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Hexagonal boron nitride (hBN) hosts luminescent defects possessing spin qualities compatible with quantum sensing protocols at room temperature. Vacancies, in particular, are readily obtained via exposure to high-energy ion beams. While the defect creation mechanism via such irradiation is well understood, the occurrence rate of optically active negatively charged vacancies ($V_B^-$) is an open question. In this work, we exploit focused helium ions to systematically generate optically active vacancy defects in hBN flakes at varying density. By comparing the density-dependent spin splitting measured by magnetic resonance to calculations based on a microscopic charge model, in which we introduce a correction term due to a constant background charge, we are able to quantify the number of $V_B^-$ defects generated by the ion irradiation. We find that only a small fraction (0.2%) of all vacancies is in the optically active, negatively charged state. Our results provide a protocol for measuring the generation efficiency of $V_B^-$, which is necessary for understanding and optimizing luminescent centers in hBN., Comment: Main manuscript: 7 pages, 4 figures; Supplemental material: 6 pages, 7 figures

This work provides a first report of ultrafast and compact photonic-electronic neuromorphic temporal leaky integrate-and-fire neuronal circuits built with Resonant Tunnelling Diodes (RTDs). We demonstrate experimentally that multiple fast (~100-200 ps) optical input pulses, arriving within a short (sub-ns long) temporal window, control the triggering of excitable responses in two different photonic-electronic RTD circuit architectures. These architectures are an electronic RTD coupled externally to a photodetector (referred to as a PD-RTD), and an integrated opto-electronic RTD device with inherent photodetection capability at infrared telecom wavelengths (referred to as an Optical RTD-PD). For both RTD systems, we reveal that the high-speed optically-triggered integrate-and-fire spiking operation can be precisely controlled by acting on the voltage bias applied to the RTD devices, or via the intensity of incoming optical pulses. Finally, we demonstrate the application of the leaky integrate-and-fire behaviour to a pattern recognition task at high-speed, with the systems triggering fast ns-long electrical spikes in response to optical inputs of weighted 4-bit digital headers., Comment: Submission manuscript, 11 pages, 6 Figures

A fundamental task in statistical learning is quantifying the joint dependence or association between two continuous random variables. We introduce a novel, fully non-parametric measure that assesses the degree of association between continuous variables $X$ and $Y$, capable of capturing a wide range of relationships, including non-functional ones. A key advantage of this measure is its interpretability: it quantifies the expected relative loss in predictive accuracy when the distribution of $X$ is ignored in predicting $Y$. This measure is bounded within the interval [0,1] and is equal to zero if and only if $X$ and $Y$ are independent. We evaluate the performance of our measure on over 90,000 real and synthetic datasets, benchmarking it against leading alternatives. Our results demonstrate that the proposed measure provides valuable insights into underlying relationships, particularly in cases where existing methods fail to capture important dependencies., Comment: This is the extended version of a paper accepted at 2025 SIAM International Conference on Data Mining (SDM'25)

Open string amplitudes at tree level have been studied for over fifty years, but there is no known analytic form for general $n$-point amplitudes, and their conventional representation in terms of worldsheet integrals does not make many of their most basic physical properties manifest. Recently, a formulation of these amplitudes exposing the underlying "binary geometry" via the use of "$u$" variables has given us many insights into their basic features. In this paper, we initiate a systematic exploration of fundamental aspects of open string amplitudes from this new point of view. We begin by giving explicit expressions for the factorization of amplitudes at general massive levels. We then study the asymptotic behavior when subsets of kinematic variables become large, delineating regimes with exponential (generalized hard scattering) and power-law (generalized Regge) behavior. We also give precise expressions for the asymptotics, which reveal another example of the recently observed property of factorization away from poles. We finally derive new recursion relations and infinite series representations for the amplitude, and for five points, we present a new closed-form expression for the amplitude that for the first time gives its analytic continuation to all of kinematic space., Comment: 54 pages, 17 figures

