Search

We compute the production rates for two, three, four and five jets in the hadronic decay of a Higgs boson in its two dominant decay modes to bottom quarks and gluons to third order in the QCD coupling constant. The five-, four- and three-jet rates are obtained from a next-to-next-to-leading order (NNLO) calculation of Higgs decay to three jets, while the two-jet rate is inferred at next-to-next-to-next-to-leading order (N$^3$LO) from the inclusive decay rate. Our results show distinct differences in the dependence of the jet rates on the jet resolution parameter between the two decay modes, supporting the aim of discriminating different Higgs boson decay channels via classic QCD observables.

We determine the value of the strong coupling $\alpha_\text{s}$ and study its running over a wide range of scales as probed by the dijet production process at hadron colliders, based on an NNLO QCD analysis of LHC dijet data. From a large subset of these data a value of $\alpha_\text{s} (m_\text{Z}) = 0.1178 \pm 0.0022$ is obtained for the strong coupling at the scale of the Z-boson mass $m_\text{Z}$, using the invariant mass of the dijet system to select the scale where $\alpha_\text{s}$ is probed. The combination of different data sets enhances the reach and precision of the analysis in the mutli-TeV range and allows for the first determination of $\alpha_\text{s}$ up to scales of 7 TeV. Complementing the LHC data with dijet cross sections measured at the HERA electron-proton collider, the kinematic range is extended to test the running of the strong coupling towards smaller scales. Our results exhibit excellent agreement with predictions based on the renormalization group equation of QCD, and represent a comprehensive test of the asymptotic behavior of QCD, spanning more than three orders of magnitude in energy scale., Comment: 5 pages plus appendix and supplementary material

Detecting hallucinations in large language models (LLMs) is critical for enhancing their reliability and trustworthiness. Most research focuses on hallucinations as deviations from information seen during training. However, the opaque nature of an LLM's parametric knowledge complicates the understanding of why generated texts appear ungrounded: The LLM might not have picked up the necessary knowledge from large and often inaccessible datasets, or the information might have been changed or contradicted during further training. Our focus is on hallucinations involving information not used in training, which we determine by using recency to ensure the information emerged after a cut-off date. This study investigates these hallucinations by detecting them at sentence level using different internal states of various LLMs. We present HalluRAG, a dataset designed to train classifiers on these hallucinations. Depending on the model and quantization, MLPs trained on HalluRAG detect hallucinations with test accuracies ranging up to 75 %, with Mistral-7B-Instruct-v0.1 achieving the highest test accuracies. Our results show that IAVs detect hallucinations as effectively as CEVs and reveal that answerable and unanswerable prompts are encoded differently as separate classifiers for these categories improved accuracy. However, HalluRAG showed some limited generalizability, advocating for more diversity in datasets on hallucinations., Comment: 19 pages, 3 figures

With ESA's PLATO space mission set for launch in December 2026, a new photometric legacy and a future of new scientific discoveries await. In this work we investigate PLATO's potential for observing pulsating stars across the Hertzsprung-Russell diagram as part of the PLATO Complimentary Science program (PLATO-CS). Specifically, a PLATO mock asteroseismic catalogue (MOCKA) of intermediate to massive stars is presented as a benchmark to highlight the asteroseismic yield of PLATO-CS in a quantitative way. MOCKA includes simulations of $\beta$~Cephei, slowly pulsating B (SPB), $\delta$~Scuti, $\gamma$~Doradus, RR Lyrae, Cepheid, hot subdwarf, and white dwarf stars. In particular, main-sequence gravity (g) mode pulsators are of interest as some of these stars form an important foundation for the scientific calibration of PLATO. MOCKA is based on a magnitude limited ($G\lesssim17$) \textit{Gaia} catalogue and is a product of realistic end-to-end \texttt{PlatoSim} simulations of stars for the first PLATO pointing field in the Southern hemisphere, which will be observed for a minimally 2-yr duration. We show that an abundant spectrum of frequencies is achievable across a wide range of magnitudes and co-pointing PLATO cameras. Within the magnitude limited regimes simulated ($G \lesssim 14$ for $\gamma$~Doradus stars and $G \lesssim 16$ for SPB stars) the dominant g-mode frequency is recovered in more than $95\%$ of the cases. MOCKA help us to understand the limits of the PLATO mission as well as highlight the opportunities to push astrophysics beyond current stellar models. All data products of this paper are made available to the community for further exploration. The key data products of MOCKA are the magnitude limited \textit{Gaia} catalogue of the first PLATO pointing field, together with fully reduced light curves from multi-camera observations for each pulsation class.

