on utilities that are logarithmic in the allocated resource fraction. We first derive this logarithmic form from fairness-throughput trade-offs in wireless network slicing, and then prove that the induced stage game admits a unique Nash equilibrium NE. For the repeated play, we prove convergence to this NE under three behavioral models: (i) all agents use Online Gradient Descent (OGD), (ii) all agents use Dual Averaging with a quadratic regularizer (DAQ) (a variant of the Follow-the-Regularized leader algorithm), and (iii) all agents play myopic best responses (BR). Our convergence results hold even when agents use personalized learning rates in OGD and DAQ (e.g., tuned to optimize individual regret bounds), and they extend to a broader class of utilities that meet a certain sufficient condition. Finally, we complement our theoretical results with extensive simulations of the repeated Kelly game under several behavioral models, comparing them in terms of convergence speed to the NE, and per-agent time-average utility. The results suggest that BR achieves the fastest convergence and the highest time-average utility, and that convergence to the stage-game NE may fail under heterogeneous update rules.

YouTube has today become the primary news source for many users, which raises concerns about the role its recommendation algorithm can play in the spread of misinformation and political polarization. Prior work in this area has mainly analyzed how recommendations evolve based on users' watch history within the platform. Nevertheless, recommendations can also depend on off-platform browsing activity that Google collects via trackers on news websites, a factor that has not been considered so far. To fill this gap, we propose a sock-puppet-based experimental framework that automatically interacts with news media articles and then collects YouTube recommendations to measure how cross-site tracking affects the political and misinformation content users see. Moreover, by running our audits in both tracking-permissive and tracking-restrictive browser environments, we assess whether common privacy-focused browsers can protect users from tracking-driven political and misinformation bubbles on YouTube.

Optical tweezers confine a particle in an intensity-defined potential well by engaging its local multipoles. In this picture, eliminating far-field scattering from the particle should cancel the optical force, as the multipole moments underpinning the conventional optomechanical response vanish. We show that certain resonant states, such as, e.g., the hybrid anapole state, enable qualitatively different optical manipulation, nonlocal by nature, where the optical force exhibits nontrivial spatial variations absent in conventional tweezing, establishing a new framework for manipulating resonant nanoparticles.

This paper examines the latent functional block structure of Japan's production network using interregional input-output data. To isolate non-trivial production linkages, we first estimate a structural gravity model to account for spatial frictions and economic scale, and then apply a weighted stochastic blockmodel (SBM) to the resulting residual network. Because these residual linkages often connect distant regions, the SBM is well suited to grouping region-industry pairs based on their shared macroeconomic roles. The results reveal that even after explicitly filtering out the mechanical effects of geographic proximity, the network is organized into functional blocks that maintain a high degree of regional coherence. Beyond this baseline spatial clustering, we find evidence of cross-regional integration, a structural bifurcation between manufacturing and urban services in metropolitan areas, and broadly spanning primary sectors. These findings provide a network-based perspective on regional coordination, offering guidance for how structurally defined production blocks-rather than simple geographic proximity-can inform wide-area policy design.

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) generalizes the conventional diagonal RIS (D-RIS) by introducing tunable inter-element connections, offering enhanced wave manipulation capabilities. However, realizing the advantages of BD-RIS requires accurate channel state information (CSI), whose acquisition becomes significantly more challenging due to the increased number of channel coefficients, leading to prohibitively large pilot training overhead in BD-RIS-aided multi-user multiple-input multiple-output (MU-MIMO) systems. Existing studies reduce pilot overhead by exploiting the channel correlations induced by the Kronecker-product or multi-linear structure of BD-RIS-aided channels, which neglect the spatial correlation among antennas and the statistical correlation across RIS-user channels. In this paper, we propose a learning-based channel estimation framework, namely the joint training scattering matrix learning and channel estimation framework (JTSMLCEF), which jointly optimizes the BD-RIS training scattering matrix and estimates the cascaded channels in an end-to-end manner to achieve accurate channel estimation and reduce the pilot overhead. The proposed JTSMLCEF follows a two-phase channel estimation protocol to enable adaptive training scattering matrix optimization with a training scattering matrix optimizer (TSMO) and cascaded channel estimation with a dual-attention channel estimator (DACE). Specifically, the DACE is designed with intra-user and inter-user attention modules to capture the multi-dimensional correlations in multi-user cascaded channels. Simulation results demonstrate the superiority of JTSMLCEF. Compared with the current state-of-the-art method, it reduces the pilot overhead by $80\%$ while further reducing the normalized mean squared error (NMSE) by $82.6\%$ and $92.5\%$ in indoor and urban micro-cell (UMi) scenarios, respectively.

