Search

Motivation BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP's first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Results Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. Availability and implementation BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d. [ABSTRACT FROM AUTHOR]

We present a search for transient radio sources on time-scales of seconds to hours at 144 MHz using the LOFAR Two-metre Sky Survey (LoTSS). This search is conducted by examining short time-scale images derived from the LoTSS data. To allow imaging of LoTSS on short time-scales, a modern imaging procedure and fast filtering strategy are introduced. This includes sky model source subtraction, no cleaning or primary beam correction, a simple source finder, fast filtering schemes, and source catalogue matching. This new strategy is first tested by injecting simulated transients, with a range of flux densities and durations, into the data. We find the limiting sensitivity to be 113 and 6 mJy for 8 s and 1 h transients, respectively. The new imaging and filtering strategies are applied to 58 fields of the LoTSS survey, corresponding to LoTSS-DR1 (2 per cent of the survey). One transient source is identified in the 8 s and 2 min snapshot images. The source shows 1 min duration flare in the 8 h observation. Our method puts the most sensitive constraints on/estimates of the transient surface density at low frequencies at time-scales of seconds to hours; <4.0 × 10−4 deg−2 at 1 h at a sensitivity of 6.3 mJy; 5.7 × 10−7 deg−2 at 2 min at a sensitivity of 30 mJy; and 3.6 × 10−8 deg−2 at 8 s at a sensitivity of 113 mJy. In the future, we plan to apply the strategies presented in this paper to all LoTSS data. [ABSTRACT FROM AUTHOR]

We present extended point spread function (PSF) models for the Hyper Suprime-Cam Subaru Strategic Program Public Data Release 3 (HSC-SSP PDR3) in all g, r, i, Z , and Y -bands. Due to its 8.2 m primary mirror and long exposure periods, HSC combines deep images with wide-field coverage. Both properties make HSC one of the most suitable observing facilities for low-surface brightness (LSB) studies, which are particularly sensitive to the PSF. By applying a median stacking technique of point-like sources with different brightness, we show how to construct the HSC-SSP PDR3 PSF models to an extent of R ∼ 5.6 arcmin. These models are appropriate for the HSC-PDR3 intermediate-state data which do not have applied the final aggressive background subtraction. The intermediate-state data is especially stored for users interested in large extended objects, where our new PSFs provide them with a crucial tool to characterise LSB properties at large angles. We demonstrate that our HSC PSFs behave reasonably in two scenarios. In the first one, we generate 2D models of a bright star, showing no evidence of residual structures across the five bands. In the second scenario, we recreate the PSF-scattered light on mock images with special consideration of the effect of this additional flux on LSB measurements. Despite the well-behaved nature of the HSC-PDR3 PSFs, there is a non-negligible impact on the faint light present in the mock images. This impact could lead to incorrect LSB measurements if a proper star subtraction is not applied. The PSF models are publicly available to the astronomical community at github.com/luciagarate/HSC_PSFs (DOI: 10.5281/zenodo.11273118). [ABSTRACT FROM AUTHOR]

Adaptive optics (AO) systems correct optical phase aberrations of the incoming light generated by the atmosphere. To do so simultaneous estimators of the atmospheric turbulence parameters are required. For the family of wide-field AO (WFAO) systems, this information must be stratified in altitude. Among these vectorized estimations, wind profiling in altitude is needed for the reduction of temporal errors in AO loops or for the estimation of turbulence coherence time. This paper proposes a turbulence wind profiler called image processing based peak tracking algorithm (ipta). ipta is an image-processing based approach that automatically and reliably estimates wind velocity for several turbulent layers along the line of sight. The estimation of each wind layer is achieved by tracking peaks produced in cross-correlation maps from pairs of wavefront sensors (WFSs) slopes using the technique known as SLODAR (slope detection and ranging). Results for simulated and on-sky WFS data sets demonstrate that ipta outperforms one of the state of the art wind profiler methods [the profiler covariance parametrization of wind velocity (CAW)] in terms of accuracy and speed. Results also show that, in terms of execution time, our method scales better when the number of WFS lenslets is increased. Being an open source and reliable tool, we believe ipta can be a useful wind profiler for the AO community. [ABSTRACT FROM AUTHOR]

The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near-infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired 'target' point spread function (PSF). We present here an initial application of imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the 'leakage'), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moiré patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper ('Paper II') explores the implications for weak lensing analyses. [ABSTRACT FROM AUTHOR]

In the realm of radio astronomy, the detection of radio frequency interference (RFI) stands as a pivotal pursuit. This study performs a novel comparative analysis of deep learning methodologies and introduces a novel transfer learning method, called fine-tuning. We compare various aspects and factors relating to this problem, including supervised fully convolutional network (FCN) architectures used within the literature, loss functions, regularization techniques, and training methodologies, to establish the most effective strategies for RFI detection. Moreover, the relationship between parameters, FLOPS, and inference times are examined. Fine-tuning involves pre-training models with low-quality aoflagger reference outputs, a very popular and accessible RFI flagging software package, and thereafter re-training the models with high-quality reference outputs. We utilize two data sets: real observations from LOFAR and simulated data from HERA. The mean squared error (MSE) loss function emerges as a robust performer if a high recall is desired. In contrast, the binary cross-entropy (BCE) loss function excels in generalization but falls short in classification performance. The Dice loss function emerges as the top performer, maximizing the F1 score and thereby serving as the choice for our further investigations. Notably, we highlight the important role of data quality and model capacity. In particular, we find that low-capacity models exhibit resilience when trained with low-quality flags from aoflagger , showcasing their ability to mitigate overfitting and overflagging tendencies. In contrast, high-capacity models excel when trained with high-quality flags. Fine-tuning proved to be an effective method to unlearn the overflagging tendencies of aoflagger , whilst requiring very little data. [ABSTRACT FROM AUTHOR]

