A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields

The search for galaxy cluster members with deep learning of panchromatic HST imaging and extensive spectroscopy

Context. The next generation of extensive and data-intensive surveys are bound to produce a vast amount of data, which can be efficiently dealt with using machine-learning and deep-learning methods to explore possible correlations within the multi-dimensional parameter space. Aims. We explore the classification capabilities of convolution neural networks (CNNs) to identify galaxy cluster members (CLMs) by using Hubble Space Telescope (HST) images of fifteen galaxy clusters at redshift 0.19≲ z≲ 0.60, observed as part of the CLASH and Hubble Frontier Field programmes. Methods. We used extensive spectroscopic information, based on the CLASH-VLT VIMOS programme combined with MUSE observations, to define the knowledge base. We performed various tests to quantify how well CNNs can identify cluster members on ht basis of imaging information only. Furthermore, we investigated the CNN capability to predict source memberships outside the training coverage, in particular, by identifying CLMs at the faint end of the magnitude distributions. Results. We find that the CNNs achieve a purity-completeness rate ≳ 90%, demonstrating stable behaviour across the luminosity and colour of cluster galaxies, along with a remarkable generalisation capability with respect to cluster redshifts. We concluded that if extensive spectroscopic information is available as a training base, the proposed approach is a valid alternative to catalogue-based methods because it has the advantage of avoiding photometric measurements, which are particularly challenging and time-consuming in crowded cluster cores. As a byproduct, we identified 372 photometric cluster members, with mag(F814) < 25, to complete the sample of 812 spectroscopic members in four galaxy clusters RX J2248-4431, MACS J0416-2403, MACS J1206-0847 and MACS J1149+2223. Conclusions. When this technique is applied to the data that are expected to become available from forthcoming surveys, it will be an efficient tool for a variety of studies requiring CLM selection, such as galaxy number densities, luminosity functions, and lensing mass reconstruction

New high-precision strong lensing modeling of Abell 2744. Preparing for JWST observations

We present a new strong lensing (SL) model of the Hubble Frontier Fields galaxy cluster Abell 2744, at z=0.3072, by exploiting archival Hubble Space Telescope (HST) multi-band imaging and Multi Unit Spectroscopic Explorer (MUSE) follow-up spectroscopy. The lens model considers 90 spectroscopically confirmed multiple images (from 30 background sources), which represents the largest secure sample for this cluster field prior to the recently acquired James Webb Space Telescope observations. The inclusion of the sub-structures within several extended sources as model constraints allows us to accurately characterize the inner total mass distribution of the cluster and the position of the cluster critical lines. We include the lensing contribution of 225 cluster members, 202 of which are spectroscopically confirmed. We also measure the internal velocity dispersion of 85 cluster galaxies to independently estimate the role of the subhalo mass component in the lens model. We investigate the effect of the cluster environment on the total mass reconstruction of the cluster core with two different mass parameterizations. We consider the mass contribution from three external clumps, either based on previous weak-lensing studies, or extended HST imaging of luminous members around the cluster core. In the latter case, the observed positions of the multiple images are better reproduced, with a remarkable accuracy of 0.37", a factor of $\sim2$ smaller than previous lens models. We develop and make publicly available a Strong Lensing Online Tool (SLOT) to exploit the predictive power and the full statistical information of this and future models, through a simple graphical interface. We plan to apply our high-precision SL model to the first analysis of the GLASS-JWST-ERS program, specifically to measure the intrinsic physical properties of high-$z$ galaxies from robust magnification maps.Comment: 14 pages, 9 figures, 4 table

Searching for galaxy-scale strong-lenses in galaxy clusters with deep networks -- I: methodology and network performance

Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological simulations. We search for galaxy-galaxy strong-lensing systems in HST multi-band imaging of galaxy cluster cores from the CLASH and HFF programs by exploring the classification capabilities of deep learning techniques. Convolutional neural networks are trained utilising highly-realistic simulations of galaxy-scale strong lenses injected into the HST cluster fields around cluster members. To this aim, we take advantage of extensive spectroscopic information on member galaxies in 16 clusters and the accurate knowledge of the deflection fields in half of these from high-precision strong lensing models. Using observationally-based distributions, we sample magnitudes, redshifts and sizes of the background galaxy population. By placing these sources within the secondary caustics associated with cluster galaxies, we build a sample of ~3000 galaxy-galaxy strong lenses which preserve the full complexity of real multi-colour data and produce a wide diversity of strong lensing configurations. We study two deep learning networks processing a large sample of image cutouts in three HST/ACS bands, and we quantify their classification performance using several standard metrics. We find that both networks achieve a very good trade-off between purity and completeness (85%-95%), as well as good stability with fluctuations within 2%-4%. We characterise the limited number of false negatives and false positives in terms of the physical properties of the background sources and cluster members. We also demonstrate the neural networks' high degree of generalisation by applying our method to HST observations of 12 clusters with previously known galaxy-scale lensing systems.Comment: 17 pages, 13 figures, to be published on A&

The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403 and MACS J1149.5+2223

The wavelength dependence of the Kormendy relation (KR) is well characterised at low-redshift, but poorly studied at intermediate redshifts. The KR provides information on the evolution of the population of early-type galaxies (ETGs), therefore, by studying it, we may shed light on the assembly processes of these objects and their size evolution. Since studies at different redshifts are generally conducted in different rest-frame wavebands, investigating whether there is a wavelength dependence of the KR is fundamental to interpret the conclusions we might draw from it. We analyse the KRs of the three Hubble Frontier Fields clusters, Abell S1063 (z = 0.348), MACS J0416.1-2403 (z = 0.396), and MACS J1149.5+2223 (z = 0.542), as a function of wavelength. This is the first time the KR of ETGs has been explored consistently in such a large range of wavelength at intermediate redshifts. We exploit very deep HST photometry, ranging from the observed B-band to the H-band, and VLT/MUSE integral field spectroscopy. We improve the structural parameters estimation we performed in a previous work (Tortorelli et al. 2018) by means of a newly developed Python package called morphofit (Tortorelli&Mercurio 2023). With its use on cluster ETGs, we find that the KR slopes smoothly increase with wavelength from the optical to the near-infrared bands in all three clusters, with the intercepts getting fainter at lower redshifts due to the passivisation of the ETGs population. The slope trend is consistent with previous findings at lower redshifts. The slope increase with wavelength implies that smaller size ETGs are more centrally concentrated than larger size ETGs in the near-infrared with respect to the optical regime. Since different bands probe different stellar populations in galaxies, the slope increase also implies that smaller ETGs have stronger internal gradients with respect to larger ETGs.Comment: Submitted to Astronomy and Astrophysics in the form of letter to the Editor, 5 pages, 1 figure, 1 tabl

Candidate Cluster Members with Deep learning

The next generation of extensive and data-intensive surveys are bound to produce a vast amount of data, which can be efficiently dealt with using machine-learning and deep-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the classification capabilities of convolution neural networks (CNNs) to identify galaxy cluster members (CLMs) by using Hubble Space Telescope (HST) images of fifteen galaxy clusters at redshift 0.19≲z≲0.60, observed as part of the CLASH and Hubble Frontier Field programmes. We used extensive spectroscopic information, based on the CLASH-VLT VIMOS programme combined with MUSE observations, to define the knowledge base. We performed various tests to quantify how well CNNs can identify cluster members on ht basis of imaging information only. Furthermore, we investigated the CNN capability to predict source memberships outside the training coverage, in particular, by identifying CLMs at the faint end of the magnitude distributions. We find that the CNNs achieve a purity-completeness rate ≳90%, demonstrating stable behaviour across the luminosity and colour of cluster galaxies, along with a remarkable generalisation capability with respect to cluster redshifts. We concluded that if extensive spectroscopic information is available as a training base, the proposed approach is a valid alternative to catalogue-based methods because it has the advantage of avoiding photometric measurements, which are particularly challenging and time-consuming in crowded cluster cores. As a byproduct, we identified 372 photometric cluster members, with mag(F814)<25, to complete the sample of 812 spectroscopic members in four galaxy clusters RX J2248-4431, MACS J0416-2403, MACS J1206-0847 and MACS J1149+2223. When this technique is applied to the data that are expected to become available from forthcoming surveys, it will be an efficient tool for a variety of studies requiring CLM selection, such as galaxy number densities, luminosity functions, and lensing mass reconstruction

