A simulation-based training framework for machine-learning applications in ARPES
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915456663683072 |
|---|---|
| author | Na, MengXing Zhou, Chris Dufresne, Sydney K. Y. Michiardi, Matteo Damascelli, Andrea |
| author_facet | Na, MengXing Zhou, Chris Dufresne, Sydney K. Y. Michiardi, Matteo Damascelli, Andrea |
| contents | In recent years, angle-resolved photoemission spectroscopy (ARPES) has advanced significantly in its ability to probe more observables and simultaneously generate multi-dimensional datasets. These advances present new challenges in data acquisition, processing, and analysis. Machine learning (ML) models can drastically reduce the workload of experimentalists; however, the lack of training data for ML -- and in particular deep learning -- is a significant obstacle. In this work, we introduce an open-source synthetic ARPES spectra simulator - aurelia - for the purpose of generating the large datasets necessary to train ML models. As a demonstration, we train a convolutional neural network to evaluate ARPES spectra quality -- a critical task performed during the initial sample alignment phase of the experiment. We benchmark the simulation-trained model against actual experimental data and find that it can assess the spectra quality more accurately than human analysis, and swiftly identify the optimal measurement region with high precision. Thus, we establish that simulated ARPES spectra can be an effective proxy for experimental spectra in training ML models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A simulation-based training framework for machine-learning applications in ARPES Na, MengXing Zhou, Chris Dufresne, Sydney K. Y. Michiardi, Matteo Damascelli, Andrea Materials Science Machine Learning Computational Physics In recent years, angle-resolved photoemission spectroscopy (ARPES) has advanced significantly in its ability to probe more observables and simultaneously generate multi-dimensional datasets. These advances present new challenges in data acquisition, processing, and analysis. Machine learning (ML) models can drastically reduce the workload of experimentalists; however, the lack of training data for ML -- and in particular deep learning -- is a significant obstacle. In this work, we introduce an open-source synthetic ARPES spectra simulator - aurelia - for the purpose of generating the large datasets necessary to train ML models. As a demonstration, we train a convolutional neural network to evaluate ARPES spectra quality -- a critical task performed during the initial sample alignment phase of the experiment. We benchmark the simulation-trained model against actual experimental data and find that it can assess the spectra quality more accurately than human analysis, and swiftly identify the optimal measurement region with high precision. Thus, we establish that simulated ARPES spectra can be an effective proxy for experimental spectra in training ML models. |
| title | A simulation-based training framework for machine-learning applications in ARPES |
| topic | Materials Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2508.15983 |