Deeper Learning in Astronomy
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866911818951163904 |
|---|---|
| author | Scott, Douglas Frolop, Ali |
| author_facet | Scott, Douglas Frolop, Ali |
| contents | It is well known that the best way to understand astronomical data is through machine learning, where a "black box" is set up, inside which a kind of artificial intelligence learns how to interpret the features in the data. We suggest that perhaps there may be some merit to a new approach in which humans are used instead of machines to understand the data. This may even apply to fields other than astronomy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19937 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deeper Learning in Astronomy Scott, Douglas Frolop, Ali Instrumentation and Methods for Astrophysics It is well known that the best way to understand astronomical data is through machine learning, where a "black box" is set up, inside which a kind of artificial intelligence learns how to interpret the features in the data. We suggest that perhaps there may be some merit to a new approach in which humans are used instead of machines to understand the data. This may even apply to fields other than astronomy. |
| title | Deeper Learning in Astronomy |
| topic | Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2403.19937 |