Evolution of the structural and fracture design of the ISS payloads due to the new launchers, specifically applied to Biolab IEC-2 projects

The relatively young discipline of astronautics represents one of the scientifically most fascinating and technologically advanced achievements of our time. The human exploration in space does not offer only extraordinary research possibilities but also demands high requirements from man and technology. The space environment provides a lot of attractive experimental tools towards the understanding of fundamental mechanism in natural sciences. It has been shown that especially reduced gravity and elevated radiation, two distinctive factors in space, influence the behavior of biological systems significantly. For this reason one of the key objectives on board of an earth orbiting laboratory is the research in the field of life sciences, covering the broad range from botany, human physiology and crew health up to biotechnology. The Columbus Module is the only European low gravity platform that allows researchers to perform ambitious experiments in a continuous time frame up to several months. Biolab is part of the initial outfitting of the Columbus Laboratory; it is a multi-user facility supporting research in the field of biology, e.g. effect of microgravity and space radiation on cell cultures, micro-organisms, small plants and small invertebrates. The Biolab IEC are projects designed to work in the automatic part of Biolab. In this moment in the TO-53 department of Airbus Defence & Space (formerly Astrium) there are two experiments that are in phase C/D of the development and they are the subject of this thesis: CELLRAD and CYTOSKELETON. They will be launched in soft configuration, that means packed inside a block of foam that has the task to reduce the launch loads on the payload. Until 10 years ago the payloads which were launched in soft configuration were supposed to be structural safe by themselves and a specific structural analysis could be waived on them; with the opening of the launchers market to private companies (that are not under the direct control of the international space agencies), the requirements on the verifications of payloads are changed and they have become much more conservative. In 2012 a new random environment has been introduced due to the new Space-X launch specification that results to be particularly challenging for the soft launched payloads. The last ESA specification requires to perform structural analysis on the payload for combined loads (random vibration, quasi-steady acceleration and pressure). The aim of this thesis is to create FEM models able to reproduce the launch configuration and to verify that all the margins of safety are positive and to show how they change because of the new Space-X random environment. In case the results are negative, improved design solution are implemented. Based on the FEM result a study of the joins has been carried out and, when needed, a crack growth analysis has been performed.

Machine learning "as a service" for high energy physics (MLaaS4HEP): evolution of a framework for ML-based physics analyses

The scientific success of the LHC experiments at CERN highly depends on the availability of computing resources which efficiently store, process, and analyse the amount of data collected every year. This is ensured by the Worldwide LHC Computing Grid infrastructure that connect computing centres distributed all over the world with high performance network. LHC has an ambitious experimental program for the coming years, which includes large investments and improvements both for the hardware of the detectors and for the software and computing systems, in order to deal with the huge increase in the event rate expected from the High Luminosity LHC (HL-LHC) phase and consequently with the huge amount of data that will be produced. Since few years the role of Artificial Intelligence has become relevant in the High Energy Physics (HEP) world. Machine Learning (ML) and Deep Learning algorithms have been successfully used in many areas of HEP, like online and offline reconstruction programs, detector simulation, object reconstruction, identification, Monte Carlo generation, and surely they will be crucial in the HL-LHC phase. This thesis aims at contributing to a CMS R&D project, regarding a ML "as a Service" solution for HEP needs (MLaaS4HEP). It consists in a data-service able to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources. This framework has been updated adding new features in the data preprocessing phase, allowing more flexibility to the user. Since the MLaaS4HEP framework is experiment agnostic, the ATLAS Higgs Boson ML challenge has been chosen as physics use case, with the aim to test MLaaS4HEP and the contribution done with this work.