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With the increasing prevalence and diversity of robots interacting in the real world, there is need for flexible, on-the-fly planning and cooperation. Large Language Models are starting to be explored in a multimodal setup for communication, coordination, and planning in robotics. Existing approaches generally use a single agent building a plan, or have multiple homogeneous agents coordinating for a simple task. We present a decentralised, dialogical approach in which a team of agents with different abilities plans solutions through peer-to-peer and human-robot discussion. We suggest that argument-style dialogues are an effective way to facilitate adaptive use of each agent's abilities within a cooperative team. Two robots discuss how to solve a cleaning problem set by a human, define roles, and agree on paths they each take. Each step can be interrupted by a human advisor and agents check their plans with the human. Agents then execute this plan in the real world, collecting rubbish from people in each room. Our implementation uses text at every step, maintaining transparency and effective human-multi-robot interaction.

Background: Cluster states in $^{13}$N are extremely difficult to measure due to the unavailability of $^{9}$B+$\alpha$ elastic scattering data. Purpose: Using $\beta$-delayed charged-particle spectroscopy of $^{13}$O, clustered states in $^{13}$N can be populated and measured in the 3$\alpha$+p decay channel. Method: One-at-a-time implantation/decay of $^{13}$O was performed with the Texas Active Target Time Projection Chamber (TexAT TPC). 149 $\beta 3\alpha p$ decay events were observed and the excitation function in $^{13}$N reconstructed. Results: Four previously unknown $\alpha$-decaying excited states were observed in $^{13}$N at an excitation energy of 11.3 MeV, 12.4 MeV, 13.1 MeV and 13.7 MeV decaying via the 3$\alpha$+p channel. Conclusion: These states are seen to have a [$^{9}\mathrm{B}(\mathrm{g.s}) \bigotimes \alpha$/ $p+^{12}\mathrm{C}(0_{2}^{+})$], [$^{9}\mathrm{B}(\frac{1}{2}^{+}) \bigotimes \alpha$], [$^{9}\mathrm{B}(\frac{5}{2}^{+}) \bigotimes \alpha$] and [$^{9}\mathrm{B}(\frac{5}{2}^{+}) \bigotimes \alpha$] structure respectively. A previously-seen state at 11.8 MeV was also determined to have a [$p+^{12}\mathrm{C}(\mathrm{g.s.})$/ $p+^{12}\mathrm{C}(0_{2}^{+})$] structure. The overall magnitude of the clustering is not able to be extracted however due to the lack of a total width measurement. Clustered states in $^{13}$N (with unknown magnitude) seem to persist from the addition of a proton to the highly $\alpha$-clustered $^{12}$C. Evidence of the $\frac{1}{2}^{+}$ state in $^{9}$B was also seen to be populated by decays from $^{13}$N$^{\star}$., Comment: arXiv admin note: substantial text overlap with arXiv:2302.14111

Stellar feedback in dwarf galaxies remains, to date, poorly explored, yet is crucial to understanding galaxy evolution in the early Universe. In particular, pre-supernova feedback has recently been found to play a significant role in regulating and disrupting star formation in larger spiral galaxies, but it remains uncertain if it also plays this role in dwarfs. We study the ionised gas properties and stellar content of individual star-forming regions across three nearby, low-metallicity, dwarf starburst galaxies (J0921, KKH046, and Leo P) to investigate how massive stars influence their surroundings and how this influence changes as a function of environment. To achieve this, we extracted integrated spectra of 30 HII regions from archival VLT/MUSE integral field spectroscopic observations of these three dwarf starburst galaxies. We fitted the HII regions' main emission lines with Gaussian profiles to derive their oxygen abundances, electron densities, and luminosities, and we used the Stochastically Lighting Up Galaxies (SLUG) code to derive the stellar mass, age, and bolometric luminosity of the stellar populations driving the HII regions. We then quantified two pre-supernova stellar feedback mechanisms, namely the direct radiation pressure and photoionisation feedback, and explored how feedback strength varies with HII region properties. Our findings suggest that stellar feedback has less of an impact on evolved regions, with both the pressure of the ionised gas and the direct radiation pressure decreasing as a function of HII region size. We also find that these stellar feedback mechanisms are dependent on the metallicity of the HII regions. These findings extend results from stellar feedback studies of more massive star-forming galaxies to the low-mass, low-metallicity regime., Comment: The paper has been published in A&A

