Thomas Edwards


I am a PhD student at GRAPPA (Gravitation AstroParticle Physics Amsterdam) centre of excellence. My work is focussed on detecting dark matter by any means necessary. Here is my personal webpage!

                                   

About Me


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 direct and indirect detection methods as well theoretical studies.


My Picture

Research


Dark Matter Detection

Dark Matter (DM) constitutes the most abundant form of matter in the late Universe. Importantly, DM has only been observed through its gravitational imprint on the Universe. The particle nature of DM remains unknown although there is a large program of ongoing and upcoming experiments that will hopefully find the first non-graviational signal. I am interested primarily in direct and indirect detection of DM and importantly in the complementarity of the two approaches.

Indirect detection requires the assumptation that DM can annihilate or decay into standard model particles that we can observe in the sky. The difficultly with this appraoch is accounting for the uncertainties associated with both the DM clustering properties and the astrophysical backgrounds. Fortunately a huge variety of precise telescopes have been built which provide a wealth of data to analyse. At the same time our theoretical understanding of galaxy formation and astrophysical processes has deapened, slowly reducing these uncertainties and hopefully leading to a discovery in the near future.

Direct detection methods are in general labratory based experiments in which it is possible to control the backgrounds to an extraordinary precision. Since the Earth is constantly moving through a wind of DM, the hope is that through building bigger and bigger detecotrs we will observe a signature of dark matter interacting with the nucleons of the detector.



Statistical Methods

The lack of a clear signal associated to DM has led many to re assess the likelihood of a discovery in the near future. Assessing this probability is a complicated question which we have been trying to provide the statistical tools to answer these types of questions for a wide variety of experiments and DM models.

It has recently become clear that many experiments will be required in order to learn the true nature of any future signal. I am interested in developing the techniques that allow physicists assess the optimal set of expeirments given a limited amount of funding and only a weak theoretical prior.

Publications


Statistical challenges in the search for dark matter (Editor) [arxiv:1807.09273]

Comment on "Understanding the γ-ray emission from the globular cluster 47 Tuc: evidence for dark matter?" [arxiv:1807.08800]

Bayesian Model Comparison and Analysis of the Galactic Disk Population of Gamma-Ray Millisecond Pulsars [arxiv:1805.11097]

Dark Matter Model or Mass, but Not Both: Assessing Near-Future Direct Searches with Benchmark-free Forecasting [arxiv:1805.04117]

swordfish: Efficient Forecasting of New Physics Searches without Monte Carlo [arxiv:1712.05401]

A Fresh Approach to Forecasting in Astroparticle Physics and Dark Matter Searches [arxiv:1704.05458]

The Volumetric Rate of Superluminous Supernovae at z~1 [arxiv:1605.05250]

Talks


Invisibles 2018, September, 2018

Title: Dark Matter Model or Mass: Benchmark-Free Forecasting for Future Detectors

TeVPA, August, 2018

Title: Bayesian Model Comparison and Analysis of the Galactic Disk Population of Gamma-Ray Millisecond Pulsars

Identification of Dark Matter, July, 2018

Title: Dark Matter Model or Mass: Benchmark-Free Forecasting for Future Detectors

Dark Side of the Universe, June, 2018

Title: Signal Diversity in Dark Matter Searches

Preparing for Dark Matter Particle Discovery, June, 2018

Title: Mass or Model: Signal Diversity in Dark Matter Searches

Statistical Challenges in the Search for Dark Matter, Feburary, 2018

Title: Fast Forecasting for Counting Experiments

Accelerating the Search for Dark Matter with Mahine Learning, January, 2018

Title: Fast Forecasting for Counting Experiments

DANuCo, August, 2017

Title: Dark Information: Forecasting with the Fisher Matrix

TeVPA Particle Astrophysics, September, 2016

Title: Population synthesis of Fermi LAT sources: A Bayesian analysis using posterior predictive distributions

Amsterdam-Paris-Stockholm 6th Meeting, August, 2016

Title: Gamma-ray luminosity function of Millisecond Pulsars and 3FGL population study


21st Symposium on Astroparticle Physics in the Netherlands, April, 2016

Title: Bayesian Analysis of the Gamma-ray luminosity function of Millisecond Pulsars




Posters


Invisibles 2018, September, 2018

Title: Dark Matter Model or Mass: Benchmark-Free Forecasting for Future Detectors

Code


Swordfish

In this coding package we implement some of the routines laid out in 1704.05458 for calculating exclusion limits and the discovery reach in a fast and efficient manner. In addition there are routines to visualise the Fisher matrix by plotting geodesics, streamlines or ellipses based off its major and minor eigenvectors. We are now working on a series of physics examples but the for now the functionality is explained in two jupyter examples.


My Picture My Picture

PSRdist

A useful wrapper for the YMW16 electron density model of the galaxy. Able to calculate the probability for a pulsar to exist at a some distance by performing a Monte-Carlo of the 30 uncertain YMW16 model parameters.

Contact


Feel free to send me an email!

+31-(0)-20-525-7312