Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/1911.01490 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909887604195328 |
|---|---|
| author | Jungbluth, Anna Gitiaux, Xavier Maloney, Shane A. Shneider, Carl Wright, Paul J. Kalaitzis, Alfredo Deudon, Michel Baydin, Atılım Güneş Gal, Yarin Muñoz-Jaramillo, Andrés |
| author_facet | Jungbluth, Anna Gitiaux, Xavier Maloney, Shane A. Shneider, Carl Wright, Paul J. Kalaitzis, Alfredo Deudon, Michel Baydin, Atılım Güneş Gal, Yarin Muñoz-Jaramillo, Andrés |
| contents | Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to super-resolve low-resolution magnetic field images and translate between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1911_01490 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses Jungbluth, Anna Gitiaux, Xavier Maloney, Shane A. Shneider, Carl Wright, Paul J. Kalaitzis, Alfredo Deudon, Michel Baydin, Atılım Güneş Gal, Yarin Muñoz-Jaramillo, Andrés Solar and Stellar Astrophysics Machine Learning Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to super-resolve low-resolution magnetic field images and translate between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs. |
| title | Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics & Losses |
| topic | Solar and Stellar Astrophysics Machine Learning |
| url | https://arxiv.org/abs/1911.01490 |