Studying the Universe through radio telescope observation is crucial. However, radio telescopes capture not only signals from the universe but also various interfering signals, known as radio frequency interference (RFI). The presence of RFI can significantly impact data analysis. Ensuring the accuracy, reliability, and scientific integrity of research findings by detecting and mitigating or eliminating RFI in observational data, presents a persistent challenge in radio astronomy. In this study, we proposed a novel deep learning model called EMSCA-UNet for RFI detection. The model employs multiscale convolutional operations to extract RFI features of various scale sizes. Additionally, an attention mechanism is utilized to assign different weights to the extracted RFI feature maps, enabling the model to focus on vital features for RFI detection. We evaluated the performance of the model using real data observed from the 40 m radio telescope at Yunnan Observatory. Furthermore, we compared our results to other models, including U-Net, RFI-Net, and R-Net, using four commonly employed evaluation metrics: precision, recall, F1 score, and IoU. The results demonstrate that our model outperforms the other models on all evaluation metrics, achieving an average improvement of approximately 5 per cent compared to U-Net. Our model not only enhances the accuracy and comprehensiveness of RFI detection but also provides more detailed edge detection while minimizing the loss of useful signals. [ABSTRACT FROM AUTHOR]

One challenge for applying current weak lensing analysis tools to the Nancy Grace Roman Space Telescope is that individual images will be undersampled. Our companion paper presented an initial application of Imcom – an algorithm that builds an optimal mapping from input to output pixels to reconstruct a fully sampled combined image – on the Roman image simulations. In this paper, we measure the output noise power spectra, identify the sources of the major features in the power spectra, and show that simple analytic models that ignore sampling effects underestimate the power spectra of the coadded noise images. We compute the moments of both idealized injected stars and fully simulated stars in the coadded images, and their one- and two-point statistics. We show that the idealized injected stars have root-mean-square ellipticity errors (1–6) × 10−4 per component depending on the band; the correlation functions are ≥2 orders of magnitude below requirements, indicating that the image combination step itself is using a small fraction of the overall Roman second moment error budget, although the fourth moments are larger and warrant further investigation. The stars in the simulated sky images, which include blending and chromaticity effects, have correlation functions near the requirement level (and below the requirement level in a wide-band image constructed by stacking all four filters). We evaluate the noise-induced biases in the ellipticities of injected stars, and explain the resulting trends with an analytical model. We conclude by enumerating the next steps in developing an image coaddition pipeline for Roman. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the software package propane , written for the r data analysis language. propane combines the full range of wcslib projections with the C++ image manipulation routines provided by the cimg library. propane offers routines for image warping and combining (including stacking), and various related tasks such as image alignment tweaking and pixel masking. It can stack an effectively unlimited number of target frames using multiple parallel cores, and offers threading for many lower level routines. It has been used for a number of current and upcoming large surveys, and we present a range of its capabilities and features. propane is already available under a permissive open-source LGPL-3 license at github.com/asgr/ProPane (DOI: 10.5281/zenodo.10057053). [ABSTRACT FROM AUTHOR]

The Chinese Space Station Telescope (CSST) is scheduled to launch soon, which is expected to provide a vast amount of image potentially containing low-surface brightness galaxies (LSBGs). However, detecting and characterizing LSBGs is known to be challenging due to their faint surface brightness, posing a significant hurdle for traditional detection methods. In this paper, we propose LSBGnet, a deep neural network specifically designed for automatic detection of LSBGs. We established LSBGnet-SDSS model using data set from the Sloan Digital Sky Survey (SDSS). The results demonstrate a significant improvement compared to our previous work, achieving a recall of 97.22 per cent and a precision of 97.27 per cent on the SDSS test set. Furthermore, we use the LSBGnet-SDSS model as a pre-training model, employing transfer learning to retrain the model with LSBGs from Dark Energy Survey (DES), and establish the LSBGnet-DES model. Remarkably, after retraining the model on a small DES sample, it achieves over 90 per cent precision and recall. To validate the model's capabilities, we utilize the trained LSBGnet-DES model to detect LSBG candidates within a selected 5 sq. deg area in the DES footprint. Our analysis reveals the detection of 204 LSBG candidates, characterized by a mean surface brightness range of |$23.5\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}\le \bar{\mu }_{\text{eff}}(g)\le 26.8\ \mathrm{ mag}\ \mathrm{ arcsec}^{-2}$| and a half-light radius range of 1.4 arcsec ≤ r 1/2 ≤ 8.3 arcsec. Notably, 116 LSBG candidates exhibit a half-light radius ≥2.5 arcsec. These results affirm the remarkable performance of our model in detecting LSBGs, making it a promising tool for the upcoming CSST. [ABSTRACT FROM AUTHOR]

The Chinese Space Station Telescope (abbreviated as CSST) is a future advanced space telescope. Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey. While recent research on celestial object recognition has progressed, the rapid and efficient identification of high-resolution local celestial images remains challenging. In this study, we conducted galaxy and NSC image classification research using deep learning methods based on data from the Hubble Space Telescope. We built a local celestial image data set and designed a deep learning model named HR-CelestialNet for classifying images of the galaxy and NSC. HR-CelestialNet achieved an accuracy of 89.09 per cent on the testing set, outperforming models such as AlexNet, VGGNet, and ResNet, while demonstrating faster recognition speeds. Furthermore, we investigated the factors influencing CSST image quality and evaluated the generalization ability of HR-CelestialNet on the blurry image data set, demonstrating its robustness to low image quality. The proposed method can enable real-time identification of celestial images during CSST survey mission. [ABSTRACT FROM AUTHOR]

This paper introduces a technique called needlet Karhunen–Loéve (NKL), which cleans both polarized and unpolarized foregrounds from H  i intensity maps by applying a Karhunen–Loéve transform on the needlet coefficients. In NKL, one takes advantage of correlations not only along the line of sight, but also between different angular regions, referred to as 'chunks'. This provides a distinct advantage over many of the standard techniques applied to map space that one finds in the literature, which do not consider such spatial correlations. Moreover, the NKL technique does not require any priors on the nature of the foregrounds, which is important when considering polarized foregrounds. We also introduce a modified version of Generalized Needlet Internal Linear Combination (GNILC), referred to as MGNILC, which incorporates an approximation of the foregrounds to improve performance. The NKL and MGNILC techniques are tested on simulated maps which include polarized foregrounds. Their performance is compared to the GNILC, generalized morphological component analysis, independent component analysis, and principal component analysis techniques. Two separate tests were performed. One at 1.84 < z < 2.55 and the other at 0.31 < z < 0.45. NKL was found to provide the best performance in both tests, providing a factor of 10–50 improvement over GNILC at |$k \lt 0.1\, {\rm hMpc^{-1}}$| in the higher redshift case and |$k \lt 0.03 \, {\rm hMpc^{-1}}$| in the lower redshift case. However, none of the methods were found to recover the power spectrum satisfactorily at all baryon acoustic oscillations scales. [ABSTRACT FROM AUTHOR]

This study investigated the genetic basis of carrot root shape traits using composite interval mapping in two biparental populations (n = 119 and n = 128). The roots of carrot F2:3 progenies were grown over 2 years and analyzed using a digital imaging pipeline to extract root phenotypes that compose market class. Broad-sense heritability on an entry-mean basis ranged from 0.46 to 0.80 for root traits. Reproducible quantitative trait loci (QTL) were identified on chromosomes 2 and 6 on both populations. Colocalization of QTLs for phenotypically correlated root traits was also observed and coincided with previously identified QTLs in published association and linkage mapping studies. Individual QTLs explained between 14 and 27% of total phenotypic variance across traits, while four QTLs for length-to-width ratio collectively accounted for up to 73% of variation. Predicted genes associated with the OFP-TRM (OVATE Family Proteins—TONNEAU1 Recruiting Motif) and IQD (IQ67 domain) pathway were identified within QTL support intervals. This observation raises the possibility of extending the current regulon model of fruit shape to include carrot storage roots. Nevertheless, the precise molecular mechanisms through which this pathway operates in roots characterized by secondary growth originating from cambium layers remain unknown. [ABSTRACT FROM AUTHOR]

Motivation Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio. Results To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset—CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking. Availability and implementation The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer. [ABSTRACT FROM AUTHOR]

The low surface brightness (LSB) regime (μg  ≳  26 mag arcsec−2) comprises a vast, mostly unexplored discovery space, from dwarf galaxies to the diffuse interstellar medium. Accessing this regime requires precisely removing instrumental signatures and light contamination, including, most critically, night sky emission. This is not trivial, as faint astrophysical and instrumental contamination can bias sky models at the precision needed to characterize LSB structures. Using idealized synthetic images, we assess how this bias impacts two common LSB-oriented sky-estimation algorithms: (1) masking and parametric modelling, and (2) stacking and smoothing dithered exposures. Undetected flux limits both methods by imposing a pedestal offset to all derived sky models. Careful, deep masking of fixed sources can mitigate this, but source density always imposes a fundamental limit. Stellar scattered light can contribute ∼28–29 mag arcsec−2 of background flux even in low-density fields; its removal is critical prior to sky estimation. For complex skies, image combining is an effective non-parametric approach, although it strongly depends on observing strategy and adds noise to images on the smoothing kernel scale. Preemptive subtraction of fixed sources may be the only practical approach for robust sky estimation. We thus tested a third algorithm, subtracting a preliminary sky-subtracted coadd from exposures to isolate sky emission. Unfortunately, initial errors in sky estimation propagate through all subsequent sky models, making the method impractical. For large-scale surveys like Legacy Survey of Space and Time, where key science goals constrain observing strategy, masking and modelling remain the optimal sky estimation approach, assuming stellar scattered light is removed first. [ABSTRACT FROM AUTHOR]