QS Vir is a low-accretion rate cataclysmic variable (CV), or pre-CV, as the M dwarf companion is just filling its Roche lobe. We recently identified radio emission from QS Vir in the Very Large Array Sky Survey, at a flux of ~1 mJy. The origin of radio emission from CVs is not fully understood, with evidence for synchrotron emission from jets and other coherent plasma emission processes, such as electron cyclotron maser emission (ECME) or plasma radiation. Our aim is to constrain the radio emission mechanism for QS Vir, through spectroscopic, polarisation, and time variability measurements, all while checking for correlated X-ray variations. We took 3 epochs of new observations with the VLA in S, C, and X bands, with full Stokes polarisation information, complemented by near-simultaneous Swift/XRT X-ray data. Radio spectra are extracted to search for emission features characteristic of coherent plasma emission processes (e.g. high circular polarisation and narrow-band emission). We fit the X-ray spectra with absorbed power-laws, finding no strong X-ray variability. QS Vir showed a nearly flat radio spectrum, with fluxes of 0.4-0.6 mJy in all bands. Swift/XRT showed L_X ~ 5x10^29 erg/s in all observations. We identified strong, variable circular polarisation, ranging from 33+/-3% in S band in the last observation, to <11% in the middle observation in all bands. Linear polarisation was not detected, with upper limits of at most 15%. Intriguingly, the S-band spectra show circularly polarised spectral bumps (width ~0.5 GHz) that rise and decay within <5 minutes. We suggest that the radio emission from QS Vir consists of two components: a relatively constant, low-polarisation flat-spectrum component and a band-limited, rapidly variable, and strongly circularly polarised component. This latter coherent component may be associated with ECME or plasma radiation.

Context. The PLAnetary Transits and Oscillations of stars (PLATO) mission will observe the same area of the sky continuously for at least two years in an effort to detect transit signals of an Earth-like planet orbiting a solar-like star. Aims. We aim to study how short-term solar-like variability caused by oscillations and granulation would affect PLATO's ability to detect and size Earth if PLATO were to observe the Solar System itself. Methods. We injected Earth-like transit signals onto real solar data taken by the Helioseismic and Magnetic Imager (HMI) instrument. We isolated short-term stellar variability by removing any variability with characteristic timescales longer than five hours. We then added a noise model for a variety of different stellar magnitudes computed by PlatoSim assuming an observation by all 24 normal cameras. We first compared four different commonly used treatments of correlated noise in the time domain. We then tried to recover pairs of transit signals. Finally, we performed transit fits using realistic priors on planetary and stellar parameters. Results. We find that short-term solar-like variability affects the correct retrieval of Earth-like transit signals in PLATO data. Variability models accounting for variations with typical timescales at the order of one hour are sufficient to mitigate these effects. For bright targets (8.5 - 10.5 mag), the transit signal of an Earth analogue can reliably be detected in PLATO data. For faint targets the results of transit search algorithms have to be verified by transit-fitting algorithms to avoid false positive detections being flagged. For bright targets (V-mag $\leq$ 9.5), the radius of an Earth-like planet orbiting a solar-like star can be correctly determined at a precision of 3% or less, assuming that at least two transit events are observed and the characteristics of the host star are well understood., Comment: 12 pages, 5 figures, accepted for publication in A&A

