Pixel based galaxy morphology classification: Difference between revisions

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==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==
Data comes from Legacy Survey in DECaLS region
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. [[File:GZD-5_Class_tree|thumb]]
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==


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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]

Latest revision as of 05:41, 6 April 2022

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

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.

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..

Here is the repository on Github