We propose a holographic complexity origin for the third law of black-hole mechanics and weak cosmic censorship. In both complexity equals action and complexity equals volume prescriptions, the relative complexity between subextremal and extremal AdS black holes diverges logarithmically. For overcharged RN-AdS, explicit calculations in both prescriptions show that the near-singularity action terms are power-law divergent or finite, while the maximal-volume contribution is finite. Thus, the extremal-to-naked relative complexity also diverges, obstructing finite-time transitions.

Low-resolution spectrographs used to have difficulties in determining redshifts of galaxies at $z\approx1$ and $z\approx3$. Spectral emission and absorption lines of magnesium and iron redshifted to $z\approx1$ fall close to hydrogen, silicon, and oxygen lines at $z\approx3$. Here, we demonstrate that even with modern, integrated field unit spectrographs, this task remains challenging. Hamilton's Object, a blue star-forming galaxy, gravitationally lensed into three multiple images by the galaxy cluster SDSS J223010.47-081017.8, is such a case. Using the Blue Keck Cosmic Web Imager (KCWI) its redshift was determined as $z=0.82$, while its MOIRCS spectrum hinted at $z=3.201$. To resolve the ambiguity, we completely reanalyse the Blue KCWI spectra of all three multiple images including the star-forming region in the outskirts. We employ a new data reduction pipeline, PypeIt, signal enhancement, and line fitting by Python-routines. The reevaluation confirms the previous result based on six absorption features, $z=0.820 \pm 0.001$, and four emission features, $z=0.821 \pm 0.002$. The alternative $z=3.199\pm 0.003$, based on six absorption and two emission lines, is a worse fit, also compared to other spectra. Moreover, we find the MOIRCS spectrum inconclusive: observations cover two of three multiple images, with the slit for image C only covering its central bulge; furthermore the pixel-to-wavelength calibration requires a nightsky emission-line calibration due to a missing calibration arc lamp. New MOIRCS observations are needed to verify that Hamilton's Object has the smallest separation in angular diameter distance between lensing cluster and source galaxy among the known cluster-scale strong lenses.

Recent high-resolution spectroscopy with the James Webb Space Telescope (JWST) has confirmed the presence of a strong He II $\lambda1640$ emitting clump in the vicinity of GN-z11, with only upper limits on its metallicity. To explain the peculiar properties of this source, now termed Hebe, a cluster of metal-free, Population III (Pop III) stars has been invoked. A less likely source for the hard UV ionizing radiation could be an accreting supermassive black hole (SMBH) embedded inside Hebe. We here provide further constraints on what could power the observed emission lines in Hebe. Comparing with cosmological simulations of Pop III star cluster formation, we assess the maximum Pop III stellar mass that could plausibly form at the location of Hebe, finding stellar masses of a few $10^5\,M_{\odot}$, consistent with those inferred from the observations. Modeling the continuum spectral energy distribution arising from an accreting SMBH, we derive He II and H I ionizing rates and the resulting recombination line luminosities, providing a less natural fit for the combined observations. We thus confirm the interpretation of Hebe as a remarkable, primordial object, with the most plausible power source provided by a massive cluster of Pop III stars, at the limit of what is allowed within the standard model of first star formation.

We present sagui, a modular framework for the analysis of multi-band imaging data in spatially resolved galaxies, with synergies to integral-field spectroscopy (IFS). Building on the spectro-spatial paradigm introduced by capivara for IFS data, sagui extends this approach to imaging datasets, enabling a coherent, pixel-level treatment of spatial and spectral information across multiple bands. The method follows a two-stage strategy: a starlet-based decomposition is first used to identify and mask spatial structures across multiple scales while suppressing noise, and a spectral-similarity analysis then partitions the image into coherent pixel groups that preserve spectral consistency. In addition to compact and high-contrast structures, the framework incorporates a dedicated statistical treatment, based on a copula transform, to identify and recover faint, diffuse low-surface-brightness components. We demonstrate the method across a diverse range of galaxy morphologies, highlighting its ability to characterize complex spatial structures, including clumps, bars, interacting systems, and low-surface-brightness features. As a case study, we apply it to eleven morphologically diverse galaxies from the James Webb Space Telescope Advanced Deep Extragalactic Survey in the GOODS--South field. sagui is released under an MIT license and is available at https://rafaelsdesouza.github.io/sagui/.

