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Understanding how light modifies long-range order is key to improve our ability to control material functionality on an ultrafast timescale. Transient spatial heterogeneity has been proposed in many materials, but isolating the dynamics of different regions experimentally has been challenging. Here we address this issue and measure the dynamics of orbital order melting in the layered manganite, La0.5Sr1.5MnO4, and isolate the surface dynamics from the bulk for the first time. Bulk measurements show orbital order is rapidly suppressed, but the correlation length surprisingly increases. However, the surface dynamics, show a stronger suppression and a significant decrease in correlation length. By isolating the surface changes, we find that light preferentially melts a less ordered surface and the loss of long-range order is likely driven by the formation of local and disordered polarons. Melting the disordered surface effectively increases the average correlation of the bulk probed volume, resolving the contradictory response. These results show that surface scattering methods are necessary to understand both surface and bulk dynamics in heterogeneous materials., Comment: 22 pages, 8 figures

Measuring the spectral phase of a pulse is key for performing wavelength resolved ultrafast measurements in the few femtosecond regime. However, accurate measurements in real experimental conditions can be challenging. We show that the reflectivity change induced by coherent phonons in a quantum material can be used to infer the spectral phase of an optical probe pulse with few-femtosecond accuracy., Comment: 5 figures

We present the discovery of Ursa Major III/UNIONS 1, the least luminous known satellite of the Milky Way, which is estimated to have an absolute V-band magnitude of $+2.2^{+0.4}_{-0.3}$ mag, equivalent to a total stellar mass of 16$^{+6}_{-5}$ M$_{\odot}$. Ursa Major III/UNIONS 1 was uncovered in the deep, wide-field Ultraviolet Near Infrared Optical Northern Survey (UNIONS) and is consistent with an old ($\tau > 11$ Gyr), metal-poor ([Fe/H] $\sim -2.2$) stellar population at a heliocentric distance of $\sim$ 10 kpc. Despite being compact ($r_{\text{h}} = 3\pm1$ pc) and composed of so few stars, we confirm the reality of Ursa Major III/UNIONS 1 with Keck II/DEIMOS follow-up spectroscopy and identify 11 radial velocity members, 8 of which have full astrometric data from $Gaia$ and are co-moving based on their proper motions. Based on these 11 radial velocity members, we derive an intrinsic velocity dispersion of $3.7^{+1.4}_{-1.0}$ km s$^{-1}$ but some caveats preclude this value from being interpreted as a direct indicator of the underlying gravitational potential at this time. Primarily, the exclusion of the largest velocity outlier from the member list drops the velocity dispersion to $1.9^{+1.4}_{-1.1}$ km s$^{-1}$, and the subsequent removal of an additional outlier star produces an unresolved velocity dispersion. While the presence of binary stars may be inflating the measurement, the possibility of a significant velocity dispersion makes Ursa Major III/UNIONS 1 a high priority candidate for multi-epoch spectroscopic follow-ups to deduce to true nature of this incredibly faint satellite., Comment: 21 pages, 9 figures, 3 tables; Accepted for publication in ApJ

The recently discovered stellar system Ursa Major III/UNIONS 1 (UMa3/U1) is the faintest known Milky Way satellite to date. With a stellar mass of $16^{+6}_{-5}\,\rm M_\odot$ and a half-light radius of $3\pm1$pc, it is either the darkest galaxy ever discovered or the faintest self-gravitating star cluster known to orbit the Galaxy. Its line-of-sight velocity dispersion suggests the presence of dark matter, although current measurements are inconclusive because of the unknown contribution to the dispersion of potential binary stars. We use $N$-body simulations to show that, if self-gravitating, the system could not survive in the Milky Way tidal field for much longer than a single orbit (roughly 0.4Gyr), which strongly suggests that the system is stabilized by the presence of large amounts of dark matter. If UMa3/U1 formed at the center of a ~$10^9\rm M_\odot$ cuspy LCDM halo, its velocity dispersion would be predicted to be of order ~1km/s. This is roughly consistent with the current estimate, which, neglecting binaries, places $\sigma_{\rm los}$ in the range 1 to 4km/s. Because of its dense cusp, such a halo should be able to survive the Milky Way tidal field, keeping UMa3/U1 relatively unscathed until the present time. This implies that UMa3/U1 is plausibly the faintest and densest dwarf galaxy satellite of the Milky Way, with important implications for alternative dark matter models and for the minimum halo mass threshold for luminous galaxy formation in the LCDM cosmology. Our results call for multi-epoch high-resolution spectroscopic follow-up to confirm the dark matter content of this extraordinary system., Comment: updated to match accepted version

