A complete radio study of SNR G15.4+0.1 from new GMRT observations

The supernova remnant G15.4+0.1 is considered to be the possible counterpart of the gamma-ray source HESSJ1818-154. With the goal of getting a complete view of this remnant and understanding the nature of the gamma-ray flux, we conducted a detailed radio study that includes the search for pulsations and a model of the broadband emission for the G15.4+0.1/HESSJ1818-154 system. Low-frequency imaging at 624 MHz and pulsar observations at 624 and 1404 MHz towards G15.4+0.1 were carried out with the Giant Metrewave Radio Telescope (GMRT). We correlated the new radio data with observations of the source at X-ray and infrared wavelengths from XMM-Newton and Herschel observatories, respectively. To characterize the neutral hydrogen medium (HI) towards G15.4+0.1, we used data from the Southern Galactic Plane Survey. We modelled the spectral energy distribution using both hadronic and leptonic scenarios. From the combination of the new GMRT observations with existing data, we derived a continuum spectral index alpha=-0.62+-0.03 for the whole remnant. The local synchrotron spectra of G15.4+0.1, calculated from the combination of the GMRT data with 330 MHz observations from the VLA, tends to be flatter in the central part of the remnant, accompanying the region where the blast wave is impinging molecular gas. No spectral index trace was found indicating the radio counterpart to the pulsar wind nebula proposed from X-ray observations. In addition, the search for radio pulsations yielded negative results. Emission at far-infrared wavelengths is observed in the region where the SNR shock is interacting with dense molecular clumps. We also identified HI features forming a shell that wraps most of the outer border of G15.4+0.1. Characteristic parameters were estimated for the shocked HI gas. We found that either a purely hadronic or leptonic model is compatible with the broadband emission known so far.Comment: 11 pages, 9 figures, accepted for publication in Astronomy & Astrophysic

Automated detection and characterization of Antarctic basal units using radar sounding data: demonstration in Institute Ice Stream, West Antarctica

Basal units – visibly distinct englacial structures near the ice-bed interface – warrant investigation for a number of reasons. Many are of unknown composition and origin, characteristics that could provide substantial insight into subglacial processes and ice-sheet history. Their significance, moreover, is not limited to near-bed depths; these units appear to dramatically influence the flow of surrounding ice. In order to enable improved characterization of these features, we develop and apply an algorithm that allows for the automatic detection of basal units. We use a tunable layer-optimized SAR processor to distinguish these structures from the bed, isochronous englacial layers and the ice-sheet surface, presenting a conceptual framework for the use of radio-echo character in the identification of ice-sheet features. We also outline a method by which our processor could be used to place observational constraints on basal units’ configuration, composition and provenance

Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region

Contrasting non-dynamic and dynamic models of the water-energy nexus in small, off-grid Mediterranean islands

Water and energy supply in small Mediterranean islands are strictly interrelated and face a large number of challenging issues, mainly caused by the distance from the mainland, the lack of accessible and safe potable water sources, and the high seasonal variability of the water and energy demands driven by touristic fluxes. The energy system generally relies on carbon intensive, expensive stand-alone diesel generators, while potable water supply is provided by tank vessels. Although this combination provides essential services for local communities, it is often economically and environmentally unsustainable due to high operational costs and greenhouse gas (GHG) emissions. A traditional approach to improve the sustainability and the efficiency of the water and energy systems is to couple renewable energy sources (RES) with water supply technologies (e.g., desalination), in order to obtain efficient planning solutions (i.e. RES capacity, desalination plant capacity) in a least-cost fashion. However, this approach is generally non-dynamic and optimizes the power allocation using fixed electricity loads as a surrogate of the actual water demand supplied by the desalination plant through the water distribution network. Although this load reflects the actual water demand on the long-term (i.e. monthly or annual time scale), it could strongly deviate from the real water demand if we consider shorter time scales (i.e. daily or hourly), over which the water distribution network is able to store and move water in space and time. In this work, we comparatively analyse this traditional non-dynamic model of the water-energy nexus with a novel dynamic modelling approach, where the operation of both the nexus components (i.e. power allocation and operations of the water distribution network) is conjunctively optimized with respect to multiple economic and sustainability indicators (e.g., net present costs, GHG emissions, water supply deficit, RES penetration). This comparative analysis is performed over the real case study of the Italian Ustica island in the Mediterranean Sea. Preliminary results show the effectiveness of the dynamic approach in improving the static solution with respect to almost all the system performance metrics considered

