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The Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy 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 disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 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 crowdsourcing. 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., Comment: 27 pages, 26 figures, 5 tables, published in A&A

The INSPIRE project has built the largest sample of ultra-compact massive galaxies (UCMGs) at 0.12, and the other stellar population parameters.We use the Full-Index-Fitting method to fit the INSPIRE spectra to single stellar population (SSP) models. This allows us to measure, for the first time, the low-mass end slope of the IMF, as well as stellar metallicity [M/H], [Mg/Fe], [Ti/Fe] and [Na/Fe] ratios, and study correlations between them and the DoR. Similarly to normal-sized galaxies, UCMGs with larger stellar masses have overall higher metallicities. We found a correlation between the low-mass end of the IMF slope and the DoR, that, however, breaks down for systems with a more extended SFH. An even stronger dependency is found between the IMF and the fraction of mass formed at high-z. At equal velocity dispersion and metallicity, galaxies with a higher DoR have a dwarf-richer IMF than that of low-DoR counterparts. This might indicate that the cosmic epoch and formation mechanisms influence the fragmentation of the star formation cloud and hence might be the explanation for IMF variations detected in massive ETGs., Comment: 16 pages, 11 figures, submitted to MNRAS

Galaxy sizes correlate with many other important properties of galaxies, and the cosmic evolution of galaxy sizes is an important observational diagnostic for constraining galaxy evolution models. The effective radius is probably the most widely used indicator of galaxy size. We used the TNG50-SKIRT Atlas to investigate the wavelength dependence of the effective radius of galaxies at optical and near-infrared (NIR) wavelengths. We find that, on average, the effective radius in every band exceeds the stellar mass effective radius, and that this excess systematically decreases with increasing wavelength. The optical g-band (NIR Ks-band) effective radius is on average 58% (13%) larger than the stellar mass effective radius. Effective radii measured from dust-obscured images are systematically larger than those measured from dust-free images, although the effect is limited (8.7% in the g-band, 2.1% in the Ks-band). We find that stellar population gradients are the dominant factor (about 80%) in driving the wavelength dependence of the effective radius, and that differential dust attenuation is a secondary factor (20%). Comparing our results to recent observational data, we find offsets in the absolute values of the median effective radii, up to 50% for the population of blue galaxies. We find better agreement in the slope of the wavelength dependence of the effective radius, with red galaxies having a slightly steeper slope than green-blue galaxies. Comparing our effective radii with those of galaxies from the Siena Galaxy Atlas in separate bins in z-band absolute magnitude and g-z colour, we find excellent agreement for the reddest galaxies, but again significant offsets for the blue populations: up to 70% for galaxies around Mz=-21.5. This difference in median effective radius for the bluer galaxies is most probably due to (abridged...)., Comment: 11 pages, 10 figures, accepted for publication in A&A

Galaxy morphology is a powerful diagnostic to assess the realism of cosmological hydrodynamical simulations. Determining the morphology of simulated galaxies requires the generation of synthetic images through 3D radiative transfer post-processing that properly accounts for different stellar populations and interstellar dust attenuation. We use the SKIRT code to generate the TNG50-SKIRT Atlas, a synthetic UV to near-infrared broadband image atlas for a complete stellar-mass selected sample of 1154 galaxies extracted from the TNG50 cosmological simulation at $z=0$. The images have a high spatial resolution (100 pc) and a wide field of view (160 kpc). In addition to the dust-obscured images, we also release dust-free images and physical parameter property maps with matching characteristics. As a sanity check and preview application we discuss the UVJ diagram of the galaxy sample. We investigate the effect of dust attenuation on the UVJ diagram and find that it affects both the star-forming and the quiescent galaxy populations. The quiescent galaxy region is polluted by younger and star-forming highly inclined galaxies, while dust attenuation induces a separation in inclination of the star-forming galaxy population, with low-inclination galaxies remaining at the blue side of the diagram and high-inclination galaxies systematically moving towards the red side. This image atlas can be used for a variety of other applications, including galaxy morphology studies and the investigation of local scaling relations. We publicly release the images and parameter maps, and we invite the community to use them., Comment: 13 pages, 7 figures, accepted for publication in A&A

The FAMU experiment at RIKEN-RAL is a muonic atom experiment with the aim to determine the Zemach radius of the proton by measuring the 1s hyperfine splitting in muonic hydrogen. The activity of the FAMU Collaboration in the years 2015-2023 enabled the final optimisation of the detector-target setup as well as the gas working condition in terms of temperature, pressure and gas mixture composition. The experiment has started its data taking in July 2023. The status of the detector setup for the 2023 experimental runs, for the beam characterisation and muonic X-ray detection in the 100-200 keV energy range, is presented and discussed., Comment: Submitted to JINST