We report on the discovery of a significant and compact over-density of old and metal-poor stars in the KiDS survey (data release 4). The discovery is confirmed by deeper HSC-SSC data revealing the old Main Sequence Turn-Off of a stellar system located at a distance from the sun of $D_{\sun}=145^{+14}_{-13}$~kpc in the direction of the Sextans constellation. The system has absolute integrated magnitude ($M_V=-3.9^{+0.4}_{-0.3}$), half-light radius ($r_h=193^{+61}_{-46}$~pc), and ellipticity ($e=0.46^{+0.11}_{-0.15}$) typical of Ultra Faint Dwarf galaxies (UFDs). The central surface brightness is near the lower limits of known local dwarf galaxies of similar integrated luminosity, as expected for stellar systems that escaped detection until now. The distance of the newly found system suggests that it is likely a satellite of our own Milky Way, consequently, we tentatively baptise it Sextans~II (KiDS-UFD-1)., Comment: 7 pages, 5 figures. Accepted for publication on A&A Letters

Predicting lithium-ion battery lifetime is one of the greatest unsolved problems in battery research right now. Recent years have witnessed a surge in lifetime prediction papers using physics-based, empirical, or data-driven models, most of which have been validated against the remaining capacity (capacity fade) and sometimes resistance (power fade). However, there are many different combinations of degradation mechanisms in lithium-ion batteries that can result in the same patterns of capacity and power fade, making it impossible to find a unique validated solution. Experimentally, degradation mode analysis involving measuring the loss of lithium inventory, loss of active material at both electrodes, and electrode drift/slippage has emerged as a state-of-the-art requirement for cell degradation studies. In this paper we coupled five degradation mechanisms together for the first time. We also showed how three models with different levels of complexity can all fit the remaining capacity and resistance well, but only the model with five coupled degradation mechanisms could also fit the degradation modes at all temperatures. This work proves that validating only against capacity and power fade is no longer sufficient, and state-of-the-art experimental and modelling degradation studies should include degradation mode analysis for validation in the future.

Ultrafast manipulation of vibrational coherence is an emergent route to control the structure of solids. However, this strategy can only induce long-range correlations and cannot modify atomic structure locally, which is required in many technologically-relevant phase transitions. Here, we demonstrate that ultrafast lasers can generate incoherent structural fluctuations which are more efficient for material control than coherent vibrations, extending optical control to a wider range of materials. We observe that local, non-equilibrium lattice distortions generated by a weak laser pulse reduce the energy barrier to switch between insulating and metallic states in vanadium dioxide by 6%. Seeding inhomogeneous structural-fluctuations presents an alternative, more energy efficient, route for controlling materials that may be applicable to all solids, including those used in data and energy storage devices.

Dwarf galaxies are valuable laboratories for dynamical studies related to dark matter and galaxy evolution, yet it is currently unknown just how physically extended their stellar components are. Satellites orbiting the Galaxy's potential may undergo tidal stripping by the host, or alternatively, may themselves have accreted smaller systems whose debris populates the dwarf's own stellar halo. Evidence of these past interactions, if present, is best searched for in the outskirts of the satellite. However, foreground contamination dominates the signal at these large radial distances, making observation of stars in these regions difficult. In this work, we introduce an updated algorithm for application to Gaia data that identifies candidate member stars of dwarf galaxies, based on spatial, color-magnitude and proper motion information, and which allows for an outer component to the stellar distribution. Our method shows excellent consistency with spectroscopically confirmed members from the literature despite having no requirement for radial velocity information. We apply the algorithm to all $\sim$60 Milky Way dwarf galaxy satellites, and we find 9 dwarfs (Bo\"otes 1, Bo\"otes 3, Draco 2, Grus 2, Segue 1, Sculptor, Tucana 2, Tucana 3, and Ursa Minor) that exhibit evidence for a secondary, low-density outer profile. We identify many member stars which are located beyond 5 half-light radii (and in some cases, beyond 10). We argue these distant stars are likely tracers of dwarf stellar haloes or tidal streams, though ongoing spectroscopic follow-up will be required to determine the origin of these extended stellar populations., Comment: 25 pages, 13 figures, 4 tables, published in MNRAS