We introduce Broadcast by Balanced Saturation (BBS), a general class of tree-based pipelined broadcast algorithms that optimizes communication efficiency across diverse network topologies, with a particular emphasis on large message sizes. By addressing spanning tree construction and communication task scheduling, two fundamental theoretical challenges in broadcasting, BBS offers a unified and flexible framework that operates effectively under varied network constraints. The algorithm maximizes aggregated throughput while simultaneously addressing topology constraints, synchronization overhead, bandwidth limitations and contention. Using SimGrid under standard assumptions, including full-duplex and one-port communication, various algorithms were evaluated on Mesh, Butterfly, Dragonfly, and Fat-Tree topologies. Results demonstrate that BBS consistently outperforms both general-purpose and topology-aware broadcast algorithms across a wide range of topologies and message sizes, establishing it as a robust and high-performance solution for large-scale systems.

Fast Radio Bursts (FRBs) are among the most intriguing phenomena observed in radio astronomy. So far, about 130 FRB signals have been confirmed and characterized by different surveys, and the CHIME telescope has recently reported a new catalog of 4539 bursts. Therefore, these numbers are expected to increase in the coming years. The detection, or lack thereof, of lensed FRB events can be used to probe Primordial Black Holes (PBHs) as a fraction of dark matter. We investigate the potential of three upcoming radio telescopes, LOFAR2.0, FAST Core Array, and BINGO, to test the PBH scenario. We forecast that LOFAR2.0 will constrain $f_{\mathrm{PBH}} < 0.16$ for PBH masses $M_{\rm PBH}>1\,{M_{\odot}}$, while FAST Core Array and BINGO will restrict $f_{\mathrm{PBH}} < 0.39$ for $M_{\rm PBH}>10\,{M_{\odot}}$ and $M_{\rm PBH}>10^{-1}\,{M_{\odot}}$, respectively. Despite the existence of stricter constraints, FRB lensing offers an independent and complementary probe of PBHs in the Universe, which will improve in the future.

We study two problems concerning the Stiefel manifolds $V_{n,2}$ and their products with spheres. First, we address a rigidity problem: we determine, for most values of~$n$, all self-maps of $V_{n,2}$ that are homotopic to an almost diffeomorphism. Second, we classify smooth closed manifolds tangentially homotopy equivalent to $V_{n,2} \times S^k$ up to almost diffeomorphism, for $k = 3, 5$ or $7 \leq k, k \neq 2^i - 2 \ \text{and} \ Dim(V_{n,2} \times S^k) \neq 2^i - 2$. Our method is to find explicit inverses in the structure set via normal invariants of specific tangential homotopy equivalences. In favourable cases -- notably $V_{12,2} \times S^3$, $V_{16,2} \times S^3$, $V_{10,2} \times S^5$ -- the classification is complete: every such manifold is almost diffeomorphic to $V_{n,2} \mathbin{\#} Σ\times S^k$ for some exotic sphere $Σ$. In the general case, we identify inverses for a large subgroup of $\operatorname{Im}(η)$ and provide a reasonable direction for the remainder.

