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We present a study of the galaxy merger and interaction activity within the Hyperion Proto-supercluster at z~2.5 in an effort to assess the occurrence of galaxy mergers and interactions in contrast to the coeval field and their impact on the build up of stellar mass in high density environments at higher-z. For this work, we utilize data from the Charting Cluster Construction with VUDS and ORELSE Survey (C3VO) along with extensive spectroscopic and photometric datasets available for the COSMOS field, including the HST-Hyperion Survey. To evaluate potential merger and interaction activity, we measure the fraction of galaxies with close kinematic companions ($f_{ckc}$) both within Hyperion and the coeval field by means of a Monte Carlo (MC) methodology developed in this work that probabilistically employs our entire combined spectroscopic and photometric dataset. We validate our $f_{ckc}$ MC methodology on a simulated lightcone built from the GAlaxy Evolution and Assembly semi-analytic model, and we determine correction factors that account for the underlying spectroscopic sampling rate of our dataset. We find that galaxies in Hyperion have close kinematic companions $\gtrsim 2\times$ more than galaxies in the field and measure a corrected $f_{ckc}=49_{-7.8}^{+7.4}$% for Hyperion and a corrected $f_{ckc}=23_{-1.3}^{+1.2}$% for the surrounding field; a $>3\sigma$ difference. This increase in $f_{ckc}$ indicates an enhancement in the merger and interaction activity within Hyperion and matches the trend seen in other structures. The rate of merger and interactions within the field implied from our field $f_{ckc}$ measurement is well aligned with values measured from other observations in similar redshift ranges. The enhanced $f_{ckc}$ measured within Hyperion suggests that merger and interaction activity play an important role in the mass growth of galaxies in denser environments at higher z., Comment: 23 pages, 10 figures, 4 tables. Submitted to A&A. Comments welcome

With the 4-meter Multi-Object Spectroscopic Telescope (4MOST) expected to provide an influx of transient spectra when it begins observations in early 2026 we consider the potential for real-time classification of these spectra. We investigate three extant spectroscopic transient classifiers: the Deep Automated Supernova and Host classifier (DASH), Next Generation SuperFit (NGSF) and SuperNova IDentification (SNID), with a focus on comparing the efficiency and purity of the transient samples they produce. We discuss our method for simulating realistic, 4MOST-like, host-galaxy contaminated spectra and determining quality cuts for each classifier used to ensure pure SN Ia samples while maintaining efficient classification in other transient classes. We investigate the classifiers individually and in combinations. We find that a combination of DASH and NGSF can produce a SN Ia sample with a purity of 99.9% while successfully classifying 70% of SNe Ia. However, it struggles to classify non-SN Ia transients. We investigate photometric cuts to transient magnitude and transient flux fraction, finding that both can be used to improve transient classification efficiencies by 7--25% depending on the transient subclass. Finally, we present an example classification plan for live classification and the predicted purities and efficiencies across five transient classes: Ia, Ibc, II, superluminous and non-supernova transients., Comment: Submitted to MNRAS

Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that takes advantage of generative neural networks to perform these three tasks at once with very high efficiency. We use two different generative networks, an autoencoder-like network and U-Net, to reconstruct the rest-frame spectrum (after redshifting). The autoencoder-like network operates similarly to the classical PCA, learning templates (eigenspectra) from the training set and returning modeling parameters. The U-Net, in contrast, functions as an end-to-end model and shows an advantage in noise reduction. By reconstructing spectra, we can achieve classification, redshift estimation, and anomaly detection in the same framework. Each rest-frame reconstructed spectrum is extended to the UV and a small part of the infrared (covering the blueshift of stars). Owing to the high computational efficiency of deep learning, we scan the chi-squared value for the entire type and redshift space and find the best-fitting point. Our results show that generative networks can achieve accuracy comparable to the classical PCA methods in spectral modeling with higher efficiency, especially achieving an average of $>98\%$ classification across all classes ($>99.9\%$ for star), and $>99\%$ (stars), $>98\%$ (galaxies) and $>93\%$ (quasars) redshift accuracy under cosmology research requirements. By comparing different peaks of chi-squared curves, we define the ``robustness'' in the scanned space, offering a method to identify potential ``anomalous'' spectra. Our approach provides an accurate and high-efficiency spectrum modeling tool for handling the vast data volumes from future spectroscopic sky surveys., Comment: 17 pages and 16 figures. Submitted to MNRAS

CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around ${\sim}$150 massive galaxy clusters, from the local universe out to z=0.45. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for ${\sim}$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.5$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters, enabling the study of galaxy pre-processing and the role of the evolving environment on galaxy evolution. In this paper we present and characterize the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable., Comment: 11 pages, 9 figures, plus references and appendix containing catalog tables, submitted to A&A

The Charting Cluster Construction with VUDS and ORELSE (C3VO) survey is an ongoing imaging and spectroscopic campaign aiming to map out the growth of structure up to $z\sim5$ and was born from the combination of the Visible Multi-Object Spectrograph Ultra Deep Survey and the Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey. As we previously accomplished with the ORELSE survey, we apply our technique known as Voronoi tessellation Monte Carlo (VMC) mapping to search for serendipitous galaxy overdensities at $2 14$ and $2 14.5$. In the C3VO fields, we were able to recover many of the previously known structures in the literature as well as find hundreds of new overdensity candidates, once again demonstrating the powerful capabilities of VMC mapping when applied to wide-field optical and infrared galaxy evolution surveys at ever higher redshifts., Comment: 39 pages, 16 figures, 7 tables, published by ApJ

We conduct a study on the relationship between galaxy environments and their active galactic nuclei (AGN) activity at high redshifts ($2.010^{12.8}M_\odot$) overdense ($\sigma_\delta>5.0$) peak, normalized with respect to the size of the peak. These findings indicate that the prevalence of AGN activity is highly dependent on the environment in which a host galaxy resides, even at early times in the formation history of the Universe., Comment: 15 pages, 11 figures, submitted to A&A, comments welcome

Reactions in solution require "contact" between the reagents. We can predict the rate at which reagents come into "contact" (at least in dilute conditions), but if the initial collision does not lead to reaction, what happens then? The collision rates in solution-phase reactions are generally described (explicitly or implicitly) with the Smoluchowski equation. Unfortunately, that model describes coagulations in gases, not reactions in solutions. The model is memory-less, i.e., collisions are treated as random processes, unaware of each other in space and time. The reality is that unreactive collisions create memory: particles (even molecules) "remember" they just collided, i.e, the probability of collision depends on how far back in time their prior collision happened. As we show here, this purely geometric and statistical fact is valid as long as their size is larger than the Kuhn length of their Brownian motion in solution. Under these conditions, particles in solution form, even in the absence of attractive interactions, relatively long-lived "clusters" kept together by the statistical unlikeliness of separating. We show here through Brownian dynamics simulations that, as a result of this memory, the collision rates and the lifetimes of these clusters, differently from what predicted by Smoluchowski's model, are proportional to the ratio between the radius of the colliders and the Kuhn length of their path in solution, with a coefficient close to unity!, Comment: 4 figures + 3 supporting information figures