We report on three optically bright, ∼16th mag, point sources within 10 arcsec of each other that vanished within 1 h, based on two consecutive exposures at Palomar Observatory on 1952 July 19 (POSS I Red and Blue). The three point sources have continued to be absent in telescope exposures during 71 yr with detection thresholds of ∼21st mag. We obtained two deep exposures with the 10.4-m Gran Telescopio Canarias on 2023 April 25 and 27 in r and g band, both reaching magnitude 25.5 (3σ). The three point sources are still absent, implying they have dimmed by more than 10 mag within an hour back in 1952. When bright in 1952, the most isolated transient source has a profile nearly the same as comparison stars, implying the sources are subarcsec in angular size and they exhibit no elongation due to movement. This triple transient has observed properties similar to other cases where groups of transients ('multiple transients') have appeared and vanished in a small region within a plate exposure. The explanation for these three transients and the previously reported cases remains unclear. Models involving background objects that are optically luminous for less than 1 h coupled with foreground gravitational lensing seem plausible. If so, a significant population of massive objects with structure serving as the lenses, to produce three images, are required to explain the subhour transients. [ABSTRACT FROM AUTHOR]

Magnetic reconnection is often considered as the primary particle acceleration mechanism in a magnetized blazar zone environment. The majority of radiation in the reconnection layer comes from plasmoids and their mergers. In particular, plasmoid mergers can produce strong multiwavelength flares and major variations in synchrotron polarization signatures. However, radiative properties of plasmoid mergers have not been well explored due to difficulties in tracking the merging processes. Here we use an image processing method that combines the magnetic vector potential and density to identify isolated and merging plasmoids. We find that this method can clearly distinguish radiation contributions from isolated plasmoids, merging plasmoids, and the primary current sheet of reconnection. This new method enables us to study the radiative properties of plasmoids and mergers statistically. Our results show that isolated plasmoids have similar emissivity regardless of their sizes, and they generally have non-zero polarization degree (PD) due to their quasi-circular shape. Flares due to plasmoid mergers have relative amplitudes that are antiproportional to the size ratio of the plasmoids participating in the mergers. Finally, only mergers between plasmoids of comparable sizes (width ratio ≲5) can lead to significant spectral hardening and polarization angle (PA) variations; the amplitude of the PA variations is between 0 and 180° and has a mean value of 90°. Our analyses on 2D simulations can pave the way for future analyses and machine learning techniques on radiative properties of 3D magnetic reconnection simulations. [ABSTRACT FROM AUTHOR]

We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure (∼0.2 s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image. [ABSTRACT FROM AUTHOR]

Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets. [ABSTRACT FROM AUTHOR]

Determination of individual age is one essential step in the accurate assessment of fish stocks. In non-tropical environments, the manual count of ring-like growth patterns in fish otoliths (ear stones) is the standard method. It relies on visual means and individual judgment and thus is subject to bias and interpretation errors. The use of automated pattern recognition based on machine learning may help to overcome this problem. Here, we employ two deep learning methods based on Convolutional Neural Networks (CNNs). The first approach utilizes the Mask R-CNN algorithm to perform object detection on the major otolith reading axes. The second approach employs the U-Net architecture to perform semantic segmentation on the otolith image in order to segregate the regions of interest. For both methods, we applied a simple postprocessing to count the rings on the output masks returned, which corresponds to the age prediction. Multiple benchmark tests indicate the promising performance of our implemented approaches, comparable to recently published methods based on classical image processing and traditional CNN implementation. Furthermore, our algorithms showed higher robustness compared to the existing methods, while also having the capacity to extrapolate missing age groups and to adapt to a new domain or data source. [ABSTRACT FROM AUTHOR]

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as noise and galactic foreground contamination that pose a challenge for accurate detection. In this study, we present the SegU-Net v2 framework, an enhanced version of our convolutional neural network, built to identify neutral and ionized regions in the 21-cm signal contaminated with foreground emission. We trained our neural network on 21-cm image data processed by a foreground removal method based on Principal Component Analysis achieving an average classification accuracy of 71 per cent between redshift z  = 7 and 11. We tested SegU-Net v2 against various foreground removal methods, including Gaussian Process Regression, Polynomial Fitting, and Foreground-Wedge Removal. Results show comparable performance, highlighting SegU-Net v2 's independence on these pre-processing methods. Statistical analysis shows that a perfect classification score with |${\rm AUC}=95~{{\ \rm per\ cent}}$| is possible for 8 < z < 10. While the network prediction lacks the ability to correctly identify ionized regions at higher redshift and differentiate well the few remaining neutral regions at lower redshift due to low contrast between 21-cm signal, noise, and foreground residual in images. Moreover, as the photon sources driving reionization are expected to be located inside ionized regions, we show that SegU-Net v2 can be used to correctly identify and measure the volume of isolated bubbles with |$V_{\rm ion}\gt (10\, {\rm cMpc})^3$| at z > 9, for follow-up studies with infrared/optical telescopes to detect these sources. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and deep learning techniques are playing an increasing role in astronomy to deal with the data avalanche. Here we describe an application for finding resolved planetary nebulae (PNe) in crowded, wide-field, narrow-band Hα survey imagery in the Galactic plane, to test and facilitate more objective, reproducible, efficient and reliable trawls for them. PNe are important for studying the late-stage stellar evolution of low-mass to intermediate-mass stars. However, the confirmed ∼3800 Galactic PNe fall far short of the numbers expected. Traditional visual searching for resolved PNe is time-consuming because of the large data size and areal coverage of modern astronomical surveys. The training and validation data set of our algorithm was built with the INT Photometric Hα Survey (IPHAS) and true PNe from the Hong Kong/AAO/Strasbourg Hα (HASH) data base. Our algorithm correctly identified 444 PNe in the validation set of 454 PNe, with only 16 explicable 'false' positives, achieving a precision rate of 96.5 per cent and a recall rate of 97.8 per cent. After transfer learning, it was then applied to the VST Photometric Hα Survey of the Southern Galactic plane and bulge (VPHAS+), examining 979 out of 2284 survey fields, each covering 1° × 1°. It returned ∼20 000 detections, including 2637 known PNe and other kinds of catalogued non-PNe. A total of 815 new high-quality PNe candidates were found, 31 of which were selected as top-quality targets for optical spectroscopic follow-up. We found that 74 per cent of them are true, likely, and possible PNe. Representative preliminary confirmatory spectroscopy results are presented here to demonstrate the effectiveness of our techniques, with full details to be given in our forthcoming paper. [ABSTRACT FROM AUTHOR]

