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Instance Space Analysis is a methodology to evaluate algorithm performance across diverse problem fields. Through visualisation and exploratory data analysis techniques, Instance Space Analysis offers objective, data-driven insights into the diversity of test instances, algorithm behaviour, and algorithm strengths and weaknesses. As such, it supports automated algorithm selection and synthetic test instance generation, increasing testing reliability in optimisation, machine learning, and scheduling fields. This paper introduces instancespace, a Python package that implements an automated pipeline for Instance Space Analysis. This package supports research by streamlining the testing process, providing unbiased metrics, and facilitating more informed algorithmic design and deployment decisions, particularly for complex and safety-critical systems.

Plasma kinetics, for both flat and curved spacetime, is conventionally performed on the mass shell, a 7--dimensional time-phase space with a Vlasov vector field, also known as the Liouville vector field. The choice of this time-phase space encodes the parameterisation of the underling 2nd order ordinary differential equations. By replacing the Vlasov vector on time-phase space with a bivector on an 8--dimensional sub-bundle of the tangent bundle, we create a parameterisation free version of Vlasov theory. This has a number of advantages, which include working for lightlike and ultra-relativistic particles, non metric connections, and metric-free and premetric theories. It also works for theories where no time-phase space can exist for topological topological reasons. An example of this is when we wish to consider all geodesics, including spacelike geodesics. We extend the particle density function to a 6--form on the subbundle of the tangent space, and define the transport equations, which correspond to the Vlasov equation. We then show how to define the corresponding 3--current on spacetime. We discuss the stress-energy tensor needed for the Einstein-Vlasov system. This theory can be generalised to create parameterisation invariant Vlasov theories for many 2nd order theories, on arbitrary manifolds. The relationship to sprays and semi-sprays is given and examples from Finsler geometry are also given., Comment: 28 pages 6 figures

In semiconductor hole spin qubits, low magnetic field ($B$) operation extends the coherence time ($T_\mathrm{2}^*$) but proportionally reduces the gate speed. In contrast, singlet-triplet (ST) qubits are primarily controlled by the exchange interaction ($J$) and can thus maintain high gate speeds even at low $B$. However, a large $J$ introduces a significant charge component to the qubit, rendering ST qubits more vulnerable to charge noise when driven. Here, we demonstrate a highly coherent ST hole spin qubit in germanium, operating at both low $B$ and low $J$. By modulating $J$, we achieve resonant driving of the ST qubit, obtaining an average gate fidelity of $99.68\%$ and a coherence time of $T_\mathrm{2}^*=1.9\,\mu$s. Moreover, by applying the resonant drive continuously, we realize a dressed ST qubit with a tenfold increase in coherence time ($T_\mathrm{2\rho}^*=20.3\,\mu$s). Frequency modulation of the driving signal enables universal control, with an average gate fidelity of $99.64\%$. Our results demonstrate the potential for extending coherence times while preserving high-fidelity control of germanium-based ST qubits, paving the way for more efficient operations in semiconductor-based quantum processors.

We explore spectroscopic and photometric methods for identifying high-redshift galaxies containing an Active Galactic Nucleus (AGN) with JWST observations. After demonstrating the limitations of standard optical methods, which appear ineffective in the low-metallicity environment of the early universe, we evaluate alternative diagnostic techniques using the current JWST observational capabilities. Our analysis focuses on line ratios and equivalent widths (EWs) of UV emission lines: CIV, HeII $\lambda$1640, OIII] $\lambda$1665, and CIII], and the faint optical line, HeII $\lambda$4686. We find that the most valuable diagnostic quantities for finding AGN are the line ratios: (CIII] + CIV) / HeII $\lambda$1640 and CIII] / HeII $\lambda$1640, as well as the EW of HeII $\lambda$1640. For more reliable AGN identification, the HeII $\lambda$1640 and OIII] $\lambda$1665 lines would need to be detected separately. We show that the HeII $\lambda$1640/H$\beta$ ratio effectively separates AGN from star-forming galaxies, though it is contingent on a low dust content. We also show that in order to effectively use these diagnostics, future observations require longer exposure times, especially for galaxies at $z > 6$. Subsequently, we plot three real high-redshift sources on these diagrams which present strong UV emission lines. However, in order to classify them as strong AGN candidates, further study is needed due to the blending of HeII + OIII] and unreliable optical lines. Lastly, we carry out a selection process using spectral energy distribution (SED) fitting with EAZY to identify strong AGN candidates in the JADES NIRCam photometry. One galaxy in our sample emerged as a strong AGN candidate, supported by both photometric selection and strong UV emission. We present a sample of similar AGN candidates in the JADES data based on this method., Comment: submitted MNRAS, 21 pages

