Galactic fountain flows in high-resolution hydrodynamical simulations of galaxy disks

Feedback from the most massive components of a young stellar cluster deeply affects the surrounding ISM driving an expanding over-pressured hot gas cavity in it. In spiral galaxies these structures may have sufficient energy to break the disk and eject large amount of material into the halo. The cycling of this gas, which eventually will fall back onto the disk, is known as galactic fountains. We aim at better understanding the dynamics of such fountain flow in a Galactic context, frame the problem in a more dynamic environment possibly learning about its connection and regulation to the local driving mechanism and understand its role as a metal diffusion channel. The interaction of the fountain with a hot corona is hereby analyzed, trying to understand the properties and evolution of the extraplanar material. We perform high resolution hydrodynamical simulations with the moving-mesh code AREPO to model the multi-phase ISM of a Milky Way type galaxy. A non-equilibrium chemical network is included to self consistently follow the evolution of the main coolants of the ISM. Spiral arm perturbations in the potential are considered so that large molecular gas structures are able to dynamically form here, self shielded from the interstellar radiation field. We model the effect of SN feedback from a new-born stellar cluster inside such a giant molecular cloud, as the driving force of the fountain. Passive Lagrangian tracer particles are used in conjunction to the SN energy deposition to model and study diffusion of freshly synthesized metals. We find that both interactions with hot coronal gas and local ISM properties and motions are equally important in shaping the fountain. We notice a bimodal morphology where most of the ejected gas is in a cold $10^4$ K clumpy state while the majority of the affected volume is occupied by a hot diffuse medium. While only about 20\% of the produced metals stay local, most of them quickly diffuse through this hot regime to great scales.

High resolution study of narrow tailed radio galaxies in clusters

Clusters of galaxies are the most massive and large gravitationally bounded systems in the whole Universe. Their study is of fundamental importance to constrain cosmological parameters and to obtain informations regarding various kind of emission in different wavebands. In particular, in the radio domain, beside the diffuse emission, the study is focused on the radio galaxies emission. Radio galaxies in clusters can have peculiar morphology, since they interact with the intracluster medium (ICM) in which they are embedded. Particularly, in this thesis we focused our attention on the so-called Narrow-Angle Tailed radio galaxies (NAT), which present radio jets that are bent at extreme angle, up to 90 degrees, from their original orientation. Some NAT show a narrow extended structure and the two radio tails are not resolved even with high resolution radio observations. An example is provided by the source IC310, in the Perseus Cluster, whose structure has been recently interpreted as due to Doppler boosting effects of a relativistic jet oriented at a small angle with respect to the line of sight. If the structure is due to relativistic effects, this implies that the jets are relativistic at about 400 kpc from the core, but this is in contrast with unified models, which predict that for low-power radio source (NAT are classified as FRI radio galaxies) the jets decelerate to sub-relativistic speed within a few kpc from the core. To investigate this scientific topic, in this thesis we have analyzed the innermost structure of a sample of eleven radio galaxies showing a very narrow NAT structure. We can conclude that the structure of these radio galaxies is different from that of IC310. These radio galaxies are indeed strongly influenced by environmental effects and are similar to classical NAT sources.

Analysis of the high-resolution ro-vibrational spectrum of DC3N in the far and mid infrared regions

Cyanoacetylene HC3N is a molecule of great astronomical importance and it has been observed in many interstellar environments. Its deuterated form DC3N has been detected in number of sources from external galaxies to Galactic interstellar clouds, star-forming regions and planetary atmospheres. All these detections relied on previous laboratory investigations, which however still lack some essential information concerning its infrared spectrum. In this project, high-resolution ro-vibrational spectra of DC3N have been recorded in two energy regions: 150 – 450 cm-1 and 1800 – 2800 cm-1. In the first window the ν7← GS, 2ν7 ← ν7, ν5 ← ν7, ν5+ν7 ← 2ν7, ν6+ν7 → 2v7, 4ν7 ← 2ν7 bands have been assigned, while in the second region the three stretching fundamental bands ν1, ν2, ν3 have been observed and analysed. The 150 – 450 cm-1 region spectra have been recorded at the AILES beamline at the SOLEIL synchrotron (France), the 1800 – 2800 cm-1 spectra at the Department of Industrial Chemistry “Toso Montanari” in Bologna. In total, 2299 transitions have been assigned. Such experimental transition, together with data previously recorded for DC3N, were included in a least-squares fitting procedure from which several spectroscopic parameters have been determined with high precision and accuracy. They include rotational, vibrational and resonance constants. The spectroscopic data of DC3N have been included in a line catalog for this molecule in order to assist future astronomical observations and data interpretation. A paper which includes this research work has been published (M. Melosso, L. Bizzocchi, A. Adamczyk, E. Cane, P. Caselli, L. Colzid, L. Dorea, B. M. Giulianob, J.-C. Guillemine, M-A. Martin-Drumel, O. Piralif, A. Pietropolli Charmet , D. Prudenzano, V. M. Rivillad, F. Tamassia, Extensive ro-vibrational analysis of deuterated-cyanoacetylene (DC3N) from millimeter wavelengths to the infrared domain, Jour. of Quant. Spectr. and Rad. Tran. 254, 107221, 2020).

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.