Radiative association processes leading to the formation of AgH in cold astrophysical environments are investigated for the first time using full quantum scattering theory. High accuracy potential energy curves and transition dipole moments for the low-lying electronic states (X$^1Σ^+$, A$^1Σ^+$, $1^1Π$, $3^1Σ^+$, $2^1Π$) are computed employing the internally contracted multireference configuration interaction method with Davidson correction. Vibrationally and rotationally resolved radiative association cross sections are calculated for transitions from these initial states to the ground X$^1Σ^+$ state. Prominent shape resonances arising from quasi-bound rovibrational levels behind centrifugal barriers are identified, with the $2^1Π\to$ X$^1Σ^+$ channel exhibiting the strongest contribution at low collision energies. Stimulated radiative association under blackbody radiation fields (up to $T = 20\,000$ K) produces modest enhancements, predominantly in the ground-state channel. Thermal rate coefficients computed over 10$^{-1}$--$10^4$~K reveal a general decreasing trend with temperature for all channels. The results provide essential kinetic data for astrochemical models of transition-metal hydride formation in low-temperature interstellar and circumstellar environments.

We present a determination of the charm- and bottom-quark masses using the heavy-quark step-scaling strategy. Renormalization is performed in small volumes where relativistic bottom quarks can be simulated directly. A sequence of finite-volume simulations connects this calculation to large-volume CLS ensembles, where simulations at physical light and strange quark masses provide reliable control over low-energy hadronic physics. In all but the smallest volume, the B-scale is reached by interpolating between relativistic heavy-quark data and the static limit. The resulting quark masses are obtained with good precision, with subdominant systematic uncertainties that differ from, and thus complement, those of standard large-volume determinations.

We investigate the rate functions that emerge in our previous works towards large deviation principle for the matrix liberation process driven by the unitary Brownian motion as well as the unitary Brownian motion itself. Our approach is grounded in the viewpoint of the martingale problem. Specifically, we formulate and solve a "free martingale problem" within this framework, which provides a new perspective on the underlying stochastic structure.

The seminal work of Kurzweil (1955) provides for any fixed badly approximable $α$ and monotonically decreasing $ψ$ a Khintchine-type statement on the set of the inhomogeneous real parameters $γ$ for which $\lVert n α+ γ\rVert \leq ψ(n)$ has infinitely many integer solutions, and further shows that the assumption of $α$ being badly approximable is necessary. In this article, we generalize Kurzweil's statement to restricting $n \in \mathcal{A}$, where $\mathcal{A} \subseteq \mathbb{N}$ is a set with some multiplicative structure. We show that for badly approximable $α$, the result of Kurzweil extends to a general class of sets $\mathcal{A}$, which allows us to establish the Kurzweil-type result in particular along the primes and along the sums of two squares. Furthermore, we construct non-trivial sets $\mathcal{A}$ where the assumption of $α$ being badly approximable is necessary. In particular, this criterion applies to $\mathcal{A}$ being the set of square-free numbers, providing a novel characterization of the badly approximable numbers. These statements in particular allow for improving the best known bounds for $\lVert n α+ γ\rVert \leq ψ(n)$ for infinitely many $n \in \mathcal{A}$ for fixed badly approximable $α$ and for various sets $\mathcal{A}$ of number-theoretic interest when accepting an exceptional set for $γ$ of Lebesgue measure $0$.

Passkeys have recently emerged as a passwordless authentication mechanism, yet their usability in captive portals remains unexplored. This paper presents an empirical, comparative usability study of passkeys and passwords in a Wi-Fi hotspot using a captive portal. We conducted a controlled laboratory experiment with 50 participants following a split-plot design across Android and Windows platforms, using a router implementing the FIDO2CAP protocol. Our results show a tendency for passkeys to be perceived as more usable than passwords during login, although differences are not statistically significant. Independent of the authentication method, captive portal limitations negatively affected user experience and increased error rates. We further found that passkeys are generally easy to configure on both platforms, but platform-specific issues introduce notable usability challenges. Based on quantitative and qualitative findings, we derive design recommendations to improve captive portal authentication, including the introduction of usernameless authentication flows, improved captive portal detection mechanisms, and user interface design changes.

