“Pixel based galaxy morphology classification”的版本间差异
跳到导航
跳到搜索
无编辑摘要 |
无编辑摘要 |
||
| （未显示同一用户的2个中间版本） | |||
| 第1行： | 第1行： | ||
==Introduction== |
==Introduction== |
||
This is a project about morphological galaxies classification. The amazing point is that we're researching an algorithm to seperate background and sources for decreasing the pollution. |
This is a project about morphological galaxies classification. The amazing point is that we're researching an algorithm to seperate background and sources for decreasing the pollution. |
||
==Dataset== |
==Dataset== |
||
Training data comes from Legacy Survey in DECaLS region and the model will be applied to out DECaLS region (MzLS and BASS project). |
|||
==Labels== |
==Labels== |
||
[[File:Tree.png|150px|right|jumengting]] |
|||
Mike Walmsley trained on DECaLS region with GZD-1/2/5 catalog and GZD-5 class tree with active learning and then form a more confident catalog called catalog_auto. I set a threshold (e.g. 0.5) on each question to split and get labels of each picture. The GZD-5 label tree showed as follows. |
Mike Walmsley trained on DECaLS region with GZD-1/2/5 catalog and GZD-5 class tree with active learning and then form a more confident catalog called catalog_auto. I set a threshold (e.g. 0.5) on each question to split and get labels of each picture. The GZD-5 label tree showed as follows. |
||
GZD-5_Class_tree.png |
|||
==DECaLS introduction== |
==DECaLS introduction== |
||
* The Dark Energy Camera (DECam) on the Blanco 4m telescope, located at the Cerro Tololo Inter-American Observatory. |
|||
==Out DECaLS introduction== |
==Out DECaLS introduction== |
||
| 第21行： | 第22行： | ||
Updating.. |
Updating.. |
||
[https://github.com/Astro-Astre/Pixel-based_DeepLearningTechnic Here is the repository] |
[https://github.com/Astro-Astre/Pixel-based_DeepLearningTechnic Here is the repository on Github] |
||
2022年4月6日 (三) 05:41的最新版本
Introduction
This is a project about morphological galaxies classification. The amazing point is that we're researching an algorithm to seperate background and sources for decreasing the pollution.
Dataset
Training data comes from Legacy Survey in DECaLS region and the model will be applied to out DECaLS region (MzLS and BASS project).
Labels
Mike Walmsley trained on DECaLS region with GZD-1/2/5 catalog and GZD-5 class tree with active learning and then form a more confident catalog called catalog_auto. I set a threshold (e.g. 0.5) on each question to split and get labels of each picture. The GZD-5 label tree showed as follows.
DECaLS introduction
- The Dark Energy Camera (DECam) on the Blanco 4m telescope, located at the Cerro Tololo Inter-American Observatory.
Out DECaLS introduction
Architecture
- Simple CNN
- Dense Net
- Xception
Loss function
- Cross Entropy
- Focal loss
Else
Updating..