We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of galaxies by training an artificial neural network (ANN) to recognise the relationships between the equivalent widths of spectral indices and the mass-weighted ages of galaxies estimated by the MAGPHYS model in data release 3 (DR3) of the Galaxy and Mass Assembly (GAMA) survey. We discuss the optimisation of our hyperparameters extensively and investigate the application of a custom loss function to reduce the influence of errors in our input data. To quantify the quality of our predictions we calculate the mean squared error (MSE), mean absolute error (MAE) and R^2 score for which we find MSE = 0.020, MAE = 0.108 and R^2 = 0.530. We find our predicted ages have a similar distribution with standard deviation sigma_p = 0.182 compared with the GAMA true ages sigma_t = 0.207. This is achieved in approximately 23s to train our ANN on an 11th Gen Intel Core i9-11900H running at 2.50GHz using 32GB of RAM. We report our results for when light-weighted ages are used to train the ANN, which improves the accuracy of the predictions. Finally, we detail an evaluation of our method relating to physical properties and compare with other ML techniques to encourage future applications of ML techniques in Astronomy., Comment: 20 pages, 19 figures, Published in MNRAS - please use arXiv version

Searches for dark matter with liquid xenon time projection chamber experiments have traditionally focused on the region of the parameter space that is characteristic of weakly interacting massive particles, ranging from a few GeV/$c^2$ to a few TeV/$c^2$. Models of dark matter with a mass much heavier than this are well motivated by early production mechanisms different from the standard thermal freeze-out, but they have generally been less explored experimentally. In this work, we present a re-analysis of the first science run (SR1) of the LZ experiment, with an exposure of $0.9$ tonne$\times$year, to search for ultraheavy particle dark matter. The signal topology consists of multiple energy deposits in the active region of the detector forming a straight line, from which the velocity of the incoming particle can be reconstructed on an event-by-event basis. Zero events with this topology were observed after applying the data selection calibrated on a simulated sample of signal-like events. New experimental constraints are derived, which rule out previously unexplored regions of the dark matter parameter space of spin-independent interactions beyond a mass of 10$^{17}$ GeV/$c^2$., Comment: 9 pages, 7 figures

Formal Modelling is often used as part of the design and testing process of software development to ensure that components operate within suitable bounds even in unexpected circumstances. In this paper, we use predictive formal modelling (PFM) at runtime in a human-swarm mission and show that this integration can be used to improve the performance of human-swarm teams. We recruited 60 participants to operate a simulated aerial swarm to deliver parcels to target locations. In the PFM condition, operators were informed of the estimated completion times given the number of drones deployed, whereas in the No-PFM condition, operators did not have this information. The operators could control the mission by adding or removing drones from the mission and thereby, increasing or decreasing the overall mission cost. The evaluation of human-swarm performance relied on four key metrics: the time taken to complete tasks, the number of agents involved, the total number of tasks accomplished, and the overall cost associated with the human-swarm task. Our results show that PFM modelling at runtime improves mission performance without significantly affecting the operator's workload or the system's usability., Comment: This work has been accepted in HRI '24 LBR track. It consist of 5 pages, 2 figures, and 2 tables. This is the author's submitted manuscript, for your personal use. Not for redistribution. The definitive Version of Record will be published in the Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI '24 Companion), https://doi.org/10.1145/3610978.3640725

In this thesis, we use classical, semi-classical and quantum-mechanical methods to simulate chemical reaction dynamics inside of an optical cavity. Within such a cavity, by selectively coupling vibrational modes of the reactants to the vacuum state of light, recent experiments have observed significant changes in reaction rates and equilibrium constants - all without any external input of energy. We investigate the dynamics of both a single reaction and an ensemble of N identical reactions coupled to the cavity. In our single reactant studies, we find significant modification to the rate of reaction and to its quantum-mechanical equilibrium constant. All of the effects observed in our single molecule studies are however found to diminish as the number of reactants is increased. For any experimentally relevant number of molecules, the cavity effects on the reaction rate and the equilibrium constant are therefore shown to be negligible within all theories considered in this thesis. This thesis therefore does not offer any explanation for the experimental observations. It does however highlight issues with all current theoretical work on this topic, and provides suggestions - in light of the results presented here and in recent literature - as to what might be required to explain these effects., Comment: undergrad thesis

