A spectroscopic and photometric study of MSP companions in Galactic Globular Clusters

This Thesis is devoted to the study of the optical companions of Millisecond Pulsars in Galactic Globular Clusters (GCs) as a part of a large project started at the Department of Astronomy of the Bologna University, in collaboration with other institutions (Astronomical Observatory of Cagliari and Bologna, University of Virginia), specifically dedicated to the study of the environmental effects on passive stellar evolution in galactic GCs. Globular Clusters are very efficient “Kilns” for generating exotic object, such as Millisecond Pulsars (MSP), low mass X-ray binaries(LMXB) or Blue Straggler Stars (BSS). In particular MSPs are formed in binary systems containing a Neutron Star which is spun up through mass accretion from the evolving companion (e.g. Bhattacharia & van den Heuvel 1991). The final stage of this recycling process is either the core of a peeled star (generally an Helium white dwarf) or a very light almos exhausted star, orbiting a very fast rotating Neutron Star (a MSP). Despite the large difference in total mass between the disk of the Galaxy and the Galactic GC system (up a factor 103), the percentage of fast rotating pulsar in binary systems found in the latter is very higher. MSPs in GCs show spin periods in the range 1.3 ÷ 30ms, slowdown rates ˙P 1019 s/s and a lower magnetic field, respect to ”normal” radio pulsars, B 108 gauss . The high probability of disruption of a binary systems after a supernova explosion, explain why we expect only a low percentage of recycled millisecond pulsars respect to the whole pulsar population. In fact only the 10% of the known 1800 radio pulsars are radio MSPs. Is not surprising, that MSP are overabundant in GCs respect to Galactic field, since in the Galactic Disk, MSPs can only form through the evolution of primordial binaries, and only if the binary survives to the supernova explosion which lead to the neutron star formation. On the other hand, the extremely high stellar density in the core of GCs, relative to most of the rest of the Galaxy, favors the formation of several different binary systems, suitable for the recycling of NSs (Davies at al. 1998). In this thesis we will present the properties two millisecond pulsars companions discovered in two globular clusters, the Helium white dwarf orbiting the MSP PSR 1911-5958A in NGC 6752 and the second case of a tidally deformed star orbiting an eclipsing millisecond pulsar, PSR J1701-3006B in NGC6266

Data driven regularization models of non-linear ill-posed inverse problems in imaging

Imaging technologies are widely used in application fields such as natural sciences, engineering, medicine, and life sciences. A broad class of imaging problems reduces to solve ill-posed inverse problems (IPs). Traditional strategies to solve these ill-posed IPs rely on variational regularization methods, which are based on minimization of suitable energies, and make use of knowledge about the image formation model (forward operator) and prior knowledge on the solution, but lack in incorporating knowledge directly from data. On the other hand, the more recent learned approaches can easily learn the intricate statistics of images depending on a large set of data, but do not have a systematic method for incorporating prior knowledge about the image formation model. The main purpose of this thesis is to discuss data-driven image reconstruction methods which combine the benefits of these two different reconstruction strategies for the solution of highly nonlinear ill-posed inverse problems. Mathematical formulation and numerical approaches for image IPs, including linear as well as strongly nonlinear problems are described. More specifically we address the Electrical impedance Tomography (EIT) reconstruction problem by unrolling the regularized Gauss-Newton method and integrating the regularization learned by a data-adaptive neural network. Furthermore we investigate the solution of non-linear ill-posed IPs introducing a deep-PnP framework that integrates the graph convolutional denoiser into the proximal Gauss-Newton method with a practical application to the EIT, a recently introduced promising imaging technique. Efficient algorithms are then applied to the solution of the limited electrods problem in EIT, combining compressive sensing techniques and deep learning strategies. Finally, a transformer-based neural network architecture is adapted to restore the noisy solution of the Computed Tomography problem recovered using the filtered back-projection method.