Upcoming imaging surveys will use weak gravitational lensing to study the large-scale structure of the Universe, demanding sub-per cent accuracy for precise cosmic shear measurements. We present a new differentiable implementation of our perturbation-based shear estimator (fpfs), using jax , which is publicly available as part of a new suite of analytic shear algorithms called anacal. This code can analytically calibrate the shear response of any non-linear observable constructed with the fpfs shapelets and detection modes utilizing autodifferentiation (ad), generalizing the formalism to include a family of shear estimators with corrections for detection and selection biases. Using the ad capability of jax , it calculates the full Hessian matrix of the non-linear observables, which improves the previously presented second-order noise bias correction in the shear estimation. As an illustration of the power of the new anacal framework, we optimize the effective galaxy number density in the space of the generalized shear estimators using an LSST-like galaxy image simulation for the 10 yr LSST. For the generic shear estimator, the magnitude of the multiplicative bias | m | is below 3 × 10−3 (99.7 per cent confidence interval), and the effective galaxy number density is improved by 5 per cent . We also discuss some planned future additions to the anacal software suite to extend its applicability beyond the fpfs measurements. [ABSTRACT FROM AUTHOR]

Chronic changes on kidney biopsy specimens include increasing amounts of arteriosclerosis, glomerulosclerosis, interstitial fibrosis and tubular atrophy, enlarged nephron size, and reduced nephron number. These chronic changes are difficult to accurately assess by visual inspection but are reasonably quantified using morphometry. This review describes the various patient populations that have undergone morphometric analysis of kidney biopsies. The common approaches to morphometric analysis are described. The chronic kidney disease outcomes associated with various chronic changes by morphometry are also summarized. Morphometry enriches the characterization of chronicity on a kidney biopsy and this can supplement the pathologist's diagnosis. Artificial intelligence image processing tools are needed to automate the annotations needed for practical morphometric analysis of kidney biopsy specimens in routine clinical care. [ABSTRACT FROM AUTHOR]

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid , and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research. [ABSTRACT FROM AUTHOR]

The recent serendipitous discovery of a new population of short duration X-ray transients, thought to be associated with collisions of compact objects or stellar explosions in distant galaxies, has motivated efforts to build up statistical samples by mining X-ray telescope archives. Most searches to date, however, do not fully exploit recent developments in the signal and imaging processing research domains to optimize searches for short X-ray flashes. This paper addresses this issue by presenting a new source detection pipeline, STATiX (Space and Time Algorithm for Transients in X-rays), which directly operates on three-dimensional X-ray data cubes consisting of two spatial and one temporal dimension. The algorithm leverages wavelet transforms and the principles of sparsity to denoise X-ray observations and then detect source candidates on the denoised data cubes. The light curves of the detected sources are then characterized using the Bayesian blocks algorithm to identify flaring periods. We describe the implementation of STATiX in the case of XMM–Newton data, present extensive validation and performance verification tests based on simulations and also apply the pipeline to a small subset of seven XMM–Newton observations, which are known to contain transients sources. In addition to known flares in the selected fields, we report a previously unknown short duration transient found by our algorithm that is likely associated with a flaring Galactic star. This discovery demonstrates the potential of applying STATiX to the full XMM–Newton archive. [ABSTRACT FROM AUTHOR]

We report on the detection of three icy cavities on the nucleus of comet 67P/Churyumov–Gerasimenko. They were identified on high-resolution anaglyphs built from images acquired by the Optical, Spectroscopic, and Infrared Remote Imaging System instrument aboard the Rosetta spacecraft on 2016 April 9 and 10. Visually, they appear as bright patches of typically 15 to 30 m across whose large reflectances and spectral slopes in the visible substantiate the presence of sub-surface water ice. Using a new high-resolution photogrammetric shape model, we determined the three-dimensional shape of these cavities whose depth ranges from 20 to 47 m. Spectral slopes were interpreted with models combining water ice and refractory dark material and the water ice abundances in the cavities were found to amount to a few per cent. The determination of the lifetime of the icy cavities was strongly biased by the availability of appropriate and favourable observations, but we found evidence of values of up to 2 yr. The icy cavities were found to be connected to jets well documented in past studies. A thermal model allowed us to track their solar insolation over a large part of the orbit of the comet and a transitory bright jet on 2015 July 18 was unambiguously linked to the brief illumination of the icy bottom of one of the cavities. These cavities are likely to be the first potential subsurface access points detected on a cometary nucleus and their lifetimes suggest that they reveal pristine sub-surface icy layers or pockets rather than recently recondensed water vapour. [ABSTRACT FROM AUTHOR]