It has been a long-standing challenge to find a geometric object underlying the cosmological wavefunction for Tr($\phi^3$) theory, generalizing associahedra and surfacehedra for scattering amplitudes. In this note we describe a new class of polytopes -- "cosmohedra" -- that provide a natural solution to this problem. Cosmohedra are intimately related to associahedra, obtained by "blowing up" faces of the associahedron in a simple way, and we provide an explicit realization in terms of facet inequalities that further "shave" the facet inequalities of the associahedron. We also discuss a novel way for computing the wavefunction from cosmohedron geometry that extends the usual connection with polytope canonical forms. We illustrate cosmohedra with examples at tree-level and one loop; the close connection to surfacehedra suggests the generalization to all loop orders. We also briefly describe "cosmological correlahedra" for full correlators. We speculate on how the existence of cosmohedra might suggest a "stringy" formulation for the cosmological wavefunction/correlators, generalizing the way in which the Minkowski sum decomposition of associahedra naturally extend particle to string amplitudes., Comment: 55 pages, 18 figures

Rotation-powered pulsars represent the main class of identified gamma-ray sources in the Galaxy. The wealth of observational data collected by the AGILE and Fermi gamma-ray space telescopes in the GeV range, and by ground-based Cherenkov telescopes in the TeV band provide invaluable insights into how relativistic plasmas dissipate and accelerate particles. Decoding the information contained in the gamma-ray pulses profile is an important step to understand how pulsars work. In this study, we aim at putting an ab initio plasma model of pulsar magnetospheres to the test, in light of the most recent gamma-ray observations in the GeV and TeV bands. To this end, we present of a new series of global particle-in-cell simulations of an inclined pulsar magnetosphere. High-quality synthetic pulse profiles in the synchrotron and inverse Compton channels are reconstructed to study in greater details their morphology and their energy dependence. We also perform a fit of observed lightcurves with the model, using the third Fermi-LAT gamma-ray pulsar catalog. Reconnection in the wind current sheet powers synchrotron and inverse Compton emission. The modeled pulse profiles reproduce some of the salient features of observed gamma-ray pulsars, including the mysterious Vela-like lightcurves, such as: the generic double-peaked structure, the presence of a bridge or third peak in between the main pulses, the pulse narrowing with increasing energy. The bolometric synchrotron radiative efficiency is strictly limited by the reconnection rate. Our global kinetic simulations are able to match observed pulse profiles. Such direct comparisons will help drive and focus future simulation developments., Comment: 16 pages, 13 figures, 1 appendix, submitted to Astronomy and Astrophysics

Heterostructures made from 2D transition-metal dichalcogenides are known as ideal platforms to explore excitonic phenomena ranging from correlated moir\'e excitons to degenerate interlayer exciton ensembles. So far, it is assumed that the atomic reconstruction appearing in some of the heterostructures gives rise to a dominating localization of the exciton states. We demonstrate that excitonic states in reconstructed MoSe$_2$/WSe$_2$ heterostructures can extend well beyond the moir\'e periodicity of the investigated heterostructures. The results are based on real-space calculations yielding a lateral potential map for interlayer excitons within the strain-relaxed heterostructures and corresponding real-space excitonic wavefunctions. We combine the theoretical results with cryogenic photoluminescence experiments, which support the computed level structure and relaxation characteristics of the interlayer excitons.

Coral reefs are crucial to marine biodiversity and rely on a delicate symbiotic relationship between corals and zooxanthellae algae. Water temperature variations, however, disrupt this association, leading to coral bleaching events that severely affect marine ecosystems. This study presents a mathematical model for the population dynamics of coral and symbiont species considering the coral symbiont network and recurrent warming events. The model incorporates thermal tolerances of species and coupled growth dynamics (between corals and symbionts) to investigate how network structure and thermal tolerance influence the species' growth. Using real data from different ocean regions, results reveal that network connectivity plays a significant role in population growth after successive warming events, with generalist species demonstrating greater growth across all regions analyzed. The comparatively higher correlation between node degree and final population also emphasizes the impact of ecological network structure on species growth, offering valuable insights into coral reef population dynamics under climate change. This research highlights the need to consider network structure beyond species' thermal tolerances when evaluating the ecological responses of corals to environmental changes., Comment: 13 pages, 6 figures

Chernyshev, Rauch, and Rautenbach proved that every connected graph on $n$ vertices with less than $\frac{11}{5}n-\frac{18}{5}$ edges has a vertex cut that induces a forest, and conjectured that the same remains true if the graph has less than $3n-6$ edges. We improve their result by proving that every connected graph on $n$ vertices with less than $\frac{9}{4}n$ edges has a vertex cut that induces a forest. We also study weaker versions of the problem that might lead to an improvement on the bound obtained.