The formation of "stellar halos" in dwarf galaxies have been discussed in terms of early mergers or Galactic tides, although fluctuations in the gravitational potential due to stellar feedback is also a possible candidate mechanism. A Bayesian algorithm is used to find new candidate members in the extreme outskirts of the Sculptor dwarf galaxy. Precise metallicities and radial velocities for two distant stars are measured from their spectra taken with the Gemini South GMOS spectrograph. The radial velocity, proper motion and metallicity of these targets are consistent with Sculptor membership. As a result, the known boundary of the Sculptor dwarf extends now out to an elliptical distance of $\sim10$ half-light radii, which corresponds to a projected physical distance of $\sim3$ kpc. As reported in earlier work, the overall distribution of radial velocities and metallicities indicate the presence of a more spatially and kinematically dispersed metal-poor population that surrounds the more concentrated and colder metal-rich stars. Sculptor's density profile shows a "kink" in its logarithmic slope at a projected distance of $\sim25$ arcmin (620 pc), which we interpret as evidence that Galactic tides have helped to populate the distant outskirts of the dwarf. We discuss further ways to test and validate this tidal interpretation for the origin of these distant stars., Comment: Accepted for publication on MNRAS. Figure 1 now displays all the candidate members. arXiv admin note: text overlap with arXiv:2301.13214

The Stokes-Einstein-Sutherland (SES) equation is at the foundation of statistical physics, relating a particle's diffusion coefficient and size with the fluid viscosity, temperature and the boundary condition for the particle-solvent interface. It is assumed that it relies on the separation of scales between the particle and the solvent, hence it is expected to break down for diffusive transport on the molecular scale. This assumption is however challenged by a number of experimental studies showing a remarkably small, if any, violation, while simulations systematically report the opposite. To understand these discrepancies, analytical ultracentrifugation experiments are combined with molecular simulations, both performed at unprecedented accuracies, to study the transport of buckminsterfullerene C60 in toluene at infinite dilution. This system is demonstrated to clearly violate the conditions of slow momentum relaxation. Yet, through a linear response to a constant force, the SES equation can be recovered in the long time limit with no more than 4% uncertainty both in experiments and in simulations. This nonetheless requires partial slip on the particle interface, extracted consistently from all the data. These results, thus, resolve a long-standing discussion on the validity and limits of the SES equation at the molecular scale., Comment: The data that support the findings of this study are openly available in Zenodo at http://doi.org/10.5281/zenodo.8281244, reference number 8281244

Particle filtering methods can be applied to estimation problems in discrete spaces on bounded domains, to sample from and marginalise over unknown hidden states. As in continuous settings, problems such as particle degradation can arise: proposed particles can be incompatible with the data, lying in low probability regions or outside the boundary constraints, and the discrete system could result in all particles having weights of zero. In this paper we introduce the Lifebelt Particle Filter (LBPF), a novel method for robust likelihood estimation in low-valued count problems. The LBPF combines a standard particle filter with one (or more) lifebelt particles which, by construction, lie within the boundaries of the discrete random variables, and therefore are compatible with the data. A mixture of resampled and non-resampled particles allows for the preservation of the lifebelt particle, which, together with the remaining particle swarm, provides samples from the filtering distribution, and can be used to generate unbiased estimates of the likelihood. The main benefit of the LBPF is that only one or few, wisely chosen, particles are sufficient to prevent particle collapse. Differently from other methods, there is no need to increase the number of particles, and therefore the computational effort, in regions of the parameter space that generate less likely hidden states. The LBPF can be used within a pseudo-marginal scheme to draw inferences on static parameters, $ \boldsymbol{\theta} $, governing the system. We address here the estimation of a parameter governing probabilities of deaths and recoveries of hospitalised patients during an epidemic.

Filamentary structures have been found nearly ubiquitously in molecular clouds and yet their formation and evolution is still poorly understood. We examine a segment of Taurus Molecular Cloud 1 (TMC-1) that appears as a single, narrow filament in continuum emission from dust. We use the Regularized Optimization for Hyper-Spectral Analysis (ROHSA), a Gaussian decomposition algorithm which enforces spatial coherence when fitting multiple velocity components simultaneously over a data cube. We analyze HC$_5$N (9-8) line emission as part of the Green Bank Ammonia Survey (GAS) and identify three velocity-coherent components with ROHSA. The two brightest components extend the length of the filament, while the third component is fainter and clumpier. The brightest component has a prominent transverse velocity gradient of $2.7 \pm 0.1$ km s$^{-1}$ pc$^{-1}$ that we show to be indicative of gravitationally induced inflow. In the second component, we identify regularly spaced emission peaks along its length. We show that the local minima between pairs of adjacent HC$_5$N peaks line up closely with submillimetre continuum emission peaks, which we argue is evidence for fragmentation along the spine of TMC-1. While coherent velocity components have been described as separate physical structures in other star-forming filaments, we argue that the two bright components identified in HC$_5$N emission in TMC-1 are tracing two layers in one filament: a lower density outer layer whose material is flowing under gravity towards the higher density inner layer of the filament., Comment: 17 pages, 8 figures; Accepted for publication to MNRAS