We present the deepest J −Ks near-infrared photometry of the globular cluster M5 (NGC 5904) from observations taken with the Gemini South Adaptive Optics Imager in tandem with the Gemini Multi-conjugate adaptive optics System (GeMS) on the 8.1-m Gemini South telescope. Point spread function (PSF) photometry was carried out using a spatially variable PSF, zero-point calibrations based on correlations to a standard photometric catalogue, colour corrections, and crowding corrections. The latter corrections provided a new challenge given the field variations of the adaptive optics corrections in the central crowded regions of this cluster. The final photometric precision in our J− Ks colour–magnitude diagram exposes a dispersion among the lower main-sequence stars of M5 for the first time. This dispersion occurs below a main-sequence knee due to variations in the helium and CNO (carbon, nitrogen, and oxygen) abundances from multiple stellar populations, consistent with results from the bright evolved stars in this cluster from ultraviolet to near-infrared Hubble Space Telescope photometry and ground-based spectroscopy. This paper completes our original GeMS quality analysis programme, providing insights into adaptive optics analyses in crowded fields. [ABSTRACT FROM AUTHOR]

It is crucial for a deeper understanding of the formation and evolution of galaxies in the Universe to study stellar mass (M *) and star formation rate (SFR). Traditionally, astronomers infer the properties of galaxies from spectra, which are highly informative, but expensive and hard to be obtained. Fortunately, modern sky surveys obtained a vast amount of high-spatial-resolution photometric images. The photometric images are obtained relatively economically than spectra, and it is very helpful for related studies if M * and SFR can be estimated from photometric images. Therefore, this paper conducted some preliminary researches and explorations on this regard. We constructed a deep learning model named Galaxy Efficient Network (GalEffNet) for estimating integrated M * and specific star formation rate (sSFR) from Dark Energy Spectroscopic Instrument galaxy images. The GalEffNet primarily consists of a general feature extraction module and a parameter feature extractor. The research results indicate that the proposed GalEffNet exhibits good performance in estimating M * and sSFR, with σ reaching 0.218 and 0.410 dex. To further assess the robustness of the network, prediction uncertainty was performed. The results show that our model maintains good consistency within a reasonable bias range. We also compared the performance of various network architectures and further tested the proposed scheme using image sets with various resolutions and wavelength bands. Furthermore, we conducted applicability analysis on galaxies of various sizes, redshifts, and morphological types. The results indicate that our model performs well across galaxies with various characteristics and indicate its potentials of broad applicability. [ABSTRACT FROM AUTHOR]

Radio galaxies exhibit a rich diversity of morphological characteristics, which make their classification into distinct types a complex challenge. To address this challenge effectively, we introduce an innovative approach for radio galaxy classification using COSFIRE filters. These filters possess the ability to adapt to both the shape and orientation of prototype patterns within images. The COSFIRE approach is explainable, learning-free, rotation-tolerant, efficient, and does not require a large training set. To assess the efficacy of our method, we conducted experiments on a benchmark radio galaxy data set comprising of 1180 training samples and 404 test samples. Notably, our approach achieved an average accuracy rate of 93.36 per cent. This achievement outperforms contemporary deep learning models, and it is the best result ever achieved on this data set. Additionally, COSFIRE filters offer better computational performance, ∼20 × fewer operations than the DenseNet-based competing method (when comparing at the same accuracy). Our findings underscore the effectiveness of the COSFIRE filter-based approach in addressing the complexities associated with radio galaxy classification. This research contributes to advancing the field by offering a robust solution that transcends the orientation challenges intrinsic to radio galaxy observations. Our method is versatile in that it is applicable to various image classification approaches. [ABSTRACT FROM AUTHOR]

We previously constructed TheCellVision.org , a central repository for visualizing and mining data from yeast high-content imaging projects. At its inception, TheCellVision.org housed two high-content screening (HCS) projects providing genome-scale protein abundance and localization information for the budding yeast Saccharomyces cerevisiae , as well as a comprehensive analysis of the morphology of its endocytic compartments upon systematic genetic perturbation of each yeast gene. Here, we report on the expansion of TheCellVision.org by the addition of two new HCS projects and the incorporation of new global functionalities. Specifically, TheCellVision.org now hosts images from the Cell Cycle Omics project, which describes genome-scale cell cycle-resolved dynamics in protein localization, protein concentration, gene expression, and translational efficiency in budding yeast. Moreover, it hosts PIFiA, a computational tool for image-based predictions of protein functional annotations. Across all its projects, TheCellVision.org now houses >800,000 microscopy images along with computational tools for exploring both the images and their associated datasets. Together with the newly added global functionalities, which include the ability to query genes in any of the hosted projects using either yeast or human gene names, TheCellVision.org provides an expanding resource for single-cell eukaryotic biology. [ABSTRACT FROM AUTHOR]

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity. [ABSTRACT FROM AUTHOR]