Since the advent of software-defined networking (SDN), Traffic Engineering (TE) has been highlighted as one of the key applications that can be achieved through software-controlled protocols (e.g. PCEP and MPLS). Being one of the most complex challenges in networking, TE problems involve difficult decisions such as allocating flows, either via splitting them among multiple paths or by using a reservation system, to minimize congestion. However, creating an optimized solution is cumbersome and difficult as traffic patterns vary and change with network scale, capacity, and demand. AI methods can help alleviate this by finding optimized TE solutions for the best network performance. SDN-based TE tools such as Teal, Hecate and more, use classification techniques or deep reinforcement learning to find optimal network TE solutions that are demonstrated in simulation. Routing control conducted via source routing tools, e.g., PolKA, can help dynamically divert network flows. In this paper, we propose a novel framework that leverages Hecate to practically demonstrate TE on a real network, collaborating with PolKA, a source routing tool. With real-time traffic statistics, Hecate uses this data to compute optimal paths that are then communicated to PolKA to allocate flows. Several contributions are made to show a practical implementation of how this framework is tested using an emulated ecosystem mimicking a real P4 testbed scenario. This work proves valuable for truly engineered self-driving networks helping translate theory to practice., Comment: 10 pages, 12 figures

In order to reach a precision of $0.2\%$ on the hadronic vacuum polarization (HVP) contribution to the anomalous magnetic moment of the muon, $(g-2)_\mu$, such that the full Standard-Model prediction matches in precision the direct experimental measurement, it is crucial to include the leading, O($\alpha$) electromagnetic corrections to HVP. In this work, we determine an important contribution to the latter from a diagram comprised of two two-point quark-loops, connected by the internal photon and gluons. This ultraviolet-finite correction is calculated from lattice QCD using a coordinate-space formalism, where photons are treated in the continuum and infinite volume. Our result amounts to a $(-0.89\pm 0.18)\%$ correction to the leading-order HVP contribution to $(g-2)_\mu$. To overcome the worsening statistical noise at large distances, our analysis is combined with phenomenological models featuring light pseudoscalar mesons with masses below 1 GeV. In particular, our data show a very steep mass dependence as dictated by the charged pion loop. Similarly, the other diagrams appearing in the O$(\alpha)$-corrections to the HVP with the internal photon connecting two separate quark loops are also ultraviolet-finite and can be computed with the same formalism., Comment: 46 pages, 11 figures, 6 tables

Molten salts are important in a number of energy applications, but the fundamental mechanisms operating in ionic liquids are poorly understood, particularly at higher temperatures. This is despite their candidacy for deployment in solar cells, next-generation nuclear reactors, and nuclear pyroprocessing. We perform extensive molecular dynamics simulations over a variety of molten chloride salt compositions at varying temperature and pressures to calculate the thermodynamic and transport properties of these liquids. Using recent developments in the theory of liquid thermophysical properties, we interpret our results on the basis of collective atomistic dynamics (phonons). We find that the properties of ionic liquids well explained by their collective dynamics, as in simple liquids. In particular, we relate the decrease of heat capacity, viscosity, and thermal conductivity to the loss of transverse phonons from the liquid spectrum. We observe the singular dependence of the isochoric heat capacity on the mean free path of phonons, and the obeyance of the Stokes-Einstein equation relating the viscosity to the mass diffusion. The transport properties of mixtures are more complicated compared to simple liquids, however viscosity and thermal conductivity are well guided by fundamental bounds proposed recently. The kinematic viscosity and thermal diffusivity lie very close to one another and obey the theoretical fundamental bounds determined solely by fundamental physical constants. Our results show that recent advances in the theoretical physics of liquids are applicable to molten salts mixtures, and therefore that the evolution and interplay of properties common to all liquids may act as a guide to a deeper understanding of these mixtures.

We present a structural analysis of 521 galaxy candidates at 6.5 < z < 12.5, with $SNR > 10\sigma$ in the F444W filter, taken from the EPOCHS v1 sample, consisting of uniformly reduced deep JWST NIRCam data, covering the CEERS, JADES GOOD-S, NGDEEP, SMACS0723, GLASS and PEARLS surveys. We use standard software to fit single S\'ersic models to each galaxy in the rest-frame optical and extract their parametric structural parameters (S\'ersic index, half-light radius and axis-ratio), and \texttt{Morfometryka} to measure their non-parametric concentration and asymmetry parameters. We find a wide range of sizes for these early galaxies, but with a strong galaxy-size mass correlation up to $z \sim 12$ such that galaxy sizes continue to get progressively smaller in the high-redshift regime, following $R_{e} = 2.74 \pm 0.49 \left( 1 + z \right) ^{-0.79 \pm 0.08}$ kpc. Using non-parametric methods we find that galaxy merger fractions, classified through asymmetry parameters, at these redshifts remain consistent with those in literature, maintaining a value of $f_{m} \sim 0.12 \pm 0.07$ showing little dependence with redshift when combined with literature at $z > 4$. We find that galaxies which are smaller in size also appear rounder, with an excess of high axis-ratio objects. Finally, we artificially redshift a subsample of our objects to determine how robust the observational trends we see are, determining that observed trends are due to real evolutionary effects, rather than being a consequence of redshift effects., Comment: 35 pages, 13 figures. Submitted to ApJ on 19/12/2024. Comments to corresponding author welcome at lewi.westcott@manchester.ac.uk

We present our calculation of the isospin-violating part of the hadronic vacuum polarisation (HVP) contribution to muon $(g-2)$ in lattice QCD at the $SU(3)_{\mathrm{f}}$ symmetric point. The computation of the contributing fully connected diagrams with one internal photon as well as the computation of the only (mass) counterterm are shown. The latter is determined from the charged-neutral kaon mass splitting. We employ coordinate-space methods and a photon propagator which is regulated \`a la Pauli-Villars with a cutoff scale $\Lambda$ well below the lattice cutoff. This regularization makes it possible for us to do crosschecks of individual contributions with calculations in the continuum. Our continuum extrapolated results show little to no dependence on $\Lambda$. This makes our final limit $\Lambda \rightarrow \infty$ straightforward.