In scenarios where synchronous generators (SGs) and grid-following renewable energy sources (GFLR) are co-located, existing research, which mainly focuses on the first-swing stability of SGs, often overlooks ongoing dynamic interactions between GFLRs and SGs throughout the entire rotor swing period. To address this gap, this study first reveals that the angle oscillations of SG can cause periodic grid voltage fluctuations, potentially triggering low-voltage ride-through (LVRT) control switching of GFLR repeatedly. Then, the periodic energy changes of SGs under "circular" and "rectangular" LVRT limits are analyzed. The results indicate that circular limits are detrimental to SG's first-swing stability, while rectangular limits and their slow recovery strategies can lead to SG's multi-swing instability. Conservative stability criteria are also proposed for these phenomena. Furthermore, an additional controller based on feedback linearization is introduced to enhance the entire period transient stability of SG by adjusting the post-fault GFLR output current. Finally, the efficacy of the analysis is validated through electromagnetic transient simulations and controller hardware-in-the-loop (CHIL) tests.

This paper examines how trade policy uncertainty influences the correlation between U.S. stock indices and short-term government bonds. The objective is to assess whether policy-related shocks, especially those linked to trade tensions, alter the traditional stock-T bill relationship and its implications for investors. We extend the Dynamic Conditional Correlation (DCC) framework by incorporating exogenous variables to account for external shocks. Three specifications are analyzed: one using the Trade Policy Uncertainty (TPU) index, one including a dummy variable reflecting presidential-cycle effects, and one combining both through an interaction term. The analysis is based on daily data for major U.S. stock indices and the 3-month Treasury bill. Results indicate that trade policy uncertainty exerts a significant effect on stock-T bill correlations. Moreover, its influence becomes stronger under specific political conditions, suggesting that political agendas can amplify the impact of trade-related shocks on financial markets. Crucially, augmenting the DCC framework with trade-policy-related variables improves also the economic relevance of correlation forecasts. Therefore, this study contributes to the literature by explicitly integrating policy-related uncertainty into correlation modeling through an augmented DCC framework. The findings provide new insights for portfolio allocation and risk management in environments characterized by heightened trade tensions.

This paper investigates the dynamics of spin-2 Bose-Einstein condensates (BECs) with rotation and spin-orbit coupling (SOC). In order to better simulate the dynamics, we present an efficient high-order compact splitting Fourier spectral method. This method splits the Hamiltonian into a linear part, which consists of the Laplace, rotation and SOC terms, and a nonlinear part that includes all the remaining terms. The wave function is well approximated by the Fourier spectral method and is numerically accessed with discrete Fast Fourier transform (FFT). For linear subproblem, the handling of rotation term and SOC term poses a major challenge. Using a function mapping based on rotation, we can integrate the linear subproblem exactly and explicitly. This mapping we propose not only helps eliminate the rotation term, but also prevents the SOC term from evolving into a time-dependent form. The nonlinear subproblem is integrated analytically in physical space. Such "compact" splitting involves only two operators and facilitates the design of high-order splitting schemes. Our method is spectrally accurate in space and high order in time. It is efficient, explicit, unconditionally stable and simple to implement. In addition, we derive some dynamical properties and carry out a systematic study, including accuracy and efficiency tests, dynamical property verification, the SOC effects and dynamics of vortex lattice.

Oxyfluorides containing up to 80 wt% recycled glass from end-of-life solar panels have been investigated. Reduced processing temperature and high transparency have shown that the material has potential for optical applications. In this work, cerium-doped samples were investigated. Spectroscopic study reveals the presence of Ce$^{3+}$, and luminescence from these ions and oxygen-deficient centers was detected. Raman demonstrated that cerium affects the glass network by promoting polymerization. In turn, thermal analysis indicated some changes in the crystallization events between 500-800 $^o$ C, which were confirmed by in situ X-ray powder diffraction measurements. Crystallization of fluorite, xonotlite, and combeite was confirmed, while other phases give minor contributions to the XRD patterns. Cerium addition reduced the formation of xonotlite, mainly above 700 $^o$ C. The potential applications of the material and the further studies required are discussed.

