Argon Purification Studies and a Novel Liquid Argon Re-circulation System

Future giant liquid argon (LAr) time projection chambers (TPCs) require a purity of better than 0.1 parts per billion (ppb) to allow the ionised electrons to drift without significant capture by any electronegative impurities. We present a comprehensive study of the effects of electronegative impurity on gaseous and liquid argon scintillation light, an analysis of the efficacy of various purification chemicals, as well as the Liverpool LAr setup, which utilises a novel re-circulation purification system. Of the impurities tested - Air, O_2, H_2O, N_2 and CO_2 in the range of between 0.01 ppm to 1000 ppm - H_2O was found to have the most profound effect on gaseous argon scintillation light, and N_2 was found to have the least. Additionally, a correlation between the slow component decay time and the total energy deposited with 0.01 ppm - 100 ppm O_2 contamination levels in liquid argon has been established. The superiority of molecular sieves over anhydrous complexes at absorbing Ar gas, N_2 gas and H_2O vapour has been quantified using BET isotherm analysis. The efficiency of Cu and P_2O5 at removing O_2 and H_2O impurities from 1 bar N6 argon gas at both room temperature and -130 ^oC was investigated and found to be high. A novel, highly scalable LAr re-circulation system has been developed. The complete system, consisting of a motorised bellows pump operating in liquid and a purification cartridge, were designed and built in-house. The system was operated successfully over many days and achieved a re-circulation rate of 27 litres/hour and high purity

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration