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We present a new solar radio imaging system implemented through the upgrade of the large single-dish telescopes of the Italian National Institute for Astrophysics (INAF), not originally conceived for solar observations. During the development and early science phase of the project (2018 – 2020), we obtained about 170 maps of the entire solar disk in the 18 – 26 GHz band, filling the observational gap in the field of solar imaging at these frequencies. These solar images have typical resolutions in the 0.7 – 2 arcmin range and a brightness temperature sensitivity <10 K. Accurate calibration adopting the Supernova Remnant Cas A as a flux reference provided typical errors <3% for the estimation of the quiet-Sun level components and active regions flux measurements. As the first early scientific result of the project, we present a catalog of radio continuum solar imaging observations with Medicina 32-m and SRT 64-m radio telescopes, including the multi-wavelength identification of active regions, their brightness and spectral characterization. The interpretation of the observed emission as thermal bremsstrahlung components combined with gyro-magnetic variable emission paves the way for the use of our system for long-term monitoring of the Sun. We also discuss useful outcomes both for solar physics (e.g., study of the chromospheric network dynamics) and space weather applications (e.g., flare precursors studies).

In the framework of the Astronomical Validation and Early Science activities of the Sardinia Radio Telescope (SRT, www.srt.inaf.it), we performed 22 GHz imaging observations of SNR W44 and IC443. Thanks to the single-dish imaging performances of SRT and innovative ad hoc imaging techniques, we obtained maps that provide a detailed view of the structure of the remnants. We are planning to exploit the high-frequency radio data of SNRs to better characterize the spatially-resolved spectra and search for possible spectral steepening or breaks in selected SNR regions, assessing the high-energy tail of the region-dependent electron distribution.

We present single-dish imaging of the well-known Supernova Remnants (SNRs) IC443 and W44 at 1.5 GHz and 7 GHz with the recently commissioned 64-m diameter Sardinia Radio Telescope (SRT). Our images were obtained through on-the-fly mapping techniques, providing antenna beam oversampling, automatic baseline subtraction and radio-frequency interference removal. It results in high-quality maps of the SNRs at 7 GHz, which are usually lacking and not easily achievable through interferometry at this frequency due to the very large SNR structures. SRT continuum maps of our targets are consistent with VLA maps carried out at lower frequencies (at 324 MHz and 1.4 GHz), providing a view of the complex filamentary morphology. New estimates of the total flux density are given within 3% and 5% error at 1.5 GHz and 7 GHz respectively, in addition to flux measurements in different regions of the SNRs.

In the framework of the Sardinia Radio Telescope (SRT) Early Science Program, we obtained single-dish high-resolution imaging of the Supernova Remnants IC443 and W44 at 7 GHz. By coupling them with SRT 1.5 GHz maps, we provided spatially-resolved spectral measurements that are highlighting a spread in spectral slope distribution. The observed features range from flat or slightly inverted spectra corresponding to bright radio limbs and filaments, to relatively steep spectra in fainter radio regions. Different theoretical possibilities explaining the above challenging findings are discussed. In particular, we exclude that the observed region-dependent wide spread in spectral slope distribution could be related to absorption processes. Our high-frequency results can be directly related to distinct electron populations in the SNRs including secondary hadronic electrons and resulting from different shocks conditions and/or undergoing different cooling processes. Integrated fluxes associated with the whole SNRs obtained by SRT in comparison with previous results in the literature support the evidence for a slight spectral steepening above 1 GHz for both sources, which could be related to primary electrons or more likely secondary hadronic electrons cut-offs.

Background The central-line associated bloodstream infections (CLABSI) are the most common healthcare-associated infections in childhood. Despite the international data available on healthcare-associated infections in selected groups of patients, there is a lack of large and good quality studies. The present survey is the first prospective study monitoring for 6 months the occurrence of central-line associated bloodstream infections in all departments of an Italian tertiary care children’s university hospital. Methods The study involved all children aged less than 18 years admitted to Meyer Children’s University Hospital, Florence, Italy who had a central line access between the October 15 th , 2014 and the April 14 th , 2015. CLABSI were defined according to the Center for Disease Control and Prevention criteria. CLABSI incidence rates with 95% confidence limits were calculated and stratified for the study variables. For each factor the relative risk and 95% confidence intervals were evaluated. Statistical analysis was performed using the statistical software SPSS for Windows, version 22.0 (SPSS Inc., Chicago, IL), p  < 0.05 was considered statistically significant. Results CLABSI rate was 3.73/1000 (95% CI: 2.54–5.28) central line-days. A higher CLABSI incidence was seen with female gender ( p  = 0.045) and underlying medical conditions (excepting prematurity, surgical diseases and malignancy) ( p  = 0.06). In our study 5 infections, were caused by extended-spectrum β-lactamase producing organisms and in one case by carbapenem-resistant Klebsiella pneumoniae . Conclusions Our study confirms the spreading of multi-resistant pathogens as causes of healthcare associated infections in children. An increased incidence rate of CLABSI in our study was related to underlying medical conditions. Pediatric studies focusing on healthcare infections in this type of patients should be done in order to deepen our understanding on associated risk factors and possible intervention areas.

