Fundamental properties of the dark and the luminous matter from Low Surface Brightness discs

Dark matter (DM) is one of the biggest mystery in the Universe. After a brief discussion of the past DM evidences and the main proposed candidates and scenarios for the DM phenomenon, I will focus on rotating disc galaxies giving a special attention to the Low Surface Brightness (LSB) galaxies. The main observational properties related to the baryonic matter in LSBs, analysed over the last decades, will be briefly recalled. Next, I will show the main results that I obtained by means of the mass modelling of the LSBs rotation curves (URC analysis). Finally, the results will be compared to those of different kinds of galaxies and contextualised in the large DM phenomenon

Thirteenth International Laser Radar Conference

One hundred fifteen papers were presented in both oral and poster sessions. The topics of the conference sessions were: spaceborne lidar applications; extinction/visibility; differential absorption lidar; winds and tropospheric studies; middle atmosphere; clouds and multiple scattering; pollution studies; and new systems

Multimodal Navigation for Accurate Space Rendezvous Missions

© Cranfield University 2021. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerRelative navigation is paramount in space missions that involve rendezvousing between two spacecraft. It demands accurate and continuous estimation of the six degree-of-freedom relative pose, as this stage involves close-proximity-fast-reaction operations that can last up to five orbits. This has been routinely achieved thanks to active sensors such as lidar, but their large size, cost, power and limited operational range remain a stumbling block for en masse on-board integration. With the onset of faster processing units, lighter and cheaper passive optical sensors are emerging as the suitable alternative for autonomous rendezvous in combination with computer vision algorithms. Current vision-based solutions, however, are limited by adverse illumination conditions such as solar glare, shadowing, and eclipse. These effects are exacerbated when the target does not hold cooperative markers to accommodate the estimation process and is incapable of controlling its rotational state. This thesis explores novel model-based methods that exploit sequences of monoc ular images acquired by an on-board camera to accurately carry out spacecraft relative pose estimation for non-cooperative close-range rendezvous with a known artificial target. The proposed solutions tackle the current challenges of imaging in the visible spectrum and investigate the contribution of the long wavelength infrared (or “thermal”) band towards a combined multimodal approach. As part of the research, a visible-thermal synthetic dataset of a rendezvous approach with the defunct satellite Envisat is generated from the ground up using a realistic orbital camera simulator. From the rendered trajectories, the performance of several state-of-the-art feature detectors and descriptors is first evaluated for both modalities in a tailored scenario for short and wide baseline image processing transforms. Multiple combinations, including the pairing of algorithms with their non-native counterparts, are tested. Computational runtimes are assessed in an embedded hardware board. From the insight gained, a method to estimate the pose on the visible band is derived from minimising geometric constraints between online local point and edge contour features matched to keyframes generated offline from a 3D model of the target. The combination of both feature types is demonstrated to achieve a pose solution for a tumbling target using a sparse set of training images, bypassing the need for hardware-accelerated real-time renderings of the model. The proposed algorithm is then augmented with an extended Kalman filter which processes each feature-induced minimisation output as individual pseudo measurements, fusing them to estimate the relative pose and velocity states at each time-step. Both the minimisation and filtering are established using Lie group formalisms, allowing for the covariance of the solution computed by the former to be automatically incorporated as measurement noise in the latter, providing an automatic weighing of each feature type directly related to the quality of the matches. The predicted states are then used to search for new feature matches in the subsequent time-step. Furthermore, a method to derive a coarse viewpoint estimate to initialise the nominal algorithm is developed based on probabilistic modelling of the target’s shape. The robustness of the complete approach is demonstrated for several synthetic and laboratory test cases involving two types of target undergoing extreme illumination conditions. Lastly, an innovative deep learning-based framework is developed by processing the features extracted by a convolutional front-end with long short-term memory cells, thus proposing the first deep recurrent convolutional neural network for spacecraft pose estimation. The framework is used to compare the performance achieved by visible-only and multimodal input sequences, where the addition of the thermal band is shown to greatly improve the performance during sunlit sequences. Potential limitations of this modality are also identified, such as when the target’s thermal signature is comparable to Earth’s during eclipse.PH