Long non-coding RNAs (lncRNAs) serve as crucial regulators in numerous biological processes. Although they share sequence similarities with messenger RNAs (mRNAs), lncRNAs perform entirely different roles, providing new avenues for biological research. The emergence of next-generation sequencing technologies has greatly advanced the detection and identification of lncRNA transcripts and deep learning-based approaches have been introduced to classify long non-coding RNAs (lncRNAs). These advanced methods have significantly enhanced the efficiency of identifying lncRNAs. However, many of these methods are devoid of robustness and accuracy due to the extended length of the sequences involved. To tackle this issue, we have introduced a novel pre-trained bidirectional encoder representation called LoRA-BERT. LoRA-BERT is designed to capture the importance of nucleotide-level information during sequence classification, leading to more robust and satisfactory outcomes. In a comprehensive comparison with commonly used sequence prediction tools, we have demonstrated that LoRA-BERT outperforms them in terms of accuracy and efficiency. Our results indicate that, when utilizing the transformer model, LoRA-BERT achieves state-of-the-art performance in predicting both lncRNAs and mRNAs for human and mouse species. Through the utilization of LoRA-BERT, we acquire valuable insights into the traits of lncRNAs and mRNAs, offering the potential to aid in the comprehension and detection of diseases linked to lncRNAs in humans.

Music digitalization has introduced new forms of composition known as "musical borrowings", where composers use elements of existing songs -- such as melodies, lyrics, or beats -- to create new songs. Using Who Sampled data and Google Trends, we examine how the popularity of a borrowing song affects the original. Employing Regression Discontinuity Design (RDD) for short-term effects and Granger Causality for long-term impacts, we find evidence of causal popularity boosts in some cases. Borrowee songs can revive interest in older tracks, underscoring economic dynamics that may support fairer compensation in the music industry., Comment: To appear in 1st Latin American Music Information Retrieval Workshop

With over a billion active users, TikTok's video-sharing service is currently one of the largest social media websites. This rise in TikTok's popularity has made the website a central platform for music discovery. In this paper, we analyze how TikTok helps to revitalize older songs. To do so, we use both the popularity of songs shared on TikTok and how the platform allows songs to propagate to other places on the Web. We analyze data from TokBoard, a website measuring such popularity over time, and Google Trends, which captures songs' overall Web search interest. Our analysis initially focuses on whether TokBoard can cause (Granger Causality) popularity on Google Trends. Next, we examine whether TikTok and Google Trends share the same virality patterns (via a Bass Model). To our knowledge, we are one of the first works to study song re-popularization via TikTok., Comment: To appear in the 1st Latin American Music Information Retrieval Workshop

Deep learning (DL) models in medical imaging face challenges in generalizability and robustness due to variations in image acquisition parameters (IAP). In this work, we introduce a novel method using conditional denoising diffusion generative models (cDDGMs) to generate counterfactual magnetic resonance (MR) images that simulate different IAP without altering patient anatomy. We demonstrate that using these counterfactual images for data augmentation can improve segmentation accuracy, particularly in out-of-distribution settings, enhancing the overall generalizability and robustness of DL models across diverse imaging conditions. Our approach shows promise in addressing domain and covariate shifts in medical imaging. The code is publicly available at https: //github.com/pedromorao/Counterfactual-MRI-Data-Augmentation