Observatories need to measure and evaluate the scientific output and overall impact of their facilities. An observatory bibliography consists of the papers published using that observatory's data, typically gathered by searching the major journals for relevant keywords. Recently, the volume of literature and methods by which the publications pool is evaluated has increased. Efficient and standardized procedures are necessary to assign meaningful metadata; enable user-friendly retrieval; and provide the opportunity to derive reports, statistics, and visualizations to impart a deeper understanding of the research output. In 2021, a group of observatory bibliographers from around the world convened online to continue the discussions presented in Lagerstrom (2015). We worked to extract general guidelines from our experiences, techniques, and lessons learnt. The paper explores the development, application, and current status of telescope bibliographies and future trends. This paper briefly describes the methodologies employed in constructing databases, along with the various bibliometric techniques used to analyze and interpret them. We explain reasons for non-standardization and why it is essential for each observatory to identify metadata and metrics that are meaningful for them; caution the (over-)use of comparisons among facilities that are, ultimately, not comparable through bibliometrics; and highlight the benefits of telescope bibliographies, both for researchers within the astronomical community and for stakeholders beyond the specific observatories. There is tremendous diversity in the ways bibliographers track publications and maintain databases, due to parameters such as resources, type of observatory, historical practices, and reporting requirements to funders and outside agencies. However, there are also common sets of Best Practices., Comment: 19 pages, 3 appendices

An important real-world application of multi-robot systems is multi-robot patrolling (MRP), where robots must carry out the activity of going through an area at regular intervals. Motivations for MRP include the detection of anomalies that may represent security threats. While MRP algorithms show some maturity in development, a key potential advantage has been unexamined: the ability to exploit collective perception of detected anomalies to prioritize the location ordering of security checks. This is because noisy individual-level detection of an anomaly may be compensated for by group-level consensus formation regarding whether an anomaly is likely to be truly present. Here, we examine the performance of unmodified idleness-based patrolling algorithms when given the additional objective of reaching an environmental perception consensus via local pairwise communication and a quorum threshold. We find that generally, MRP algorithms that promote physical mixing of robots, as measured by a higher connectivity of their emergent communication network, reach consensus more quickly. However, when there is noise present in anomaly detection, a more moderate (constrained) level of connectivity is preferable because it reduces the spread of false positive detections, as measured by a group-level F-score. These findings can inform user choice of MRP algorithm and future algorithm development.

Under the assumption of a simple and time-invariant gravitational potential, many Galactic dynamics techniques infer the Milky Way's mass and dark matter distribution from stellar kinematic observations. These methods typically rely on parameterized potential models of the Galaxy and must take into account non-trivial survey selection effects, because they make use of the density of stars in phase space. Large-scale spectroscopic surveys now supply information beyond kinematics in the form of precise stellar label measurements (especially element abundances). These element abundances are known to correlate with orbital actions or other dynamical invariants. Here, we use the Orbital Torus Imaging (OTI) framework that uses abundance gradients in phase space to map orbits. In many cases these gradients can be measured without detailed knowledge of the selection function. We use stellar surface abundances from the APOGEE survey combined with kinematic data from the Gaia mission. Our method reveals the vertical ($z$-direction) orbit structure in the Galaxy and enables empirical measurements of the vertical acceleration field and orbital frequencies in the disk. From these measurements, we infer the total surface mass density, $\Sigma$, and midplane volume density, $\rho_0$, as a function of Galactocentric radius and height. Around the Sun, we find $\Sigma_{\odot}(z=1.1$ kpc)$=72^{+6}_{-9}$M$_{\odot}$pc$^{-2}$ and $\rho_{\odot}(z=0)=0.081^{+0.015}_{-0.009}$ M$_{\odot}$pc$^{-3}$ using the most constraining abundance ratio, [Mg/Fe]. This corresponds to a dark matter contribution in surface density of $\Sigma_{\odot,\mathrm{DM}}(z=1.1$ kpc)$=24\pm4$ M$_{\odot}$pc$^{-2}$, and in total volume mass density of $\rho_{\odot,\mathrm{DM}}(z=0)=0.011\pm0.002$ M$_{\odot}$pc$^{-3}$. Moreover, using these mass density values we estimate the scale length of the low-$\alpha$ disc to be $h_R=2.24\pm0.06$kpc., Comment: Accepted for publication in ApJ. 19 pages, 11 figures, 3 Tables

