Dr Peter Hatfield

Abstract:

Hydrodynamical cosmological simulations have recently made great advances in reproducing galaxy mass assembly over cosmic time - as often quantified from the comparison of their predicted stellar mass functions to observed stellar mass functions from data. In this paper we compare the clustering of galaxies from the hydrodynamical cosmological simulated lightcone Horizon-AGN, to clustering measurements from the VIDEO survey observations. Using mocks built from a VIDEO-like photometry, we first explore the bias introduced into clustering measurements by using stellar masses and redshifts derived from SED-fitting, rather than the intrinsic values. The propagation of redshift and mass statistical and systematic uncertainties in the clustering measurements causes us to underestimate the clustering amplitude. We find then that clustering and halo occupation distribution (HOD) modelling results are qualitatively similar in Horizon-AGN and VIDEO. However at low stellar masses Horizon-AGN underestimates the observed clustering by up to a factor of ~3, reflecting the known excess stellar mass to halo mass ratio for Horizon-AGN low mass haloes, already discussed in previous works. This reinforces the need for stronger regulation of star formation in low mass haloes in the simulation. Finally, the comparison of the stellar mass to halo mass ratio in the simulated catalogue, inferred from angular clustering, to that directly measured from the simulation, validates HOD modelling of clustering as a probe of the galaxy-halo connection.

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.

Abstract:

Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

Abstract:

There is a wide-ranging debate about the merits and demerits of searching for, and sending messages to, extraterrestrial intelligences (SETI and METI). There is however reasonable (but not universal) consensus that replying to a message from an extraterrestrial intelligence should not be done unilaterally, without consultation with wider society and the rest of the world. But how should this consultation actually work? In this paper we discuss various ways that decision making in such a scenario could be done democratically, and gain legitimacy. In particular we consider a scientist-led response, a politician-led response, deciding a response using a referendum, and finally using citizens’ assemblies. We present the results of a survey of a representative survey of 2000 people in the UK on how they thought a response should best be determined, and finally discuss parallels to how the public is responding to scientific expertise in the COVID-19 Pandemic.