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!

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.

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.

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.

Paleo-Detectors for Galactic Supernova Neutrinos [arxiv:1906.05800]

A Unique Multi-Messenger Signal of QCD Axion Dark Matter [arxiv:1905.04686]

Digging for Dark Matter: Spectral Analysis and Discovery Potential of Paleo-Detectors [arxiv:1811.10549]

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]

A Unique Multi-Messenger Signal of QCD Axion Dark Matter [arxiv:1905.04686]

Digging for Dark Matter: Spectral Analysis and Discovery Potential of Paleo-Detectors [arxiv:1811.10549]

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]

Rencontes du Vietnam, August, 2019

Title: Paleo-detectors: Searching for Dark Matter and Galactic Supernova NeutrinosRencontes du Vietnam, August, 2019

Title: A Unique Multi-Messenger Signal of QCD Axion Dark MatterWorkshop on core-collapse supernova explosions and related physics, August, 2019

Title: Paleo Detectors for Supernova NeutrinosOslo Theory Seminar, May, 2019

Title: Novel Approaches in the Search for Dark MatterTAUP 2019, September, 2019

Title: A Unique Multi-Messenger Signal of QCD Axion Dark MatterTAUP 2019, September, 2019

Title: Pathways to Discovering Supernova NeutrinosInvisibles 2018, September, 2018

Title: Dark Matter Model or Mass: Benchmark-Free Forecasting for Future DetectorsTeVPA, August, 2018

Title: Bayesian Model Comparison and Analysis of the Galactic Disk Population of Gamma-Ray Millisecond PulsarsIdentification of Dark Matter, July, 2018

Title: Dark Matter Model or Mass: Benchmark-Free Forecasting for Future DetectorsDark Side of the Universe, June, 2018

Title: Signal Diversity in Dark Matter SearchesPreparing for Dark Matter Particle Discovery, June, 2018

Title: Mass or Model: Signal Diversity in Dark Matter SearchesStatistical Challenges in the Search for Dark Matter, Feburary, 2018

Title: Fast Forecasting for Counting ExperimentsAccelerating the Search for Dark Matter with Mahine Learning, January, 2018

Title: Fast Forecasting for Counting ExperimentsDANuCo, August, 2017

Title: Dark Information: Forecasting with the Fisher MatrixTeVPA Particle Astrophysics, September, 2016

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

Invisibles 2018, September, 2018

Title: Dark Matter Model or Mass: Benchmark-Free Forecasting for Future DetectorsIn 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. The Euclideanized signals method method is now also implemented with a number of helper functions to analyse the output. In particular an easy interface with standard python clustering algorithms as well as volume calculators, as described in 1805.04117 Swordfish has now been applied to a number of analyses, with many more to come. An example can be found here:

Here we gather all the required information to calculate signals for paleo-dectectors, as described in 1811.10549. Paleo-detectors act as long-lived direct detection (DD) experiments. Unlike DD experiments we propose to dig up extremly old minerals from far below the Earth's surface. The signal (and background) in this case comes from nano-metre scale defects in the normally organised mineral structure. Paleo-detectors could represent the most sensitive method to search for weakly interacting massive particles as shown here. The code replies on the fast recoil spectrum calculator I helped to develop with Bradley Kavanagh, WIMpy_NREFT.

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.