Science advances not only by accumulating discovered patterns but by changing how new problems and solutions are expressed. While structural indicators track scholarly attention, they offer only an indirect proxy for the reorganization of meaning. We propose a semantic geometry based on the R-P-C (references, focal publication, and citing publications) framework to quantify how a publication positions itself relative to its knowledge base and diffusion. This geometry identifies three publication types: consolidating, exploratory and balanced. Our results show that the semantic similarity and distance between a publication's knowledge base and diffusion serve as a semantic foundation for disruption, with novelty (atypical reference combinations) acting as an antecedent disturbance that triggers a semantic rupture. This is related to team size, where small teams preserve a higher potential for exploratory departures while large collaborations systematically align with paradigmatic consolidation. Crucially, this geometry explains why citation trajectories differ; consolidating research earns rapid recognition by lowering comprehension costs, while exploratory work faces high paradigm conversion costs that result in slower, more selective diffusion. Collectively, this R-P-C framework provides a robust instrument for monitoring the dynamics of scientific paradigms.

We present a hardware demonstration and resource analysis of split-evolution quantum phase estimation (SE-QPE) on a Quantinuum System Model H2 quantum computer. SE-QPE is a modification to canonical QPE for particle-conserving Hamiltonians in which controlled time evolution is replaced by CSWAP-based interference between a target register and a reference register. For factorizations of time evolution with a shared eigenbasis, SE-QPE preserves the phase-register outcome distribution of canonical QPE and, unlike with compute--uncompute substitutions, it remains compatible with non-exact eigenstates. The substitution removes controlled-simulation overhead and enables parallel evolution on two registers, reducing the depth of each phase-kickback block. Resource analysis for Trotterized double-factorized chemistry Hamiltonians shows that the substitution becomes increasingly favorable at higher phase powers and combining QPE and SE-QPE implementations can be a useful option. Over a range of FeMoco active spaces, SE-QPE reduces time evolution resources, with asymptotic reductions of about 33% in CX count, 25% in $T$ count, and an asymptotic depth ratio of $3/N$ for CX layers. On Quantinuum H2-2, a four-qubit model ethylene demonstration with explicit inverse QFT and repeated phase-kickback steps up to 8 phase bits yields distinct energies and shows the auxiliary registers provide useful error detection filters.

We derive the asymptotic formula $α(k,q)=λ_{k-1}q^k+o(q^k)$, where $α(k,q)$ is the independence number of the de Bruijn graph $B(k,q)$, and $λ_{k-1}$ is a constant arising from a variational problem on the unit $(k-1)$-dimensional cube. When $k=4$, we show the bounds $91/240\le λ_3\le 11/28$. For odd prime $k$, we analyse the binary case $q=2$ via a phase reduction on rotation orbits. For $k=11,13,17$ this yields compact orbit-marker certificates for optimal constructions. Combined with a lifting theorem by Lichiardopol, these certificates give exact formulas for $α(11,q)$, $α(13,q)$, and $α(17,q)$ for all $q\ge2$, extending the known cases $k=3,5,7$.

We present a systematic study of gas accretion and angular momentum in the circumgalactic medium (CGM) using high-resolution FIRE cosmological simulations. Our analysis includes halos spanning the critical $\sim 10^{12}\ \mathrm{M}_{\odot}$ scale where several transitions have been identified, including inner CGM virialization, the transition from bursty to steady star formation, and the emergence of thin disks. We find that the temperature of inflowing gas is correlated with the virialization of the inner CGM. CGM inflows are almost entirely cold ($T < 10^5$ K) in pre-virialized halos, while hot inflows ($T > 10^5$ K) dominate in virialized halos. When hot inflows dominate, cooling generally occurs simultaneously with circularization at galaxy radii. The dominance of hot inflows onto massive galaxies persists even at high redshift, where cold streams may coexist. Consistent with previous studies, cold inflows have higher specific angular momentum than dark matter and hot gas. However, in bursty, low-mass galaxies, cold inflows do not circularize prior to star formation, while in steady, massive galaxies, hot inflows circularize, cool, and form stars with disk-like kinematics. We additionally find that in bursty galaxies, accreted gas typically forms stars after residing in the galaxy for less than $\sim 5$ galaxy free-fall times, while in steady galaxies, gas can persist for up to $\sim 25$ free-fall times before forming stars. This highlights a key difference between star formation in bursty galaxies fed by cold accretion and steady equilibrium disks fed by hot accretion.