The WHT Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph. This new instrument will be exploited to obtain high S/N spectra of $\sim$25000 galaxies at intermediate redshifts for the WEAVE Stellar Population Survey (WEAVE-StePS). We test machine learning methods for retrieving the key physical parameters of galaxies from WEAVE-StePS-like spectra using both photometric and spectroscopic information at various S/Ns and redshifts. We simulated $\sim$105000 galaxy spectra assuming SFH with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, sSFRs, and dust extinctions. We then evaluated the ability of the random forest and KNN algorithms to correctly predict such parameters assuming no measurement errors. We checked how much the predictive ability deteriorates for different S/Ns and redshifts, finding that both algorithms still accurately estimate the ages and metallicities with low bias. The dispersion varies from 0.08-0.16 dex for ages and 0.11-0.25 dex for metallicity, depending on the redshift and S/N. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR$\gtrsim$ -11, where the bias is $\sim$ 0.01 dex and the dispersion is $\sim$ 0.10 dex. For more quiescent galaxies, with log sSFR$\lesssim$ -11, we find a higher bias, 0.61-0.86 dex, and a higher dispersion, $\sim$ 0.4 dex, for different S/Ns and redshifts. Generally, we find that the RF outperforms the KNN. Finally, the retrieved sSFR was used to successfully classify galaxies as part of the blue cloud, green valley, or red sequence. We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies even at relatively low S/N=10 per angstrom spectra with available ancillary photometric information., Comment: 19 pages, 10 + 2 figures, 4 tables, accepted in A&A

We present an analysis of the number density of galaxies as a function of stellar mass (i.e., the stellar mass function, SMF) in the COSMOS field at z~3.3, making a comparison between the SMF in overdense environments and the SMF in the coeval field. In particular, this region contains the Elent\'ari proto-supercluster, a system of 6 extended overdensities spanning ~70 cMpc on a side. A clear difference is seen in the high-mass slope of these SMFs, with overdense regions showing an increase in the ratio of high-mass galaxies to low-mass galaxies relative to the field, indicating a more rapid build-up of stellar mass in overdense environments. This result qualitatively agrees with analyses of clusters at z~1, though the differences between protocluster and field SMFs at z~3.3 are smaller. While this is consistent with overdensities enhancing the evolution of their member galaxies, potentially through increased merger rates, whether this enhancement begins in protocluster environments or even earlier in group environments is still unclear. Though the measured fractions of quiescent galaxies between the field and overdense environments do not vary significantly, implying that this stellar mass enhancement is ongoing and any starbursts triggered by merger activity have not yet quenched, we note that spectroscopic observations are biased towards star-forming populations, particularly for low-mass galaxies. If mergers are indeed responsible, high resolution imaging of Elent\'ari and similar structures at these early epochs should then reveal increased merger rates relative to the field. Larger samples of well-characterized overdensities are necessary to draw broader conclusions in these areas., Comment: 15 pages, 8 figures, plus references and appendices. Submitted to ApJ, posted after responding to referee comments. Further comments welcome

Complex non-local behavior makes designing high efficiency and multifunctional metasurfaces a significant challenge. While using libraries of meta-atoms provide a simple and fast implementation methodology, pillar to pillar interaction often imposes performance limitations. On the other extreme, inverse design based on topology optimization leverages non-local coupling to achieve high efficiency, but leads to complex and difficult to fabricate structures. In this paper, we demonstrate numerically and experimentally a shape optimization method that enables high efficiency metasurfaces while providing direct control of the structure complexity. The proposed method provides a path towards manufacturability of inverse-designed high efficiency metasurfaces.

Simulations predict that the galaxy populations inhabiting protoclusters may contribute considerably to the total amount of stellar mass growth of galaxies in the early universe. In this study, we test these predictions observationally, focusing on the Taralay protocluster (formerly PCl J1001+0220) at $z \sim 4.57$ in the COSMOS field. Leveraging data from the Charting Cluster Construction with VUDS and ORELSE (C3VO) survey, we spectroscopically confirmed 44 galaxies within the adopted redshift range of the protocluster ($4.48 < z < 4.64$) and incorporate an additional 18 such galaxies from ancillary spectroscopic surveys. Using a density mapping technique, we estimate the total mass of Taralay to be $\sim 1.7 \times 10^{15}$ M$_\odot$, sufficient to form a massive cluster by the present day. By comparing the star formation rate density (SFRD) within the protocluster (SFRD$_\text{pc}$) to that of the coeval field (SFRD$_\text{field}$), we find that SFRD$_\text{pc}$ surpasses the SFRD$_\text{field}$ by $\Delta$log(SFRD/$M_\odot$ yr$^{-1}$ Mpc$^{-3}$) = $1.08 \pm 0.32$ (or $\sim$ 12$\times$). The observed contribution fraction of protoclusters to the cosmic SFRD adopting Taralay as a proxy for typical protoclusters is $33.5\%^{+8.0\%}_{-4.3\%}$, a value $\sim$2$\sigma$ in excess of the predictions from simulations. Taralay contains three peaks that are $5\sigma$ above the average density at these redshifts. Their SFRD is $\sim$0.5 dex higher than the value derived for the overall protocluster. We show that 68% of all star formation in the protocluster takes place within these peaks, and that the innermost regions of the peaks encase $\sim 50\%$ of the total star formation in the protocluster. This study strongly suggests that protoclusters drive stellar mass growth in the early universe and that this growth may proceed in an inside-out manner., Comment: 26 pages, 14 figures, 10 tables

We present six spectroscopically confirmed massive protostructures, spanning a redshift range of $2.59000$\thinspace cMpc$^3$) of more than $2.5\sigma_{\delta}$ above the field density levels at these redshifts, have a total mass $M_{tot}\ge10^{14.8}M_\odot$ and one or more highly overdense (overdensity$\thinspace>5\sigma_{\delta}$) peaks. One of the most complex protostructures discovered is a massive ($M_{tot}=10^{15.1}M_\odot$) system at $z\sim3.47$ that contains six peaks and 55 spectroscopic members. We also discover protostructures at $z\sim3.30$ and $z\sim3.70$ that appear to at least partially overlap on sky with the protostructure at $z\sim3.47$, suggesting a possible connection. We additionally report on the discovery of three massive protostructures at $z=2.67$, 2.80, and 4.14 and discuss their properties. Finally, we discuss the relationship between star formation rate and environment in the richest of these protostructures, finding an enhancement of star formation activity in the densest regions. The diversity of the protostructures reported here provide an opportunity to study the complex effects of dense environments on galaxy evolution over a large redshift range in the early universe., Comment: 10 pages, 4 figures, 1 table

Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multi-network deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions for classified objects in each of them. It also provides redshift errors, using a network-of-networks that reproduces a Monte Carlo test on each spectrum, by randomizing their weight initialization. As a demonstration of the capability of the deep learning pipeline, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4% average classification accuracy over the 13 classes (larger than 90% for the majority of them), and an average redshift error of approximately 0.23% for galaxies and 2.1% for quasars. We further train/test the same pipeline to classify spectra and predict redshifts for a sample of 200k 4MOST mock spectra and 21k publicly released DESI spectra. On 4MOST mock data, we reach 93.4% accuracy in 10-class classification and an average redshift error of 0.55% for galaxies and 0.3% for active galactic nuclei. On DESI data, we reach 96% accuracy in (star/galaxy/quasar only) classification and an average redshift error of 2.8% for galaxies and 4.8% for quasars, despite the small sample size available. GaSNet-II can process ~40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses of Stage-IV survey observations and an ideal tool for feedback loops aimed at night-by-night survey strategy optimization., Comment: 23 pages and 31 figures. The published version of MNRAS

Higher-order relations are widespread in nature, with numerous phenomena involving complex interactions that extend beyond simple pairwise connections. As a result, advancements in higher-order processing can accelerate the growth of various fields requiring structured data. Current approaches typically represent these interactions using hypergraphs. We enhance this representation by introducing cellular sheaves for hypergraphs, a mathematical construction that adds extra structure to the conventional hypergraph while maintaining their local, higherorder connectivity. Drawing inspiration from existing Laplacians in the literature, we develop two unique formulations of sheaf hypergraph Laplacians: linear and non-linear. Our theoretical analysis demonstrates that incorporating sheaves into the hypergraph Laplacian provides a more expressive inductive bias than standard hypergraph diffusion, creating a powerful instrument for effectively modelling complex data structures. We employ these sheaf hypergraph Laplacians to design two categories of models: Sheaf Hypergraph Neural Networks and Sheaf Hypergraph Convolutional Networks. These models generalize classical Hypergraph Networks often found in the literature. Through extensive experimentation, we show that this generalization significantly improves performance, achieving top results on multiple benchmark datasets for hypergraph node classification., Comment: Accepted at Neural Information Processing Systems (NeurIPS 2023)

The upcoming generation of optical spectrographs on four meter-class telescopes, with their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage, will provide high-quality spectra for thousands of galaxies. These data will allow us to examine of the stellar population properties at intermediate redshift, an epoch that remains unexplored by large and deep surveys. We assess our capability to retrieve the mean stellar metallicity in galaxies at different redshifts and S/N, while simultaneously exploiting the UV and optical rest-frame wavelength coverage. The work is based on a comprehensive library of spectral templates of stellar populations, covering a wide range of age and metallicity values and built assuming various SFHs. We simulated realistic observations of a large sample of galaxies carried out with WEAVE at the WHT at different redshifts and S/N values. We measured all the reliable indices on the simulated spectra and on the comparison library. We then adopted a Bayesian approach to obtain the probability distribution of stellar metallicity. The analysis of the spectral indices has shown how some mid-UV indices can provide reliable constraints on stellar metallicity, along with optical indicators. The analysis of the mock observations has shown that even at S/N=10, the metallicity can be derived within 0.3 dex, in particular, for stellar populations older than 2 Gyr. Our results are in good agreement with other theoretical and observational works in the literature and show how the UV indicators can be advantageous in constraining metallicities. This is very promising for the upcoming surveys carried out with new, highly multiplexed, large-field spectrographs, such as StePS at the WEAVE and 4MOST, which will provide spectra of thousands of galaxies covering large spectral ranges at relatively high S/N., Comment: 16 pages, 14 figures. Submitted 31/03/2023, Accepted 20/07/2023

Motivated by spectroscopic confirmation of three overdense regions in the COSMOS field at $z\sim3.35$, we analyze the uniquely deep multi-wavelength photometry and extensive spectroscopy available in the field to identify any further related structure. We construct a three dimensional density map using the Voronoi tesselation Monte Carlo method and find additional regions of significant overdensity. Here we present and examine a set of six overdense structures at $3.205\sigma$ overdensity separated by up to 70 cMpc, suggestive of a proto-supercluster similar to the Hyperion system at $z\sim2.45$. Upcoming photometric surveys with JWST such as COSMOS-Web, and further spectroscopic follow-up will enable more extensive analysis of the evolutionary effects that such an environment may have on its component galaxies at these early times., Comment: Accepted for publication in MNRAS. 7 pages, 3 figures, 3 tables

The paper proposes a data-driven approach to air-to-ground channel estimation in a millimeter-wave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inappropriate for others, we propose a generalized model that uses Federated Learning (FL) for channel estimation and can predict the air-to-ground path loss between a low-altitude platform and a terrestrial terminal. To this end, our proposed FL-based Generative Adversarial Network (FL-GAN) is designed to function as a generative data model that can learn different types of data distributions and generate realistic patterns from the same distributions without requiring prior data analysis before the training phase. To evaluate the effectiveness of the proposed model, we evaluate its performance using Kullback-Leibler divergence (KL), and Wasserstein distance between the synthetic data distribution generated by the model and the actual data distribution. We also compare the proposed technique with other generative models, such as FL-Variational Autoencoder (FL-VAE) and stand-alone VAE and GAN models. The results of the study show that the synthetic data generated by FL-GAN has the highest similarity in distribution with the real data. This shows the effectiveness of the proposed approach in generating data-driven channel models that can be used in different regions

With the advent of 5G and the anticipated arrival of 6G, there has been a growing research interest in combining mobile networks with Non-Terrestrial Network platforms such as low earth orbit satellites and Geosynchronous Equatorial Orbit satellites to provide broader coverage for a wide range of applications. However, integrating these platforms is challenging because Line-Of-Sight (LOS) estimation is required for both inter satellite and satellite-to-terrestrial segment links. Machine Learning (ML) techniques have shown promise in channel modeling and LOS estimation, but they require large datasets for model training, which can be difficult to obtain. In addition, network operators may be reluctant to disclose their network data due to privacy concerns. Therefore, alternative data collection techniques are needed. In this paper, a framework is proposed that uses generative models to generate synthetic data for LOS estimation in non-terrestrial 6G networks. Specifically, the authors show that generative models can be trained with a small available dataset to generate large datasets that can be used to train ML models for LOS estimation. Furthermore, since the generated synthetic data does not contain identifying information of the original dataset, it can be made publicly available without violating privacy

Abridged - Stars with ZAMS masses between 140 and $260 M_\odot$ are thought to explode as pair-instability supernovae (PISNe). During their thermonuclear runaway, PISNe can produce up to several tens of solar masses of radioactive nickel, resulting in luminous transients similar to some superluminous supernovae (SLSNe). Yet, no unambiguous PISN has been discovered so far. SN2018ibb is a H-poor SLSN at $z=0.166$ that evolves extremely slowly compared to the hundreds of known SLSNe. Between mid 2018 and early 2022, we monitored its photometric and spectroscopic evolution from the UV to the NIR with 2-10m class telescopes. SN2018ibb radiated $>3\times10^{51} \rm erg$ during its evolution, and its bolometric light curve reached $>2\times10^{44} \rm erg\,s^{-1}$ at peak. The long-lasting rise of $>93$ rest-frame days implies a long diffusion time, which requires a very high total ejected mass. The PISN mechanism naturally provides both the energy source ($^{56}$Ni) and the long diffusion time. Theoretical models of PISNe make clear predictions for their photometric and spectroscopic properties. SN2018ibb complies with most tests on the light curves, nebular spectra and host galaxy, potentially all tests with the interpretation we propose. Both the light curve and the spectra require 25-44 $M_\odot$ of freshly nucleosynthesised $^{56}$Ni, pointing to the explosion of a metal-poor star with a He-core mass of 120-130 $M_\odot$ at the time of death. This interpretation is also supported by the tentative detection of [Co II]$\lambda$1.025$\mu$m, which has never been observed in any other PISN candidate or SLSN before. Powering by a central engine, such as a magnetar or a black hole, can be excluded with high confidence. This makes SN2018ibb by far the best candidate for being a PISN, to date., Comment: Accepted in A&A, the revised version includes a PISN rate estimate and an additional test with PISN models. 47 pages, main text 41 pages, 38 figures, 16 Tables