Increased usage of automated tools like deep learning in medical image segmentation has alleviated the bottleneck of manual contouring. This has shifted manual labour to quality assessment (QA) of automated contours which involves detecting errors and correcting them. A potential solution to semi-automated QA is to use deep Bayesian uncertainty to recommend potentially erroneous regions, thus reducing time spent on error detection. Previous work has investigated the correspondence between uncertainty and error, however, no work has been done on improving the "utility" of Bayesian uncertainty maps such that it is only present in inaccurate regions and not in the accurate ones. Our work trains the FlipOut model with the Accuracy-vs-Uncertainty (AvU) loss which promotes uncertainty to be present only in inaccurate regions. We apply this method on datasets of two radiotherapy body sites, c.f. head-and-neck CT and prostate MR scans. Uncertainty heatmaps (i.e. predictive entropy) are evaluated against voxel inaccuracies using Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves. Numerical results show that when compared to the Bayesian baseline the proposed method successfully suppresses uncertainty for accurate voxels, with similar presence of uncertainty for inaccurate voxels. Code to reproduce experiments is available at https://github.com/prerakmody/bayesuncertainty-error-correspondence, Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:018

To prepare for the analyses of the future PLATO light curves, we develop a deep learning model, Panopticon, to detect transits in high precision photometric light curves. Since PLATO's main objective is the detection of temperate Earth-size planets around solar-type stars, the code is designed to detect individual transit events. The filtering step, required by conventional detection methods, can affect the transit, which could be an issue for long and shallow transits. To protect transit shape and depth, the code is also designed to work on unfiltered light curves. We trained the model on a set of simulated PLATO light curves in which we injected, at pixel level, either planetary, eclipsing binary, or background eclipsing binary signals. We also include a variety of noises in our data, such as granulation, stellar spots or cosmic rays. The approach is able to recover 90% of our test population, including more than 25% of the Earth-analogs, even in the unfiltered light curves. The model also recovers the transits irrespective of the orbital period, and is able to retrieve transits on a unique event basis. These figures are obtained when accepting a false alarm rate of 1%. When keeping the false alarm rate low (<0.01%), it is still able to recover more than 85% of the transit signals. Any transit deeper than 180ppm is essentially guaranteed to be recovered. This method is able to recover transits on a unique event basis, and does so with a low false alarm rate. Thanks to light curves being one-dimensional, model training is fast, on the order of a few hours per model. This speed in training and inference, coupled to the recovery effectiveness and precision of the model make it an ideal tool to complement, or be used ahead of, classical approaches., Comment: Submitted to A&A

The Gaia mission detected many new candidate $\beta$ Cephei ($\beta$ Cep) pulsators, whose variability classification has since been confirmed from Transiting Exoplanet Survey Satellite (TESS) space photometry of the nominal mission. We aim to analyse all currently available TESS data for these $\beta$ Cep pulsators, of which 145 were new discoveries, in order to exploit their asteroseismic potential. Although they are of critical importance to improve evolution models of massive stars, $\beta$ Cep stars are under-represented in the current space photometry revolution. We extracted light curves for 216 stars from TESS full-frame images and performed frequency analysis by means of pre-whitening. Based on Gaia Data Release 3, we deduced stellar properties and compared them to those of known $\beta$ Cep stars from the literature. We developed a methodology to identify the dominant pulsation modes of the $\beta$ Cep stars from the detection of rotationally-split multiplets and Gaia and TESS amplitude ratios. We used grid modelling to gain insights into the population of $\beta$ Cep stars. Combining TESS and Gaia, we successfully identified the mode degrees for 148 stars in our sample. We find the majority to have a dominant dipole non-radial mode. Many non-radial modes show splittings in their TESS frequency spectra, which we use to calculate their envelope rotation, spin parameter, and the level of differential envelope-to-surface rotation (upper limit of about 3). We also provide relative frequency asymmetries within the multiplets, ranging from -0.15 to 0.15 with most being positive. Based on grid modelling, we provide mass, convective core mass, and age distributions for 119 stars. Our study prepares for future detailed modelling based on individual frequencies of identified modes leading towards a better understanding of these massive pulsators. (abridged), Comment: Resbumitted to A&A after taking into account all the comments from the referee