In this work, we extend and generalize our solving strategy, first introduced in [1], based on a greedy optimization algorithm and the alternating direction method (ADM) for nonlinear systems computed with multiple load steps. In particular, we combine the greedy optimization algorithm with the direct data-driven solver based on ADM which is firstly introduced in [2] and combined with the Newton-Raphson method for nonlinear elasticity in [3]. We numerically illustrate via one- and two-dimensional bar and truss structures exhibiting nonlinear strain measures and different constitutive datasets that our solving strategy generally achieves a better approximation of the globally optimal solution. This, however, comes at the expense of higher computational cost which is scaled by the number of "greedy" searches. Using this solving strategy, we reproduce the first cycle of the cyclic testing for a nylon rope that was performed at industrial testing facilities for mooring lines manufacturers. We also numerically illustrate for a truss structure that our solving strategy generally improves the accuracy and robustness in cases of an unsymmetrical data distribution and noisy data.

We confront a meso-scale continuum model, archetypical for the heteroepitaxial context, with an atomistic Vicinal Cellular Automaton (VicCA), built as a homoepitaxial counterpart, to show that in (2+1)D complex surface instabilities are fundamental growth phenomena rather than context-specific artifacts. Our approach is to first construct a Ginzburg-Landau-type model, designed to extend the previously (1+1)D Tersoff-type models in (2+1)D. We complement the continuum approach with a discrete one - the VicCA, in which we use a novel version of the potential landscape for the diffusing particles - a double-well potential located at each step edge. Notably, this framework also reproduces step bunching and step meandering - which are typically treated as incompatible in the theoretical paradigm, but coexist in real material systems. Thus we establish a cross-context correspondence at the level of obtained morphologies and morphology diagrams and, additionally, a multiscale perspective on the governing parameters, bridging the gap between the mesoscale and atomistic modeling.

We provide a practical prescription for a variant of the On-Shell scheme which does not require mixing matrix counterterms at all, i.e. $δV=0$. The scheme is based on the fact that one can always choose a basis in which there are no mixing matrices (angles) and, therefore, the corresponding counterterms are superfluous. Importantly, the prescription is model- and process-independent and is formulated entirely in terms of self-energies. As an example, we compute the 1-loop hadronic $W$-boson decay widths in the Standard Model with different renormalization schemes of the quark mixing matrix found in the literature and the one found in this paper. For full consistency, the principles of this scheme are employed both for the quark mixing matrix and for the Weinberg angle.

Phylogenetic methods, traditionally used in biology to trace the evolutionary relationships among species, are emerging as a powerful framework to reconstruct evolutionary processes in galaxies from chemical information. We apply galactic phylogenetics to study the chemical evolution of stellar populations in distinct regions of a simulated disc galaxy, assessing its capability to unveil assembly histories. We used a high-resolution simulation that follows the chemical enrichment of an isolated disc galaxy, by different stellar progenitors. We track gas particles as they turn into stars and inherit their parent gas chemical composition. Target particles are selected to store the chemical history of each chemical element considered in the simulation. Two regions were analysed: an inner ring, influenced by early bar-driven inflows, and an outer ring, shaped by spiral arms. We built phylogenetic trees for stellar populations in each region and quantified their structure using the Corrected Colless index, a standard metric of tree balance used in biology. The inner ring tree reveals a compact clade of old stars enriched by rapid SNII feedback, followed by a hierarchical sequence with increasing SNIa and AGB contributions. In contrast, the outer ring exhibits more symmetric, caterpillar-like trees with smoother abundance gradients, consistent with more prolonged star formation and efficient local mixing. Chemical enrichment rates corroborate these trends, showing fast early enrichment in the inner ring and gradual, spatially extended enrichment in the outer disc. The structural indices differ significantly between the two regions and converge robustly even for modest stellar samples (NSSP = 100). Galactic phylogenetics provides a novel and complementary tool to decode the fossil record of galaxies.

