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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Studies of the orbital background noise and the detector characteristics for the MeVCube mission

Athanasiou, Eleni January 2019 (has links)
A space camera is a promising candidate to address the non-stop rising interest for astrophysics research in the Compton regime. The MeVCube mission is intended to be launched in 2022, hosting an on-board Compton Camera. To better support the development of the instrument in this early stage, a series of feasibility studies to assess two potential launch orbits were performed. The studies were composed by a series of mission analysis simulations which permitted the characterisation of the orbital environments for the two orbital options. Several sources of background noise to the instrument were identified. The population of trapped protons and trapped electrons were simulated for the periods of Solar Minimum and Solar Maximum, as well as the levels of Galactic Cosmic Ray (GCR) flux. The performance of trade-off studies concluded that an equatorial orbit is more preferable for reducing the influence of background noise. To better estimate the environment effects at the equatorial orbit, the number of particles which can penetrate the detector shielding were simulated. The next step was to perform a series secondary studies whose aim were to simulate the induced current on the electrodes, produced by the interactions occurring within the detector. The actualisation of these simulations required the study of photon interaction with matter, the various Cadmium-Zink-Telluride (CZT) types and the how they operate, and the use of a sophisticated software to perform the appropriate simulations. COMSOL, which allows the method of FEA, was chosen as the tool to perform the simulations. The geometry of the detector voxel was primarily designed in SIEMENS NX. The geometry was inserted into COMSOL, where a number of iterations were performed to finalise the appropriate mesh size, which ensured an accurate representation of the Electric field and the Weighting potential within the detector voxel. The induced current on the electrodes was decided to be calculated via MATLAB. As a verification step it was thought useful to firstly plot the weighting potential of the three electrodes under test; the chosen anode pixel, the steering grid and the cathode. The process revealed a series of numerical errors, most likely introduced by the type of mesh chosen or by the data manipulation process via MATLAB. Significant reduction of the numerical errors would lead to more accurate values for the induced current. Unfortunately, due to time constraints this was a task that was not completed. Solving this problem would be optimal for future studies with MATLAB, as the induced current on the electrodes can be correctly calculated based on charge transport within the detector bulk. / MeVCube, DESY
2

A Machine Learning Model of Perturb-Seq Data for use in Space Flight Gene Expression Profile Analysis

Liam Fitzpatric Johnson (18437556) 27 April 2024 (has links)
<p dir="ltr">The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects but does not necessarily indicate the initial point of interference within a network. The objective of this project is to take advantage of large scale and genome-wide perturbational or Perturb-Seq datasets by using them to pre-train a generalist machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of single cell RNA sequencing data collected from CRISPR knock out screens in cell culture. The advent of generative machine learning algorithms, particularly transformers, make it an ideal time to re-assess large scale data libraries in order to grasp cell and even organism-wide genomic expression motifs. By tailoring an algorithm to learn the downstream effects of the genetic perturbations, we present a pre-trained generalist model capable of predicting the effects of multiple perturbations in combination, locating points of origin for perturbation in new datasets, predicting the effects of known perturbations in new datasets, and annotation of large-scale network motifs. We demonstrate the utility of this model by identifying key perturbational signatures in RNA sequencing data from spaceflown biological samples from the NASA Open Science Data Repository.</p>

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