Classical estimation outputs a single point estimate of an unknown $d$-dimensional vector from an observation. In this paper, we study \emph{$k$-list estimation}, in which a single observation is used to produce a list of $k$ candidate estimates and performance is measured by the expected squared distance from the true vector to the closest candidate. We compare this centralized setting with a symmetric decentralized MMSE benchmark in which $k$ agents observe conditionally i.i.d.\ measurements and each agent outputs its own MMSE estimate. On the centralized side, we show that optimal $k$-list estimation is equivalent to fixed-rate $k$-point vector quantization of the posterior distribution and, under standard regularity conditions, admits an exact high-rate asymptotic expansion with explicit constants and decay rate $k^{-2/d}$. On the decentralized side, we derive lower bounds in terms of the small-ball behavior of the single-agent MMSE error; in particular, when the conditional error density is bounded near the origin, the benchmark distortion cannot decay faster than order $k^{-2/d}$. We further show that if the error density vanishes at the origin, then the decentralized benchmark is provably unable to match the centralized $k^{-2/d}$ exponent, whereas the centralized estimator retains that scaling. Gaussian specializations yield explicit formulas and numerical experiments corroborate the predicted asymptotic behavior. Overall, the results show that, in the scaling with $k$, one observation combined with $k$ carefully chosen candidates can be asymptotically as effective as -- and in some regimes strictly better than -- this MMSE-based decentralized benchmark with $k$ independent observations.

This work develops and analyzes a variational-monolithic unfitted finite element formulation of a linear fluid-structure interaction problem in Eulerian coordinates with a fixed interface. The overall discretization is based on a backward Euler scheme in time and finite elements in space. For the spatial discretization we employ a cut finite element method on a mesh consisting of quadrilateral elements. We use a first-order in time formulation of the elasticity equations, inf-sup stable finite elements in the fluid part and Nitsche's method to incorporate the coupling conditions. Ghost penalty terms guarantee the robustness of the approach independently of the way the interface cuts the finite element mesh. The main objective is to establish stability and a priori error estimates. We prove optimal-order error estimates in space and time and substantiate them with numerical tests.

We compute the ground-state binding energy of muonic $^9$Be in two ways: first, the fully perturbative treatment of the nuclear-size effect often employed in light systems, and second, an approach that accounts for the finite-nuclear-size to all orders (and is inspired by calculations otherwise employed for heavy muonic ions). The results are compared term by term and show that both approaches agree to better than one part-per-million of the total energy. The objective of this work is twofold. The first is practical: to provide a parametrization that allows the extraction of the $^9$Be charge radius from recent and forthcoming experiments with high precision. The second is more conceptual: to act as a bridge between the community working on calculations for light systems and those focusing on heavy systems, demonstrating that the fully relativistic approach otherwise chosen for heavy systems can be enhanced to cover theoretical predictions for all charge numbers.

ProtoPol is a medium-resolution echelle spectro-polarimeter developed for Physical Research Laboratory (PRL) 1.2m and 2.5m telescopes, Mt. Abu, India. Though initially conceived to evaluate the development methodology of the echelle spectro-polarimeter, it was subsequently elevated to the level of a full-fledged back-end instrument for PRL telescopes. ProtoPol is developed on the traditional concept of using a half-wave plate with Wollaston prism to achieve the separation of two mutually orthogonal polarized beams. These separated beams are modulated and directed into an echelle spectrometer which is employs an echelle grating and two plane reflection gratings as the cross-dispersers. Therefore, the cross-dispersed spectra for two orthogonal polarized beams are recorded in multiple orders on a CCD detector. ProtoPol is designed to operate in the visible and near IR spectral range, 4000 - 9600 angstrom, with a spectral resolution ($δ$$λ$) around 0.4-0.75 angstrom. The uniqueness of ProtoPol lies in its design which has entirely been developed with commercially available off-the-shelf optical and opto-mechanical components. This feature makes ProtoPol a noteworthy development as it offers a cost-effective way to develop spectro-polarimeters with such resolutions for small-aperture (2-3m) telescopes around the world, in a much shorter development period. ProtoPol has been successfully developed and commissioned on PRL 1.2m and 2.5m telescopes since December 2023, and a variety of observations have been carried out for instrument characterization, performance verification, and scientific purposes. This is the first of the two-part research articles series, wherein we present the design and development methodology of ProtoPol, along with its laboratory characterization and performance.