The FAMU experiment aims at an indirect measurement of the Zemach radius of the proton. The measurement is carried out on muonic hydrogen atoms produced through the low-momentum (50-60 MeV/c) muon beam a the RIKEN-RAL negative muon facility. The particle flux plays an important role in this measurement, as it is proportional to the number of muonic hydrogen atoms produced, which is the target of the FAMU experimental method. The beam monitor calibration technique and results, presented here, are meant to extract a reliable estimation of the muon flux during the FAMU data taking. These measurements were carried out at the CNAO synchrotron in Pavia, Italy, using proton beams and supported by Monte Carlo simulation of the detector in Geant4., Comment: Submitted to JINST

Atomic hydrogen (HI) serves a crucial role in connecting galactic-scale properties such as star formation with the large-scale structure of the Universe. While recent numerical simulations have successfully matched the observed covering fraction of HI near Lyman Break Galaxies (LBGs) and in the foreground of luminous quasars at redshifts $z \lesssim 3$, the low-mass end remains as-of-yet unexplored in observational and computational surveys. We employ a cosmological, hydrodynamical simulation (FIREbox) supplemented with zoom-in simulations (MassiveFIRE) from the Feedback In Realistic Environments (FIRE) project to investigate the HI covering fraction of Lyman Limit Systems ($N_{\mathrm{HI}} \gtrsim 10^{17.2}$ cm$^{-2}$) across a wide range of redshifts ($z=0-6$) and halo masses ($10^8-10^{13} M_{\odot}$ at $z=0$, $10^8-10^{11} M_{\odot}$ at $z=6$) in the absence of feedback from active galactic nuclei. We find that the covering fraction inside haloes exhibits a strong increase with redshift, with only a weak dependence on halo mass for higher-mass haloes. For massive haloes ($M_{\mathrm{vir}} \sim 10^{11}-10^{12} M_{\odot}$), the radial profiles showcase scale-invariance and remain independent of mass. The radial dependence is well-captured by a fitting function. The covering fractions in our simulations are in good agreement with measurements of the covering fraction in LBGs. Our comprehensive analysis unveils a complex dependence with redshift and halo mass for haloes with $M_{\mathrm{vir}} \lesssim 10^{10} M_{\odot}$ that future observations aim to constrain, providing key insights into the physics of structure formation and gas assembly.

Convolutional neural networks (CNNs) are the state-of-the-art technique for identifying strong gravitational lenses. Although they are highly successful in recovering genuine lens systems with a high true-positive rate, the unbalanced nature of the data set (lens systems are rare), still leads to a high false positive rate. For these techniques to be successful in upcoming surveys (e.g. with Euclid) most emphasis should be set on reducing false positives, rather than on reducing false negatives. In this paper, we introduce densely connected neural networks (DenseNets) as the CNN architecture in a new pipeline-ensemble model containing an ensemble of classification CNNs and regression CNNs to classify and rank-order lenses, respectively. We show that DenseNets achieve comparable true positive rates but considerably lower false positive rates (when compared to residual networks; ResNets). Thus, we recommend DenseNets for future missions involving large data sets, such as Euclid, where low false positive rates play a key role in the automated follow-up and analysis of large numbers of strong gravitational lens candidates when human vetting is no longer feasible

We report on the discovery of a significant and compact over-density of old and metal-poor stars in the KiDS survey (data release 4). The discovery is confirmed by deeper HSC-SSC data revealing the old Main Sequence Turn-Off of a stellar system located at a distance from the sun of $D_{\sun}=145^{+14}_{-13}$~kpc in the direction of the Sextans constellation. The system has absolute integrated magnitude ($M_V=-3.9^{+0.4}_{-0.3}$), half-light radius ($r_h=193^{+61}_{-46}$~pc), and ellipticity ($e=0.46^{+0.11}_{-0.15}$) typical of Ultra Faint Dwarf galaxies (UFDs). The central surface brightness is near the lower limits of known local dwarf galaxies of similar integrated luminosity, as expected for stellar systems that escaped detection until now. The distance of the newly found system suggests that it is likely a satellite of our own Milky Way, consequently, we tentatively baptise it Sextans~II (KiDS-UFD-1)., Comment: 7 pages, 5 figures. Accepted for publication on A&A Letters

