Toward porting Astrophysics Visual Analytics Services to the European Open Science Cloud

The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated into EOSC, towards addressing the diverse astrophysics user communities needs. These services rely on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and applying machine learning techniques for detection of structures in large scale multidimensional maps

Astrophysics visual analytics services on the European Open Science Cloud

The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data, supporting science in all disciplines without geographical boundaries, so that data, software, methods and publications can be shared seamlessly as part of an Open Science community. This work presents the ongoing activities related to the implementation and integration into EOSC of Visual Analytics services for astrophysics, specifically addressing challenges related to data management, mapping and structure detection. These services provide visualisation capabilities to manage the data life cycle processes under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicking and data analysis supported with machine learning techniques, for detection of structures in large scale multidimensional maps

EMU Detection of a Large and Low Surface Brightness Galactic SNR G288.8-6.3

We present the serendipitous detection of a new Galactic Supernova Remnant (SNR), G288.8-6.3 using data from the Australian Square Kilometre Array Pathfinder (ASKAP)-Evolutionary Map of the Universe (EMU) survey. Using multi-frequency analysis, we confirm this object as an evolved Galactic SNR at high Galactic latitude with low radio surface brightness and typical SNR spectral index of $\alpha = -0.41\pm0.12$. To determine the magnetic field strength in SNR G288.8-6.3, we present the first derivation of the equipartition formulae for SNRs with spectral indices $\alpha>-0.5$. The angular size is 1.\!^\circ 8\times 1.\!^\circ 6 (107.\!^\prime 6 \times 98.\!^\prime 4) and we estimate that its intrinsic size is $\sim40$pc which implies a distance of $\sim1.3$kpc and a position of $\sim140$pc above the Galactic plane. This is one of the largest angular size and closest Galactic SNRs. Given its low radio surface brightness, we suggest that it is about 13000 years old.Comment: Accepted for publication in The Astrophysical Journa

NIKA2 observations around LBV stars Emission from stars and circumstellar material

Luminous Blue Variable (LBV) stars are evolved massive objects, previous to core-collapse supernova. LBVs are characterized by photometric and spectroscopic variability, produced by strong and dense winds, mass-loss events and very intense UV radiation. LBVs strongly disturb their surroundings by heating and shocking, and produce important amounts of dust. The study of the circumstellar material is therefore crucial to understand how these massive stars evolve, and also to characterize their effects onto the interstellar medium. The versatility of NIKA2 is a key in providing simultaneous observations of both the stellar continuum and the extended, circumstellar contribution. The NIKA2 frequencies (150 and 260 GHz) are in the range where thermal dust and free-free emission compete, and hence NIKA2 has the capacity to provide key information about the spatial distribution of circumstellar ionized gas, warm dust and nearby dark clouds; non-thermal emission is also possible even at these high frequencies. We show the results of the first NIKA2 survey towards five LBVs. We detected emission from four stars, three of them immersed in tenuous circumstellar material. The spectral indices show a complex distribution and allowed us to separate and characterize different components. We also found nearby dark clouds, with spectral indices typical of thermal emission from dust. Spectral indices of the detected stars are negative and hard to be explained only by free-free processes. In one of the sources, G79.29+0.46, we also found a strong correlation of the 1mm and 2mm continuum emission with respect to nested molecular shells at ≈1 pc from the LBV. The spectral index in this region clearly separates four components: the LBV star, a bubble characterized by free-free emission, and a shell interacting with a nearby infrared dark cloud

NIKA2 observations around LBV stars Emission from stars and circumstellar material

Luminous Blue Variable (LBV) stars are evolved massive objects, previous to core-collapse supernova. LBVs are characterized by photometric and spectroscopic variability, produced by strong and dense winds, mass-loss events and very intense UV radiation. LBVs strongly disturb their surroundings by heating and shocking, and produce important amounts of dust. The study of the circumstellar material is therefore crucial to understand how these massive stars evolve, and also to characterize their effects onto the interstellar medium. The versatility of NIKA2 is a key in providing simultaneous observations of both the stellar continuum and the extended, circumstellar contribution. The NIKA2 frequencies (150 and 260 GHz) are in the range where thermal dust and free-free emission compete, and hence NIKA2 has the capacity to provide key information about the spatial distribution of circumstellar ionized gas, warm dust and nearby dark clouds; non-thermal emission is also possible even at these high frequencies. We show the results of the first NIKA2 survey towards five LBVs. We detected emission from four stars, three of them immersed in tenuous circumstellar material. The spectral indices show a complex distribution and allowed us to separate and characterize different components. We also found nearby dark clouds, with spectral indices typical of thermal emission from dust. Spectral indices of the detected stars are negative and hard to be explained only by free-free processes. In one of the sources, G79.29+0.46, we also found a strong correlation of the 1mm and 2mm continuum emission with respect to nested molecular shells at ≈1 pc from the LBV. The spectral index in this region clearly separates four components: the LBV star, a bubble characterized by free-free emission, and a shell interacting with a nearby infrared dark cloud

Radio astronomical images object detection and segmentation: A benchmark on deep learning methods

In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest telescope in the world - the Square Kilometre Array (SKA), the task of automatic object detection and instance segmentation is crucial for source finding and analysis. In this work, we explore the performance of the most affirmed deep learning approaches, applied to astronomical images obtained by radio interferometric instrumentation, to solve the task of automatic source detection. This is carried out by applying models designed to accomplish two different kinds of tasks: object detection and semantic segmentation. The goal is to provide an overview of existing techniques, in terms of prediction performance and computational efficiency, to scientists in the astrophysics community who would like to employ machine learning in their research