Relativistic magnetic reconnection is one of the most fundamental mechanisms considered responsible for the acceleration of relativistic particles in astrophysical jets and magnetospheres of compact objects. Understanding the properties of the dissipation of magnetic fields and the formation of non-ideal electric fields is of paramount importance to quantify the efficiency of reconnection at energizing charged particles. Recent results from particle-in-cell (PIC) simulations suggest that the fundamental properties of how magnetic fields dissipate in a current sheet might be captured by an "effective resistivity" formulation, which would locally enhance the amount of magnetic energy dissipated and favor the onset of fast reconnection. Our goal is to assess this ansatz quantitatively by comparing fluid models of magnetic reconnection with a non-constant magnetic diffusivity and fully-kinetic models. We perform 2D resistive relativistic magnetohydrodynamic (ResRMHD) simulations of magnetic reconnection combined to PIC simulations using the same initial conditions (namely a Harris current sheet). We explore the impact of crucial parameters such as the plasma magnetization, its mass density, the grid resolution, and the characteristic plasma skin depth. Our ResRMHD models with effective resistivity can quantitatively reproduce the dynamics of fully-kinetic models of relativistic magnetic reconnection. In particular, they lead to reconnection rates consistent with PIC simulations, while for constant-resistivity fluid models the reconnection dynamics is generally 10 times slower. Even at modest resolutions the adoption of an effective resistivity can qualitatively capture the properties of kinetic reconnection models and produce reconnection rates compatible with collisionless models, i.e. of the order of $\sim10^{-1}$., Comment: 17 pages, 17 figures; accepted for publication on A&A

Analyzing musical influence networks, such as those formed by artist influence or sampling, has provided valuable insights into contemporary Western music. Here, computational methods like centrality rankings help identify influential artists. However, little attention has been given to how influence changes over time. In this paper, we apply Bayesian Surprise to track the evolution of musical influence networks. Using two networks -- one of artist influence and another of covers, remixes, and samples -- our results reveal significant periods of change in network structure. Additionally, we demonstrate that Bayesian Surprise is a flexible framework for testing various hypotheses on network evolution with real-world data., Comment: To appear in the Latin American Musical Information Retrieval Workshop

In recent years, deep learning has achieved formidable results in creative computing. When it comes to music, one viable model for music generation are Transformer based models. However, while transformers models are popular for music generation, they often rely on annotated structural information. In this work, we inquire if the off-the-shelf Music Transformer models perform just as well on structural similarity metrics using only unannotated MIDI information. We show that a slight tweak to the most common representation yields small but significant improvements. We also advocate that searching for better unannotated musical representations is more cost-effective than producing large amounts of curated and annotated data., Comment: Presented at the Music for Machine Learning Workshop with ECML/PKDD. To be published by Springer

A rigorous treatment of light-matter interactions typically requires an interacting quantum field theory. However, most applications of interest are handled using classical or semiclassical models, which are valid only when quantum-field fluctuations can be neglected. This approximation breaks down in scenarios involving large light intensities or degenerate matter, where additional quantum effects become significant. In this work, we address these limitations by developing a quantum kinetic framework that treats both light and matter fields on equal footing, naturally incorporating both linear and nonlinear interactions. To accurately account for light fluctuations, we introduce a photon distribution function that, together with the classical electromagnetic fields, provides a better description of the photon fluid. From this formalism, we derive kinetic equations from first principles that recover classical electrodynamical results while revealing couplings that are absent in the corresponding classical theory. Furthermore, by addressing the Coulomb interaction in the Hartree-Fock approximation, we include the role of fermionic exchange exactly in both kinetic and fluid regimes through a generalized Fock potential. The latter provides corrections not only to the electrostatic forces but also to the plasma velocity fields, which become significant in degenerate conditions., Comment: Quantum kinetic theory, light matter interaction, quantum degeneracy, warm dense matter, laser-plasma interaction

A vishing attack is a form of social engineering where attackers use phone calls to deceive individuals into disclosing sensitive information, such as personal data, financial information, or security credentials. Attackers exploit the perceived urgency and authenticity of voice communication to manipulate victims, often posing as legitimate entities like banks or tech support. Vishing is a particularly serious threat as it bypasses security controls designed to protect information. In this work, we study the potential for vishing attacks to escalate with the advent of AI. In theory, AI-powered software bots may have the ability to automate these attacks by initiating conversations with potential victims via phone calls and deceiving them into disclosing sensitive information. To validate this thesis, we introduce ViKing, an AI-powered vishing system developed using publicly available AI technology. It relies on a Large Language Model (LLM) as its core cognitive processor to steer conversations with victims, complemented by a pipeline of speech-to-text and text-to-speech modules that facilitate audio-text conversion in phone calls. Through a controlled social experiment involving 240 participants, we discovered that ViKing has successfully persuaded many participants to reveal sensitive information, even those who had been explicitly warned about the risk of vishing campaigns. Interactions with ViKing's bots were generally considered realistic. From these findings, we conclude that tools like ViKing may already be accessible to potential malicious actors, while also serving as an invaluable resource for cyber awareness programs.