In this study, we explore the characteristics of `low-mass' ($\log(M_{\star}/M_{\odot}) \leq 8$) and `intermediate-mass' ($8 \lt \log(M_{\star}/M_{\odot}) \leq 10$) host galaxies of Type Ia supernovae (SNe Ia) from the second data release (DR2) of the Zwicky Transient Facility survey and investigate their correlations with different sub-types of SNe Ia. We use the photospheric velocities measured from the Si II $\lambda$6355 feature, SALT2 light-curve stretch ($x_1$) and host-galaxy properties of SNe Ia to re-investigate the existing relationship between host galaxy mass and Si II $\lambda$6355 velocities. We also investigate sub-type preferences for host populations and show that while the more energetic and brighter 91T-like SNe Ia tends to populate the younger host populations, 91bg-like SNe Ia populate in the older populations. Our findings suggest High Velocity SNe Ia (HV SNe Ia) not only comes from the older populations but they also come from young populations as well. Therefore, while our findings can partially provide support for HV SNe Ia relating to single degenerate progenitor models, they indicate that HV SNe Ia other than being a different population, might be a continued distribution with different explosion mechanisms. We lastly investigate the specific rate of SNe Ia in the volume-limited SN Ia sample of DR2 and compare with other surveys., Comment: 13 pages, 8 figures, accepted for publication in Astronomy & Astrophysics

We present a variety of methods for training complex-valued word embeddings, based on the classical Skip-gram model, with a straightforward adaptation simply replacing the real-valued vectors with arbitrary vectors of complex numbers. In a more "physically-inspired" approach, the vectors are produced by parameterised quantum circuits (PQCs), which are unitary transformations resulting in normalised vectors which have a probabilistic interpretation. We develop a complex-valued version of the highly optimised C code version of Skip-gram, which allows us to easily produce complex embeddings trained on a 3.8B-word corpus for a vocabulary size of over 400k, for which we are then able to train a separate PQC for each word. We evaluate the complex embeddings on a set of standard similarity and relatedness datasets, for some models obtaining results competitive with the classical baseline. We find that, while training the PQCs directly tends to harm performance, the quantum word embeddings from the two-stage process perform as well as the classical Skip-gram embeddings with comparable numbers of parameters. This enables a highly scalable route to learning embeddings in complex spaces which scales with the size of the vocabulary rather than the size of the training corpus. In summary, we demonstrate how to produce a large set of high-quality word embeddings for use in complex-valued and quantum-inspired NLP models, and for exploring potential advantage in quantum NLP models.

We present a detailed investigation into the abundance and morphology of high redshift quenched galaxies at $3 < z < 7$ using James Webb Space Telescope data in the NEP, CEERS and JADES fields. Within these fields, we identify 90 candidate passive galaxies using specific star formation rates modelled with the BAGPIPES SED fitting code, which is more effective at identifying recently quenched systems than the classical UVJ method. With this sample of galaxies, we find number densities broadly consistent with other works and a rapidly evolving passive fraction of high mass galaxies ($\log_{10}{(M_{\star}/M_{\odot})} > $ 9.5) between $3 < z < 5$. We find that the fraction of galaxies with low star formation rates and mass 9.5 $ < \log_{10}{(M_{\star}/M_{\odot})} < $ 10.5 decreases from $\sim$25% at $3 < z < 4$ to $\sim$2% at $5 < z < 7$. Our passive sample of galaxies is shown to exhibit more compact light profiles compared to star-forming counterparts and some exhibit traces of AGN activity through detections in either the X-ray or radio. At the highest redshifts ($z > 6.5$) passive selections start to include examples of 'little red dots' which complicates any conclusions until their nature is better understood., Comment: 24 pages, 5 tables, 11 figures, submitted to MNRAS

Alignment is a social phenomenon wherein individuals share a common goal or perspective. Mirroring, or mimicking the behaviors and opinions of another individual, is one mechanism by which individuals can become aligned. Large scale investigations of the effect of mirroring on alignment have been limited due to the scalability of traditional experimental designs in sociology. In this paper, we introduce a simple computational framework that enables studying the effect of mirroring behavior on alignment in multi-agent systems. We simulate systems of interacting large language models in this framework and characterize overall system behavior and alignment with quantitative measures of agent dynamics. We find that system behavior is strongly influenced by the range of communication of each agent and that these effects are exacerbated by increased rates of mirroring. We discuss the observed simulated system behavior in the context of known human social dynamics.