This study evaluates the performance of Large Language Models (LLMs) as an Artificial Intelligence-based tutor for a university course. In particular, different advanced techniques are utilized, such as prompt engineering, Retrieval-Augmented-Generation (RAG), and fine-tuning. We assessed the different models and applied techniques using common similarity metrics like BLEU-4, ROUGE, and BERTScore, complemented by a small human evaluation of helpfulness and trustworthiness. Our findings indicate that RAG combined with prompt engineering significantly enhances model responses and produces better factual answers. In the context of education, RAG appears as an ideal technique as it is based on enriching the input of the model with additional information and material which usually is already present for a university course. Fine-tuning, on the other hand, can produce quite small, still strong expert models, but poses the danger of overfitting. Our study further asks how we measure performance of LLMs and how well current measurements represent correctness or relevance? We find high correlation on similarity metrics and a bias of most of these metrics towards shorter responses. Overall, our research points to both the potential and challenges of integrating LLMs in educational settings, suggesting a need for balanced training approaches and advanced evaluation frameworks., Comment: The article is an extended version of a paper presented at the International Workshop on AI in Education and Educational Research (AIEER) at ECAI-2024 (27th European Conference on Artificial Intelligence)

Precision in identifying nanometer-scale device-killer defects is crucial in both semiconductor research and development as well as in production processes. The effectiveness of existing ML-based approaches in this context is largely limited by the scarcity of data, as the production of real semiconductor wafer data for training these models involves high financial and time costs. Moreover, the existing simulation methods fall short of replicating images with identical noise characteristics, surface roughness and stochastic variations at advanced nodes. We propose a method for generating synthetic semiconductor SEM images using a diffusion model within a limited data regime. In contrast to images generated through conventional simulation methods, SEM images generated through our proposed DL method closely resemble real SEM images, replicating their noise characteristics and surface roughness adaptively. Our main contributions, which are validated on three different real semiconductor datasets, are: i) proposing a patch-based generative framework utilizing DDPM to create SEM images with intended defect classes, addressing challenges related to class-imbalance and data insufficiency, ii) demonstrating generated synthetic images closely resemble real SEM images acquired from the tool, preserving all imaging conditions and metrology characteristics without any metadata supervision, iii) demonstrating a defect detector trained on generated defect dataset, either independently or combined with a limited real dataset, can achieve similar or improved performance on real wafer SEM images during validation/testing compared to exclusive training on a real defect dataset, iv) demonstrating the ability of the proposed approach to transfer defect types, critical dimensions, and imaging conditions from one specified CD/Pitch and metrology specifications to another, thereby highlighting its versatility., Comment: 8 pages, 11 figures, to be presented at 2024 International Symposium ELMAR, and published by IEEE in the conference proceedings

ESA's PLATO mission aims the detection and characterization of terrestrial planets around solar-type stars as well as the study of host star properties. The noise-to-signal ratio (NSR) is the main performance parameter of the PLATO instrument, which consists of 24 Normal Cameras and 2 Fast Cameras. In order to justify, verify and breakdown NSR-relevant requirements the software simulator PINE was developed. PINE models the signal pathway from a target star to the digital output of a camera based on physical models and considers the major noise contributors. In this paper, the simulator's coarse mode is introduced which allows fast performance analyses on instrument level. The added value of PINE is illustrated by exemplary applications., Comment: 17 pages, 8 figures, 3 tables

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.

High-dimensional encodings based on temporal modes (TMs) of photonic quantum states provide the foundations for a highly versatile and efficient quantum information science (QIS) framework. Here, we demonstrate a crucial building block for any QIS applications based on TMs: a programmable source of maximally entangled high-dimensional TM states. Our source is based on a parametric down-conversion process driven by a spectrally shaped pump pulse, which facilitates the generation of maximally entangled TM states with a well-defined dimensionality that can be chosen programmatically. We characterize the effective dimensionality of the generated states via measurements of second-order correlation functions and joint spectral intensities, demonstrating the generation of bi-photon TM states with a controlled dimensionality in up to 20 dimensions.

The identification of a hadron in the final state of hadron-collider events that feature a leptonically decaying vector boson can provide essential information on the parton content of the colliding protons. Moreover, the study of hadrons inside jets can provide deeper insights into the fragmentation dynamics. We provide theoretical predictions for specific observables involving either the production of a $Z$ boson in association with light charged hadrons inside a jet or the production of a $W$ boson together with a charmed hadron. We present results for various fragmentation functions and compare our predictions with measurements by LHCb and ATLAS at $\sqrt{s}=13$ TeV. Our predictions are obtained using the antenna subtraction formalism which has been extended to cope with infrared singularities associated to the fragmentation processes in a hadron-collider environment at NLO accuracy.