Recent advancements in Graph Neural Networks (GNN) have facilitated their widespread adoption in various applications, including recommendation systems. GNNs have proven to be effective in addressing the challenges posed by recommendation systems by efficiently modeling graphs in which nodes represent users or items and edges denote preference relationships. However, current GNN techniques represent nodes by means of a single static vector, which may inadequately capture the intricate complexities of users and items. To overcome these limitations, we propose a solution integrating a cutting-edge model inspired by category theory: Sheaf4Rec. Unlike single vector representations, Sheaf Neural Networks and their corresponding Laplacians represent each node (and edge) using a vector space. Our approach takes advantage from this theory and results in a more comprehensive representation that can be effectively exploited during inference, providing a versatile method applicable to a wide range of graph-related tasks and demonstrating unparalleled performance. Our proposed model exhibits a noteworthy relative improvement of up to 8.53% on F1-Score@10 and an impressive increase of up to 11.29% on NDCG@10, outperforming existing state-of-the-art models such as Neural Graph Collaborative Filtering (NGCF), KGTORe and other recently developed GNN-based models. In addition to its superior predictive capabilities, Sheaf4Rec shows remarkable improvements in terms of efficiency: we observe substantial runtime improvements ranging from 2.5% up to 37% when compared to other GNN-based competitor models, indicating a more efficient way of handling information while achieving better performance. Code is available at https://github.com/antoniopurificato/Sheaf4Rec., Comment: 21 pages, 8 figures

The upcoming new generation of optical spectrographs on four-meter-class telescopes will provide valuable opportunities for forthcoming galaxy surveys through their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage. WEAVE is a new wide-field spectroscopic facility mounted on the 4.2 m William Herschel Telescope in La Palma. WEAVE-StePS is one of the five extragalactic surveys that will use WEAVE during its first five years of operations. It will observe galaxies using WEAVE MOS (~950 fibres across a field of view of ~3 deg2 on the sky) in low-resolution mode (R~5000, spanning the wavelength range 3660-9590 AA). WEAVE-StePS will obtain high-quality spectra (S/N ~ 10 per AA at R~5000) for a magnitude-limited (I_AB = 20.5) sample of ~25,000 galaxies, the majority selected at z>=0.3. The survey goal is to provide precise spectral measurements in the crucial interval that bridges the gap between LEGA-C and SDSS data. The wide area coverage of ~25 deg2 will enable us to observe galaxies in a variety of environments. The ancillary data available in each observed field (including X-ray coverage, multi-narrow-band photometry and spectroscopic redshift information) will provide an environmental characterisation for each observed galaxy. This paper presents the science case of WEAVE-StePS, the fields to be observed, the parent catalogues used to define the target sample, and the observing strategy chosen after a forecast of the expected performance of the instrument for our typical targets. WEAVE-StePS will go back further in cosmic time than SDSS, extending its reach to encompass more than ~6 Gyr, nearly half of the age of the Universe. The spectral and redshift range covered by WEAVE-StePS will open a new observational window by continuously tracing the evolutionary path of galaxies in the largely unexplored intermediate-redshift range., Comment: 15 pages, 9 figures, A&A in press

SIPGI is a spectroscopic pipeline for the data reduction of optical/near-infrared data acquired by slit-based spectrographs. SIPGI is a complete spectroscopic data reduction environment retaining the high level of flexibility and accuracy typical of the standard "by-hand" reduction methods but with a significantly higher level of efficiency. This is obtained exploiting three main concepts: 1) a built-in data organiser to classify the data, together with a graphical interface; 2) the instrument model (analytic description of the main calibration relations); 3) the design and flexibility of the reduction recipes: the number of tasks required to perform a complete reduction is minimised, preserving the possibility to verify the accuracy of the main stages of data-reduction process. The current version of SIPGI manages data from the MODS and LUCI spectrographs mounted at the Large Binocular Telescope (LBT) with the idea to extend SIPGI to support other through-slit spectrographs., Comment: 4 pages, 3 figure, to appear in proceedings of the Astronomical Data Analysis Software and Systems (ADASS) XXXII, virtual conference held 31 October - 4 November 2022

We perform a homogeneous analysis of an unprecedented set of spatially resolved scaling relations (SRs) between ISM components and other properties in the range of scales 0.3-3.4 kpc. We also study some ratios: dust-to-stellar, dust-to-gas, and dust-to-metal. We use a sample of 18 large, spiral, face-on DustPedia galaxies. All the SRs are moderate/strong correlations except the dust-HI SR that does not exist or is weak for most galaxies. The SRs do not have a universal form but each galaxy is characterized by distinct correlations, affected by local processes and galaxy peculiarities. The SRs hold starting from 0.3 kpc, and if a breaking down scale exists it is < 0.3 kpc. By evaluating all galaxies at 3.4 kpc, differences due to peculiarities of individual galaxies are cancelled out and the corresponding SRs are consistent with those of whole galaxies. By comparing subgalactic and global scales, the most striking result emerges from the SRs involving ISM components: the dust-total gas SR is a good correlation at all scales, while the dust-H2 and dust-HI SRs are good correlations at subkpc/kpc and total scales, respectively. For the other explored SRs, there is a good agreement between small and global scales and this may support the picture where the main physical processes regulating the properties and evolution of galaxies occur locally. Our results are consistent with the hypothesis of self-regulation of the SF process. The analysis of subgalactic ratios shows that they are consistent with those derived for whole galaxies, from low to high z, supporting the idea that also these ratios could be set by local processes. Our results highlight the heterogeneity of galaxy properties and the importance of resolved studies on local galaxies in the context of galaxy evolution. They also provide observational constraints to theoretical models and updated references for high-z studies., Comment: 42 pages, 11 figures and 5 tables in the main text, 2 figures and 1 table in Appendix. Accepted for publication in A&A