The kinetic stability of collisionless, sloshing beam-ion (45{\deg} pitch angle) plasma is studied in a 3D simple magnetic mirror, mimicking the Wisconsin High-temperature superconductor Axisymmetric Mirror (WHAM) experiment. The collisional Fokker-Planck code CQL3D-m provides a slowing-down beam-ion distribution to initialize the kinetic-ion/fluid-electron code Hybrid-VPIC, which then simulates free plasma decay without external heating or fueling. Over 1-10 $\mu$s, drift-cyclotron loss-cone (DCLC) modes grow and saturate in amplitude. DCLC scatters ions to a marginally-stable distribution with gas-dynamic rather than classical-mirror confinement. Sloshing ions can trap cool (low-energy) ions in an electrostatic potential well to stabilize DCLC, but DCLC itself does not scatter sloshing beam-ions into said well. Instead, cool ions must come from external sources such as charge-exchange collisions with a low-density neutral population. Manually adding cool ~1 keV ions improves beam-ion confinement ~2-5x in Hybrid-VPIC simulations, which qualitatively corroborates measurements from real mirror devices with sloshing ions., Comment: Submitted; 35 pages, 14 figures

Exoplanet atmospheric modeling is advancing from chemically diverse one-dimensional (1D) models to three-dimensional (3D) global circulation models (GCMs), which are crucial for interpreting observations from facilities like the James Webb Space Telescope (JWST) and Extremely Large Telescope (ELT). However, maintaining chemical diversity in models, especially in GCMs, is computationally expensive, limiting their complexity. Optimizing the number of reactions and species can address this tradeoff, but transparent and efficient methods for such optimization are lacking in current exoplanet literature. We aim to develop a systematic approach for reducing chemical networks in exoplanetary atmospheres while balancing accuracy and computational efficiency. Our data-driven method selects optimal reduced chemical networks based on accuracy and computational efficiency metrics. This approach can optimize networks for similar planets simultaneously, assign weights to prioritize accuracy or efficiency, and is applicable when including photochemistry. We base our method on sensitivity analysis of a typical 1D chemical kinetics model, applying principal component analysis to the sensitivities. To achieve fast and reliable network reduction, we utilize a genetic algorithm, a machine-learning optimization method that mimics natural selection. We present three schemes tailored for different priorities (accuracy, computational efficiency, and adaptability to photochemistry) that demonstrate improved performance and reduced computational costs. Our genetic algorithm-based method, the first to reduce a chemical network including photochemistry in exoplanet research, offers a versatile and efficient approach to enhance both accuracy and computational efficiency.

The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to beyond the standard model effects that can enhance production at large transverse momentum $p_{\mathrm{T}}$. The largest event fraction arises from the fully hadronic final state in which every Higgs boson decays to a bottom quark-antiquark pair ($b\bar{b}$). This introduces a combinatorial challenge known as the \emph{jet assignment problem}: assigning jets to sets representing Higgs boson candidates. Symmetry-preserving attention networks (SPA-Nets) have been been developed to address this challenge. However, the complexity of jet assignment increases when simultaneously considering both $H\rightarrow b\bar{b}$ reconstruction possibilities, i.e., two "resolved" small-radius jets each containing a shower initiated by a $b$-quark or one "boosted" large-radius jet containing a merged shower initiated by a $b\bar{b}$ pair. The latter improves the reconstruction efficiency at high $p_{\mathrm{T}}$. In this work, we introduce a generalization to the SPA-Net approach to simultaneously consider both boosted and resolved reconstruction possibilities and unambiguously interpret an event as "fully resolved'', "fully boosted", or in between. We report the performance of baseline methods, the original SPA-Net approach, and our generalized version on nonresonant $HH$ and $HHH$ production at the LHC. Considering both boosted and resolved topologies, our SPA-Net approach increases the Higgs boson reconstruction purity by 57--62\% and the efficiency by 23--38\% compared to the baseline method depending on the final state.

Disc galaxies represent a promising laboratory for the study of gravitational physics, including alternatives to dark matter, owing to the possibility of coupling rotation curves' dynamical data with strong gravitational lensing observations. In particular, Euclid, DES and LSST are predicted to observe hundreds of thousands of gravitational lenses. Here, we investigate disc galaxy strong gravitational lensing in the MOND framework. We employ the concept of equivalent Newtonian systems within the quasi-linear MOND formulation to make use of the standard lensing formalism. We derive the phantom dark matter distribution predicted for realistic disc galaxy models and study the impact of morphological and mass parameters on the expected lensing. We find purely MONDian effects dominate the lensing and generate non-trivial correlations between the lens parameters and the lensing cross section. Moreover, we show that the standard realisation of MOND predicts a number count of disc galaxy lenses of one order of magnitude higher than the dark matter-driven predictions, making it distinguishable from the latter in upcoming surveys. Finally, we show that disc galaxy gravitational lensing can be used to strongly constrain the interpolating function of MOND., Comment: 15 pages, 10 figures