500,000 to 1 million satellites are expected in the next decades, primarily to build internet constellations called megaconstellations. These megaconstellations are disposable and will constantly re-enter and be replaced, hence creating a layer of conductive particulate. Here it will be shown that the mass of the conductive particles left behind from worldwide distribution of re-entry satellites is already billions of times greater than the mass of the Van Allen Belts. From a preliminary analysis, the Debye length in spaceflight regions is significantly higher than non-spaceflight regions according to CCMC ionosphere data. As the megaconstellations grow, the Debye length of the satellite particulate may exceed that of the cislunar environment and create a conductive layer around the earth worldwide. Thus, satellite reentries may create a global band of plasma dust with a charge higher than the rest of the magnetosphere. Therefore, perturbation of the magnetosphere from conductive satellites and their plasma dust layer should be expected and should be a field of intensive research. Human activity is not only impacting the atmosphere, it is clearly impacting the ionosphere., Comment: 5 pages, 3 figures

We present the extended ALMA MaNGA QUEnching and STar formation survey, a combination of the original 46 ALMaQUEST galaxies plus new ALMA observations for a further 20 interacting galaxies. Three well-studied scaling relations are fit to the 19,999 star-forming spaxels in the extended sample, namely the resolved Schmidt-Kennicutt (rSK) relation, the resolved star forming main sequence (rSFMS) and the resolved molecular gas main sequence (rMGMS). We additionally investigate the relationship between the dynamical equilibrium pressure (PDE) and star formation rate surface density (Sigma_SFR), which we refer to as the resolved PDE (rPDE) relation. Contrary to previous studies that have focussed on normal star-forming galaxies and found an approximately linear rPDE relation, the presence of more vigourously star-forming galaxies in the extended ALMaQUEST sample reveals a marked turnover in the relation at high pressures. Although the scatter around the linear fit to the rPDE relation is similar to the other three relations, a random forest analysis, which can extract non-linear dependences, finds that PDE is unambiguously more important than either Sigma_H2 or Sigma_star for predicting Sigma_SFR. We compare the observed rPDE relation to the prediction of the pressure-regulated feedback-modulated (PRFM) model of star formation, finding that galaxies residing on the global SFMS do indeed closely follow the rPDE relation predicted by the PRFM theory. However, galaxies above and below the global SFMS show significant deviations from the model. Galaxies with high SFR are instead consistent with models that include other contributions to turbulence in addition to the local star formation feedback., Comment: Accepted for publication in MNRAS

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare forecasts of Storm Ciar\'an, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciar\'an.

Following the first science results of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time projection chamber operating from the Sanford Underground Research Facility in Lead, South Dakota, USA, we report the initial limits on a model-independent non-relativistic effective field theory describing the complete set of possible interactions of a weakly interacting massive particle (WIMP) with a nucleon. These results utilize the same 5.5 t fiducial mass and 60 live days of exposure collected for the LZ spin-independent and spin-dependent analyses while extending the upper limit of the energy region of interest by a factor of 7.5 to 270 keVnr. No significant excess in this high energy region is observed. Using a profile-likelihood ratio analysis, we report 90% confidence level exclusion limits on the coupling of each individual non-relativistic WIMP-nucleon operator for both elastic and inelastic interactions in the isoscalar and isovector bases., Comment: 17 pages 11 figures

Purpose: This study assesses the effectiveness of Deep Learning (DL) for creating synthetic CT (sCT) images in MR-guided adaptive radiation therapy (MRgART). Methods: A Cycle-GAN model was trained with MRI and CT scan slices from MR-LINAC treatments, generating sCT volumes. The analysis involved retrospective treatment plan data from patients with various tumors. sCT images were compared with standard CT scans using mean absolute error in Hounsfield Units (HU) and image similarity metrics (SSIM, PSNR, NCC). sCT volumes were integrated into a clinical treatment system for dosimetric re-evaluation. Results: The model, trained on 8405 frames from 57 patients and tested on 357 sCT frames from 17 patients, showed sCTs comparable to dCTs in electron density and structural similarity with MRI scans. The MAE between sCT and dCT was 49.2 +/- 13.2 HU, with sCT NCC exceeding dCT by 0.06, and SSIM and PSNR at 0.97 +/- 0.01 and 19.9 +/- 1.6 respectively. Dosimetric evaluations indicated minimal differences between sCTs and dCTs, with sCTs showing better air-bubble reconstruction. Conclusions: DL-based sCT generation on MR-Linacs is accurate for dose calculation and optimization in MRgART. This could facilitate MR-only treatment planning, enhancing simulation and adaptive planning efficiency on MR-Linacs.