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Promotionsvortrag Physik: "Application of Deep Learning Methods to the Search for Neutrinoless Double Beta Decay with the EXO-200 Experiment"

Jul 02
2. Juli 2020 11:00 Uhr bis 12:00 Uhr
Online Videokonferenz

Ankündigung des Promotionsvortrags von Herrn Tobias Ziegler

Proving the Majorana nature of neutrinos would establish physics beyond the Standard Model of particle physics, by demonstrating that neutrinos are their own antiparticles. To date, the best candidate for this proof is the observation of the neutrinoless double beta decay.

The EXO-200 experiment searches for the neutrinoless double beta decay in Xenon-136 with an ultra-low background time projection chamber lled with liquid xenon.

The current generation of experiments are sensitive to half-lives of this extremely rare decay of up to 10^26 yr. In this thesis, deep learning based methods are adapted for data analysis in EXO-200 from approaches used in image recognition.

These algorithms are developed in order to improve the sensitivity to the half-life of the neutrinoless double beta decay.

A deep neural network is trained to reconstruct the kinetic energy deposited in the detector. In particular, this algorithm outperforms the traditional EXO-200 reconstruction in terms of energy resolution by 10% (12%) in Phase-I (Phase-II) of EXO-200 operation. In an additional study, deep neural networks are developed to discriminate double beta decays from the dominant background interactions.

The discrimination power of these algorithms exceeds those of other discriminators which utilize classical machine learning methods.

(Vortrag auf Deutsch)

Dem Vortrag schließt sich eine Diskussion von 15 Minuten an. Vortrag und Diskussion sind aufgrund der momentanen Situation nicht öffentlich. Diesen Verfahrensteilen folgt ein ebenfalls nicht öffentliches Rigorosum von 45 Minuten.