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Control system middle layers act as a co-ordination and communication bridge between end users, including operators, system experts, scientists, and experimental users, and the low-level control system interface. This article describes a Python package -- Controls Abstraction Towards Acclerator Physics (CATAP) -- which aims to build on previous experience and provide a modern Python-based middle layer with explicit abstraction, YAML-based configuration, and procedural code generation. CATAP provides a structured and coherent interface to a control system, allowing researchers and operators to centralize higher-level control logic and device information. This greatly reduces the amount of code that a user must write to perform a task, and codifies system knowledge that is usually anecdotal. The CATAP design has been deployed at two accelerator facilities, and has been developed to produce a procedurally generated facility-specific middle layer package from configuration files to enable its wider dissemination across other machines.

Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it's impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with traditional machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model which allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filter (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE)=0.009, which was a significant improvement (30%) over the traditional Random Forest (RF), and the model performed even better at lower redshifts achieving a MSE=0.0007 (a 50% improvement over the RF) in the range of z<0.3. This method could be hugely beneficial to upcoming surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.

Euclid will cover over 14000 $deg^{2}$ with two optical and near-infrared spectro-photometric instruments, and is expected to detect around ten million active galactic nuclei (AGN). This unique data set will make a considerable impact on our understanding of galaxy evolution and AGN. In this work we identify the best colour selection criteria for AGN, based only on Euclid photometry or including ancillary photometric observations, such as the data that will be available with the Rubin legacy survey of space and time (LSST) and observations already available from Spitzer/IRAC. The analysis is performed for unobscured AGN, obscured AGN, and composite (AGN and star-forming) objects. We make use of the spectro-photometric realisations of infrared-selected targets at all-z (SPRITZ) to create mock catalogues mimicking both the Euclid Wide Survey (EWS) and the Euclid Deep Survey (EDS). Using these catalogues we estimate the best colour selection, maximising the harmonic mean (F1) of completeness and purity. The selection of unobscured AGN in both Euclid surveys is possible with Euclid photometry alone with F1=0.22-0.23, which can increase to F1=0.43-0.38 if we limit at z>0.7. Such selection is improved once the Rubin/LSST filters (a combination of the u, g, r, or z filters) are considered, reaching F1=0.84 and 0.86 for the EDS and EWS, respectively. The combination of a Euclid colour with the [3.6]-[4.5] colour, which is possible only in the EDS, results in an F1-score of 0.59, improving the results using only Euclid filters, but worse than the selection combining Euclid and LSST. The selection of composite ($f_{\rm AGN}$=0.05-0.65 at 8-40 $\mu m$) and obscured AGN is challenging, with F1<0.3 even when including ancillary data. This is driven by the similarities between the broad-band spectral energy distribution of these AGN and star-forming galaxies in the wavelength range 0.3-5 $\mu m$.

We review observations of solar activity, geomagnetic variation, and auroral visibility for the extreme geomagnetic storm on 1872 February 4. The extreme storm (referred to here as the Chapman-Silverman storm) apparently originated from a complex active region of moderate area (\approx 500 {\mu}sh) that was favorably situated near disk center (S19° E05°). There is circumstantial evidence for an eruption from this region at 9--10 UT on 1872 February 3, based on the location, complexity, and evolution of the region, and on reports of prominence activations, which yields a plausible transit time of \approx29 hr to Earth. Magnetograms show that the storm began with a sudden commencement at \approx14:27 UT and allow a minimum Dst estimate of £ -834 nT. Overhead aurorae were credibly reported at Jacobabad (British India) and Shanghai (China), both at 19°.9 in magnetic latitude (MLAT) and 24°. 2 in invariant latitude (ILAT). Auroral visibility was reported from 13 locations with MLAT below |20|° for the 1872 storm (ranging from |10°. 0|--|19°. 9| MLAT) versus one each for the 1859 storm (|17°. 3| MLAT) and the 1921 storm (|16.°2| MLAT). The auroral extension and conservative storm intensity indicate a magnetic storm of comparable strength to the extreme storms of 1859 September (25°.1 \pm 0°.5 ILAT and -949 \pm 31 nT) and 1921 May (27°.1 ILAT and -907 \pm 132 nT), which places the 1872 storm among the three largest magnetic storms yet observed.

Classical force fields (FF) based on machine learning (ML) methods show great potential for large scale simulations of materials. MLFFs have hitherto largely been designed and fitted for specific systems and are not usually transferable to chemistries beyond the specific training set. We develop a unified atomisitic line graph neural network-based FF (ALIGNN-FF) that can model both structurally and chemically diverse materials with any combination of 89 elements from the periodic table. To train the ALIGNN-FF model, we use the JARVIS-DFT dataset which contains around 75000 materials and 4 million energy-force entries, out of which 307113 are used in the training. We demonstrate the applicability of this method for fast optimization of atomic structures in the crystallography open database and by predicting accurate crystal structures using genetic algorithm for alloys.