In this paper we study the stability properties of the equilibrium point for an age-structured chemostat model with renewal boundary condition and coupled substrate dynamics under constant dilution rate. This is a complex infinite-dimensional feedback system. It has two feedback loops, both nonlinear. A positive static loop due to reproduction at the age-zero boundary of the PDE, counteracted and dominated by a negative dynamic loop with the substrate dynamics. The derivation of explicit sufficient conditions that guarantee global stability estimates is carried out by using an appropriate Lyapunov functional. The constructed Lyapunov functional guarantees global exponential decay estimates and uniform global asymptotic stability with respect to a measure related to the Lyapunov functional. From a biological perspective, stability arises because reproduction is constrained by substrate availability, while dilution, mortality, and substrate depletion suppress transient increases in biomass before age-structure effects can amplify them. The obtained results are applied to a chemostat model from the literature, where the derived stability condition is compared with existing results that are based on (necessarily local) linearization methods.

While conventional power system protection isolates faulty components only after a fault has occurred, fault prediction approaches try to detect faults before they can cause significant damage. Although initial studies have demonstrated successful proofs of concept, development is hindered by scarce field data and ineffective feature selection. To address these limitations, this paper proposes a surrogate task that uses simulation data for feature selection. This task exhibits a strong correlation (r = 0.92) with real-world fault prediction performance. We generate a large dataset containing 20000 simulations with 34 event classes and diverse grid configurations. From 1556 candidate features, we identify 374 optimal features. A case study on three substations demonstrates the effectiveness of the selected features, achieving an F1-score of 0.80 and outperforming baseline approaches that use frequency-domain and wavelet-based features.

We introduce the crisp topology for schemes as a refinement of the fpqc topology. This Grothendieck topology uses the new notion of crisp morphisms, which generalise universal injectivity from ring homomorphisms to arbitrary morphisms of schemes. We study basic properties and demonstrate that this topology is well-behaved.

We investigate the pressure-induced evolution of crystal structure and magnetism in the kagome ferromagnet Fe$_3$Sn$_2$ by combining X-ray diffraction, X-ray Emission Spectroscopy, X-ray Magnetic Circular Dichroism, and spin-polarized density functional theory calculations. X-ray diffraction reveals a structural phase transition above $\sim$20~GPa, which coincides with a pronounced reduction of the local Fe magnetic moment evidenced by X-ray emission spectroscopy, indicating a high-spin to low-spin transition. While XES probes the amplitude of the local moment, XMCD provides direct information on the orientation of the ordered magnetic moments and uncovers a rich pressure--temperature magnetic phase diagram. At room temperature, a collinear ferromagnetic phase with moments aligned along the $c$ axis persists up to the structural transition. At low temperature, a tilted magnetic configuration remains stable to significantly higher pressures, while at intermediate temperatures pressure stabilizes the low-temperature magnetic phase at the expense of the high-temperature one. Spin-polarized first-principles calculations show that, although isotropic ferromagnetic exchange interactions remain robust under compression, pressure enhances spin--orbit--driven magnetic anisotropy and Dzyaloshinskii--Moriya interactions, favoring non-collinear magnetic configurations. Our results demonstrate that pressure reshapes the magnetic energy landscape of Fe$_3$Sn$_2$ by coupling lattice, spin state, and relativistic magnetic interactions, establishing hydrostatic pressure as an effective control parameter to engineer magnetic anisotropy and potentially topological phases in kagome materials.

Quantum state transfer between distant nodes is essential for distributed quantum information processing. Existing protocols are typically optimized for specific coupling regimes, such as adiabatic dark-state transfer in the single-mode limit and pitch-and-catch schemes in the multimode regime, leaving the crossover between them without a simple and unified control strategy. Here we identify a minimal two-parameter control framework that enables high-fidelity quantum state transfer across this single-mode-to-multimode crossover in a multimode quantum channel. Using a pulse-shaped pitch-and-catch protocol controlled only by the pulse ramp rate and the emission-absorption delay, we achieve transfer fidelities exceeding 99.9%, extending pitch-and-catch protocols toward the single-mode limit without requiring dark-state protection or complex pulse design. We further demonstrate robustness against dissipation, disorder, detuning, and imperfect initialization under experimentally realistic conditions. These results provide a simple and broadly applicable framework for state transfer in multimode quantum channels, with relevance to circuit-QED and hybrid quantum-acoustic systems.