To facilitate the measurement of representational harms caused by large language model (LLM)-based systems, the NLP research community has produced and made publicly available numerous measurement instruments, including tools, datasets, metrics, benchmarks, annotation instructions, and other techniques. However, the research community lacks clarity about whether and to what extent these instruments meet the needs of practitioners tasked with developing and deploying LLM-based systems in the real world, and how these instruments could be improved. Via a series of semi-structured interviews with practitioners in a variety of roles in different organizations, we identify four types of challenges that prevent practitioners from effectively using publicly available instruments for measuring representational harms caused by LLM-based systems: (1) challenges related to using publicly available measurement instruments; (2) challenges related to doing measurement in practice; (3) challenges arising from measurement tasks involving LLM-based systems; and (4) challenges specific to measuring representational harms. Our goal is to advance the development of instruments for measuring representational harms that are well-suited to practitioner needs, thus better facilitating the responsible development and deployment of LLM-based systems., Comment: NeurIPS 2024 Workshop on Evaluating Evaluations (EvalEval)

The second data release of Type Ia supernovae (SNe Ia) observed by the Zwicky Transient Facility has provided a homogeneous sample of 3628 SNe Ia with photometric and spectral information. This unprecedented sample size enables us to better explore our currently tentative understanding of the dependence of host environment on SN Ia properties. In this paper, we make use of two-dimensional image decomposition to model the host galaxies of SNe Ia. We model elliptical galaxies as well as disk/spiral galaxies with or without central bulges and bars. This allows for the categorisation of SN Ia based on their morphological host environment, as well as the extraction of intrinsic galaxy properties corrected for both cosmological and atmospheric effects. We find that although this image decomposition technique leads to a significant bias towards elliptical galaxies in our final sample of galaxies, the overall results are robust. By successfully modelling 728 host galaxies, we find that the photometric properties of SNe Ia found in disks and in elliptical galaxies, correlate fundamentally differently with their host environment. We identified strong linear relations between light-curve stretch and our model-derived galaxy colour for both the elliptical (16.8$\sigma$) and disk (5.1$\sigma$) subpopulations of SNe Ia. Lower stretch SNe Ia are found in redder environments, which we identify as an age/metallicity effect. Within the subpopulation of SNe Ia found in disk containing galaxies, we find a significant linear trend (6.1$\sigma$) between light-curve stretch and model-derived local $r$-band surface brightness, which we link to the age/metallicity gradients found in disk galaxies. SN Ia colour shows little correlation with host environment as seen in the literature. We identify a possible dust effect in our model-derived surface brightness (3.3$\sigma$), for SNe Ia in disk galaxies., Comment: ZTF SN Ia DR2 release paper. Submitted to A&A (ZTF DR2 Special Issue)

We report the crystal structures, magnetic and thermodynamic properties of two magnetically frustrated $A$'$A$"$M_2$F$_7$-type antiferromagnets, NaCdNi$_2$F$_7$ and NaCdMn$_2$F$_7$. While NaCdNi$_2$F$_7$ forms a stable pyrochlore structure (SG: $Fd \overline{3} m$, #227) with magnetic $S$ = 1 Ni$^{2+}$ ions on the frustrated pyrochlore 16$c$ site and fully disordered non-magnetic Na$^+$/Cd$^{2+}$ ions on the pyrochlore 16$d$ site, NaCdMn$_2$F$_7$ favors the defect-fluorite structure (SG: $Fm \overline{3} m$, #225) with magnetic $S$ = 5/2 Mn$^{2+}$ and non-magnetic Na$^+$ and Cd$^{2+}$ ions fully disordered on the fluorite 4$a$ site. This is a result of the Mn$^{2+}$ ionic radius being too close to the average Na$^+$/Cd$^{2+}$ ionic radius, hindering the cationic site ordering towards the stable pyrochlore structure. In both cases, dominant antiferromagnetic interactions $\theta_{CW,Ni}$ = -91.2(5) K and $\theta_{CW,Mn}$ = -42.4(4) K are noted, with no magnetic transition until $T_{f,Ni}$ = 3.2 K and $T_{f,Mn}$ = 2.0 K, implying substantial frustration, with frustration indices $f_{Ni}$ = 28.5 and $f_{Mn}$ = 21. AC susceptibility measurements and bifurcation of ZFC/FC low-field magnetization indicate a glassy ground-state, precipitated by the magnetic-bond-disorder stemming from the inherent structural disorder.

Manipulating the electromagnetic spectrum at the single-photon level is fundamental for quantum experiments. In the visible and infrared range, this can be accomplished with atomic quantum emitters, and with superconducting qubits such control is extended to the microwave range (below 10 GHz). Meanwhile, the region between these two energy ranges presents an unexplored opportunity for innovation. We bridge this gap by scaling up a superconducting qubit to the millimeter-wave range (near 100 GHz). Working in this energy range greatly reduces sensitivity to thermal noise compared to microwave devices, enabling operation at significantly higher temperatures, up to 1 K. This has many advantages by removing the dependence on rare $^3$He for refrigeration, simplifying cryogenic systems, and providing orders of magnitude higher cooling power, lending the flexibility needed for novel quantum sensing and hybrid experiments. Using low-loss niobium trilayer junctions, we realize a qubit at 72 GHz cooled to 0.87 K using only $^4$He. We perform Rabi oscillations to establish control over the qubit state, and measure relaxation and dephasing times of 15.8 and 17.4 ns respectively. This demonstration of a millimeter-wave quantum emitter offers exciting prospects for enhanced sensitivity thresholds in high-frequency photon detection, provides new options for quantum transduction and for scaling up and speeding up quantum computing, enables integration of quantum systems where $^3$He refrigeration units are impractical, and importantly paves the way for quantum experiments exploring a novel energy range., Comment: 10 pages, 4 figures and supplement (10 pages, 10 figures)

