Accelerating computations for dark matter direct detection experiments via neural networks and GPUs
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Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Physics (MPPHS), MSc
Publicerad
2023
Författare
OLVHAMMAR, HANNA
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
There is indisputable evidence for the existence of dark matter (DM). Examples are
the rotation curves of galaxies, the velocity dispersions of galaxy clusters and dark
matter density measurements. One of the biggest questions in physics today con cerns the nature of dark matter, and the most promising theory is that dark matter
consists of one or more new particle species. To discover the nature of dark matter
particles, they need to be inferred from collider experiments or found via indirect or
direct detection. Since none of these alternatives has led to conclusive results within
the current theoretical frameworks, new approaches should be investigated. In this
thesis, sub-GeV dark matter particles are studied through interactions in direct de tection experiments described with an effective field theory (EFT). More specifically,
dark matter-induced electronic transition rates in crystal detectors are studied. The
rate of electronic transitions is described with EFT scattering amplitudes, which
introduce many model-independent coupling strengths. By computing transition
rates corresponding to different sets of EFT parameters, direct detection data can
be used for inferring properties of dark matter particles without relying on any
specific theoretical framework. Since the computation of the electronic transition
rates is very expensive, the aim of this thesis is to implement a deep neural network
for fast predictions of transition rates. Furthermore, since the neural network re quires a large data set for training, the generation of training data was accelerated
using computations on graphics processing units (GPUs). I developed two neural
networks, one with the DM mass as input and one with the DM mass and two
EFT coupling strengths as inputs, that are about 600 times faster than the original
computations and capture the overall behaviour of the transition rates. However,
the relative error of the predictions has a standard deviation of about 30% with a
mean of around 0%. On the other hand, the GPU computations are about 16 times
faster than the original computations and have negligible error while being able to
compute transition rates corresponding to all 28 coupling strengths. I conclude that
there is great potential for using both neural networks and GPUs for dark matter
research, and suggest further improvements.
Beskrivning
Ämne/nyckelord
Dark matter, transition rate, direct detection, machine learning, neural networks, GPU, CUDA, parallelisation, crystal detector