Relativistic magnetic reconnection studies have focused on symmetric configurations so far, where the upstream plasma has identical properties on each side of the layer. The boundary layer between a relativistic jet and an accretion flow forming around a supermassive black hole may present an asymmetric configuration in terms of plasma composition, bulk velocity, temperature and magnetization. In this work, we aim to conduct the first study of relativistic magnetic reconnection where the upstream plasma is composed of electron-positron pairs on one side, and electrons and ions on the other. We also investigate the role of a relativistic symmetric shear flow applied along the reconnecting field lines. We simulate magnetic reconnection using two-dimensional particle-in-cell simulations. The initial setup is adapted from a classic Harris layer without guide field, modified to accommodate plasma-composition and shear asymmetries in the upstream medium. For a composition-asymmetric setup, we find that the reconnection dynamics is driven by the electron-ion side, which is the plasma with the lowest magnetization. The energy partition favors accelerating ions at the expense of electrons even more than in a corresponding symmetric setup. With respect to shear, a super-Alfv\'enic upstream decreases the laboratory-frame reconnection rate, but, unlike in non-relativistic studies, does not shut off reconnection completely. The asymmetries examined in this work diminish the overall efficiency of electron acceleration relative to corresponding symmetric configurations. In the context of a black hole jet-disk boundary, asymmetric reconnection alone is probably not efficient at accelerating electrons to very high energies, but it might facilitate plasma mixing and particle injection for other acceleration channels at the interface., Comment: 10 pages, 9 figures, accepted for publication in Astronomy & Astrophysics

Many natural phenomena are quantified by counts of observable events, from the annihilation of quasiparticles in a lattice to predator-prey encounters on a landscape to spikes in a neural network. These events are triggered at random intervals when an underlying dynamical system occupies a set of reactive states in its phase space. We derive a general expression for the distribution of times between events in such counting processes assuming the underlying triggering dynamics is a stochastic process that converges to a stationary distribution. Our results contribute to resolving a long-standing dichotomy in the study of reaction-diffusion processes, showing the inter-reaction point process interpolates between a reaction- and a diffusion-limited regime. At low reaction rates, the inter-reaction process is Poisson with a rate depending on stationary properties of the event-triggering stochastic process. At high reaction rates, inter-reaction times are dominated by the hitting times to the reactive states. To further illustrate the power of this approach we apply our framework to obtain the counting statistics of two counting processes appearing in several biophysical scenarios. First, we study the common situation of estimating an animal's activity level by how often it crosses a detector, showing that the mean number of crossing events can decrease monotonically with the hitting rate, a seemingly 'paradoxical' result that could possibly lead to misinterpretation of experimental count data. Second, we derive the ensemble statistics for the detection of many particles, recovering and generalizing known results in the biophysics of chemosensation. Overall, we develop a unifying theoretical framework to quantify inter-event time distributions in reaction-diffusion systems that clarifies existing debates in the literature and provide examples of application to real-world scenarios., Comment: 14 pages, 3 figures

It has been a long-standing challenge to define a canonical loop integrand for non-supersymmetric gluon scattering amplitudes in the planar limit. Naive integrands are inflicted with $1/0$ ambiguities associated with tadpoles and massless external bubbles, which destroy integrand-level gauge invariance as well as consistent on-shell factorization on single loop-cuts. In this letter, we show that this essentially kinematical obstruction to defining "the" integrand for Yang-Mills theory has a structural solution, handed to us by the formulation of gluon amplitudes in terms of curves on surfaces. This defines "surface kinematics" generalizing momenta, making it possible to define "the" integrand satisfying both a (surface generalized) notion of gauge-invariance and consistent loop-cuts. The integrand also vanishes at infinity in appropriate directions, allowing it to be recursively computed for non-supersymmetric Yang-Mills theory in any number of dimensions. We illustrate these ideas through one loop for all multiplicity, and for the simplest two-loop integrand., Comment: 13 pages, 4 figures, explicit expressions at tree-level and one-loop given in supplementary materials

The article deals with finite groups in which commutators have prime power order (CPPO-groups). We show that if G is a soluble CPPO-group, then the order of the commutator subgroup G' is divisible by at most two primes.