Neural responses encode information that is useful for a variety of downstream tasks. A common approach to understand these systems is to build regression models or ``decoders'' that reconstruct features of the stimulus from neural responses. Popular neural network similarity measures like centered kernel alignment (CKA), canonical correlation analysis (CCA), and Procrustes shape distance, do not explicitly leverage this perspective and instead highlight geometric invariances to orthogonal or affine transformations when comparing representations. Here, we show that many of these measures can, in fact, be equivalently motivated from a decoding perspective. Specifically, measures like CKA and CCA quantify the average alignment between optimal linear readouts across a distribution of decoding tasks. We also show that the Procrustes shape distance upper bounds the distance between optimal linear readouts and that the converse holds for representations with low participation ratio. Overall, our work demonstrates a tight link between the geometry of neural representations and the ability to linearly decode information. This perspective suggests new ways of measuring similarity between neural systems and also provides novel, unifying interpretations of existing measures.

We present our lattice QCD result for the long-distance part of the hadronic vacuum polarization contribution, $(a_\mu^{\rm hvp})^{\rm LD}$, to the muon $g-2$ in the time-momentum representation. This is the numerically dominant, and at the same time the most challenging part regarding statistical precision. Our calculation is based on ensembles with dynamical up, down and strange quarks, employing the O($a$)-improved Wilson fermion action with lattice spacings ranging from $0.035-0.099$ fm. In order to reduce statistical noise in the long-distance part of the correlator to the per-mille level, we apply low-mode averaging and combine it with an explicit spectral reconstruction. Our result is $(a_\mu^{\rm hvp})^{\rm LD} = 423.2(4.2)_{\rm stat}(3.4)_{\rm syst}\times 10^{-10}$ in isospin-symmetric QCD, where the pion decay constant is used to set the energy scale. When combined with our previous results for the short- and intermediate-distance window observables and after including all sub-dominant contributions as well as isospin-breaking corrections, we obtain the total leading-order hadronic vacuum polarization contribution as $a_\mu^{\rm hvp} = 724.9(5.0)_{\rm stat}(4.9)_{\rm syst}\times 10^{-10}$. Our result displays a tension of 3.9 standard deviations with the data-driven estimate published in the 2020 White Paper, but leads to a SM prediction for the total muon anomalous magnetic moment that agrees with the current experimental average., Comment: 55 pages, 14 figures, 8 tables

We show that the multicomponent Kardar-Parisi-Zhang equation describes the low-energy theory for phase fluctuations in a $\mathbb{Z}_{2}$ degenerate non-equilibrium driven-dissipative condensate with global $U(1)\times U(1)$ symmetry. Using dynamical renormalisation group in spatial dimension $d=1$, we demonstrate that coupled stochastic complex Ginsburg-Landau equations exhibit an emergent stationary distribution, enforcing KPZ dynamical exponent $z=3/2$ and static roughness exponent $\chi=1/2$ for both components. By tuning intercomponent interactions, the system can access other regimes, including a fragmented condensate regime from a dynamical instability in the phase fluctuations, as well as a spacetime vortex regime driven by the non-linear terms in the coupled KPZ equations. In stable regimes, we show that in specific submanifolds relevant to polaritons, the RG fixed point offers a transformation to decoupled KPZ equations. Our findings have broad implications for understanding multicomponent KPZ systems in the long-wavelength limit., Comment: 7 pages, 3 figures with supplemental materials attached

This paper presents the Hammir tandem mirror design based on Realta Fusion's first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium, and transport combined with a new formulation of the plasma operation contours (POPCONs) technique for the tandem mirror central cell. Using this model, it is shown that an end plug utilizing high temperature superconducting magnets and modern neutral beams enables a classical tandem mirror pilot plant producing a fusion gain Q > 5. The approach here represents an important advance in tandem mirror design. The high fidelity end plug model enables calculations of heating and transport in the highly non-Maxwellian end plug to be made more accurately and the central cell POPCON technique allows consideration of a wide range of parameters in the relatively simple near-Maxwellian central cell, facilitating the selection of more optimal central cell plasmas. These advances make it possible to find more conservative classical tandem mirror fusion pilot plant operating points with lower $\beta$, temperatures, neutral beam energies, and end plug performance than designs in the literature. Despite being more conservative, it is shown that these operating points can still form the basis of a viable fusion pilot plant.