We present the discovery of PSO J191.05696$+$86.43172 (hereafter PSO J191$+$86), a new powerful radio-loud quasar (QSO) in the early Universe (z = 5.32). We discovered it by cross-matching the NRAO VLA Sky Survey (NVSS) radio catalog at 1.4 GHz with the first data release of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS PS1) in the optical. With a NVSS flux density of 74.2 mJy, PSO J191$+$86 is one of the brightest radio QSO discovered at z$\sim$5. The intensity of its radio emission is also confirmed by the very high value of radio loudness (R>300). The observed radio spectrum of PSO J191$+$86 shows a possible turnover around $\sim$1 GHz (i.e., $\sim$6 GHz in the rest frame), making it a Gigahertz-Peaked Spectrum (GPS) source. However, variability could affect the real shape of the radio spectrum, since the data in hand have been taken $\sim$25 years apart. By assuming a peak of the observed radio spectrum between 1 and 2 GHz (i.e. $\sim$ 6 and 13 GHz in the rest-frame) we found a linear size of the source of $\sim$10-30 pc and a corresponding kinetic age of 150-460 yr. This would make PSO J191$+$86 a newly born radio source. However, the large X-ray luminosity (5.3$\times$10$^{45}$ erg s$^{-1}$), the flat X-ray photon index ($\Gamma_X$=1.32) and the optical-X-ray spectral index ($\tilde{\alpha_{ox}}$=1.329) are typical of blazars. This could indicate that the non-thermal emission of PSO J191$+$86 is Doppler boosted. Further radio observations (both on arcsec and parsec scales) are necessary to better investigate the nature of this powerful radio QSO., Comment: 10 pages, 9 figures, 5 tables, Accepted for publication in A&A

We present SIPGI, a spectroscopic pipeline to reduce optical/near-infrared data from slit-based spectrographs. SIPGI is a complete spectroscopic data reduction environment which retains the high level of flexibility and accuracy typical of the standard "by-hand" reduction methods but is characterized by a significantly higher level of efficiency. This is obtained by exploiting three main concepts: $i)$ the instrument model: at the core of the data reduction is an analytic description of the main calibration relations (e.g. spectra location and wavelength calibration) that can be easily checked and adjusted on data using a graphical tool; $ii)$ a built-in data organizer that classifies the data, together with a graphical interface that helps in providing the recipes with the correct input; $iii)$ the design and flexibility of the reduction recipes: the number of tasks required to perform a complete reduction is minimized, while preserving the possibility of verifying the accuracy of the main stages of data-reduction process with provided tools. The current version of SIPGI manages data from the MODS and LUCI spectrographs mounted at the Large Binocular Telescope, and it is our plan to extend SIPGI to support other through-slit spectrographs. Meanwhile, to allow using the same approach based on the instrument model with other instruments, we have developed SpectraPy, a spectrograph independent Python library working on through-slit spectra. In its current version, SpectraPy produces two-dimensional wavelength calibrated spectra corrected by instrument distortions. The current release of SIPGI and its documentation can by downloaded from http://pandora.lambrate.inaf.it/sipgi/, while SpectraPy can be found at http://pandora.lambrate.inaf.it/SpectraPy/., Comment: 13 pages, 8 figures. This article has been published in MNRAS: Gargiulo et al., 2022. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved

Using VIPERS, we estimated a set of SFR based on photometric and spectroscopic data. We used, as estimators, photometric bands from ultraviolet to mid-infrared, and spectral lines. Assuming a reference SFR obtained from the spectral energy distribution reconstructed with Code Investigating GALaxy Emission, we estimated the reliability of each band as an SFR tracer. We used GSWLC to trace the dependence of these SFR calibrators with redshift. The far and near UV, u-band and 24-$\mu$m bands, as well as $L_{TIR}$, are found to be good SFR tracers up to $z\sim0.9$ with a strong dependence on the attenuation prescription used for the bluest bands (scatter of SFR of 0.26, 0.14, 0.15, 0.23, and 0.24dex for VIPERS, and 0.25, 0.24, 0.09, 0.12, and 0.12dex for GSWLC). The 8-$\mu$m band provides only a rough estimate of the SFR as it depends on metallicity and polycyclic aromatic hydrocarbon properties (scatter of 0.23dex for VIPERS). We estimated the scatter of rest-frame luminosity estimations from CIGALE to be 0.26, 0.14, 0.12, 0.15, and 0.20dex for FUV, NUV, ugriz, K$_{\mathrm{s}}$, and 8-24$\mu$m-$L_{\mathrm{TIR}}$). At intermediate redshift, the H$\beta$ line is a reliable SFR tracer (scatter of 0.19dex) and the [OII] line gives an equally good estimation when the metallicity from the $R_{23}$ parameter is taken into account (0.17 for VIPERS and 0.20dex for GSWLC). A calibration based on [OIII] retrieves the SFR only when additional information such as the metallicity or the ionization parameter of galaxies are used (0.26 for VIPERS and 0.20dex for GSWLC), diminishing its usability as a direct SFR tracer. Based on rest-frame luminosities estimated with CIGALE, we propose our own set of calibrations from FUV, NUV, u-band, 8, 24$\mu$m, $L_{TIR}$, H$\beta$, [OII], and [OIII]., Comment: 35 pages, 28 figures, o be published in A&A

Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevents intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require an explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at a low cost creates fertile ground for the implementation of these techniques.

In the shipping digitalisation process, the peak will be reached with the advent of a wholly autonomous and at the same time safe and reliable ship. Full autonomy could be obtained by two linked Artificial-Intelligence systems representing the ship navigator and the ship engineer that possess sensing and analysis skills, situational awareness, planning, and control capabilities. Many efforts have been made in developing onboard systems; however, the shore facilities are not ready yet to deal with these new technologies. The paper aims to present the innovative technologies and methodologies needed to develop a futuristic Vessel Traffic System. The proposed systems will aim at faultless data acquisition and processing, provide input to decision-making systems, and suggest evasive manoeuvre; to deal with hazards and systems failure without human intervention onboard. The system is composed of three different and interacting layers. The first is an artificially intelligent tool to detect and control autonomous ships, thanks to situation recognition and obstacle avoidance strategies. The second is an orchestration and management platform designed to coordinate the sensing-actuation infrastructure and the AI algorithms results made available by multiple ships, mustering edge, and distributed computing techniques to fulfil the specific harsh requirements of the sea environment. The final part is a holistic guidance-navigation-control framework to manage autonomous ships navigation in a crowded area. Eventually, a cyber-physical scenario, using both a ship digital-twin and a real model-scale ship, is suggested to test and validate the innovative system without the availability of a full-scale scenario.