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

During the early stages of galaxy evolution, a significant fraction of galaxies undergo a transitional phase between the ``blue nugget" systems, which arise from the compaction of large, active star-forming disks, and the ``red nuggets", which are red and passive compact galaxies. These objects are typically only observable with space telescopes, and detailed studies of their size, mass, and stellar population parameters have been conducted on relatively small samples. Strong gravitational lensing can offer a new opportunity to study them in detail, even with ground-based observations. In this study, we present the first 6 \textit{bona fide} sample of strongly lensed post-blue nugget (pBN) galaxies, which were discovered in the Kilo Degree Survey. By using the lensing-magnified luminosity from optical and near-infrared bands, we have derived robust structural and stellar population properties of the multiple images of the background sources. \rui{The pBN galaxies have very small sizes of $\rm R_{eff}<1.3$ kpc, high mass density inside 1 kpc of $ \rm \log (\Sigma_1/M_{\odot} \mathrm{kpc}^{-2})>9.3$, and low specific star formation rates of $\log (\mathrm{sSFR/Gyr})\lesssim0$, The size-mass and $\Sigma_1$-mass relations of this sample are consistent with those of the red nuggets, while their sSFR is close to the lower end of compact star-forming blue nugget systems at the same redshift, suggesting a clear evolutionary link between them., Comment: Accepted for publication in APJ

Hera represents the European Space Agency's inaugural planetary defense space mission and plays a pivotal role in the Asteroid Impact and Deflection Assessment international collaboration with NASA DART mission that performed the first asteroid deflection experiment using the kinetic impactor techniques. With the primary objective of conducting a detailed post-impact survey of the Didymos binary asteroid following the DART impact on its small moon called Dimorphos, Hera aims to comprehensively assess and characterize the feasibility of the kinetic impactor technique in asteroid deflection while conducting an in-depth investigation of the asteroid binary, including its physical and compositional properties as well as the effect of the impact on the surface and shape of Dimorphos. In this work, we describe the Hera radio science experiment, which will allow us to precisely estimate critical parameters, including the mass, which is required to determine the momentum enhancement resulting from the DART impact, mass distribution, rotational states, relative orbits, and dynamics of the asteroids Didymos and Dimorphos. Through a multi-arc covariance analysis, we present the achievable accuracy for these parameters, which consider the full expected asteroid phase and are based on ground radiometric, Hera optical images, and Hera to CubeSats InterSatellite Link radiometric measurements. The expected formal uncertainties for Didymos and Dimorphos GM are better than 0.01% and 0.1%, respectively, while their J2 formal uncertainties are better than 0.1% and 10%, respectively. [...]

Accelerated muon beams have been considered for next-generation studies of high-energy lepton-antilepton collisions and neutrino oscillations. However, high-brightness muon beams have not yet been produced. The main challenge for muon acceleration and storage stems from the large phase-space volume occupied by the beam, derived from the muon production mechanism through the decay of pions from proton collisions. Ionization cooling is the technique proposed to decrease the muon beam phase-space volume. Here we demonstrate a clear signal of ionization cooling through the observation of transverse emittance reduction in beams that traverse lithium hydride or liquid hydrogen absorbers in the Muon Ionization Cooling Experiment (MICE). The measurement is well reproduced by the simulation of the experiment and the theoretical model. The results shown here represent a substantial advance towards the realization of muon-based facilities that could operate at the energy and intensity frontiers., Comment: 23 pages and 5 figures

The galaxy total mass inside the effective radius encode important information on the dark matter and galaxy evolution model. Total "central" masses can be inferred via galaxy dynamics or with gravitational lensing, but these methods have limitations. We propose a novel approach, based on Random Forest, to make predictions on the total and dark matter content of galaxies using simple observables from imaging and spectroscopic surveys. We use catalogs of multi-band photometry, sizes, stellar mass, kinematic "measurements" (features) and dark matter (targets) of simulated galaxies, from Illustris-TNG100 hydrodynamical simulation, to train a Mass Estimate machine Learning Algorithm (Mela). We separate the simulated sample in passive early-type galaxies (ETGs), both "normal" and "dwarf", and active late-type galaxies (LTGs) and show that the mass estimator can accurately predict the galaxy dark masses inside the effective radius in all samples. We finally test the mass estimator against the central mass estimates of a series of low redshift (z$\leq$0.1) datasets, including SPIDER, MaNGA/DynPop and SAMI dwarf galaxies, derived with standard dynamical methods based on Jeans equations. Dynamical masses are reproduced within 0.30 dex ($\sim2\sigma$), with a limited fraction of outliers and almost no bias. This is independent of the sophistication of the kinematical data collected (fiber vs. 3D spectroscopy) and the dynamical analysis adopted (radial vs. axisymmetric Jeans equations, virial theorem). This makes Mela a powerful alternative to predict the mass of galaxies of massive stage-IV surveys' datasets.