The Southern Photometric Local Universe Survey (S-PLUS) is a project to map $\sim9300$ sq deg of the sky using twelve bands (seven narrow and five broadbands). Observations are performed with the T80-South telescope, a robotic telescope located at the Cerro Tololo Observatory in Chile. The survey footprint consists of several large contiguous areas, including fields at high and low galactic latitudes, and towards the Magellanic Clouds. S-PLUS uses fixed exposure times to reach point source depths of about $21$ mag in the $griz$ and $20$ mag in the $u$ and the narrow filters. This paper describes the S-PLUS Data Release 4 (DR4), which includes calibrated images and derived catalogues for over 3000 sq deg, covering the aforementioned area. The catalogues provide multi-band photometry performed with the tools \texttt{DoPHOT} and \texttt{SExtractor} -- point spread function (\PSF) and aperture photometry, respectively. In addition to the characterization, we also present the scientific potential of the data. We use statistical tools to present and compare the photometry obtained through different methods. Overall we find good agreement between the different methods, with a slight systematic offset of 0.05\,mag between our \PSF and aperture photometry. We show that the astrometry accuracy is equivalent to that obtained in previous S-PLUS data releases, even in very crowded fields where photometric extraction is challenging. The depths of main survey (MS) photometry for a minimum signal-to-noise ratio $S/N = 3$ reach from $\sim19.5$ for the bluer bands to $\sim21.5$ mag on the red. The range of magnitudes over which accurate \PSF photometry is obtained is shallower, reaching $\sim19$ to $\sim20.5$ mag depending on the filter. Based on these photometric data, we provide star-galaxy-quasar classification and photometric redshift for millions of objects., Comment: 26 pages, 17 figures, 14 tables, accepted for A&A

Software architecture often consists of interconnected components dispersed across source code and other development artifacts, making visualization difficult without costly additional documentation. Although some tools can automatically generate architectural diagrams, these hardly fully reflect the architecture of the system. We propose the value of automatic architecture recovery from multiple software artifacts, combined with the ability to manually adjust recovered models and automate the recovery process. We present a general approach to achieve this and describe a tool that generates architectural diagrams for a software system by analyzing its software artifacts and unifying them into a comprehensive system representation. This representation can be manually modified while ensuring that changes are reintegrated into the diagram when it is regenerated. We argue that adopting a similar approach in other types of documentation tools is possible and can render similar benefits.

We discuss the constraints and phenomenology of the $Z_2 \times Z_2$ three Higgs doublet model (3HDM) with two inert scalars, originating two dark matter (DM) particles. We elucidate the competing vacua and we submit the model to all theoretical and current experimental constraints. We find unexplored regions of parameter space and investigate their experimental signatures. In particular, we find regions where the two DM particles contribute equally to the relic DM density., Comment: 24 pages, 17 figures, revtex

Point cloud models are a common shape representation for several reasons. Three-dimensional scanning devices are widely used nowadays and points are an attractive primitive for rendering complex geometry. Nevertheless, there is not much literature on collision detection for point cloud models. This paper presents a novel collision detection algorithm for large point cloud models using voxels, octrees and bounding spheres hierarchies (BSH). The scene graph is divided in voxels. The objects of each voxel are organized into an octree. Due to the high number of points in the scene, each non-empty cell of the octree is organized in a bounding sphere hierarchy, based on an R-tree hierarchy like structure. The BSH hierarchies are used to group neighboring points and filter out very quickly parts of objects that do not interact with other models. Points derived from laser scanned data typically are not segmented and can have arbitrary spatial resolution thus introducing computational and modeling issues. We address these issues and our results show that the proposed collision detection algorithm effectively finds intersections between point cloud models since it is able to reduce the number of bounding volume checks and updates.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

The differential cross section for $\gamma p\rightarrow K^+\Lambda(1520)$ was measured from threshold to a centre-of-mass energy of 2090\,MeV at forward angles at the BGOOD experiment. The high statistical precision and resolution in centre-of-mass energy and angle allows a detailed characterisation of this low-momentum transfer kinematic region. The data agree with a previous LEPS measurement and support effective Lagrangian models that indicate that the contact term dominates the cross section near threshold., Comment: 6 pages, 7 figures