A method for selecting and/or rejecting leptons from charm semileptonic decays based on the tagging of the secondary vertex using a hadron track is introduced. The method is developed for dimuon Drell-Yan measurements in LHCb using full simulations in proton-proton collisions at $\sqrt{s}=13.6$ TeV. We focus on the invariant mass range between 2.9 and 5 GeV/$c^2$ with single muon transverse momentum larger than 1 GeV/$c$. A novel strategy is detailed for background rejection, achieving an improvement of the signal over background of a factor $\sim 4$ at an efficiency of 81% while maintaining the Drell-Yan kinematic distributions largely unbiased except at the acceptance edges. Moreover, a second approach is presented for the construction of unbiased background-pure samples of single muons from charm decays, achieving a charm efficiency of 21.4% at a Drell-Yan efficiency of 1.1%.

The GraphAlg domain-specific language for graph algorithms enables user-defined algorithms in graph databases. In this work we show how GraphAlg is built on top of the formal MATLANG language for matrix manipulation. Starting from MATLANG, we describe the extensions to MATLANG and the syntactic sugar needed to derive GraphAlg. Furthermore, we prove that any GraphAlg program can be simulated in an extension of for-MATLANG that supports simultaneous induction.

Vectorlike leptons are nonchiral, colorless fermions from new physics beyond the Standard Model, appearing in many theoretical extensions. We investigate the prospect for detecting the single production of a singlet vectorlike lepton that mixes with the $τ$ lepton at the Large Hadron Collider. The corresponding final states are classified as the three- and four-lepton search channels. The machine learning algorithm XGBoost is employed to enhance signal-background discrimination. Our analysis indicates that, at $\sqrt{s} = 14~\mathrm{TeV}$ with an integrated luminosity of $3000~\mathrm{fb}^{-1}$ under the assumption of negligible systematic uncertainties, the expected $2σ$ exclusion limits in the three- and four-lepton channels can reach vectorlike lepton masses up to $500$ and $405~\mathrm{GeV}$ in the parameter region allowed by the electroweak oblique parameter constraint, respectively. These findings demonstrate that machine learning techniques can substantially improve the sensitivity of collider searches for vectorlike leptons.

As Neural Cellular Automata (NCAs) are increasingly applied outside of the toy models in Artificial Life, there is a pressing need to understand how they behave and to build appropriate routes to interpret what they have learnt. By their very nature, the benefits of training NCAs are balanced with a lack of interpretability: we can engineer emergent behaviour, but have limited ability to understand what has been learnt. In this paper, we apply a variety of techniques to pry open the NCA black box and glean some understanding of what it has learnt to do. We apply techniques from manifold learning (principal components analysis and both dense and sparse autoencoders) along with techniques from topological data analysis (persistent homology) to capture the NCA's underlying behavioural manifold, with varying success. Results show that when analysis is performed at a macroscopic level (i.e. taking the entire NCA state as a single data point), the underlying manifold is often quite simple and can be captured and analysed quite well. When analysis is performed at a microscopic level (i.e. taking the state of individual cells as a single data point), the manifold is highly complex and more complicated techniques are required in order to make sense of it.

We introduce a no-reaction-plane (no-RP) method for extracting directed (\(v_1\)) and elliptic (\(v_2\)) flows in heavy-ion collisions, which eliminates the need for event-plane reconstruction. %by scanning over fixed test angles and using simple count asymmetries. The method is validated with PHSD model simulations of Au+Au collisions at \(\sqrt{s_{NN}} = 9.2\) GeV at freeze-out (for impact parameters \(b = 4-8\) fm). We demonstrate that the two asymmetries for each harmonic contribute equally, i.e., \(\langle A_{\mathrm{ud}}^2\rangle \approx \langle A_{\mathrm{lr}}^2\rangle\) and \(\langle A_1^2\rangle \approx \langle A_2^2\rangle\), so that a single asymmetry measurement suffices for a good flow estimate. Event-by-event comparisons with direct calculations using the true reaction plane yield Pearson correlation coefficients of 0.956 for \(v_2\) and 0.834 for \(v_1\), confirming that the no-RP method captures flow fluctuations well enough.

