Galaxy formation and evolution

Abstract:

Born in rapidly evolving mini-halos during the first billion years of the Universe, super- massive black holes (SMBH) feed from gas flows spanning many orders of magnitude, from the cosmic web in which they are embedded to their event horizon. As such, accretion onto SMBHs constitutes a formidable challenge to tackle numerically, and currently requires the use of sub-grid models to handle the flow on small, unresolved scales. In this paper, we study the impact of resolution on the accretion pattern of SMBHs initially inserted at the heart of dense galactic gas clouds, using a custom super-Lagrangian refinement scheme to resolve the black hole (BH) gravitational zone of influence. We find that once the self-gravitating gas cloud host is sufficiently well re- solved, accretion onto the BH is driven by the cloud internal structure, independently of the BH seed mass, provided dynamical friction is present during the early stages of cloud collapse. For a pristine gas mix of hydrogen and helium, a slim disc develops around the BH on sub-parsec scales, turning the otherwise chaotic BH accretion duty cycle into an episodic one, with potentially important consequences for BH feedback. In the presence of such a nuclear disc, BH mass growth predominantly occurs when infalling dense clumps trigger disc instabilities, fuelling intense albeit short-lived gas accretion episodes.

Abstract:

The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload onboard the satellite TechDemoSat-1, used to study the radiation environment in Low Earth Orbit (635 km). LUCID operated from 2014 to 2017, collecting over 2.1 million frames of radiation data from its five Timepix detectors on board. LUCID is one of the first uses of the Timepix detector technology in open space, with the data providing useful insight into the performance of this technology in new environments. It provides high-sensitivity imaging measurements of the mixed radiation field, with a wide dynamic range in terms of spectral response, particle type and direction. The data has been analysed using computing resources provided by GridPP, with a new machine learning algorithm that uses the Tensorflow framework. This algorithm provides a new approach to processing Medipix data, using a training set of human labelled tracks, providing greater particle classification accuracy than other algorithms. For managing the LUCID data, we have developed an online platform called Timepix Analysis Platform at School (TAPAS). This provides a swift and simple way for users to analyse data that they collect using Timepix detectors from both LUCID and other experiments. We also present some possible future uses of the LUCID data and Medipix detectors in space.