This paper presents the third data release of the INvestigating Stellar Population In RElics (INSPIRE) project, comprising 52 ultra-compact massive galaxies (UCMGs) observed with the X-Shooter spectrograph. We measure integrated stellar velocity dispersion, [Mg/Fe] abundances, ages, and metallicities for all the INSPIRE objects. We thus infer star formation histories and confirm the existence of a degree of relicness (DoR), defined in terms of the fraction of stellar mass formed by $z=2$, the time at which a galaxy has assembled 75\% of its mass, and the final assembly time. Objects with a high DoR assembled their stellar mass at early epochs, while low-DoR objects show a non-negligible fraction of later-formed populations and hence a spread in ages and metallicities. A higher DoR correlates with larger [Mg/Fe], super-solar metallicity, and larger velocity dispersion values. The 52 UMCGs span a large range of DoR from 0.83 to 0.06, with 38 of them having formed more than 75\% of their mass by $z=2$. Of these, nine are extreme relics (DoR$>0.7$), since they formed the totality ($>99\%$) of their stellar mass by redshift $z=2$. The remaining 14 UCMGs cannot be considered relics, as they are characterised by more extended star formation histories. With INSPIRE, we built the first sizeable sample of relics outside the local Universe, up to $z\sim0.4$, increasing the number of confirmed relics by a factor of $>10$, and opening up an important window to explain the mass assembly of massive galaxies in the high-z Universe., Comment: accepted for publication on MNRAS, 20 pages, 15 figures, 3 tables

We propose a mechanism for engineering chiral interactions in Rydberg atoms via a directional antiblockade condition, where an atom can change its state only if an atom to its right (or left) is excited. The scalability of our scheme enables us to explore the many-body dynamics of kinetically constrained models with unidirectional character. We observe non-ergodic behavior via either scars, confinement, or localization, upon simply tuning the strength of two driving fields acting on the atoms. We discuss how our mechanism persists in the presence of classical noise and how the degree of chirality in the interactions can be tuned, opening towards the frontier of directional, strongly correlated, quantum mechanics using neutral atoms arrays., Comment: 5+5 pages, 3+3 figures; close to published version

A muon collider would enable the big jump ahead in energy reach that is needed for a fruitful exploration of fundamental interactions. The challenges of producing muon collisions at high luminosity and 10~TeV centre of mass energy are being investigated by the recently-formed International Muon Collider Collaboration. This Review summarises the status and the recent advances on muon colliders design, physics and detector studies. The aim is to provide a global perspective of the field and to outline directions for future work., Comment: 118 pages, 103 figures

We compare the structural properties and dark matter content of star-forming galaxies taken from the CAMELS cosmological simulations to the observed trends derived from the SPARC sample in the stellar mass range $[10^{9}, 10^{11}]\,\textrm{M}_{\odot}$, to provide constraints on the value of cosmological and astrophysical (SN- and AGN-related) parameters. We consider the size-, internal DM fraction-, internal DM mass- and total-stellar mass relations for all the 1065 simulations from the IllustrisTNG, SIMBA and ASTRID suites of CAMELS, and search for the parameters that minimize the $\chi^{2}$ with respect to the observations. For the IllustrisTNG suite, we find the following constraints for the cosmological parameters: $\Omega_{\textrm{m}} = 0.27_{-0.05}^{+0.01}$, $\sigma_{8} = 0.83_{-0.11}^{+0.08}$ and $S_{8} = 0.78_{-0.09}^{+0.03}$, which are consistent within $1\sigma$ with the results from the nine-year WMAP observations. SN feedback-related astrophysical parameters, which describe the departure of outflow wind energy per unit star formation rate and wind velocity from the reference IllustrisTNG simulations, assume the following values: $A_{\textrm{SN1}} = 0.48_{-0.16}^{+0.25}$ and $A_{\textrm{SN2}} = 1.21_{-0.34}^{+0.03}$, respectively. Therefore, simulations with a lower value of outflow wind energy per unit star formation rate with respect to the reference illustrisTNG simulation better reproduce the observations. Simulations based on SIMBA and ASTRID suites predict central dark matter masses substantially larger than those observed in real galaxies, which can be reconciled with observations only by requiring values of $\Omega_{\textrm{m}}$ inconsistent with cosmological constraints for SIMBA, or simulations characterized by unrealistic galaxy mass distributions for ASTRID., Comment: 24 pages, 10 figures, 9 tables. Accepted by MNRAS for publication; Added a reference to sec. 4.1

