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The solar wind: acceleration mechanisms, turbulence and heating

The study of mass loss from the Sun due to the solar wind relies principally on observations from space, both "remote-sensing", using instruments for UV and EUV
images of the solar disk and white light and UV coronographs, as well as via "in situ" measurements of characteristic parameters (velocity, magnetic and electric field, density, temperature). The measurements are necessarily supported by the analysis of theoretical models and by the comparison with the results of high precision numerical simulations.
The Italian community is constantly involved in all the phases of the above mentioned study, both in the development of instruments on-board satellites and numerical codes, as well as data analysis and theoretical modeling. The magnetic turbulence in the solar wind has a decisive influence on the processes that transport energetic particles into interplanetary space. In turn, the transport influences the acceleration processes, like stochastic acceleration and so-called "diffusive shock acceleration".

Radio evidence of a minor merger in the Shapley supercluster

Jan 17, 2022

Radio evidence of a minor merger in the Shapley supercluster A group of radio astronomers led by INAF has conducted a multi-frequency and multi-band study of the Shapley Supercluster, where the formation of large structures is ongoing at the present cosmological age. Radio astronomers have discovered a radio emission that acts as a "bridge" between a cluster of galaxies and a group of galaxies

Multiwavelength snapshot of a repeating fast radio burst

Dec 09, 2021

Multiwavelength snapshot of a repeating fast radio burst With a multiwavelength campaign, a group of astronomers led by the Italian National Institute for Astrophysics (INAF) studied a repeating fast radio burst (FRB). The object FRB20201124A, discovered in November 2020, reactivated in March 2021, emitting a series of radio bursts

Classifying Seyfert Galaxies with Deep Learning

Sep 28, 2021

Classifying Seyfert Galaxies with Deep Learning Scientist uses deep learning to identify low luminous Seyfert 1.9 galaxies that are usually missed by human inspection among ten thousands of spectra. These results are published in the Astrophysical Journal Supplement Series by Yen Chen Chen, in the department of physics at Sapienza University of Rome and ICRANet