Detecting the phase transition in a strongly-interacting Fermi gas by unsupervised machine learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866929542042484736 |
|---|---|
| author | Eberz, D. Link, M. Kell, A. Breyer, M. Gao, K. Köhl, M. |
| author_facet | Eberz, D. Link, M. Kell, A. Breyer, M. Gao, K. Köhl, M. |
| contents | We study the critical temperature of the superfluid phase transition of strongly-interacting fermions in the crossover regime between a Bardeen-Cooper-Schrieffer (BCS) superconductor and a Bose-Einstein condensate (BEC) of dimers. To this end, we employ the technique of unsupervised machine learning using an autoencoder neural network which we directly apply to time-of-flight images of the fermions. We extract the critical temperature of the phase transition from trend changes in the data distribution revealed in the latent space of the autoencoder bottleneck. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_15989 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Detecting the phase transition in a strongly-interacting Fermi gas by unsupervised machine learning Eberz, D. Link, M. Kell, A. Breyer, M. Gao, K. Köhl, M. Quantum Gases We study the critical temperature of the superfluid phase transition of strongly-interacting fermions in the crossover regime between a Bardeen-Cooper-Schrieffer (BCS) superconductor and a Bose-Einstein condensate (BEC) of dimers. To this end, we employ the technique of unsupervised machine learning using an autoencoder neural network which we directly apply to time-of-flight images of the fermions. We extract the critical temperature of the phase transition from trend changes in the data distribution revealed in the latent space of the autoencoder bottleneck. |
| title | Detecting the phase transition in a strongly-interacting Fermi gas by unsupervised machine learning |
| topic | Quantum Gases |
| url | https://arxiv.org/abs/2310.15989 |