Michele Delli Veneri

The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.

We performed differential number counts down to 4.25 sigma using ALMA Band 3 calibrator images, which are known for their high dynamic range and susceptibility to various types of contamination. Estimating the fraction of contaminants is an intricate process due to correlated non-Gaussian noise, and it is often compounded by the presence of false positives generated during the cleaning phase. In addition, calibrator extensions further complicate the counting of background sources. In order to address these challenges, our strategy employs a machine learning-based approach utilizing the UMLAUT algorithm. UMLAUT assigns a value to each detection, and it considers how likely it is for there to be a genuine background source or a contaminant. With respect to this goal, we provide UMLAUT with eight observational input parameters, each automatically weighted using a gradient descent method. Our methodology significantly improves the precision of differential number counts, thus surpassing conventional techniques, including visual inspection. This study contributes to a better understanding of radio sources, particularly in the challenging sub-5 sigma regime, within the complex context of a high dynamic range of ALMA calibrator images.

The Square Kilometre Array Observatory (SKAO) will explore the radio sky to new depths in order to conduct transformational science. SKAO data products made available to astronomers will be correspondingly large and complex, requiring the application of advanced analysis techniques to extract key science findings. To this end, SKAO is conducting a series of Science Data Challenges, each designed to familiarize the scientific community with SKAO data and to drive the development of new analysis techniques. We present the results from Science Data Challenge 2 (SDC2), which invited participants to find and characterize 233 245 neutral hydrogen (H i) sources in a simulated data product representing a 2000 h SKA-Mid spectral line observation from redshifts 0.25–0.5. Through the generous support of eight international supercomputing facilities, participants were able to undertake the Challenge using dedicated computational resources. Alongside the main challenge, ‘reproducibility awards’ were made in recognition of those pipelines which demonstrated Open Science best practice. The Challenge saw over 100 participants develop a range of new and existing techniques, with results that highlight the strengths of multidisciplinary and collaborative effort. The winning strategy – which combined predictions from two independent machine learning techniques to yield a 20 per cent improvement in overall performance – underscores one of the main Challenge outcomes: that of method complementarity. It is likely that the combination of methods in a so-called ensemble approach will be key to exploiting very large astronomical data sets.

The light curve of Gaia23bab (=SPICY 97589) shows two significant (ΔG > 2 mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source’s quiescent spectral energy distribution indicates an embedded (A _V > 5 mag) pre-main-sequence star, with optical accretion emission and mid-infrared disk emission. This characterization is supported by the source’s membership in an embedded cluster in the star-forming cloud DOBASHI 1604 at a distance of 900 ± 45 pc. Thus, the brightening events are probable accretion outbursts, likely of EX Lup-type.

The light curve of Gaia23bab (= SPICY 97589) shows two significant ($ΔG>2$ mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source's quiescent spectral energy distribution indicates an embedded ($A_V>5$ mag) pre-main-sequence star, with optical accretion emission and mid-infrared disk emission. This characterization is supported by the source's membership in an embedded cluster in the star-forming cloud DOBASHI 1604 at a distance of $900\pm45$~pc. Thus, the brightening events are probable accretion outbursts, likely of EX Lup-type.

The Atacama large millimeter/submillimeter array with the planned electronic upgrades will deliver an unprecedented number of deep and high resolution observations. Wider fields of view are possible with the consequential cost of image reconstruction. Alternatives to commonly used applications in image processing have to be sought and tested. Advanced image reconstruction methods are critical to meet the data requirements needed for operational purposes. Astrostatistics and astroinformatics techniques are employed. Evidence is given that these interdisciplinary fields of study applied to synthesis imaging meet the Big Data challenges and have the potential to enable new scientific discoveries in radio astronomy and astrophysics.

We present a Deep-Learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a Convolutional Autoencoder for source detection within the spatial domain of the integrated data cubes, a Recurrent Neural Network (RNN) for denoising and peak detection within the frequency domain, and four Residual Neural Networks (ResNets) for source characterization. The combination of spatial and frequency information improves completeness while decreasing spurious signal detection. To train and test the pipeline, we developed a simulation algorithm able to generate realistic ALMA observations, i.e. both sky model and dirty cubes. The algorithm simulates always a central source surrounded by fainter ones scattered within the cube. Some sources were spatially superimposed in order to test the pipeline deblending capabilities. The detection performances of the pipeline were compared to those of other methods and significant improvements in performances were achieved. Source morphologies are detected with subpixel accuracies obtaining mean residual errors of 10⁻³ pixel (0.1 mas) and 10⁻¹ mJy/beam on positions and flux estimations, respectively. Projection angles and flux densities are also recovered within |$10\%$| of the true values for |$80\%$| and |$73\%$| of all sources in the test set, respectively. While our pipeline is fine-tuned for ALMA data, the technique is applicable to other interferometric observatories, as SKA, LOFAR, VLBI, and VLTI.