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Due to potential pitch reduction, the semiconductor industry is adopting High-NA EUVL technology. However, its low depth of focus presents challenges for High Volume Manufacturing. To address this, suppliers are exploring thinner photoresists and new underlayers/hardmasks. These may suffer from poor SNR, complicating defect detection. Vision-based ML algorithms offer a promising solution for semiconductor defect inspection. However, developing a robust ML model across various image resolutions without explicit training remains a challenge for nano-scale defect inspection. This research's goal is to propose a scale-invariant ADCD framework capable to upscale images, addressing this issue. We propose an improvised ADCD framework as SEMI-SuperYOLO-NAS, which builds upon the baseline YOLO-NAS architecture. This framework integrates a SR assisted branch to aid in learning HR features by the defect detection backbone, particularly for detecting nano-scale defect instances from LR images. Additionally, the SR-assisted branch can recursively generate upscaled images from their corresponding downscaled counterparts, enabling defect detection inference across various image resolutions without requiring explicit training. Moreover, we investigate improved data augmentation strategy aimed at generating diverse and realistic training datasets to enhance model performance. We have evaluated our proposed approach using two original FAB datasets obtained from two distinct processes and captured using two different imaging tools. Finally, we demonstrate zero-shot inference for our model on a new, originating from a process condition distinct from the training dataset and possessing different Pitch characteristics. Experimental validation demonstrates that our proposed ADCD framework aids in increasing the throughput of imaging tools for defect inspection by reducing the required image pixel resolutions.

We investigate the estimation of treatment effects from a sample that is stratified on the binary treatment status. In the case of unconfounded assignment where the potential outcomes are independent of the treatment given covariates, we show that standard estimators of the average treatment effect are inconsistent. In the case of an endogenous treatment and a binary instrument, we show that the IV estimator is inconsistent for the local average treatment effect. In both cases, we propose simple alternative estimators that are consistent in stratified samples, assuming that the fraction treated in the population is known or can be estimated.

Volume measurement of a paraganglioma (a rare neuroendocrine tumor that typically forms along major blood vessels and nerve pathways in the head and neck region) is crucial for monitoring and modeling tumor growth in the long term. However, in clinical practice, using available tools to do these measurements is time-consuming and suffers from tumor-shape assumptions and observer-to-observer variation. Growth modeling could play a significant role in solving a decades-old dilemma (stemming from uncertainty regarding how the tumor will develop over time). By giving paraganglioma patients treatment, severe symptoms can be prevented. However, treating patients who do not actually need it, comes at the cost of unnecessary possible side effects and complications. Improved measurement techniques could enable growth model studies with a large amount of tumor volume data, possibly giving valuable insights into how these tumors develop over time. Therefore, we propose an automated tumor volume measurement method based on a deep learning segmentation model using no-new-UNnet (nnUNet). We assess the performance of the model based on visual inspection by a senior otorhinolaryngologist and several quantitative metrics by comparing model outputs with manual delineations, including a comparison with variation in manual delineation by multiple observers. Our findings indicate that the automatic method performs (at least) equal to manual delineation. Finally, using the created model, and a linking procedure that we propose to track the tumor over time, we show how additional volume measurements affect the fit of known growth functions.

Magnetic sounding using data collected from the Juno mission can be used to provide constraints on Jupiter's interior. However, inwards continuation of reconstructions assuming zero electrical conductivity and a representation in spherical harmonics are limited by the enhancement of noise at small scales. Here we describe new reconstructions of Jupiter's internal magnetic field based on physics-informed neural networks and either the first 33 (PINN33) or the first 50 (PINN50) of Juno's orbits. The method can resolve local structures, and allows for weak ambient electrical currents. Our models are not hampered by noise amplification at depth, and offer a much clearer picture of the interior structure. We estimate that the dynamo boundary is at a fractional radius of 0.8. At this depth, the magnetic field is arranged into longitudinal bands, and strong local features such as the great blue spot appear to be rooted in neighbouring structures of oppositely signed flux.