Context: The far-infrared (FIR) and sub-millimeter (submm) emissivity of the Milky Way (MW) cirrus is an important benchmark for dust grain models. Dust masses in other galaxies are generally derived from the FIR/submm using the emission properties of these MW-calibrated models. Aims: We seek to derive the FIR/submm emissivity in nine nearby spiral galaxies to check its compatibility with MW cirrus measurements. Methods: We obtained values of the emissivity at 70 to 500 um, using maps of dust emission from the Herschel satellite and of gas surface density from the THINGS and HERACLES surveys on a scale generally corresponding to 440 pc. We studied the variation of the emissivity with the surface brightness ratio I(250um)/I(500um), a proxy for the intensity of the interstellar radiation field heating the dust. Results: We find that the average value of the emissivity agrees with MW estimates for pixels sharing the same color as the cirrus, namely, for I(250um)/I(500um) = 4.5. For I(250um)/I(500um) > 5, the measured emissivity is instead up to a factor ~2 lower than predicted from MW dust models heated by stronger radiation fields. Regions with higher I(250um)/I(500um) are preferentially closer to the galactic center and have a higher overall (stellar+gas) surface density and molecular fraction. The results do not depend strongly on the adopted CO-to-molecular conversion factor and do not appear to be affected by the mixing of heating conditions. Conclusions: Our results confirm the validity of MW dust models at low density, but are at odds with predictions for grain evolution in higher density environments. If the lower-than-expected emissivity at high I(250um)/I(500um) is the result of intrinsic variations in the dust properties, it would imply an underestimation of the dust mass surface density of up to a factor ~2 when using current dust models., Comment: 16 pages, 11 figures, A&A accepted

The discovery and spectroscopic confirmation of Hyperion, a proto-supercluster at z~2.47, provides an unprecedented opportunity to study distant galaxies in the context of their large-scale environment. We carry out deep narrow-band imaging of a ~1*1 deg^2 region around Hyperion and select 157 Lya emitters (LAEs). The inferred LAE overdensity is delta_g~40 within an effective volume of 30*20*15 cMpc^3, consistent with the fact that Hyperion is composed of multiple protoclusters and will evolve into a super-cluster with a total mass of M_tot ~1.4*10^15 M_sun at z=0. The distribution of LAEs closely mirrors that of known spectroscopic members, tracing the protocluster cores and extended filamentary arms connected to them, suggesting that they trace the same large-scale structure. By cross-correlating the LAE positions with HI tomography data, we find weak evidence that LAEs may be less abundant in the highest HI regions, perhaps because Lya is suppressed in such regions. The Hyperion region hosts a large population of active galactic nuclei (AGN), ~12 times more abundant than that in the field. The prevalence of AGN in protocluster regions hints at the possibility that they may be triggered by physical processes that occur more frequently in dense environments, such as galaxy mergers. Our study demonstrates LAEs as reliable markers of the largest cosmic structures. When combined with ongoing and upcoming imaging and spectroscopic surveys, wide-field narrow-band imaging has the potential to advance our knowledge in the formation and evolution of cosmic structures and of their galaxy inhabitants.

Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.

Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths. The scheduler acting as an RL agent learns in real-time the optimal policy for path selection, path rate allocation and redundancy estimation for flow protection. The scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.

We present an optical/near-IR survey of 11 variable young stars (EXors and EXor candidates) aimed at deriving and monitoring their accretion properties. About 30 optical and near-infrared spectra ($\Re$ $\sim$ 1500-2000) were collected between 2014-2019 with the Large Binocular Telescope (LBT). From the spectral analysis we have derived the accretion luminosity and mass accretion rate, the visual extinction, the temperature and density of the permitted line formation region, and the signature of the outflowing matter. Two sources (ASASSN-13db and iPTF15afq) have been observed in outburst and quiescence, three during a high-level of brightness (XZ Tau, PV Cep, and NY Ori), and the others in quiescence. These latter have accretion luminosity and mass accretion rates in line with the values measured in classical T Tauri stars of similar mass. All sources observed more than once present accretion variability. The most extreme case is ASASSN-13db, for which the mass accretion rate decreases by two orders of magnitude from the outburst peak in 2015 to quiescence in 2017. Also, in NY Ori the accretion luminosity decreases by a factor 25 in one year. In 80% of the sample we detect the [OI]630 nm line, a tracer of mass loss. From the variability of the H$\alpha$/[OI]630, ratio, we conclude that mass accretion variations are larger than mass loss variations. From the analysis of the HI recombination lines a correlation is suggested between the density of the line formation region and the level of accretion activity of the source., Comment: To appear in ApJ

This paper presents a proof-of-concept implementation of the AI-as-a-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems. In addition, we implemented a data-gathering subsystem to collect data from different sensors, i.e., wearables and cameras, to automatize stress recognition. The system was attached for testing to a driving simulation software, CARLA, which allows testing the approach's feasibility with minimum cost and without putting at risk drivers and passengers. At the core of the relative subsystems, different learning algorithms were implemented using Deep Neural Networks, Recurrent Neural Networks, and Reinforcement Learning.

We report on a LUCI/Large Binocular Telescope near-infrared (NIR) spectrum of PSO J030947.49+271757.31 (hereafter PSO J0309+27), the highest redshift blazar known to date (z$\sim$6.1). From the C$\rm IV$$\lambda$1549 broad emission line we found that PSO J0309+27 is powered by a 1.45$^{+1.89}_{-0.85}$$\times$10$^9$M$_{\odot}$ supermassive black hole (SMBH) with a bolometric luminosity of $\sim$8$\times$10$^{46}$ erg s$^{-1}$ and an Eddington ratio equal to 0.44$^{+0.78}_{-0.35}$. We also obtained new photometric observations with the Telescopio Nazionale Galileo in J and K bands to better constrain the NIR Spectral Energy Distribution of the source. Thanks to these observations, we were able to model the accretion disk and to derive an independent estimate of the black hole mass of PSO J0309+27, confirming the value inferred from the virial technique. The existence of such a massive SMBH just $\sim$900 million years after the Big Bang challenges models of the earliest SMBH growth, especially if jetted Active Galactic Nuclei are associated to a highly spinning black hole as currently thought. Indeed, in a Eddington-limited accretion scenario and assuming a radiative efficiency of 0.3, typical of a fast rotating SMBH, a seed black hole of more than 10$^6$ M$_{\odot}$ at z = 30 is required to reproduce the mass of PSO J0309+27 at redshift 6. This requirement suggests either earlier periods of rapid black hole growth with super-Eddington accretion and/or that only part of the released gravitational energy goes to heat the accretion disk and feed the black hole., Comment: 10 pages, 5 figures, 2 tables; Accepted to publication in A&A

We present a study of optically-selected Type II AGN at 0.5 < z < 0.9 from the VIPERS and VVDS surveys, to investigate the connection between AGN activity and physical properties of their host galaxies. The host stellar mass is estimated through spectral energy distribution fitting with the CIGALE code, and star formation rates are derived from the [OII]$\lambda$3727 $\r{A}$ line luminosity. We find that 49% of the AGN host galaxies are on or above the main sequence (MS), 40% lie in the sub-MS locus, and 11% in the quiescent locus. Using the [OIII]$\lambda$5007 $\r{A}$ line luminosity as a proxy of the AGN power, we find that at fixed AGN power Type II AGN host galaxies show a bimodal behaviour: systems with host galaxy stellar mass <10$^{10}$ M$_{\odot}$, reside along the MS or in the starbursts locus (high-SF Type II AGN), while systems residing in massive host-galaxies (>10$^{10}$ M$_{\odot}$) show a lower level of star formation (low-SF Type II AGN). At all stellar masses, the offset from the MS is positively correlated with the AGN power. We interpret this correlation as evidence of co-evolution between the AGN and the host, possibly due to the availability of cold gas. In the most powerful AGN with host galaxies below the MS we find a hint, though weak, of asymmetry in the [OIII] line profile, likely due to outflowing gas, consistent with a scenario in which AGN feedback removes the available gas and halts the star formation in the most massive hosts., Comment: Accepted for publication in A&A