Publicly traded companies must disclose financial information under regulations of the Securities and Exchange Commission (SEC) and the Generally Accepted Accounting Principles (GAAP). The eXtensible Business Reporting Language (XBRL), as an XML-based financial language, enables standardized and machine-readable reporting, but accurate tag selection from large taxonomies remains challenging. Existing fine-tuning-based methods struggle to distinguish highly similar XBRL tags, limiting performance in financial data matching. To address these issues, we introduce XBRLTagRec, an end-to-end framework for automated financial numeral tagging. The framework generates semantic tag documents with a fine-tuned FLAN-T5-Large model, retrieves relevant candidates via semantic similarity, and applies zero-shot re-ranking with ChatGPT-3.5 to select the optimal tag. Experiments on the FNXL dataset show that XBRLTagRec outperforms the state-of-the-art FLAN-FinXC framework, achieving 2.64%-4.47% improvements in Hits@1 and Macro metrics. These results demonstrate its effectiveness in large-scale and semantically complex tag matching scenarios.

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will produce unprecedentedly deep and wide photometric catalogs, enabling transformative studies of faint stellar systems such as the research of ultra-faint dwarf galaxies (UFDs). A critical challenge for these studies is reliable star-galaxy separation at faint magnitudes, where compact background galaxies increasingly contaminate stellar samples. This work aims to assess the performance of supervised machine-learning techniques for star-galaxy separation in LSST-like data, quantify the relative importance of morphological and photometric information, and identify the most effective combinations of input features for minimizing galaxy contamination while preserving stellar completeness in the faint regime relevant for UFD searches. We apply a Random Forest classifier to observations of the Extended Chandra Deep Field South from LSST Data Preview 1 (DP1), the deepest field observed within the DP1. We construct a curated sample of bona fide stars and galaxies using spectroscopic data, Gaia DR3, and multi-band photometric catalogs. We train and validate the classifier using several configurations of LSST-based input features, including multi-band colors, the LSST morphological parameter refExtendedness, and photometric uncertainties. We find that LSST multi-band photometry alone delivers a good star-galaxy separation, significantly outperforming morphology-based classification at faint magnitudes. Colors involving the u-band are essential to provide a robust star galaxy separation. Furthermore, explicitly including photometric uncertainties as input features yields the best overall performance. Across all configurations that include all the six LSST filters, galaxy contamination remains negligible almost the whole magnitude range probed in this work (i.e. r < 27.5 mag). [abridged]

The highest performance atomic clocks are based on interrogation of ultra-narrow optical transitions. There is now significant interest in developing these systems as a source of GNSS-independent time in deployed, dynamic environments. We report on the development and field trial of a portable optical atomic clock interrogating the 10mHz wide $^1$S$_0\rightarrow ^3$P$_0$ transition in ytterbium-171. To enable measurement of this ultra-narrow transition in a deployed setting we combine an atom-vapor based pre-stabilization reference with all-digital control and continuous clock spectroscopy of a transversely-cooled thermal atomic beam. Characterization of the short-term frequency stability within the lab demonstrates a modified Allan deviation of $2\times 10^{-14}/\sqrtτ$ for integration times up to 100s, reaching a best performance of $1.9\times 10^{-15}$ at 200s. The clock demonstrated the same performance after transport and install aboard a ship for field trial, and operated uninterrupted for multiple days whilst at sea. These results show a pathway towards truly portable optical frequency references based on the interrogation of ultra-narrow transitions.

We introduce a multilayer superconducting microwave resonator with sub-Ohm impedance optimized for high coupling strength to single electron spins. The design minimizes the magnetic far-field and therefore achieves a Purcell factor $F_P > 10^{15}$. We show several ways to fabricate this type of resonator and present resonators with an intrinsic $Q$-factor exceeding $2 \cdot 10^4$ at the single-photon level. We further characterize these resonators in magnetic fields up to $500 \, \text{mT}$. Finally, we evaluate the impact of the achievable Purcell factor on single-spin detection through photon counting and dispersive readout.