We present the discovery of Bo\"otes V, a new ultra-faint dwarf galaxy candidate. This satellite is detected as a resolved overdensity of stars during an ongoing search for new Local Group dwarf galaxy candidates in the UNIONS photometric dataset. It has a physical half-light radius of 26.9$^{+7.5}_{-5.4}$ pc, a $V$-band magnitude of $-$4.5 $\pm$ 0.4 mag, and resides at a heliocentric distance of approximately 100 kpc. We use Gaia DR3 astrometry to identify member stars, characterize the systemic proper motion, and confirm the reality of this faint stellar system. The brightest star in this system was followed up using Gemini GMOS-N long-slit spectroscopy and is measured to have a metallicity of [Fe/H] $=$ $-$2.85 $\pm$ 0.10 dex and a heliocentric radial velocity of $v_r$ = 5.1 $\pm$ 13.4 km s$^{-1}$. Bo\"otes V is larger (in terms of scale radius), more distant, and more metal-poor than the vast majority of globular clusters. It is likely that Bo\"otes V is an ultra-faint dwarf galaxy, though future spectroscopic studies will be necessary to definitively classify this object., Comment: 13 pages, 7 figures, 3 tables. Accepted for publication in the AAS Journals. Please note that this paper was submitted in coordination with the work of William Cerny et al. 2022. These authors independently discovered this same satellite so our two research groups have coordinated the submission of these discovery papers

Novel Monte Carlo methods to generate samples from a target distribution, such as a posterior from a Bayesian analysis, have rapidly expanded in the past decade. Algorithms based on Piecewise Deterministic Markov Processes (PDMPs), non-reversible continuous-time processes, are developing into their own research branch, thanks their important properties (e.g., correct invariant distribution, ergodicity, and super-efficiency). Nevertheless, practice has not caught up with the theory in this field, and the use of PDMPs to solve applied problems is not widespread. This might be due, firstly, to several implementational challenges that PDMP-based samplers present with and, secondly, to the lack of papers that showcase the methods and implementations in applied settings. Here, we address both these issues using one of the most promising PDMPs, the Zig-Zag sampler, as an archetypal example. After an explanation of the key elements of the Zig-Zag sampler, its implementation challenges are exposed and addressed. Specifically, the formulation of an algorithm that draws samples from a target distribution of interest is provided. Notably, the only requirement of the algorithm is a closed-form function to evaluate the target density of interest, and, unlike previous implementations, no further information on the target is needed. The performance of the algorithm is evaluated against another gradient-based sampler, and it is proven to be competitive, in simulation and real-data settings. Lastly, we demonstrate that the super-efficiency property, i.e. the ability to draw one independent sample at a lesser cost than evaluating the likelihood of all the data, can be obtained in practice., Comment: Small edits from previous version following some minor revisions requested

Using light to control transient phases in quantum materials is an emerging route to engineer new properties and functionality, with both thermal and non-thermal phases observed out of equilibrium. Transient phases are expected to be heterogeneous, either through photo-generated domain growth or by generating topological defects, and this impacts the dynamics of the system. However, this nanoscale heterogeneity has not been directly observed. Here we use time- and spectrally resolved coherent X-ray imaging to track the prototypical light induced insulator-to-metal phase transition in vanadium dioxide on the nanoscale with femtosecond time resolution. We show that the early-time dynamics are independent of the initial spatial heterogeneity and observe a 200 fs switch to the metallic phase. A heterogeneous response emerges only after hundreds of picoseconds. Through spectroscopic imaging, we reveal that the transient metallic phase is a highly orthorhombically strained rutile metallic phase, an interpretation that is in contrast to those based on spatially averaged probes. Our results demonstrate the critical importance of spatially and spectrally resolved measurements for understanding and interpreting the transient phases of quantum materials.