Galaxy mergers are a key driver of galaxy formation and evolution, including the triggering of AGN and star formation to a still unknown degree. We thus investigate the impact of galaxy mergers on star formation and AGN activity using a sample of 3,330 galaxies at $z = [4.5, 8.5]$ from eight JWST fields (CEERS, JADES GOODS-S, NEP-TDF, NGDEEP, GLASS, El-Gordo, SMACS-0723, and MACS-0416), collectively covering an unmasked area of 189 arcmin$^2$. We focuses on star formation rate (SFR) enhancement, AGN fraction, and AGN excess in major merger ($\mu > 1/4$) close-pair samples, defined by $\Delta z < 0.3$ and projected separations $r_p < 100$ kpc, compared to non-merger samples. We find that SFR enhancement occurs only at $r_p < 20$ kpc, with values of $0.25 \pm 0.10$ dex and $0.26 \pm 0.11$ dex above the non-merger medians for $z = [4.5, 6.5]$ and $z = [6.5, 8.5]$, respectively. No other statistically significant enhancements in galaxy sSFR or stellar mass are observed at any projected separation or redshift bin. We also compare our observational results with predictions from the SC-SAM simulation and find no evidence of star formation enhancement in the simulations at any separation range. Finally, we examine the AGN fraction and AGN excess, finding that the fraction of AGNs in AGN-galaxy pairs, relative to the total AGN population, is $3.25^{+1.50}_{-1.06}$ times greater than the fraction of galaxy pairs relative to the overall galaxy population at the same redshift. We find that nearly all AGNs have a companion within 100 kpc and observe an excess AGN fraction in close-pair samples compared to non-merger samples. This excess is found to be $1.26 \pm 0.06$ and $1.34 \pm 0.06$ for AGNs identified via the inferred BPT diagram and photometric SED selection, respectively., Comment: 17 Pages, 7 Figures, Submitted to MNRAS

We investigate strategies to suppress phase separation and reduce threading dislocation densities (TDD) in AlGaInAs compositionally graded buffers (CGBs) that span the lattice constant range from GaAs to InP. Combining results from high resolution x-ray diffraction, cathodoluminescence, transmission electron microscopy, and photovoltaic device measurements, we correlate choices of epitaxial growth conditions with the defect structure of the CGBs and subsequent device performance. Both the use of substrates with high misorientation off (100) towards the (111)A plane and Zn-doping instead of Si-doping are shown to suppress phase separation and reduce TDD. We demonstrate a 0.74 eV GaInAs device grown on a (411)A GaAs substrate using a Zn-doped AlGaInAs CGB with TDD = 3.5 +/- 0.2 x 106 cm^-2 that has a bandgap-open circuit voltage offset of only 0.434 V measured under the AM1.5G solar spectrum. We characterized this device under high-intensity irradiance from a 1570 nm laser and measured a 31.9% peak efficiency laser power conversion efficiency at 3.6 W/cm2. These results provide a roadmap to the manufacture of laser- and thermal-power conversion devices with the performance and cost-effectiveness needed to drive adoption of these technologies at scale., Comment: 15 pages, 8 figures

In order to be in control of the $\alpha'$ derivative expansion, geometric string compactifications are understood in the context of a large volume approximation. In this letter, we consider the reduction of these higher derivative terms, and propose an improved estimate on the large volume approximation using numerical Calabi-Yau metrics obtained via machine learning methods. Further to this, we consider the $\alpha'^3$ corrections to numerical Calabi-Yau metrics in the context of IIB string theory. This correction represents one of several important contributions for realistic string compactifications -- alongside, for example, the backreaction of fluxes and local sources -- all of which have important consequences for string phenomenology. As a simple application of the corrected metric, we compute the change to the spectrum of the scalar Laplacian.

We perform molecular dynamics simulations of molten Lithium Fluoride using the MACE-MP-0 (small) machine learnt interatomic potential and the classical Buckingham and Born-Huggins-Mayer potentials. We find that the MACE-MP-0, out-of-the-box, is able to accurately reproduce the experimental viscosity across the liquid state. Whilst the previous predicted viscosities from classical potentials are under-predicted, which has previously been attributed to a suppressed melting temperature. We find that the melting temperature simulated by MACE-MP-0, simply by heating a crystal structure, is significantly closer to the experimental melting temperature of LiF., Comment: 7 pages, 5 figures

A number of popular transfer learning methods rely on grid search to select regularization hyperparameters that control over-fitting. This grid search requirement has several key disadvantages: the search is computationally expensive, requires carving out a validation set that reduces the size of available data for model training, and requires practitioners to specify candidate values. In this paper, we propose an alternative to grid search: directly learning regularization hyperparameters on the full training set via model selection techniques based on the evidence lower bound ("ELBo") objective from variational methods. For deep neural networks with millions of parameters, we specifically recommend a modified ELBo that upweights the influence of the data likelihood relative to the prior while remaining a valid bound on the evidence for Bayesian model selection. Our proposed technique overcomes all three disadvantages of grid search. We demonstrate effectiveness on image classification tasks on several datasets, yielding heldout accuracy comparable to existing approaches with far less compute time.

