I work in theoretical astroparticle physics as part of the Institute of Physics at the University of Amsterdam, GRAPPA. Originally from south-east London I moved to Southampton in 2010 to complete my undergraduate degree in Physics with Astronomy after which I went to Cambridge to complete Part III of the mathematical tripos. I am now interested in probing the nature of dark matter (DM) through indirect detection methods as well theoretical studies.
The search for DM has led to many large experiments generating huge amounts of data in the hope of detecting a signal. I am interested in putting this information into a statistically rigorous framework allowing one to make accurate inferences from the data. Combining this with theoretical studies of the potential models will hopefully provide provide a window into this mysterious part of the Universe.
DM is thought to only weakly interact with the Standard Model of particles but it may be able to self-annihilate and in the process produce electromagnetic waves which we can observe in the sky. In order to observe such a signal it is important to understand alternative processes that could lead something similar. In the case of indirect detection this means trying to accurately model all the astrophysical sources and disentangle the vast array of objects we find in our Universe.
Recently there has been a debate regarding the nature of an excess of gamma ray emission, consistent with a DM signal, from the centre of our galaxy observed by the Fermi LAT. It is also possible that a population of unresolved millisecond pulsars could produce a similar signal and much work is still needed to sway the debate one way or another
Understanding DM distribution throughout the universe is a complex problem, leading researchers to perform large scale simulations of patches of the Universe. These simulations have become increasingly complex over the past several years, now attempting to correctly model galactic astrophysics such as supernova feedback to resolve DM distributions in the highly non-linear regime of gravitational interactions. Analysing the output of these simulations will help to provide an insight into potential observational targets in the hope of finding DM signals.
In between what we observe and our theoretical models sits statistics. Statistics allows us to quantify the amount of information we can infer about the parameters in our physical models. Since there has been no verified detection of dark matter to date, the name of the game is to set limits on the most theoretically motivated models. Obviously we hope to eventually find dark matter but in the mean time statistics can help to find the best methods to probe parts of the parameter space previously unknown.
Machine learning has in recent years become a central part of modern physics. Due to the complexity of our detectors and theoretical models, computationally expensive calculations are now the norm. The techniques developed in the field of machine learning allow physicists to perform the necessary complex calculations when a super-computer is not enough.
Information geometry is a field of mathematics that uses the techniques of differential geometry and applies them to probability distributions. It is an extremely promising avenue to apply similar techniques to physical problems to reveal optimal experimental design and data collection procedures.
1. 21st Symposium on Astroparticle Physics in the Netherlands, April, 2016
Title: Bayesian Analysis of the Gamma-ray luminosity function of Millisecond Pulsars
2. Amsterdam-Paris-Stockholm 6th Meeting, August, 2016
Title: Gamma-ray luminosity function of Millisecond Pulsars and 3FGL population study
3. TeVPA Particle Astrophysics, September, 2016Title: Population synthesis of Fermi LAT sources: A Bayesian analysis using posterior predictive distributions
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