Predicting lithium-ion battery degradation is worth billions to the global automotive, aviation and energy storage industries, to improve performance and safety and reduce warranty liabilities. However, very few published models of battery degradation explicitly consider the interactions between more than two degradation mechanisms, and none do so within a single electrode. In this paper, the first published attempt to directly couple more than two degradation mechanisms in the negative electrode is reported. The results are used to map different pathways through the complicated path dependent and non-linear degradation space. Four degradation mechanisms are coupled in PyBaMM, an open source modelling environment uniquely developed to allow new physics to be implemented and explored quickly and easily. Crucially it is possible to see 'inside' the model and observe the consequences of the different patterns of degradation, such as loss of lithium inventory and loss of active material. For the same cell, five different pathways that can result in end-of-life have already been found, depending on how the cell is used. Such information would enable a product designer to either extend life or predict life based upon the usage pattern. However, parameterization of the degradation models remains as a major challenge, and requires the attention of the international battery community., Comment: First version submitted to Energy and Environmental Science on 15th November 2021. Second version submitted to Physical Chemistry: Chemical Physics on 25th January 2022

The impact of an applied electric field on the exchange coupling parameters has been investigated based on first-principles electronic structure calculations by means of the KKR Green function method. The calculations have been performed for a Fe film, free-standing and deposited on two different substrates, having 1 monolayer (ML) thickness to minimize the effect of screening of the electric field typical for metallic systems. By comparing the results for the free-standing Fe ML with those for Fe on the various substrates, we could analyze the origin of the field-induced change of the exchange interactions. Compared to the free-standing Fe ML, in particular rather pronounced changes have been found for the Fe/Pt(111) system due to the localized electronic states at the Fe/Pt interface, which are strongly affected by the electric field and which play an important role for the Fe-Fe exchange interactions.

Performing model selection for coupled hidden Markov models (CHMMs) is highly challenging, owing to the large dimension of the hidden state process. Whilst in principle the hidden state process can be marginalized out via forward filtering, in practice the computational cost of doing so increases exponentially with the number of coupled Markov chains, making this approach infeasible in most applications. Monte Carlo methods can be utilized, but despite many remarkable developments in model selection methodology, generic approaches continue to be ill-suited for such high-dimensional problems. Here we develop specialized solutions for CHMMs with weak inter-chain dependencies. Specifically we construct effective proposal distributions for the hidden state process that remain computationally viable as the number of chains increases, and that require little user input or tuning. This methodology is particularly applicable to individual-level infectious disease models characterized as CHMMs, in which each chain represents an individual, and the coupling represents contact between individuals. Since the only significant contacts are between susceptible and infectious individuals, and since multiple infection pathways are often possible, the resulting CHMMs naturally have low inter-chain dependencies. We demonstrate the utility of our methodology with an application to a study of highly pathogenic avian influenza in chickens.

Infectious diseases remain one of the major causes of human mortality and suffering. Mathematical models have been established as an important tool for capturing the features that drive the spread of the disease, predicting the progression of an epidemic and hence guiding the development of strategies to control it. Another important area of epidemiological interest is the development of geostatistical methods for the analysis of data from spatially referenced prevalence surveys. Maps of prevalence are useful, not only for enabling a more precise disease risk stratification, but also for guiding the planning of more reliable spatial control programmes by identifying affected areas. Despite the methodological advances that have been made in each area independently, efforts to link transmission models and geostatistical maps have been limited. Motivated by this fact, we developed a Bayesian approach that combines fine-scale geostatistical maps of disease prevalence with transmission models to provide quantitative, spatially explicit projections of the current and future impact of control programs against a disease. These estimates can then be used at a local level to identify the effectiveness of suggested intervention schemes and allow investigation of alternative strategies. The methodology has been applied to lymphatic filariasis in East Africa to provide estimates of the impact of different intervention strategies against the disease., Comment: 48 pages

Between pandemics, the influenza virus exhibits periods of incremental evolution via a process known as antigenic drift. This process gives rise to a sequence of strains of the pathogen that are continuously replaced by newer strains, preventing a build up of immunity in the host population. In this paper, a parsimonious epidemic model is defined that attempts to capture the dynamics of evolving strains within a host population. The `evolving strains' epidemic model has many properties that lie in-between the Susceptible-Infected-Susceptible and the Susceptible-Infected-Removed epidemic models, due to the fact that individuals can only be infected by each strain once, but remain susceptible to reinfection by newly emerged strains. Coupling results are used to identify key properties, such as the time to extinction. A range of reproduction numbers are explored to characterize the model, including a novel quasi-stationary reproduction number that can be used to describe the re-emergence of the pathogen into a population with `average' levels of strain immunity, analogous to the beginning of the winter peak in influenza. Finally the quasi-stationary distribution of the evolving strains model is explored via simulation., Comment: 34 pages, 7 figures, in review