Modeling the Formation of Clouds in Brown Dwarf Atmospheres

Because the opacity of clouds in substellar mass object (SMO) atmospheres depends on the composition and distribution of particle sizes within the cloud, a credible cloud model is essential for accurately modeling SMO spectra and colors. We present a one--dimensional model of cloud particle formation and subsequent growth based on a consideration of basic cloud microphysics. We apply this microphysical cloud model to a set of synthetic brown dwarf atmospheres spanning a broad range of surface gravities and effective temperatures (g_surf = 1.78 * 10^3 -- 3 * 10^5 cm/s^2 and T_eff = 600 -- 1600 K) to obtain plausible particle sizes for several abundant species (Fe, Mg2SiO4, and Ca2Al2SiO7). At the base of the clouds, where the particles are largest, the particle sizes thus computed range from ~5 microns to over 300 microns in radius over the full range of atmospheric conditions considered. We show that average particle sizes decrease significantly with increasing brown dwarf surface gravity. We also find that brown dwarfs with higher effective temperatures have characteristically larger cloud particles than those with lower effective temperatures. We therefore conclude that it is unrealistic when modeling SMO spectra to apply a single particle size distribution to the entire class of objects.Comment: 25 pages; 8 figures. We have added considerable detail describing the physics of the cloud model. We have also added discussions of the issues of rainout and the self-consistent coupling of clouds with brown dwarf atmospheric models. We have updated figures 1, 3, and 4 with new vertical axis labels and new particle sizes for forsterite and gehlenite. Accepted to the Astrophysical Journal, Dec. 2, 200

The impact of viscous self-assembled phase formation on the ageing and atmospheric lifetimes of organic aerosol proxies

Atmospheric aerosols are key components of the atmosphere. They nucleate cloud droplets and facilitate the transport of particle-bound components around the atmosphere. This has an impact on the climate and air quality. The phase state of atmospheric aerosols can affect their ability to form cloud droplets and their atmospheric lifetime. Organic compounds are found in aerosol emissions. Some of these compounds, such as fatty acids, are surface active and can affect the cloud formation potential of an aerosol particle. Specifically, oleic acid is an unsaturated fatty acid found as a major component of cooking emissions. Recently, a study on an oleic acid aerosol proxy has shown that the formation of viscous 3-D self-assembled nanostructures is possible, affecting oleic acid reactivity. This thesis takes this novel concept of nanostructure formation in atmospheric aerosol proxies and aims to explore what the potential atmospheric impact could be. This was done by applying X-ray scattering techniques to levitated droplets and surface coatings of the fatty acid aerosol proxy. Development of a method for determining reaction kinetics from these measurements is presented along with a first quantification of the effect of self-assembly on reaction kinetics, later being extended to different nanostructures. Experiments on reactivity and water uptake described here probe the proxy from nanometre-scale films to micrometre-scale droplets and bulk mixtures, demonstrating the versatility of the range of experimental techniques used to probe the proxy. The atmospheric implications of nanostructure formation are discussed via numerical modelling and observations from experiments on these proxies, highlighting the potential impact on aerosol atmospheric lifetime and implications for the atmosphere

A theory of representation learning in deep neural networks gives a deep generalisation of kernel methods

The successes of modern deep neural networks (DNNs) are founded on their ability to transform inputs across multiple layers to build good high-level representations. It is therefore critical to understand this process of representation learning. However, we cannot use standard theoretical approaches involving infinite width limits, as they eliminate representation learning. We therefore develop a new infinite width limit, the representation learning limit, that exhibits representation learning mirroring that in finite-width networks, yet at the same time, remains extremely tractable. For instance, the representation learning limit gives exactly multivariate Gaussian posteriors in deep Gaussian processes with a wide range of kernels, including all isotropic (distance-dependent) kernels. We derive an elegant objective that describes how each network layer learns representations that interpolate between input and output. Finally, we use this limit and objective to develop a flexible, deep generalisation of kernel methods, that we call deep kernel machines (DKMs). We show that DKMs can be scaled to large datasets using methods inspired by inducing point methods from the Gaussian process literature, and we show that DKMs exhibit superior performance to other kernel-based approaches

Austro-German Revivals: (Re)constructing Acoustic Recordings

This album was recorded during the final year of Inja Stanović’s Leverhulme-funded research project (Re)constructing Early Recordings: a guide for historically-informed performance. As the title suggests, this project focused upon the technologies used to produce early recordings and, more specifically, the ways in which those recordings reveal performance practices of the past. The project was highly practical: a series of performing musicians were invited to produce brand new recordings, albeit using early recording technologies, period instruments, and historically-informed performance practices. The aim was to reconstruct and study the circumstances in which historic recordings were originally produced, in order that we might better understand what they reliably preserve of past performing musicians that are, nowadays, a rich source of inspiration for musicians of the present. This is a rather unusual research project in which we made a series of recordings using wax discs (modelled on what recording artists did around 1910 - 1920). My part in the research project was as recording engineer and audio post-production specialist. The album (as per all recorded albums throughout history) has the musicians/performers as central figures, but as a research document (which this very much is) my role was crucial in terms of finding an appropriate context and method in which these recordings could be presented to a public audience

The Deuterium-Burning Mass Limit for Brown Dwarfs and Giant Planets

There is no universally acknowledged criterion to distinguish brown dwarfs from planets. Numerous studies have used or suggested a definition based on an object's mass, taking the ~13-Jupiter mass (M_J) limit for the ignition of deuterium. Here, we investigate various deuterium-burning masses for a range of models. We find that, while 13 M_J is generally a reasonable rule of thumb, the deuterium fusion mass depends on the helium abundance, the initial deuterium abundance, the metallicity of the model, and on what fraction of an object's initial deuterium abundance must combust in order for the object to qualify as having burned deuterium. Even though, for most proto-brown dwarf conditions, 50% of the initial deuterium will burn if the object's mass is ~(13.0 +/- 0.8)M_J, the full range of possibilities is significantly broader. For models ranging from zero-metallicity to more than three times solar metallicity, the deuterium burning mass ranges from ~11.0 M_J (for 3-times solar metallicity, 10% of initial deuterium burned) to ~16.3 M_J (for zero metallicity, 90% of initial deuterium burned).Comment: "Models" section expanded, references added, accepted by Ap