Non-thermal phenomena in galaxy clusters: the LOFAR revolution

Turbulence introduced into the intra-cluster medium (ICM) through cluster merger events transfers energy to non-thermal components (relativistic particles and magnetic fields) and can trigger the formation of diffuse synchrotron radio sources. Owing to their steep synchrotron spectral index, such diffuse sources can be better studied at low radio frequencies. In this respect, the LOw Frequency ARray (LOFAR) is revolutionizing our knowledge thanks to its unprecedented resolution and sensitivity below 200 MHz. In this Thesis we focus on the study of radio halos (RHs) by using LOFAR data. In the first part of this work we analyzed the largest-ever sample of galaxy clusters observed at radio frequencies. This includes 309 Planck clusters from the Second Data Release of the LOFAR Two Metre Sky Survey (LoTSS-DR2), which span previously unexplored ranges of mass and redshift. We detected 83 RHs, half of which being new discoveries. In 140 clusters we lack a detected RH; for this sub-sample we developed new techniques to derive upper limits to their radio powers. By comparing detections and upper limits, we carried out the first statistical analysis of populations of clusters observed at low frequencies and tested theoretical formation models. In the second part of this Thesis we focused on ultra-steep spectrum radio halos. These sources are almost undetected at GHz frequencies, but are thought to be common at low frequencies. We presented LOFAR observations of two interesting clusters hosting ultra-steep spectrum radio halos. With complementary radio and X-ray observations we constrained the properties and origin of these targets.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.

Georges Lemaître: a visionary contribute to the quest of the world model

The aim of this dissertation is to present the sequence of events which brought the scientific community of the early 20th century to conceive an expanding Universe born from a single origin. Among the facts here reported, some are well-known, some others instead are little-known backstories, not so easy neither to obtain nor to trust. Indeed, several matters shown in this thesis, now as then, create a battleground among scientists. Amid the numerous personalities whose contributions are discussed in this work, the main protagonist is surely Georges Lemaître, who managed to combine – without overlapping – his being both a priest and a scientist. The first chapter is dedicated to his biography, from his childhood in Belgium, to his early adulthood between England and the USA, to his success in the scientific community. The second and the third chapter explain how the race to the understanding of a Universe which not only expands, but also originated from a singularity, developed. The Belgian priest’s discoveries, as shown, were challenged by other important scientists, who, in several cases, Lemaître had a friendly relationship with. As a consequence, the fourth and final chapter deals with the multiple relations that the priest managed to build, thanks to his politeness and kindness. Moreover, it is also covered Lemaître’s personal connection with the Church and religion, without forgetting the personalities that influenced him – above all, Saint Thomas Aquinas. As a conclusion to this thesis, two appendices gather not only a summary of Lemaître’s works which are not already described in the chapters, but also the biographies of all the characters presented in this dissertation.

Benchmarking ’Expressing Without Asserting’ approaches on a large Wikidata dump

Semantic Web technologies provide the means to express the knowledge in a formal and standardized manner, enabling machines to automatically derive meaning from the data. Often this knowledge is uncertain or different degrees of certainty may be assigned to the same statements. This is the case in many fields of study such as in Digital Humanities, Science and Arts. The challenge relies on the fact that our knowledge about the surrounding world is dynamic and may evolve based on new data coming from the latest discoveries. Furthermore we should be able to express conflicting, debated or disputed statements in an efficient, effective and consistent way without the need of asserting them. We call this approach 'Expressing Without Asserting' (EWA). In this work we identify all existing methods that are compatible with actual Semantic Web standards and enable us to express EWA. In our research we were able to prove that existing reification methods such as Named Graphs, Singleton Properties, Wikidata Statements and RDF-Star are the most suitable methods to represent in a reliable way EWA. Next we compare these methods with our own method, namely Conjectures from a quantitative perspective. Our main objective was to put Conjectures into stress tests leveraging enormous datasets created ad hoc using art-related Wikidata dumps and measure the performance in various triplestores in relation with similar concurrent methods. Our experiments show that Conjectures are a formidable tool to express efficiently and effectively EWA. In some cases, Conjectures outperform state of the art methods such as singleton and Rdf-Star exposing their great potential. Is our firm belief that Conjectures represent a suitable solution to EWA issues. Conjectures in their weak form are fully compatible with Semantic Web standards, especially with RDF and SPARQL. Furthermore Conjectures benefit from comprehensive syntax and intuitive semantics that make them easy to learn and adapt.