Huber, David ; Marchukov, Oleksandr V. ; Hammer, Hans-Werner ; Volosniev, Artem G. (2021):
Morphology of three-body quantum states from machine learning. (Publisher's Version)
In: New Journal of Physics, 23 (6), IOP Publishing, ISSN 1367-2630,
DOI: 10.26083/tuprints-00019366,
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Item Type: | Article |
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Origin: | Secondary publication via sponsored Golden Open Access |
Status: | Publisher's Version |
Title: | Morphology of three-body quantum states from machine learning |
Language: | English |
Abstract: | The relative motion of three impenetrable particles on a ring, in our case two identical fermions and one impurity, is isomorphic to a triangular quantum billiard. Depending on the ratio κ of the impurity and fermion masses, the billiards can be integrable or non-integrable (also referred to in the main text as chaotic). To set the stage, we first investigate the energy level distributions of the billiards as a function of 1/κ ∈ [0, 1] and find no evidence of integrable cases beyond the limiting values 1/κ = 1 and 1/κ = 0. Then, we use machine learning tools to analyze properties of probability distributions of individual quantum states. We find that convolutional neural networks can correctly classify integrable and non-integrable states. The decisive features of thewave functions are the normalization and a large number of zero elements, corresponding to the existence of a nodal line. The network achieves typical accuracies of 97%, suggesting that machine learning tools can be used to analyze and classify the morphology of probability densities obtained in theory or experiment. |
Journal or Publication Title: | New Journal of Physics |
Volume of the journal: | 23 |
Issue Number: | 6 |
Place of Publication: | Darmstadt |
Publisher: | IOP Publishing |
Collation: | 20 Seiten |
Classification DDC: | 500 Naturwissenschaften und Mathematik > 530 Physik |
Divisions: | 05 Department of Physics > Institute of Nuclear Physics |
Date Deposited: | 25 Aug 2021 12:37 |
Last Modified: | 24 Jun 2022 18:02 |
DOI: | 10.26083/tuprints-00019366 |
Corresponding Links: | |
URN: | urn:nbn:de:tuda-tuprints-193666 |
Additional Information: | Keywords: quantum billiards, machine learning, impurity systems, quantum chaos |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/19366 |
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