My Story. Digital Storytelling across Europe for Social Cohesion

‘My Story’ (Mysty) is a pan-European, Erasmus+ funded Digital Storytelling project focused on intercultural competency. It has eight partners (HE, secondary schools and NGOs) across four countries (Austria, Italy, Hungary and the UK) and involves the collection, editing and uploading of digital stories to a shared ‘toolbox’. These stories focus on ‘food’, ‘family’ and ‘festival’ and act as a platform for diversity awareness and digital upskilling. The project is driven by the principle that innovative teaching resources form part of broader pedagogic strategies that can actively help tackle issues of diversity common across the EU. The paper discusses the process the project went through, some of its challenges and its results and, on the basis of these, looks at the role digital storytelling as a way of expressing different ethical, cultural or personal issues

Object narratives, imaginings and multilingual communities: young people’s digital stories in the making

This paper draws on research from a global 5-year project, Critical Connections: Multilingual Digital Storytelling (2012-2017), which links language and intercultural learning with literacy, active citizenship and the arts. A critical ethnographic approach was adopted in the research project and the multilingual digital stories were an integral part of the research process. With the project’s focus on multilingualism and creation of bilingual digital texts, young people had to imagine how to use language in new contexts, uncover narratives around objects, and negotiate interfaces between different cultural landscapes. The research findings revealed the complexity of multilingual digital storytelling and how young people (aged 6-18 years old) learnt to become meaning makers discovering their own voices in unfamiliar contexts. Through these digital stories the young people forged strong links with the past and created new multilingual communities

My Story. Digital Storytelling across Europe for Social Cohesion

‘My Story’ (Mysty) is a pan-European, Erasmus+ funded Digital Storytelling project focused on intercultural competency. It has eight partners (HE, secondary schools and NGOs) across four countries (Austria, Italy, Hungary and the UK) and involves the collection, editing and uploading of digital stories to a shared ‘toolbox’. These stories focus on ‘food’, ‘family’ and ‘festival’ and act as a platform for diversity awareness and digital upskilling. The project is driven by the principle that innovative teaching resources form part of broader pedagogic strategies that can actively help tackle issues of diversity common across the EU. The paper discusses the process the project went through, some of its challenges and its results and, on the basis of these, looks at the role digital storytelling as a way of expressing different ethical, cultural or personal issues

Cool Stars and Contingencies: Stellar Characterisation in the Solar Neighbourhood

The coming decades will see stellar parameters like temperature, radius, and metallicity produced for many millions of new stars as part of both current and upcoming massive stellar surveys. However, for any new insights into stellar or Galactic physics to reach their full potential, these parameters need to be calibrated by a library of benchmark stars whose properties are as fundamental and model independent as possible. Thus was the motivation for my PhD work as presented here: to extend the currently available library of stellar standards, and to use this library as part of the calibration strategy for upcoming stellar spectroscopic surveys. For stellar temperature and radius, there is nothing more accurate or precise than when these properties are calculated from the angular diameters measured by long-baseline optical interferometry. Using VLTI/PIONIER, I inducted a new set of 10 stars into the ranks of stellar benchmarks, and confirmed another six with higher precision than ever before. These results, precise to the 1% level, will serve as critical benchmarks for all current and future surveys in Galactic archaeology. One such survey was the planned FunnelWeb Survey. A low resolution spectroscopic survey of two million of the brightest stars in the southern hemisphere, FunnelWeb was built around the novel TAIPAN instrument and its ability to rapidly reconfigure its 150 optical fibres in parallel. This high-multiplex capability and broad range in targeted magnitudes combined to provide unique challenges in optimising survey efficiency and yield. Here I present a sky tiling algorithm for this class of survey, including a six step priority scale and overlapping magnitude bins, able to efficiently allocate observing fields to a high level of survey completeness. A more traditional survey involves observing stars one at a time using a high efficiency spectrograph with greater resolving power. WiFeS on the ANU 2.3m Telescope is one such instrument, and is well suited to a survey of exoplanet host stars identified by NASA's TESS Mission. The result was the spectroscopic characterisation of 92 cool dwarfs and transit light curve modelling of 100 planet candidates. Given known complexities modelling cool dwarf atmospheres, I quantified model deficiencies at predicting optical fluxes, and developed an empirical photometric relation to determine cool dwarf metallicity independently of spectroscopy. This large and uniform sample will prove instrumental to future demographic studies of planets around cool dwarfs - a historically small, but now rapidly growing sample thanks to TESS. My PhD research has provided new insight into the stars and planets of the Solar Neighbourhood and improved our ability to calibrate broader surveys of our Galaxy as a whole. While I have demonstrated the strength of tried and tested methods, my work has made apparent the need for non-traditional analysis techniques for dealing with challenging regions of the parameter space - techniques that should be well suited to the data-rich environment stellar astronomers will soon find themselves in