التفاصيل البيبلوغرافية
| العنوان: |
Learning from machine learning: optimization of the Bose-Einstein condensate of the thulium atom at a 1064 trap |
| المؤلفون: |
Kumpilov, D. A., Pershin, D. A., Cojocaru, I. S., Khlebnikov, V. A., Pyrkh, I. A., Rudnev, A. E., Fedotova, E. A., Khoruzhii, K. A., Aksentsev, P. A., Gaifutdinov, D. V., Zykova, A. K., Tsyganok, V. V., Akimov, A. V. |
| سنة النشر: |
2023 |
| المجموعة: |
ArXiv.org (Cornell University Library) |
| مصطلحات موضوعية: |
Quantum Physics |
| الوصف: |
Bose-Einstein condensation is an intriguing phenomenon that has garnered significant attention in recent decades. The number of atoms within the condensate determines the scale of experiments that can be performed, making it crucial for quantum simulations. Consequently, a condensate of thulium atoms at a 1064-nm dipole trap was successfully achieved, and optimization of the atom count was performed. Surprisingly, the number of atoms exhibited saturation, closely resembling the count achieved in a dipole trap at 532 nm. Drawing insights from machine learning results, it was concluded that a 3-body recombination process was likely limiting the number of atoms. This limitation was successfully overcome by leveraging Fano-Feshbach resonances. Additionally, optimization of the cooling time was implemented. |
| نوع الوثيقة: |
text |
| اللغة: |
unknown |
| Relation: |
http://arxiv.org/abs/2311.06795 |
| الاتاحة: |
http://arxiv.org/abs/2311.06795 |
| رقم الانضمام: |
edsbas.5983C8A |
| قاعدة البيانات: |
BASE |