Disentangling the radio flux contribution from star formation (SF) and active-galactic-nuclei (AGNs) activity is a long-standing problem in extragalactic astronomy, since at frequencies of ≲ 10 GHz, both processes emit synchrotron radiation. We present in this work the general objectives of the PARADIGM (PAnchromatic high-Resolution Analysis of DIstant Galaxy Mergers) project, a multi-instrument concept to explore SF and mass assembly of galaxies. We introduce two novel general approaches for a detailed multiscale study of the radio emission in local (ultra) luminous infrared galaxies (U/LIRGs). In this work, we use archival interferometric data from the Very Large Array (VLA) centred at ∼ 6 GHz (C band) and present new observations from the e -Multi-Element Radio-Linked Interferometer Network (e -MERLIN) for UGC 5101, VV 705, VV 250, and UGC 8696. Using our image decomposition methods, we robustly disentangle the radio emission into distinct components by combining information from the two interferometric arrays. We use e -MERLIN as a probe of the core-compact radio emission (AGN or starburst) at ∼ 20 pc scales, and as a probe of nuclear diffuse emission, at scales ∼100–200 pc. With VLA, we characterize the source morphology and the flux density on scales from ∼200 pc up to and above 1 kpc. As a result, we find deconvolved and convolved sizes for nuclear regions from ∼10 to ∼200 pc. At larger scales, we find sizes of 1.5–2 kpc for diffuse structures (with effective sizes of ∼ 300–400 pc). We demonstrate that the radio emission from nuclear extended structures (∼ 100 pc) can dominate over core-compact components, providing a significant fraction of the total multiscale SF output. We establish a multiscale radio tracer for SF by combining information from different instruments. Consequently, this work sets a starting point to potentially correct for overestimations of AGN fractions and underestimates of SF activity. [ABSTRACT FROM AUTHOR]

The hot intracluster medium (ICM) provides a unique laboratory to test multiscale physics in numerical simulations and probe plasma physics. Utilizing archival Chandra observations, we measure density fluctuations in the ICM in a sample of 80 nearby (z ≲ 1) galaxy clusters and infer scale-dependent velocities within regions affected by mergers (r < R 2500c), excluding cool-cores. Systematic uncertainties (e.g. substructures, cluster asymmetries) are carefully explored to ensure robust measurements within the bulk ICM. We find typical velocities ∼220 (300) km s−1 in relaxed (unrelaxed) clusters, which translate to non-thermal pressure fractions ∼4 (8) per cent, and clumping factors ∼1.03 (1.06). We show that density fluctuation amplitudes could distinguish relaxed from unrelaxed clusters in these regions. Comparison with density fluctuations in cosmological simulations shows good agreement in merging clusters. Simulations underpredict the amplitude of fluctuations in relaxed clusters on length scales <0.75  R 2500c, suggesting these systems are most sensitive to 'missing' physics in the simulations. In clusters hosting radio haloes, we examine correlations between gas velocities, turbulent dissipation rate, and radio emission strength/efficiency to test turbulent re-acceleration of cosmic ray electrons. We measure a weak correlation, driven by a few outlier clusters, in contrast to some previous studies. Finally, we present upper limits on effective viscosity in the bulk ICM of 16 clusters, showing it is systematically suppressed by at least a factor of 8, and the suppression is a general property of the ICM. Confirmation of our results with direct velocity measurements will be possible soon with XRISM. [ABSTRACT FROM AUTHOR]

The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work, we present a Deep Learning (DL) based morphological catalogue built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves a purity rate of 98.5 per cent in accurately distinguishing between spiral, as part of the larger category of LT galaxies, and elliptical, belonging to ET galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalogue of galaxies, we recover the expected colour–magnitude diagram in which LT galaxies display bluer colours than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalogue with 164 314 objects complete up to r petro < 18, covering ∼1800 deg2, from which ∼55 000 are classified as high reliability, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues. [ABSTRACT FROM AUTHOR]

The microtubule motor dynein is critical for the assembly and positioning of mitotic spindles. In Caenorhabditis elegans , these dynein functions have been extensively studied in the early embryo but remain poorly explored in other developmental contexts. Here, we use a hypomorphic dynein mutant to investigate the motor's contribution to asymmetric stem cell–like divisions in the larval epidermis. Live imaging of seam cell divisions that precede formation of the seam syncytium shows that mutant cells properly assemble but frequently misorient their spindle. Misoriented divisions misplace daughter cells from the seam cell row, generate anucleate compartments due to aberrant cytokinesis, and disrupt asymmetric cell fate inheritance. Consequently, the seam becomes disorganized and populated with extra cells that have lost seam identity, leading to fatal epidermal rupture. We show that dynein orients the spindle through the cortical GOA-1Gα–LIN-5NuMA pathway by directing the migration of prophase centrosomes along the anterior–posterior axis. Spindle misorientation in the dynein mutant can be partially rescued by elongating cells, implying that dynein-dependent force generation and cell shape jointly promote correct asymmetric division of epithelial stem cells. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [−1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented data set while testing is performed using GRMHD simulated data, as indicated by the high R 2 score. Consequently, we propose that GANs can be employed as cost-effective models for black hole image generation and reliably augment training data sets for other parametrization algorithms. [ABSTRACT FROM AUTHOR]

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra -like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 per cent at an 89 per cent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 per cent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC 4765, NGC 533, NGC 2300, NGC 3091, NGC 4073, NGC 4125, and NGC 5129) and a number of potential cavity candidates. [ABSTRACT FROM AUTHOR]