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

The upcoming JUICE and Europa Clipper missions to Jupiter's Galilean satellites will provide radio science tracking measurements of both spacecraft. Such data are expected to significantly help estimating the moons' ephemerides and related dynamical parameters. However, the two missions will yield an imbalanced dataset, with no flybys planned at Io, condensed over less than six years. Current ephemerides' solutions for the Galilean moons, on the other hand, rely on ground-based astrometry collected over more than a century which, while being less accurate, bring very valuable constraints on the long-term dynamics of the system. An improved solution for the Galilean satellites' complex dynamics could however be achieved by exploiting the existing synergies between these different observation sets. To quantify this, we merged simulated JUICE and Clipper radio science data with existing ground-based astrometric and radar observations, and performed the inversion. Our study specifically focusses on the resulting formal uncertainties in the moons' states, as well as Io's and Jupiter's tidal dissipation parameters. Adding astrometry stabilises the moons' state solution, especially beyond the missions' timelines. It furthermore reduces the uncertainties in $1/Q$ (inverse of the tidal quality factor) by a factor two to four for Jupiter, and about 30-35\% for Io. Among all data types, classical astrometry data prior to 1960 proved particularly beneficial. We also show that ground observations of Io add the most to the solution, confirming that ground observations can fill the lack of radio science data for this specific moon. We obtained a noticeable solution improvement when exploiting the complementarity between all different observation sets. These promising simulation results thus motivate future efforts to achieve a global solution from actual JUICE and Clipper radio science data.

The Kilo Degree Survey (KiDS) is currently the only sky survey providing optical ($ugri$) plus near-infrared (NIR, $ZYHJK_S$) seeing matched photometry over an area larger than 1000 $\rm deg^2$. This is obtained by incorporating the NIR data from the VISTA Kilo Degree Infrared Galaxy (VIKING) survey, covering the same KiDS footprint. As such, the KiDS multi-wavelength photometry represents a unique dataset to test the ability of stellar population models to return robust photometric stellar mass ($M_*$) and star-formation rate (SFR) estimates. Here we use a spectroscopic sample of galaxies for which we possess $u g r i Z Y J H K_s$ ``gaussianized'' magnitudes from KiDS data release 4. We fit the spectral energy distribution from the 9-band photometry using: 1) three different popular libraries of stellar {population} templates, 2) single burst, simple and delayed exponential star-formation history models, and 3) a wide range of priors on age and metallicity. As template fitting codes we use two popular softwares: LePhare and CIGALE. We investigate the variance of the stellar masses and the star-formation rates from the different combinations of templates, star formation recipes and codes to assess the stability of these estimates and define some ``robust'' median quantities to be included in the upcoming KiDS data releases. As a science validation test, we derive the mass function, the star formation rate function, and the SFR-$M_*$ relation for a low-redshift ($z<0.5$) sample of galaxies, that result in excellent agreement with previous literature data. The final catalog, containing $\sim290\,000$ galaxies with redshift $0.01

Accurate forecasting of renewable generation is crucial to facilitate the integration of RES into the power system. Focusing on PV units, forecasting methods can be divided into two main categories: physics-based and data-based strategies, with AI-based models providing state-of-the-art performance. However, while these AI-based models can capture complex patterns and relationships in the data, they ignore the underlying physical prior knowledge of the phenomenon. Therefore, in this paper we propose MATNet, a novel self-attention transformer-based architecture for multivariate multi-step day-ahead PV power generation forecasting. It consists of a hybrid approach that combines the AI paradigm with the prior physical knowledge of PV power generation of physics-based methods. The model is fed with historical PV data and historical and forecast weather data through a multi-level joint fusion approach. The effectiveness of the proposed model is evaluated using the Ausgrid benchmark dataset with different regression performance metrics. The results show that our proposed architecture significantly outperforms the current state-of-the-art methods. These findings demonstrate the potential of MATNet in improving forecasting accuracy and suggest that it could be a promising solution to facilitate the integration of PV energy into the power grid.