We present resummed predictions at next-to-leading logarithmic accuracy matched to the exact next-to-leading order results for a set of classical event-shape observables in hadronic Higgs decays, i.e., for the channels $H\to gg$ and $H\to b\bar{b}$. We furthermore consider soft-drop grooming of the hadronic final states and derive corresponding $\text{NLO}+\text{NLL}^\prime$ predictions for the groomed thrust observable. Differences in the QCD radiation pattern of gluon- and quark-initiated final states are imprinted in the event-shape distributions, offering separation power for the two decay channels. In particular, we show that ungroomed event shapes in $H\to gg$ decays develop a considerably harder spectrum than in $H\to b\bar b$ decays. We highlight that soft-drop grooming can substantially alter this behaviour, unless rather inclusive grooming parameters are chosen., Comment: 24 pages, 6 figures, version accepted for publication in JHEP

Jet production from hadronic Higgs decays at future lepton colliders will have significantly different phenomenological implications than jet production via off-shell photon and $Z$-boson decays, owing to the fact that Higgs bosons decay to both pairs of quarks and gluons. We compute observables involving flavoured jets in hadronic Higgs decays to three partons at Born level including next-to-leading order corrections in QCD (i.e up to $\mathcal{O}(\alpha_\mathrm{S}^2))$. The calculation is performed in the framework of an effective theory in which the Higgs boson couples directly to gluons and massless $b$-quarks retaining a non-vanishing Yukawa coupling. For the energy of the leading and subleading flavoured jet, the angular separation and the invariant mass of the leading $b$-$\bar{b}$ pair, we contrast the results obtained in both Higgs decay categories and using either of the infrared-safe flavoured jet algorithms flavour-$k_\mathrm{T}$ and flavour-dressing., Comment: 14 pages, 6 figures

In semiconductor manufacturing, lithography has often been the manufacturing step defining the smallest possible pattern dimensions. In recent years, progress has been made towards high-NA (Numerical Aperture) EUVL (Extreme-Ultraviolet-Lithography) paradigm, which promises to advance pattern shrinking (2 nm node and beyond). However, a significant increase in stochastic defects and the complexity of defect detection becomes more pronounced with high-NA. Present defect inspection techniques (both non-machine learning and machine learning based), fail to achieve satisfactory performance at high-NA dimensions. In this work, we investigate the use of the Slicing Aided Hyper Inference (SAHI) framework for improving upon current techniques. Using SAHI, inference is performed on size-increased slices of the SEM images. This leads to the object detector's receptive field being more effective in capturing small defect instances. First, the performance on previously investigated semiconductor datasets is benchmarked across various configurations, and the SAHI approach is demonstrated to substantially enhance the detection of small defects, by approx. 2x. Afterwards, we also demonstrated application of SAHI leads to flawless detection rates on a new test dataset, with scenarios not encountered during training, whereas previous trained models failed. Finally, we formulate an extension of SAHI that does not significantly reduce true-positive predictions while eliminating false-positive predictions., Comment: 12 pages, 9 figures, to be presented at International Conference on Machine Intelligence with Applications (ICMIA), and to be published in conference proceedings by AIP

Monitoring the number of jumps for volleyball players during training or a match can be crucial to prevent injuries, yet the measurement requires considerable workload and cost using traditional methods such as video analysis. Also, existing methods do not provide accurate differentiation between different types of jumps. In this study, an unobtrusive system with a single inertial measurement unit (IMU) on the waist was proposed to recognize the types of volleyball jumps. A Multi-Layer Temporal Convolutional Network (MS-TCN) was applied for sample-wise classification. The model was evaluated on ten volleyball players and twenty-six volleyball players, during a lab session with a fixed protocol of jumping and landing tasks, and during four volleyball training sessions, respectively. The MS-TCN model achieved better performance than a state-of-the-art deep learning model but with lower computational cost. In the lab sessions, most jump counts showed small differences between the predicted jumps and video-annotated jumps, with an overall count showing a Limit of Agreement (LoA) of 0.1+-3.40 (r=0.884). For comparison, the proposed algorithm showed slightly worse results than VERT (a commercial jumping assessment device) with a LoA of 0.1+-2.08 (r=0.955) but the differences were still within a comparable range. In the training sessions, the recognition of three types of jumps exhibited a mean difference from observation of less than 10 jumps: block, smash, and overhead serve. These results showed the potential of using a single IMU to recognize the types of volleyball jumps. The sample-wise architecture provided high resolution of recognition and the MS-TCN required fewer parameters to train compared with state-of-the-art models., Comment: NA

