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Christopher Harrison

Publications and source records attributed to Christopher Harrison.

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Singing Materials: Initial experiments in applying sonification to phonon spectra

Solid materials may appear static, but at the atomic scale they are in constant vibrational motion. These vibrations, described by phonons, govern many key material properties, including structural stability, mechanical strength, optical behaviour, and thermal transport. Understanding phonon physics is therefore central to the rational design of materials with targeted functionalities. Singing Materials is a research project that explores how sonification can be applied to this domain. In this work, we introduce `SingingMaterials`, a modular Python package for sonifying materials simulation data. The software interfaces with the Materials Project database and is designed to be extensible, enabling the incorporation of additional sonification strategies and data sources. Built using the Sonification Toolkit `STRAUSS`, the current implementation supports three core approaches: spectral, synthesised, and sample-based. We demonstrate these approaches using phonon density-of-states data and evaluate their effectiveness through a user study, investigating whether listeners can distinguish differences in material properties from their auditory representations. The results show that sonification can provide an interpretable and complementary approach for exploring vibrational materials data.

cond-mat.mtrl-sci

KURVS: chemical properties from multiple strong line calibrations for star-forming galaxies at $z\sim1.5$

Gas-phase oxygen abundance (metallicity) properties can be constrained through emission line analyses, and are of great importance to investigate galaxy evolution histories. We present an analysis of the integrated and spatially-resolved rest-frame optical emission line properties of the ionised gas in 43 star-forming galaxies at $z\sim1.5$ in the KMOS Ultra-deep Rotational Velocity Survey (KURVS). Using the [NII]$\lambda6584$/H$\alpha$ (N$_2$), ([OII]$\lambda\lambda3727,9+$[OIII]$\lambda\lambda4959,5007$)/H$\beta$ (R23), and for the first time [NII]$\lambda6584$/[OII]$\lambda\lambda3727,9$ (N$_2$O$_2$) indicators at this redshift, we measure the gas-phase metallicities and their radial gradients. On $\sim4$-kpc scales metallicity gradients measured from N$_2$O$_2$ and those measured from N$_2$ are in good agreement when considering the spatial distributions of dust in each galaxy, as parameterised by dust attenuation radial gradients. We report a nearly flat metallicity gradient distribution typically at $z\sim1.5$, with the 50th, 16th and 84th percentiles at $0.01$, $-0.03$, and $0.05$ dex kpc$^{-1}$, respectively. The findings agree well with previous observational studies and simulations at this epoch. We ascribe the observed negative metallicity gradients to a natural result from self-regulating systems, and the positive ones to potential galactic fountains and higher merger rates.

astro-ph.GA

Bioinformatics Computational Cluster Batch Task Profiling with Machine Learning for Failure Prediction

Motivation: Traditional computational cluster schedulers are based on user inputs and run time needs request for memory and CPU, not IO. Heavily IO bound task run times, like ones seen in many big data and bioinformatics problems, are dependent on the IO subsystems scheduling and are problematic for cluster resource scheduling. The problematic rescheduling of IO intensive and errant tasks is a lost resource. Understanding the conditions in both successful and failed tasks and differentiating them could provide knowledge to enhancing cluster scheduling and intelligent resource optimization. Results: We analyze a production computational cluster contributing 6.7 thousand CPU hours to research over two years. Through this analysis we develop a machine learning task profiling agent for clusters that attempts to predict failures between identically provision requested tasks.

cs.DC