Autonomous vehicles (AVs) generate a massive amount of multi-modal data that once collected and processed through Machine Learning algorithms, enable AI-based services at the Edge. In fact, not all these data contain valuable, and informative content but only a subset of the relative attributes should be exploited at the Edge. Therefore, enabling AVs to locally extract such a subset is of utmost importance to limit computation and communication workloads. Achieving a consistent subset of data in a distributed manner imposes the AVs to cooperate in finding an agreement on what attributes should be sent to the Edge. In this work, we address such a problem by proposing a federated feature selection algorithm where all the AVs collaborate to filter out, iteratively, the redundant or irrelevant attributes in a distributed manner, without any exchange of raw data. This solution builds on two components: a Mutual-Information-based feature selection algorithm run by the AVs and a novel aggregation function based on the Bayes theorem executed on the Edge. Our federated feature selection algorithm provably converges to a solution in a finite number of steps. Such an algorithm has been tested on two reference datasets: MAV with images and inertial measurements of a monitored vehicle, WESAD with a collection of samples from biophysical sensors to monitor a relative passenger. The numerical results show that the fleet finds a consensus with both the datasets on the minimum achievable subset of features, i.e., 24 out of 2166 (99\%) in MAV and 4 out of 8 (50\%) in WESAD, preserving the informative content of data.

We publicly release the spectroscopic and photometric redshift catalog of the sources detected with Chandra in the field of the $z$=6.3 quasar SDSS J1030+0525. This is currently the fifth deepest X-ray field, and reaches a 0.5-2 keV flux limit $f_{\rm 0.5-2}$=6$\times$10$^{-17}$ erg s$^{-1}$ cm$^{-2}$. By using two independent methods, we measure a photometric redshift for 243 objects, while 123 (51%) sources also have a spectroscopic redshift, 110 of which coming from an INAF-Large Binocular Telescope (LBT) Strategic Program. We use the spectroscopic redshifts to determine the quality of the photometric ones, and find it in agreement with that of other X-ray surveys which used a similar number of photometric data-points. In particular, we measure a sample normalized median absolute deviation $\sigma_{NMAD}$=1.48||$z_{phot}$-$z_{spec}$||/(1+$z_{spec}$)=0.065. We use these new spectroscopic and photometric redshifts to study the properties of the Chandra J1030 field. We observe several peaks in our spectroscopic redshift distribution between $z$=0.15 and $z$=1.5, and find that the sources in each peak are often distributed across the whole Chandra field of view. This evidence confirms that X-ray selected AGN can efficiently track large-scale structures over physical scales of several Mpc. Finally, we computed the Chandra J1030 $z>$3 number counts: while the spectroscopic completeness at high-redshift of our sample is limited, our results point towards a potential source excess at $z\geq$4, which we plan to either confirm or reject in the near future with dedicated spectroscopic campaigns., Comment: 21 pages, 15 figures. Accepted for publication in Astronomy and Astrophysics

This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum. TEACHING puts forward a human-centred vision leveraging the physiological, emotional, and cognitive state of the users as a driver for the adaptation and optimization of the autonomous applications. It does so by building a distributed, embedded and federated learning system complemented by methods and tools to enforce its dependability, security and privacy preservation. The paper discusses the main concepts of the TEACHING approach and singles out the main AI-related research challenges associated with it. Further, we provide a discussion of the design choices for the TEACHING system to tackle the aforementioned challenges

Methods. We have modelled a sample of ~800 nearby galaxies, spanning a wide range of metallicity, gas fraction, specific star formation rate and Hubble stage. We have derived the dust properties of each object from its spectral energy distribution. Through an additional level of analysis, we have inferred the timescales of dust condensation in core-collapse supernova ejecta, grain growth in cold clouds and dust destruction by shock waves. Throughout this paper, we have adopted a hierarchical Bayesian approach, resulting in a single large probability distribution of all the parameters of all the galaxies, to ensure the most rigorous interpretation of our data. Results. We confirm the drastic evolution with metallicity of the dust-to-metal mass ratio (by two orders of magnitude), found by previous studies. We show that dust production by core-collapse supernovae is efficient only at very low-metallicity, a single supernova producing on average less than ~0.03 Msun/SN of dust. Our data indicate that grain growth is the dominant formation mechanism at metallicity above ~1/5 solar, with a grain growth timescale shorter than ~50 Myr at solar metallicity. Shock destruction is relatively efficient, a single supernova clearing dust on average in at least ~1200 Msun/SN of gas. These results are robust when assuming different stellar initial mass functions. In addition, we show that early-type galaxies are outliers in several scaling relations. This feature could result from grain thermal sputtering in hot X-ray emitting gas, an hypothesis supported by a negative correlation between the dust-to-stellar mass ratio and the X-ray photon rate per grain. Finally, we confirm the well-known evolution of the aromatic-feature-emitting grain mass fraction as a function of metallicity and interstellar radiation field intensity. Our data indicate the relation with metallicity is significantly stronger., Comment: 64 pages, 36 figures, accepted for publication in Astronomy & Astrophysics

Investigating the dust heating mechanisms in galaxies provides a deeper understanding of how the internal energy balance drives their evolution. Over the last decade, radiative transfer simulations based on the Monte Carlo method have underlined the role of the various stellar populations heating the diffuse dust. Beyond the expected heating through ongoing star formation, both older stellar population (> 8Gyr) and even AGN can contribute energy to the infrared emission of diffuse dust. Here, we examine how the radiation of an external heating source, like the less massive galaxy NGC5195, in the M51 interacting system, could affect the heating of the diffuse dust of its parent galaxy, NGC5194, and vice versa. To quantify the exchange of energy between the two galaxies we use SKIRT, a state-of-the-art Monte Carlo radiative transfer code. In the interest of modelling, the assumed centre-to-centre distance separation between the two galaxies is 10kpc. Our model reproduces the global SED of the system, and it closely matches the observed images. In total, 40.7% of the intrinsic stellar radiation of the combined system is absorbed by dust. Furthermore, we quantify the contribution of the various dust heating sources in the system, and find that the young stellar population of NGC5194 is the predominant dust-heating agent, with a global heating fraction of 71.2%. Another 23% is provided by the older stellar population of the same galaxy, while the remaining 5.8% has its origin in NGC5195. Locally, we find that the regions of NGC5194 closer to NGC5195 are significantly affected by the radiation field of the latter, with the absorbed energy fraction rising up to 38%. The contribution of NGC5195 remains under the percentage level in the outskirts of the disc of NGC5194. This is the first time that the heating of the diffuse dust by a companion galaxy is quantified in a nearby interacting system.

We report on the spectroscopic confirmation of a large scale structure around the luminous, z=6.31 QSO SDSS~J1030+0524, that is powered by a billion solar mass black hole. The structure is populated by at least six members, four Lyman Break Galaxies (LBGs) and two Lyman Alpha Emitters (LAEs). The four LBGs have been identified among a sample of 21 i-band dropouts with z{AB}<25.5 selected up to projected separations of 5 physical Mpc (15 arcmin) from the QSO. Their redshifts have been determined through up to 8hr-long multi-object spectroscopic observations at 8-10m class telescopes. The two LAEs have been identified in a 6hr VLT/MUSE observation centered on the QSO. The redshifts of the six galaxies cover the range 6.129-6.355. Assuming that peculiar velocities are negligible, this range corresponds to radial separations of +/-5 physical Mpc from the QSO, that is comparable to the projected scale of the observed LBG distribution on the sky. We conservatively estimate that this structure is significant at >3.5 sigma level, and that the level of the galaxy overdensity is at least 1.5-2 within the large volume sampled (~780 physical Mpc^3). The spectral properties of the six member galaxies (Lyalpha strength and UV luminosity) are similar to those of field galaxies at similar redshifts. This is the first spectroscopic identification of a galaxy overdensity around a super-massive black hole in the first billion years of the Universe. Our finding lends support to the idea that the most distant and massive black holes form and grow within massive (>10^{12} Msun) dark matter halos in large scale structures, and that the absence of earlier detections of such systems was likely due to observational limitations., Comment: 8 pages including Appendix, 5 figures, accepted as a letter on Astronomy & Astrophysics. v2: minor changes in Table 1 caption and Figs. 2 & 3 labels

We present the first nonparametric morphological analysis of a set of spiral galaxies from UV to submm wavelengths. Our study is based on high-quality multi-wavelength imaging for nine well-resolved spiral galaxies from the DustPedia database, combined with nonparametric morphology indicators calculated in a consistent way using the {\tt{StatMorph}} package. We measure the half-light radius, the concentration index, the asymmetry index, the smoothness index, the Gini coefficient and the $M_{20}$ indicator in various wavebands from UV to submm wavelengths, as well as in stellar mass, dust mass and star formation rate maps. We find that the interstellar dust in galaxies is distributed in a more extended, less centrally concentrated, more asymmetric, and more clumpy way than the stars. This is particularly evident when comparing morphological indicators based on the stellar mass and dust mass maps. This should serve as a warning sign against treating the dust in galaxies as a simple smooth component. We argue that the nonparametric galaxy morphology of galaxies from UV to submm wavelengths is an interesting test for cosmological hydrodynamics simulations., Comment: 9 pages, 5 figures, accepted for publication in A&A

The Lyman-$\alpha$ (Ly$\alpha$) emission line has been ubiquitously used to confirm and study high redshift galaxies. We report on the line morphology as seen in the 2D spectra from the VIMOS Ultra Deep Survey in a sample of 914 Ly$\alpha$ emitters from a parent sample of 4192 star-forming galaxies at $2

Dust emission, an important diagnostic of star formation and ISM mass throughout the Universe, can be powered by sources unrelated to ongoing star formation. In the framework of the DustPedia project we have set out to disentangle the radiation of the ongoing star formation from that of the older stellar populations. This is done through detailed, 3D radiative transfer simulations of face-on spiral galaxies. In this particular study, we focus on NGC 1068, which contains an active galactic nucleus (AGN). The effect of diffuse dust heating by AGN (beyond the torus) was so far only investigated for quasars. This additional dust heating source further contaminates the broadband fluxes on which classic galaxy modelling tools rely to derive physical properties. We aim to fit a realistic model to the observations of NGC 1068 and quantify the contribution of the several dust heating sources. Our model is able to reproduce the global spectral energy distribution of the galaxy. It matches the resolved optical and infrared images fairly well, but deviates in the UV and the submm. We find a strong wavelength dependency of AGN contamination to the broadband fluxes. It peaks in the MIR, drops in the FIR, but rises again at submm wavelengths. We quantify the contribution of the dust heating sources in each 3D dust cell and find a median value of 83% for the star formation component. The AGN contribution is measurable at the percentage level in the disc, but quickly increases in the inner few 100 pc, peaking above 90%. This is the first time the phenomenon of an AGN heating the diffuse dust beyond its torus is quantified in a nearby star-forming galaxy. NGC 1068 only contains a weak AGN, meaning this effect can be stronger in galaxies with a more luminous AGN. This could significantly impact the derived star formation rates and ISM masses for such systems., Comment: 18 pages, 11 figures, accepted for publication in A&A

We compare the spectral energy distributions (SEDs) and inferred physical properties for simulated and observed galaxies at low redshift. We exploit UV-submillimetre mock fluxes of ~7000 z=0 galaxies from the EAGLE suite of cosmological simulations, derived using the radiative transfer code SKIRT. We compare these to ~800 observed galaxies in the UV-submillimetre range, from the DustPedia sample of nearby galaxies. To derive global properties, we apply the SED fitting code CIGALE consistently to both data sets, using the same set of ~80 million models. The results of this comparison reveal overall agreement between the simulations and observations, both in the SEDs and in the derived physical properties, with a number of discrepancies. The optical and far-infrared regimes, and the scaling relations based upon the global emission, diffuse dust and stellar mass, show high levels of agreement. However, the mid-infrared fluxes of the EAGLE galaxies are overestimated while the far-UV domain is not attenuated enough, compared to the observations. We attribute these discrepancies to a combination of galaxy population differences between the samples, and limitations in the subgrid treatment of star-forming regions in the EAGLE-SKIRT post-processing recipe. Our findings show the importance of detailed radiative transfer calculations and consistent comparison, and provide suggestions for improved numerical models., Comment: 17 pages, 14 figures, accepted for publication in MNRAS

We present the discovery of PSO J030947.49+271757.31, the radio brightest (23.7 mJy at 1.4 GHz) active galactic nucleus (AGN) at z>6.0. It was selected by cross-matching the NRAO VLA Sky Survey and the Panoramic Survey Telescope and Rapid Response System PS1 databases and its high-z nature was confirmed by a dedicated spectroscopic observation at the Large Binocular Telescope. A pointed Neil Gehrels $Swift$ Observatory XRT observation allowed us to measure a flux of $\sim$3.4$\times$10$^{-14}$ erg s$^{-1}$ cm$^{-2}$ in the [0.5-10] keV energy band, which also makes this object the X-ray brightest AGN ever observed at z>6.0. Its flat radio spectrum ($\alpha_{\nu r}$<0.5), very high radio loudness (R>10$^3$), and strong X-ray emission, compared to the optical, support the hypothesis of the blazar nature of this source. %i.e. a radio-loud (RL) AGN with the relativistic jet pointed toward us. Assuming that this is the only blazar at this redshift in the surveyed area of sky, we derive a space density of blazars at z$\sim$6 and with M$_{1450 \mbox{\AA}}$ < -25.1 of 5.5$^{+11.2}_{-4.6}$$\times$10$^{-3}$ Gpc$^{-3}$. From this number, and assuming a reasonable value of the bulk velocity of the jet ($\Gamma$=10), we can also infer a space density of the entire radio-loud AGN population at z$\sim$6 with the same optical/UV absolute magnitude of 1.10$^{+2.53}_{-0.91}$ Gpc$^{-3}$. Larger samples of blazars will be necessary to better constrain these estimates., Comment: 7 pages, 8 figures, 3 tables, Accepted to publication in A&A Letters. Typo corrections