Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
| Autor: | Junwei Liu, Gyu-Boong Jo, Zejian Ren, Jeongwon Lee, Elnur Hajiyev, Chengdong He, Entong Zhao |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Science
FOS: Physical sciences General Physics and Astronomy Quantum simulator 01 natural sciences General Biochemistry Genetics and Molecular Biology Article 010305 fluids & plasmas Momentum 0103 physical sciences Statistical physics 010306 general physics Wave function Quantum Ultracold gases Physics Quantum Physics Multidisciplinary Artificial neural network Computational science Observable General Chemistry Fermion Quantum Gases (cond-mat.quant-gas) Physics - Data Analysis Statistics and Probability Condensed Matter::Strongly Correlated Electrons Quantum simulation Condensed Matter - Quantum Gases Quantum Physics (quant-ph) Fermi gas Data Analysis Statistics and Probability (physics.data-an) |
| Zdroj: | Nature Communications Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021) |
| ISSN: | 2041-1723 |
| Popis: | The power of machine learning (ML) provides the possibility of analyzing experimental measurements with an unprecedented sensitivity. However, it still remains challenging to probe the subtle effects directly related to physical observables and to understand physics behind from ordinary experimental data using ML. Here, we introduce a heuristic machinery by using machine learning analysis. We use our machinery to guide the thermodynamic studies in the density profile of ultracold fermions interacting within SU($N$) spin symmetry prepared in a quantum simulator. Although such spin symmetry should manifest itself in a many-body wavefuction, it is elusive how the momentum distribution of fermions, the most ordinary measurement, reveals the effect of spin symmetry. Using a fully trained convolutional neural network (NN) with a remarkably high accuracy of $\sim$94$\%$ for detection of the spin multiplicity, we investigate how the accuracy depends on various less-pronounced effects with filtered experimental images. Guided by our machinery, we directly measure a thermodynamic compressibility from density fluctuations within the single image. Our machine learning framework shows a potential to validate theoretical descriptions of SU($N$) Fermi liquids, and to identify less-pronounced effects even for highly complex quantum matter with minimal prior understanding. Comment: 11 pages, 5 figures, 1 table |
| Databáze: | OpenAIRE |
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