We present the measurement of Baryon Acoustic Oscillations (BAO) from the Lyman-$\alpha$ (Ly$\alpha$) forest of high-redshift quasars with the first-year dataset of the Dark Energy Spectroscopic Instrument (DESI). Our analysis uses over $420\,000$ Ly$\alpha$ forest spectra and their correlation with the spatial distribution of more than $700\,000$ quasars. An essential facet of this work is the development of a new analysis methodology on a blinded dataset. We conducted rigorous tests using synthetic data to ensure the reliability of our methodology and findings before unblinding. Additionally, we conducted multiple data splits to assess the consistency of the results and scrutinized various analysis approaches to confirm their robustness. For a given value of the sound horizon ($r_d$), we measure the expansion at $z_{\rm eff}=2.33$ with 2\% precision, $H(z_{\rm eff}) = (239.2 \pm 4.8) (147.09~{\rm Mpc} /r_d)$ km/s/Mpc. Similarly, we present a 2.4\% measurement of the transverse comoving distance to the same redshift, $D_M(z_{\rm eff}) = (5.84 \pm 0.14) (r_d/147.09~{\rm Mpc})$ Gpc. Together with other DESI BAO measurements at lower redshifts, these results are used in a companion paper to constrain cosmological parameters.

Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of our knowledge, this is the first deterministic model that is aiming to achieve disentanglement based on autoencoders without regularizers. The proposed model is compared to seven state-of-the-art generative models based on autoencoders and evaluated based on five supervised disentanglement metrics. The experimental results show that the proposed model can have better disentanglement when variances of each features are different. We believe that this model leads to a new field for disentanglement learning based on autoencoders without regularizers.

One of the key challenges in performing muon experiments is knowing which temperatures and applied fields to measure at, and how many muon decays should be measured at each temperature/field combination to get the most useful dataset. We have developed a technique using Fisher information which, for a given muon asymmetry function, can analytically calculate the number of muon decays required to obtain a given error on the parameters of the asymmetry model. Here, we report on the results of our project, in particular applying our methodology to the problem of knowing the best choice of applied longitudinal fields for ionic diffusion experiments.

The discovery of new materials is often constrained by the need for large labelled datasets or expensive simulations. In this study, we explore the use of Disentangling Autoencoders (DAEs) to learn compact and interpretable representations of spectral data in an entirely unsupervised manner. We demonstrate that the DAE captures physically meaningful features in optical absorption spectra, relevant to photovoltaic (PV) performance, including a latent dimension strongly correlated with the Spectroscopic Limited Maximum Efficiency (SLME)--despite being trained without access to SLME labels. This feature corresponds to a well-known spectral signature: the transition from direct to indirect optical band gaps. Compared to Principal Component Analysis (PCA) and a beta-Variational Autoencoder (beta-VAE), the DAE achieves superior reconstruction fidelity, improved correlation with efficiency metrics, and more compact encoding of relevant features. We further show that the DAE latent space enables more efficient discovery of high-performing PV materials, identifying top candidates using fewer evaluations than both VAE-guided and random search. These results highlight the potential of DAEs as a powerful tool for unsupervised structure-property learning and suggest broad applicability to other areas of materials discovery where labeled data is limited but rich structure is present in raw signals.

Auroral records found in historical archives and cosmogenic isotopes found in natural archives have served as sound proxies of coronal mass ejections (CMEs) and solar energetic particles (SEPs), respectively, for dates prior to the onset of telescopic sunspot observations in 1610. These space weather events constitute a significant threat to a modern civilization, because of its increasing dependency on an electronic infrastructure. Recent studies have identified multiple extreme space weather events derived from solar energetic particles (SEPs) in natural archives, such as the event in 660 BCE. While the level of solar activity around 660 BCE is of great interest, this had not been within the coverage of the hitherto-known datable auroral records in historical documents that extend back to the 6th century BCE. Therefore, we have examined Assyrian astrological reports in the 8th and 7th centuries BCE, identified three observational reports of candidate aurorae, and dated these reports to approximately 680 BCE -- 650 BCE. The Assyrian cuneiform tablets let us extend the history of auroral records and solar activity by a century. These cuneiform reports are considered to be the earliest datable records of candidate aurorae and they support the concept of enhanced solar activity suggested by the cosmogenic isotopes from natural archives.

Modelling has become a third distinct line of scientific enquiry, alongside experiments and theory. Molecular dynamics (MD) simulations serve to interpret, predict and guide experiments and to test and develop theories. A major limiting factor of MD simulations is system size and in particular the difficulty in handling, storing and processing trajectories of very large systems. This limitation has become significant as the need to simulate large system sizes of the order of billions of atoms and beyond has been steadily growing. Examples include interface phenomena, composite materials, biomaterials, melting, nucleation, atomic transport, adhesion, radiation damage and fracture. More generally, accessing new length and energy scales often brings qualitatively new science, but this has currently reached a bottleneck in MD simulations due to the traditional methods of storing and post-processing trajectory files. To address this challenge, we propose a new paradigm of running MD simulations: instead of storing and post-processing trajectory files, we calculate key system properties on-the-fly. Here, we discuss the implementation of this idea and on-the-fly calculation of key system properties in the general-purpose MD code, DL_POLY. We discuss code development, new capabilities and the calculation of these properties, including correlation functions, viscosity, thermal conductivity and elastic constants. We give examples of these on-the-fly calculations in very large systems. Our developments offer a new way to run MD simulations of large systems efficiently in the future.