In this paper, we propose a unified compression algorithm for distributed nonconvex opitmization with both the locally- and globally-bounded communication compressors, including 1-bit compressors, saturating quantizers, and the globally-bounded compressors with both relative and absolute compression errors, as well as additional arbitrary bounded noise. We provide a rigorous convergence analysis in nonconvex settings and establish linear convergence under the Polyak-Lojasiewicz (P-L) condition. Notably, we establish an $\mathcal{O}(1/\sqrt{T})$ convergence rate for the locally-bounded class in the distributed nonconvex setting, matching that achieved by the centralized algorithms with 1-bit compressors, where $T$ denotes the total number of iterations. Moreover, one initial uncompressed communication round further yields an order-wise improvement to $\mathcal{O}(1/T^{2/3})$. For the P-L setting and the globally-bounded class, we recover state-of-the-art convergence rates.

The discrimination of quantum states is fundamental to quantum information processing. Inverse-squeezing Kennedy (IS-Kennedy) receivers can outperform the coherent-state BPSK Helstrom benchmark at the same energy by converting transmitter-side squeezing into an effective coherent-state separation gain, without violating the Helstrom bound for the squeezed-state alphabet. This work investigates how squeezing mismatch degrades this mechanism. We show that imperfect inverse squeezing transforms the ideally nulled output into a residually squeezed state, thereby altering the photon-number statistics before detection. This residual-squeezing picture reveals a strong physical asymmetry between squeezing-magnitude and squeezing-phase mismatches. Magnitude mismatch produces an energy-independent error floor in the high-signal-energy regime, whereas phase mismatch generates a residual squeezing term that grows with signal energy. In the small-residual-squeezing regime, this leads to a polynomial growth of the leading error contribution and a rapid collapse of the SQL advantage. We also identify a parity-step effect in photon-number-resolving detection: because the nulled residual squeezed vacuum contains only even photon numbers, increasing detector resolution improves the high-energy robustness only when the effective saturation threshold crosses the next even photon number. These results identify phase locking as the dominant bottleneck for IS-Kennedy-type non-Gaussian receivers under unitary squeezing mismatch and provide design guidelines for robust squeezed-state quantum receivers.

We study spatio-temporal increments of the solutions to nonlinear parabolic SPDEs on a bounded interval with Dirichlet, Neumann, or Robin boundary conditions. We identify the exact local and uniform spatio-temporal moduli of continuity for the sample functions of the solutions. These moduli of continuity results imply the existence of random points in space-time at which spatio-temporal oscillations are exceptionally large. We also establish small-ball probability estimates and Chung-type laws of the iterated logarithm for spatio-temporal increments. Our method yields extension of some of these results to the open KPZ equation on the unit interval with inhomogeneous Neumann boundary conditions. Our key ingredients include new strong local non-determinism results for linear stochastic heat equation under various types of boundary conditions, and detailed estimates for the errors in linearization of spatio-temporal increments of the solution to the nonlinear equation.

Establishing a notion of the quantum state that applies consistently across space and time could be a crucial step toward formulating a relativistic quantum theory. We give an operational meaning to multipartite quantum states over arbitrary regions in spacetime through a causally agnostic measurement, a measurement scheme that can be consistently implemented independently of the causal relation between the regions. We prove that such measurements can always be implemented with interferometry, also known as the scattering circuit technique, wherein the conventional density operator, the recently developed quantum state over time (QSOT), and the process matrix formalisms smoothly merge. This framework allows for a systematic study of mixed states in the temporal setting, which turn out to be crucial for modeling quantum non-Markovianity. Based on this, we demonstrate that two different ensembles of quantum dynamics can be represented by the same QSOT, indicating that they cannot be distinguished through interferometry. Moreover, our formalism reveals a new type of spatiotemporal correlation between two quantum dynamics that originates from synchronized propagation in time under time-reversal symmetry. We show that quantum systems with such correlation can be utilized as a reference frame to distinguish certain dynamics indistinguishable under time-reversal symmetry.