We study a class of supersymmetric Froggatt-Nielsen (FN) models with multiple $U(1)$ symmetries and Standard Model (SM) singlets inspired by heterotic string compactifications on Calabi-Yau threefolds. The string-theoretic origin imposes a particular charge pattern on the SM fields and FN singlets, dividing the latter into perturbative and non-perturbative types. Employing systematic and heuristic search strategies, such as genetic algorithms, we identify charge assignments and singlet VEVs that replicate the observed mass and mixing hierarchies in the quark sector, and subsequently refine the Yukawa matrix coefficients to accurately match the observed values for the Higgs VEV, the quark and charged lepton masses and the CKM matrix. This bottom-up approach complements top-down string constructions and our results demonstrate that string FN models possess a sufficiently rich structure to account for flavour physics. On the other hand, the limited number of distinct viable charge patterns identified here indicates that flavour physics imposes tight constraints on string theory models, adding new constraints on particle spectra that are essential for achieving a realistic phenomenology., Comment: 49 pages, 4 figures, 10 tables

Image representations (artificial or biological) are often compared in terms of their global geometry; however, representations with similar global structure can have strikingly different local geometries. Here, we propose a framework for comparing a set of image representations in terms of their local geometries. We quantify the local geometry of a representation using the Fisher information matrix, a standard statistical tool for characterizing the sensitivity to local stimulus distortions, and use this as a substrate for a metric on the local geometry in the vicinity of a base image. This metric may then be used to optimally differentiate a set of models, by finding a pair of "principal distortions" that maximize the variance of the models under this metric. We use this framework to compare a set of simple models of the early visual system, identifying a novel set of image distortions that allow immediate comparison of the models by visual inspection. In a second example, we apply our method to a set of deep neural network models and reveal differences in the local geometry that arise due to architecture and training types. These examples highlight how our framework can be used to probe for informative differences in local sensitivities between complex computational models, and suggest how it could be used to compare model representations with human perception.

The Magellanic Stream, a tidal tail of diffuse gas falling onto the Milky Way, formed by interactions between the Small and Large Magellanic Clouds, is primarily composed of neutral atomic hydrogen (HI). The deficiency of dust and the diffuse nature of the present gas make molecular formation rare and difficult, but if present, could lead to regions potentially suitable for star formation, thereby allowing us to probe conditions of star formation similar to those at high redshifts. We search for HCO$^+$, HCN, HNC, and C$_2$H using the highest sensitivity observations of molecular absorption data from the Atacama Large Millimeter Array to trace these regions, comparing with HI archival data to compare these environments in the Magellanic Stream to the HI column density threshold for molecular formation in the Milky Way. We also compare the line of sight locations with confirmed locations of stars, molecular hydrogen, and OI detections, though at higher sensitivities than the observations presented here. We find no detections to a 3$\sigma$ significance, despite four sightlines having column densities surpassing the threshold for molecular formation in the diffuse regions of the Milky Way. Here we present our calculations for the upper limits of the column densities of each of these molecular absorption lines, ranging from $3 \times 10^{10}$ to $1 \times 10^{13}$ cm$^{-2}$. The non-detection of HCO$^+$ suggests that at least one of the following is true: (i) $X_{HCO^+, \mathrm{MS}}$ is significantly lower than the Milky Way value; (ii) that the widespread diffuse molecular gas observed in the Milky Way's diffuse ISM does not have a direct analog in the MS; (iii) the HI-to-H$_2$ transition occurs in the MS at a higher surface density in the MS than in the LMC or SMC; or (iv) molecular gas exists in the MS, but only in small, dense clumps., Comment: 19 pages, 7 figures, 4 tables, Accepted for publication in PASA

It has recently been proved that a third law of black hole mechanics does not hold for Einstein-Maxwell theory coupled to a massless charged scalar field: there exist solutions that describe gravitational collapse to form an exactly extremal Reissner-Nordstr\"om black hole in finite time. In this paper it is proved that such solutions do not exist in theories with matter fields satisfying a local mass-charge inequality. In such a theory, if a 2-surface has the same metric, extrinsic curvature, and Maxwell field as a cross-section of an extremal Reissner-Nordstr\"om horizon then this surface cannot have a compact interior and so cannot be a horizon cross-section of a black hole formed in gravitational collapse. This result is proved using spinorial techniques, which are also used to prove a mass-charge inequality for a modified version of the Dougan-Mason quasi-local mass., Comment: 23 pages v2: typos corrected

Rapid and robust parameter estimation of gravitational-wave sources is a key component of modern multi-messenger astronomy. We present a novel and straightforward method for rapid parameter estimation of gravitational-wave sources that uses metric-based importance sampling. The method enables robust parameter estimation of binary neutron star and binary black hole binaries and is trivially parallelized, enabling full parameter estimation in seconds with modest resources. The algorithm achieves an average 35% effective sampling efficiency for the majority of aligned-spin neutron star binaries sources. Surprisingly, this approach is also highly efficient for analyzing the full 15-dimensional parameter space of typical binary black holes, with 20% efficiency achieved for a source detected primarily by the twin LIGO observatories and 9% for a network of three comparable sensitivity observatories. This method can serve immediate use to improve the low-latency data products of the gravitational-wave observatory network and may be a key component of how the millions of sources observed by next-generation observatories could be analyzed. The approach can also be broadly applied for problems where an approximate likelihood metric-space can be constructed., Comment: 8 pages, 3 figures, release at https://github.com/gwastro/games-rapid-pe