Gravitational lensing due to the large-scale distribution of matter in the cosmos distorts the primordial Cosmic Microwave Background (CMB) and thereby induces new, small-scale $B$-mode polarization. This signal carries detailed information about the distribution of all the gravitating matter between the observer and CMB last scattering surface. We report the first direct evidence for polarization lensing based on purely CMB information, from using the four-point correlations of even- and odd-parity $E$- and $B$-mode polarization mapped over $\sim30$ square degrees of the sky measured by the POLARBEAR experiment. These data were analyzed using a blind analysis framework and checked for spurious systematic contamination using null tests and simulations. Evidence for the signal of polarization lensing and lensing $B$-modes is found at 4.2$\sigma$ (stat.+sys.) significance. The amplitude of matter fluctuations is measured with a precision of $27\%$, and is found to be consistent with the Lambda Cold Dark Matter ($\Lambda$CDM) cosmological model. This measurement demonstrates a new technique, capable of mapping all gravitating matter in the Universe, sensitive to the sum of neutrino masses, and essential for cleaning the lensing $B$-mode signal in searches for primordial gravitational waves.

Image reconstruction enhanced by regularizers, e.g., to enforce sparsity, low rank or smoothness priors on images, has many successful applications in vision tasks such as computer photography, biomedical and spectral imaging. It has been well accepted that non-convex regularizers normally perform better than convex ones in terms of the reconstruction quality. But their convergence analysis is only established to a critical point, rather than the global optima. To mitigate the loss of guarantees for global optima, we propose to apply the concept of \textit{invexity} and provide the first list of proved invex regularizers for improving image reconstruction. Moreover, we establish convergence guarantees to global optima for various advanced image reconstruction techniques after being improved by such invex regularization. To the best of our knowledge, this is the first practical work applying invex regularization to improve imaging with global optima guarantees. To demonstrate the effectiveness of invex regularization, numerical experiments are conducted for various imaging tasks using benchmark datasets.

Designing well-defined magnetic nanomaterials is crucial for various applications and it demands a comprehensive understanding of their magnetic properties at the microscopic level. In this study, we investigate the contributions to the total anisotropy of Mn-Co mixed spinel nanoparticles. By employing neutron measurements sensitive to the spatially resolved surface anisotropy with sub-\AA\space resolution, we reveal an additional contribution to the anisotropy constant arising from shape anisotropy and interparticle interactions. Our findings shed light on the intricate interplay between chemical composition, microstructure, morphology, and surface effects, providing valuable insights for the design of advanced magnetic nanomaterials for AC biomedical applications, such as cancer treatment by magnetic fluid hyperthermia.

Efficient data streaming is essential for real-time data analytics, visualization, and machine learning model training, particularly when dealing with high-volume datasets. Various streaming technologies and serialization protocols have been developed to cater to different streaming requirements, each performing differently depending on specific tasks and datasets involved. This variety poses challenges in selecting the most appropriate combination, as encountered during the implementation of streaming system for the MAST fusion device data or SKA's radio astronomy data. To address this challenge, we conducted an empirical study on widely used data streaming technologies and serialization protocols. We also developed an extensible, open-source software framework to benchmark their efficiency across various performance metrics. Our study uncovers significant performance differences and trade-offs between these technologies, providing valuable insights that can guide the selection of optimal streaming and serialization solutions for modern data-intensive applications. Our goal is to equip the scientific community and industry professionals with the knowledge needed to enhance data streaming efficiency for improved data utilization and real-time analysis.

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.

Beamline I22 at Diamond Light Source is dedicated to the study of soft matter systems from both biological and materials science. The beamline can operate in the range 3.7 keV to 22 keV for transmission SAXS and 14 keV to 20 keV for microfocus SAXS with beamsizes 240 x 60 {\mu}m$^{2}$ spot [Full width half maximum (FWHM) Horizontal (H) x Vertical (V)] at sample for the main beamline, and approximately 10 x 10 {\mu}m$^{2}$ for the dedicated microfocussing platform. There is a versatile sample platform for accommodating a range of facility, and user developed, sample environments. The high brilliance of the insertion device source on I22 allows structural investigation of materials under extreme environments (for example fluid flow at high pressures and temperatures). I22 provides reliable access to millisecond timescales, essential to understanding kinetic processes such as early folding events in proteins or structural evolution in polymers and colloids.