Predicted gamma-ray image of SN 1006 due to inverse Compton emission

We propose a method to synthesize the inverse Compton (IC) γ-ray image of a supernova remnant starting from the radio (or hard X-ray) map and using results of the spatially resolved X-ray spectral analysis. The method is successfully applied to SN 1006. We found that synthesized IC γ-ray images of SN 1006 show morphology in nice agreement with that reported by the High Energy Stereoscopic System (HESS) collaboration. The good correlation found between the observed very high energy γ-ray and X-ray/radio appearance can be considered as evidence of the fact that the γ-ray emission of SN 1006 observed by HESS is leptonic in origin, although a hadronic origin may not be excluded.Fil: Petruk, O.. Institute for Applied Problems in Mechanics and Mathematics; UcraniaFil: Bocchino, F.. Istituto Nazionale Di Astrofísica. Osservatorio Astronómico Di Palermo; ItaliaFil: Miceli, M.. Istituto Nazionale Di Astrofísica. Osservatorio Astronómico Di Palermo; ItaliaFil: Dubner, Gloria Mabel. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Castelletti, Gabriela Marta. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Orlando, S.. Istituto Nazionale Di Astrofísica. Osservatorio Astronómico Di Palermo; ItaliaFil: Iakubovskyi, D.. Bogolyubov Institute for Theoretical Physics; UcraniaFil: Telezhinsky, I.. Kiev National Taras Shevchenko University; Ucrani

Use of artificial intelligence to automatically predict the optimal patient-specific inversion time for late gadolinium enhancement imaging. Tool development and clinical validation

Introduction With the worldwide diffusion of cardiac magnetic resonance (CMR), demand on image quality has grown. CMR late gadolinium enhancement (LGE) imaging provides critical diagnostic and prognostic information, and guides management. The identification of optimal Inversion Time (TI), a time-sensitive parameter closely linked to contrast kinetics, is pivotal for correct myocardium nulling. However, determining the optimal TI can be challenging in some diseases and for less experienced operators. Purpose To develop and test an artificial intelligence tool to automatically predict the personalised optimal TI in LGE imaging. Methods The tool, named THAITI, consists of a Random Forest regression model. It considers, as input parameters, patient-specific TI determinants (age, gender, weight, height, kidney function, heart rate) and CMR scan-specific TI determinants (B0, contrast type and dose, time elapsed from contrast injection). THAITI was trained on 219 patients (3585 images) with mixed conditions who underwent CMR (1.5T; Gadobutrol; averaged, MOCO, free-breathing true-FISP IR [1]) for clinical reasons. The dataset was split with a 90–10 policy: 90% of data for training, and 10% for testing. THAITI’s hyperparameters were optimised by embedding k-fold cross validation into an evolutionary computation algorithm, and the best performing model was finally evaluated on the test set. A graphical user interface was also developed. Clinical validation was performed on 55 consecutive patients, randomised to experimental (THAITI-set TI) vs control (operator-set TI) group. Image quality was assessed blindly by 2 independent experienced operators by a 4-points Likert scale, and by means of the contrast/enhancement ratio (CER) (i.e., signal intensity of enhanced/remote myocardium ratio). Results In the testing set, the TI predicted by THAITI differed from the ground truth by ≥ 5ms in 16% of cases. At clinical validation, myocardial nulling quality did not differ between the experimental vs the control group either by CER or visual assessment, with an overall "optimal" or "good" nulling in 96% vs 93%, respectively. Conclusions Using main determinants of contrast kinetics, THAITI efficiently predicted the optimal TI for CMR-LGE imaging. The tool works as a stand-alone on laptops/mobile devices, not requiring adjunctive scanner technology and thus has great potential for diffusion, including in small or recently opened CMR services, and in low-resource settings. Additional development is ongoing to increase generalisability (multi-vendor, multi-sequence, multi-contrast) and to test its potential to further improve CMR-LGE image quality and reduce the need for repeated imaging for inexperienced operators. Figure 1. Top: THAITI interface. Bottom: examples of experimental group CMR-LGE imaging. Table 1. Control vs experimental group. Data expressed as absolute number (%), mean ± SD, median [IQR]. ⧧ T-test; * Chi-square