My PhD project title is
Observations and Modelling of Intensity Time series for Biomedical
and Astrophysical Applications. This PhD was a joint venture between Queen's University Belfast, and Randox Laboratories
to develop cutting-edge statistical techniques with application in astrophysical and biomedical analysis.
During the PhD I have developed strong data analysis skills,
primarily through the development of algorithms for the large-scale statistical analysis and modelling of below-noise-floor signals,
which were applied to astrophysical and to industrial biomedical datasets. These statistical techniques led to novel discoveries of stellar nanoflares,
which hold the potential to answer key questions about the nature of flaring in stars. Applying these techniques to biomedical data has allowed
for the development of cutting-edge noise suppression and dynamic range software.
I also created bespoke image feature recognition software for industry biomedical use.
This recognition software identifies key features within an image in under a second, and has led to a 98.8% reduction in downstream processing times.
These results highlight the interoperability of astrophysical data analysis skills to industrial applications.
Written and spoken communication skills have been extensively developed, through academic publications,
technical documents, presenting at numerous conferences,
workshops and catchup meetings with industry partners.
The synthesis of industry and academic experience has allowed me to develop excellent communication skills, and the opportunity to collaborate with
biomedical colleagues from a range of different skill-sets and job-roles.
I've developed extensive coding experience, primarily working in IDL and Python.
Role required leading an investigation into a manufacturing defect. Required strong interpersonal skills, in order to interact with team members and deliver the changes required. Emphasis on presenting findings to management, requiring confidence and strong presentation skills. Developed my personal responsibility, as I undertook self directed tasks to solve this manufacturing problem. Gained experience with R, as well as Six-sigma manufacturing philosophy.
Several studies have documented periodic and quasi-periodic signals from the time series of dMe flare stars and other stellar sources. Such periodic signals, observed within quiescent phases (i.e., devoid of larger-scale microflare or flare activity), range in period from 1 − 1000 seconds and hence have been tentatively linked to ubiquitous p-mode oscillations generated in the convective layers of the star. As such, most interpretations for the observed periodicities have been framed in terms of magneto-hydrodynamic wave behavior. However, we propose that a series of continuous nanoflares, based upon a power-law distribution, can provide a similar periodic signal in the associated time series. Adapting previous statistical analyses of solar nanoflare signals, we find the first statistical evidence for stellar nanoflare signals embedded within the noise envelope of M- type stellar lightcurves. Employing data collected by the Next Generation Transit Survey (NGTS), we find evidence for stellar nanoflare activity demonstrating a flaring power-law index of 3.25 ± 0.20, alongside a decay timescale of 200 ± 100 s. We also find that synthetic time series, consistent with the observations of dMe flare star lightcurves, are capable of producing quasi-periodic signals in the same frequency range as p-mode signals, despite being purely comprised of impulsive signatures. Phenomena traditionally considered a consequence of wave behaviour may be described by a number of high frequency but discrete nanoflare energy events. This new physical interpretation presents a novel diagnostic capability, by linking observed periodic signals to given nanoflare model conditions.
Small-scale magnetic reconnection processes, in the form of nanoflares, have become increasingly hypothesized as important mechanisms for the heating of the solar atmosphere, for driving propagating disturbances along magnetic field lines in the Sun’s corona, and for instigating rapid jet-like bursts in the chromosphere. Unfortunately, the relatively weak signatures associated with nanoflares places them below the sensitivities of current observational instrumentation. Here, we employ Monte Carlo techniques to synthesize realistic nanoflare intensity time series from a dense grid of power-law indices and decay timescales. Employing statistical techniques, which examine the modeled intensity fluctuations with more than 107 discrete measurements, we show how it is possible to extract and quantify nanoflare characteristics throughout the solar atmosphere, even in the presence of significant photon noise. A comparison between the statistical parameters (derived through examination of the associated intensity fluctuation histograms) extracted from the Monte Carlo simulations and SDO/AIA 171 Å and 94 Å observations of active region NOAA 11366 reveals evidence for a flaring power-law index within the range of 1.82 ≤ α ≤ 1.90, combined with e-folding timescales of 385 ± 26 s and 262 ± 17 s for the SDO/AIA 171 Å and 94 Å channels, respectively. These results suggest that nanoflare activity is not the dominant heating source for the active region under investigation. This opens the door for future dedicated observational campaigns to not only unequivocally search for the presence of small-scale reconnection in solar and stellar environments, but also quantify key characteristics related to such nanoflare activity.
I attained a First class Honor in my degree. Studying physics has taught me how to work methodically, toward any goal, whether it be writing a lab report or studying for an exam. This methodical problem solving ability combined with rigorous discipline allowed me to excel in this degree, and those same skills transfer to other areas. Naturally, there was a strong development of my numeracy and IT skills. There was also a significant emphasis on ‘soft skills’ of presentation and collaboration, teaching me to how work cohesively within a team, under management and how to direct teams.
GCSE - 5A*’s and 6A’s, including A* in English Language and Maths
A Level - Chemistry -A English Literature -A Maths -A Physics -A
I was awarded the
John McDaid Memorial Prize in 2013, which is awarded each year to a student who has made an
Outstanding Contribution to College Life.