CsSn$_x$Ge$_{1-x}$I$_3$ as lead-free perovskites are promising for next generation NIR emitting perovskite LEDs due to their tunable bandgaps and stability. However, they suffer from poor light extraction efficiency, and accurate composition-specific optical data for these materials remain scarce. This study presents a DFT-FDTD framework to optimize light extraction via compositional tuning and plasmonic enhancement. First, DFT calculations were performed to obtain composition-specific complex refractive index and extinction coefficient values for $x = 0, 0.25, 0.5, 0.75$, and $1$. Results show bandgap increased from 1.331 eV for CsSnI$_3$ to 1.927 eV for CsGeI$_3$ with increasing Ge content, while refractive index ranges from 2.2 to 2.6 across compositions. These optical constants were then used as inputs for FDTD simulations of a PeLED structure with optimized Au/SiO$_2$ core-shell nanorods for plasmonic enhancement. A 12.1-fold Purcell enhancement was achieved for CsSn$_{0.25}$Ge$_{0.75}$I$_3$, while light extraction efficiency reached 25% for CsSn$_{0.5}$Ge$_{0.5}$I$_3$. LEE enhancement of 36% was obtained for CsSnI$_3$, and spectral overlap between emitter and plasmon resonance reached 96% for Sn-rich compositions. Design guidelines indicate CsSn$_{0.5}$Ge$_{0.5}$I$_3$ offers optimal balance of extraction efficiency (25%), Purcell enhancement (5.3$\times$), spectral overlap (93%), and oxidation stability for wearable and flexible optoelectronic applications, while CsSn$_{0.25}$Ge$_{0.75}$I$_3$ is recommended for applications prioritizing spontaneous emission rate.

The present research applies Graph Neural-Networks (GNNs) for energy measurement and particle identification tasks for a proposed second detector at the future Electron Ion Collider (EIC). In particular, an iron-scintillator sampling calorimeter would provide neutral hadron ($K_L$ and neutron) energy measurements and identification, as well as separation of muons from hadrons. Using detector simulations, particle hits in the detector are represented as graphs, and a GNN is trained for either classification or prediction. Furthermore, we developed a parameterization of the scintillator optical photon simulation that yields a 20-fold speed up compared to the default simulation. We find that the GNN method outperforms classical methods at the same tasks, and we report projections for the energy and timing resolution, and identification accuracy of the calorimeter. We also present an integration of the GNN method into a Multi-Objective Optimization framework, enabled by an automated pipeline of data generation, GNN training, and detector performance evaluation. We utilize the optimization to quantify the tradeoffs between different performance metrics at high and low energies when changing the detector design parameters, such as the iron/scintillator thickness.

Strongly correlated molecules often contain dense manifolds of low-lying spin states, making total-spin symmetry essential for predictive electronic-structure theory. Neural-network quantum states provide flexible variational wavefunctions, but commonly used fermionic architectures do not enforce this symmetry and can therefore converge to spin-contaminated states with misleading energies and properties. Here we introduce a spin-adapted neural-network backflow (SA-NNBF) ansatz in second quantization, which combines configuration-dependent spatial orbitals with a compressed spin eigenfunction. A projected tensor compression scheme for spin eigenfunctions and a particle-hole representation make variational Monte Carlo calculations with SA-NNBF practical for active spaces containing more than one hundred electrons. Across hydrogen chains and iron-sulfur clusters, SA-NNBF eliminates spin contamination and consistently achieves lower variational energies than standard NNBF with a comparable number of parameters. For the CAS(113e,76o) active-space model of FeMoco, SA-NNBF yields a highly compact spin-adapted variational state, achieving an energy competitive with recent spin-adapted DMRG calculations at bond dimension $D=10000$ while using orders of magnitude fewer parameters. Our work establishes a general framework for developing spin-symmetry-preserving neural-network quantum states for chemically realistic strongly correlated electrons.