The detected polarized radio emission from remnant of SN1987A opens the possibility to unveil the structure of the pre-supernova magnetic field in the circumstellar medium. Properties derived from direct measurements would be of importance for understanding the progenitor stars and their magnetic fields. As the first step to this goal, we adopted the hydrodynamic data from an elaborated three-dimensional (3-D) numerical model of SN1987A. We have developed an approximate method for `reconstruction' of 3-D magnetic field structure inside supernova remnant on the `hydrodynamic background'. This method uses the distribution of the magnetic field around the progenitor as the initial condition. With such a 3-D magneto-hydrodynamic model, we have synthesized the polarization maps for a number of SN1987A models and compared them to the observations. In this way, we have tested different initial configurations of the magnetic field as well as a structure of the synchrotron emission in SN987A. We have recovered the observed polarization pattern and we have found that the radial component of the ambient pre-supernova magnetic field should be dominant on the length-scale of the present-day radius of SN1987A. The physical reasons for such a field are discussed.

Supernova remnants (SNRs) carry vast amounts of mechanical and radiative energy that heavily influence the structural, dynamical, and chemical evolution of galaxies. To this day, more than 300 SNRs have been discovered in the Milky Way, exhibiting a wide variety of observational features. However, existing classification schemes are mainly based on their radio morphology. In this work, we introduce a novel unsupervised deep learning pipeline to analyse a representative subsample of the Galactic SNR population (\\(\\sim\\) 50% of the total) with the aim of finding a connection between their multi-wavelength features and their physical properties. The pipeline involves two stages: (1) a representation learning stage, consisting of a convolutional autoencoder that feeds on imagery from infrared and radio continuum surveys (WISE 22\\(\\mu\\)m, Hi-GAL 70 \\(\\mu\\)m and SMGPS 30 cm) and produces a compact representation in a lower-dimensionality latent space; and (2) a clustering stage that seeks meaningful clusters in the latent space that can be linked to the physical properties of the SNRs and their surroundings. Our results suggest that this approach, when combined with an intermediate uniform manifold approximation and projection (UMAP) reprojection of the autoencoded embeddings into a more clusterable manifold, enables us to find reliable clusters. Despite a large number of sources being classified as outliers, most clusters relate to the presence of distinctive features, such as the distribution of infrared emission, the presence of radio shells and pulsar wind nebulae, and the existence of dust filaments.

New advancements in radio data post-processing are underway within the SKA precursor community, aiming to facilitate the extraction of scientific results from survey images through a semi-automated approach. Several of these developments leverage deep learning (DL) methodologies for diverse tasks, including source detection, object or morphology classification, and anomaly detection. Despite substantial progress, the full potential of these methods often remains untapped due to challenges associated with training large supervised models, particularly in the presence of small and class-unbalanced labelled datasets. Self-supervised learning has recently established itself as a powerful methodology to deal with some of the aforementioned challenges, by directly learning a lower-dimensional representation from large samples of unlabelled data. The resulting model and data representation can then be used for data inspection and various downstream tasks if a small subset of labelled data is available. In this work, we explored contrastive learning methods to learn suitable radio data representation from unlabelled images taken from the ASKAP EMU and SARAO MeerKAT GPS surveys. We evaluated trained models and the obtained data representation over smaller labelled datasets, also taken from different radio surveys, in selected analysis tasks: source detection and classification, and search for objects with peculiar morphology. For all explored downstream tasks, we reported and discussed the benefits brought by self-supervised foundational models built on radio data.

We present the serendipitous discovery of a new radio-continuum ring-like object nicknamed Kyklos (J1802-3353), with MeerKAT UHF and L-band observations. The radio ring, which resembles the recently discovered odd radio circles (ORCs), has a diameter of 80 arcsec and is located just 6 deg from the Galactic plane. However, Kyklos exhibits an atypical thermal radio-continuum spectrum ({\\alpha} = -0.1 +/- 0.3), which led us to explore different possible formation scenarios. We concluded that a circumstellar shell around an evolved massive star, possibly a Wolf-Rayet, is the most convincing explanation with the present data.

KQVel is a binary system composed of a slowly rotating magnetic Ap star with a companion of unknown nature. In this paper, we report the detection of its radio emission. We conducted a multi-frequency radio campaign using the ATCA interferometer (band-names: 16cm, 4cm, and 15mm). The target was detected in all bands. The most obvious explanation for the radio emission is that it originates in the magnetosphere of the Ap star, but this is shown unfeasible. The known stellar parameters of the Ap star enable us to exploit the scaling relationship for non-thermal gyro-synchrotron emission from early-type magnetic stars. This is a general relation demonstrating how radio emission from stars with centrifugal magnetospheres is supported by rotation. Using KQVel's parameters the predicted radio luminosity is more than five orders of magnitudes lower than the measured one. The extremely long rotation period rules out the Ap star as the source of the observed radio emission. Other possible explanations for the radio emission from KQVel, involving its unknown companion, have been explored. A scenario that matches the observed features (i.e. radio luminosity and spectrum, correlation to X-rays) is a hierarchical stellar system, where the possible companion of the magnetic star is a close binary (possibly of RSCVn type) with at least one magnetically active late-type star. To be compatible with the total mass of the system, the last scenario places strong constraints on the orbital inclination of the KQVel stellar system.