We release photometric redshifts, reaching ∼0.7, for ∼14M galaxies at r ≤ 20 in the 11 500 deg2 of the SDSS north and south Galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on ugriz stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA, and BOSS surveys. Representative training sets of ∼370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the over-representation of luminous red galaxies. The CNN outputs a redshift classification that offers all the benefits of a well-behaved PDF, with a width efficiently signalling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias, and rate of catastrophic failures of the median point estimate are of order σMAD = 0.014, <Δ z norm>=0.0015, |$\eta (|\Delta z_{\rm norm}|\gt 0.05)=4{{\, \rm per\ cent}}$| on a representative test sample at r < 19.8, outperforming currently published estimates. The distributions in narrow intervals of magnitudes of the redshifts inferred for the photometric sample are in good agreement with the results of tomographic analyses. The inferred redshifts also match the photometric redshifts of the redMaPPer galaxy clusters for the probable cluster members. [ABSTRACT FROM AUTHOR]

We present wide-field, deep K -band photometry of 98 luminous early-type galaxies (ETGs) from the MASSIVE survey based on observations taken with the WIRCam instrument on the Canada–France–Hawaii Telescope. Using these images, we extract accurate total K -band luminosities (LK) and half-light radii (Re) for this sample of galaxies. We use these new values to explore the size–luminosity and Faber–Jackson relations for massive ETGs. Within this volume-limited sample, we find clear evidence for curvature in both relations, indicating that the most luminous galaxies tend to have larger sizes and smaller velocity dispersions than expected from a simple power-law fit to less luminous galaxies. Our measured relations are qualitatively consistent with the most massive elliptical galaxies forming largely through dissipationless mergers. When the sample is separated into fast and slow rotators, we find the slow rotators to exhibit similar changes in slope with increasing LK , suggesting that low-mass and high-mass slow rotators have different formation histories. The curvatures in the Re – LK and σ– LK relations cancel, leading to a relation between dynamical mass and luminosity that is well described by a single power law: Re σ2 ∝  LK b with b ≈ 1.2. This is consistent with the tilt of the fundamental plane observed in lower mass elliptical galaxies. [ABSTRACT FROM AUTHOR]

Direct imaging is an active research topic in astronomy for the detection and the characterization of young substellar objects. The very high contrast between the host star and its companions makes the observations particularly challenging. In this context, post-processing methods combining several images recorded with the pupil tracking mode of telescope are needed. In previous works, we have presented a data-driven algorithm, PACO , capturing locally the spatial correlations of the data with a multivariate Gaussian model. PACO delivers better detection sensitivity and confidence than the standard post-processing methods of the field. However, there is room for improvement due to the approximate fidelity of the PACO statistical model to the time evolving observations. In this paper, we propose to combine the statistical model of PACO with supervised deep learning. The data are first pre-processed with the PACO framework to improve the stationarity and the contrast. A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources. Finally, the trained network delivers a detection map. The photometry of detected sources is estimated by a second CNN. We apply the proposed approach to several data sets from the VLT/SPHERE instrument. Our results show that its detection stage performs significantly better than baseline methods (cADI and PCA), and leads to a contrast improvement up to half a magnitude compared to PACO. The characterization stage of the proposed method performs on average on par with or better than the comparative algorithms (PCA and PACO) for angular separation above 0.5 arcsec. [ABSTRACT FROM AUTHOR]

From Nov. 2019 to May 2020, the red supergiant star Betelgeuse experienced an unprecedented drop of brightness in the visible domain called the Great Dimming event (GDE). Large atmospheric dust clouds and large photospheric convective features are suspected to be responsible for it. To better understand the dimming event, we used mid-infrared long-baseline spectro-interferometric measurements of Betelgeuse taken with the Very Large Telescope Interferometer/Multi AperTure mid-Infrared SpectroScopic Experiment (VLTI/MATISSE) instrument before (Dec. 2018), during (Feb. 2020), and after (Dec. 2020) the GDE. We present data in the 3.98–4.15 µm range to cover SiO spectral features molecules as well as adjacent continuum. We have employed geometrical models, image reconstruction, as well as radiative transfer models to monitor the spatial distribution of SiO over the stellar surface. We find a strongly inhomogeneous spatial distribution of SiO that appears to be looking very different between our observing epochs, indicative of a vigorous activity in the stellar atmosphere. The contrast of our images is small in the pseudo-continuum for all epochs, implying that our MATISSE observations support both cold spot and dust cloud model. [ABSTRACT FROM AUTHOR]

The treatment of recurrent ovarian cancer has been based on systemic therapy. The role of secondary cytoreductive surgery has been addressed recently in several trials. Imaging plays a key role in helping the surgical team to decide which patients will have resectable disease and benefit from surgery. The role of staging laparoscopy and several imaging and clinical scores has been extensively debated in the field. In other surgical fields there have been reports of using 3D imaging software and 3D printed models to help surgeons better plan the surgical approach. To the best of our knowledge, we report the first case of a patient with recurrent ovarian cancer undergoing 3D modeling before secondary cytoreductive surgery. The 3D modeling was of most value to evaluate the extension of the disease in our patient who underwent a successful secondary cytoreductive surgery and is currently free of the disease. [ABSTRACT FROM AUTHOR]