We aim to confirm the compact nature and constrain the radio spectra of candidate persistent radio sources (PRSs) associated with repeating fast radio bursts (FRBs). We performed European VLBI Network (EVN) observations at 5 and 8 GHz targeting two candidates identified in a recent VLA survey. We measured flux densities and upper limits at milliarcsecond resolution and combined them with published VLBI data at lower frequencies to derive spectral constraints. We detect a compact source associated with FRB 20190417A at 5 GHz with a flux density of $150\pm45$ uJy, while no detection is obtained at 8 GHz. The source is unresolved and has a brightness temperature $T_{\rm b}>10^{5}$ K, confirming its non-thermal nature. Combining our measurement with VLBI data at 1.4 GHz, we derive a spectral index $α= -0.19 \pm 0.29$, consistent with a nearly flat spectrum. This makes FRB 20190417A only the second PRS with a spectral index constrained using VLBI data. The inferred luminosity places the source on the proposed $L_ν$-|RM| relation. Including this source yields a scatter of $σ_Δ= 0.65$, corresponding to $\hatα|ε| = 1.5 \pm 0.7$, consistent with forward shocks in the free-expansion phase or young pulsar wind nebulae. For the candidate PRS associated with FRB 20181030A, we report upper limits of 80 uJy at 5 GHz and 150 uJy at 8 GHz, corresponding to $L_{5\,\mathrm{GHz}} \lesssim 3.8 \times 10^{25}\ {\rm erg\ s^{-1}\ Hz^{-1}}$, and implying a steep spectral index ($α\lesssim -1.2$) if the VLA emission arises from a compact component. Our results highlight the importance of VLBI in isolating compact emission from FRB engines and provide one of the few spectral constraints for PRSs at milliarcsecond resolution. The consistency of FRB 20190417A with the $L_ν$-|RM| relation supports a nebular origin for the persistent emission.

Bayesian inference is central to modern cosmology, yet comprehensive model comparison and tension quantification remain computationally prohibitive for many researchers. To address this, we release $\texttt{unimpeded}$, a publicly available Python library and data repository providing pre-computed nested sampling and MCMC chains. We apply this resource to conduct a systematic analysis across a grid of eight cosmological models, including $Λ$CDM and seven extensions, and 39 datasets, including individual probes and their pairwise combinations. Our model comparison reveals that whilst individual datasets show varied preferences for model extensions, the base $Λ$CDM model is most frequently preferred in combined analyses, with the general trend suggesting that evidence for new physics is diluted when probes are combined. Using five complementary statistics, we quantify tensions, finding the most significant to be between DES and Planck ($σ=3.57\pm0.10$) and SH0ES and Planck ($σ=3.27\pm0.10$) within $Ω_kΛ$CDM. We characterise the $S_8$ tension as high-dimensional ($d_G=4.87\pm0.79$) and partially resolvable in certain extended models, whereas the Hubble tension is low-dimensional and persists across the model space. Caution should be exercised when combining datasets in tension. The $\texttt{unimpeded}$ data products, hosted on Zenodo, provide a powerful resource for reproducible cosmological analysis and underscore the robustness of the $Λ$CDM model against this comprehensive benchmark compilation.

Achieving fast, excitation-free quantum control is a vital challenge in modern quantum technologies. In many cases, shortcuts to adiabaticity enable fast adiabatic-like protocols, yet determining control parameters that satisfy practical constraints is often challenging in complex systems. Here, we combine an analytical shortcut to adiabaticity approach with several numerical optimization methods to boost the performance of the protocol. As a proof-of-principle for this hybrid approach, we study a particularly intricate control problem, the separation of two trapped ions. We show that this analytical-numerical approach, along with the physical insight gained through the variety of suboptimal solutions, leads to the exploration of new solutions in a complex landscape that yield improvements of up to 3 orders of magnitude. Moreover, this improvement comes with no additional cost from an experimental point of view.