Population genetic structure of Schistosoma haematobium and Schistosoma haematobium x Schistosoma bovis hybrids among school-aged children in Côte d'Ivoire

While population genetics of Schistosoma haematobium have been investigated in West Africa, only scant data are available from Cote d'Ivoire. The purpose of this study was to analyze both genetic variability and genetic structure among S. haematobium populations and to quantify the frequency of S. haematobium x S. bovis hybrids in school-aged children in different parts of Cote d'Ivoire. Urine samples were subjected to a filtration method and examined microscopically for Schistosoma eggs in four sites in the western and southern parts of Cote d'Ivoire. A total of 2692 miracidia were collected individually and stored on Whatman((R)) FTA cards. Of these, 2561 miracidia were successfully genotyped for species and hybrid identification using rapid diagnostic multiplex mitochondrial cox1 PCR and PCR Restriction Fragment Length Polymorphism (PCR-RFLP) analysis of the nuclear ITS2 region. From 2164 miracidia, 1966 (90.9%) were successfully genotyped using at least 10 nuclear microsatellite loci to investigate genetic diversity and population structure. Significant differences were found between sites in all genetic diversity indices and genotypic differentiation was observed between the site in the West and the three sites in the East. Analysis at the infrapopulation level revealed clustering of parasite genotypes within individual children, particularly in Duekoue (West) and Sikensi (East). Of the six possible cox1-ITS2 genetic profiles obtained from miracidia, S. bovis cox1 x S. haematobium ITS2 (42.0%) was the most commonly observed in the populations. We identified only 15 miracidia (0.7%) with an S. bovis cox1 x S. bovis ITS2 genotype. Our study provides new insights into the population genetics of S. haematobium and S. haematobium x S. bovis hybrids in humans in Cote d'Ivoire and we advocate for researching hybrid schistosomes in animals such as rodents and cattle in Cote d'Ivoire

CLASH-VLT: Abell S1063: Cluster assembly history and spectroscopic catalogue

Context. The processes responsible for galaxy evolution in different environments as a function of galaxy mass remain heavily debated. Rich galaxy clusters are ideal laboratories in which to distinguish the role of environmental versus mass quenching because they consist of a full range of galaxies and environments. Aims. Using the CLASH-VLT survey, we assembled an unprecedentedly large sample of 1234 spectroscopically confirmed members in Abell S1063. We found a dynamically complex structure at «zcl»= 0.3457 with a velocity dispersion σv = 1380-32+26 km s-1. We investigated cluster environmental and dynamical effects by analysing the projected phase-space diagram and the orbits as a function of galaxy spectral properties. Methods. We classified cluster galaxies according to the presence and strength of the [OII] emission line, the strength of the Hδ absorption line, and colours. We investigated the relation between the spectral classes of galaxies and their position in the projected phase-space diagram. We separately analysed red and blue galaxy orbits. By correlating the observed positions and velocities with the projected phase-space constructed from simulations, we constrained the accretion redshift of galaxies with different spectral types. Results. Passive galaxies are mainly located in the virialised region, while emission-line galaxies lie beyond r200 and are accreted into the cluster at a later time. Emission-line and post-starburst galaxies show an asymmetric distribution in projected phase-space within r200; emission-line galaxies are prominent at Δv/σ ≲ -1.5 and post-starburst galaxies at Δv/σ ≲ 1.5, suggesting that backsplash galaxies lie at high positive velocities. We find that low-mass passive galaxies are accreted into the cluster before high-mass galaxies. This suggests that we observe as passives only the low-mass galaxies that are accreted early into the cluster as blue galaxies. They had the time to quench their star formation. We also find that red galaxies move on more radial orbits than blue galaxies. This can be explained if infalling galaxies can remain blue by moving on tangential orbits

Euclid Preparation TBD. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong lensing events

Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with ≳90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band