We present next-to-leading order perturbative QCD predictions for four-jet-like event-shape observables in hadronic Higgs decays. To this end, we take into account two Higgs-decay categories: involving either the Yukawa-induced decay to a $b\bar{b}$ pair or the loop-induced decay to two gluons via an effective Higgs-gluon-gluon coupling. We present results for distributions related to the event-shape variables thrust minor, light-hemisphere mass, narrow jet broadening, $D$-parameter, and Durham four-to-three-jet transition variable. For each of these observables we study the impact of higher-order corrections and compare their size and shape in the two Higgs-decay categories. We find large NLO corrections with a visible shape difference between the two decay modes, leading to a significant shift of the peak in distributions related to the $H\to gg$ decay mode., Comment: 26 pages, 8 figures, 1 table, matches version published in JHEP

PLAnetary Transits and Oscillations of stars (PLATO) is the ESA M3 space mission dedicated to detect and characterise transiting exoplanets including information from the asteroseismic properties of their stellar hosts. The uninterrupted and high-precision photometry provided by space-borne instruments such as PLATO require long preparatory phases. An exhaustive list of tests are paramount to design a mission that meets the performance requirements, and as such, simulations are an indispensable tool in the mission preparation. To accommodate PLATO's need of versatile simulations prior to mission launch - that at the same time describe accurately the innovative but complex multi-telescope design - we here present the end-to-end PLATO simulator specifically developed for the purpose, namely PlatoSim. We show step-by-step the algorithms embedded into the software architecture of PlatoSim that allow the user to simulate photometric time series of CCD images and light curves in accordance to the expected observations of PLATO. In the context of the PLATO payload, a general formalism of modelling, end-to-end, incoming photons from the sky to the final measurement in digital units is discussed. We show the strong predictive power of PlatoSim through its diverse applicability and contribution to numerous working groups within the PLATO Mission Consortium. This involves the on-going mechanical integration and alignment, performance studies of the payload, the pipeline development and assessments of the scientific goals. PlatoSim is a state-of-the-art simulator that is able to produce the expected photometric observations of PLATO to a high level of accuracy. We demonstrate that PlatoSim is a key software tool for the PLATO mission in the preparatory phases until mission launch and prospectively beyond., Comment: 30 pages, 22 figures

The third Gaia Data Release (DR3) provided photometric time series of more than 2 million long-period variable (LPV) candidates. Anticipating the publication of full radial-velocity (RV) in DR4, this Focused Product Release (FPR) provides RV time series for a selection of LPVs with high-quality observations. We describe the production and content of the Gaia catalog of LPV RV time series, and the methods used to compute variability parameters published in the Gaia FPR. Starting from the DR3 LPVs catalog, we applied filters to construct a sample of sources with high-quality RV measurements. We modeled their RV and photometric time series to derive their periods and amplitudes, and further refined the sample by requiring compatibility between the RV period and at least one of the $G$, $G_{\rm BP}$, or $G_{\rm RP}$ photometric periods. The catalog includes RV time series and variability parameters for 9\,614 sources in the magnitude range $6\lesssim G/{\rm mag}\lesssim 14$, including a flagged top-quality subsample of 6\,093 stars whose RV periods are fully compatible with the values derived from the $G$, $G_{\rm BP}$, and $G_{\rm RP}$ photometric time series. The RV time series contain a mean of 24 measurements per source taken unevenly over a duration of about three years. We identify the great most sources (88%) as genuine LPVs, with about half of them showing a pulsation period and the other half displaying a long secondary period. The remaining 12% consists of candidate ellipsoidal binaries. Quality checks against RVs available in the literature show excellent agreement. We provide illustrative examples and cautionary remarks. The publication of RV time series for almost 10\,000 LPVs constitutes, by far, the largest such database available to date in the literature. The availability of simultaneous photometric measurements gives a unique added value to the Gaia catalog (abridged), Comment: 36 pages, 38 figures