Skip to content
/ rps-cnn Public

Trained CNN model for prediction of IGM density from 2D density and kinematic maps of simulated disc galaxies undergoing ram pressure stripping

License

Notifications You must be signed in to change notification settings

axshen/rps-cnn

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

35 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Prediction of Ram Pressure Stripping (RPS) parameters with CNNs

simulated disc galaxy

Description

We have trained CNN models to predict RPS parameters (IGM density rho_igm and relative velocity v_rel) from 2D density and kinematic maps of disc galaxies undergoing RPS.

Usage

We provide a sample of images which can be used for testing our trained model.

  1. pip install -r requirements.txt
  2. Install pre-trained model.
  3. python src/predict.py

Paper available at: http://arxiv.org/abs/2008.03460

About

Trained CNN model for prediction of IGM density from 2D density and kinematic maps of simulated disc galaxies undergoing ram pressure stripping

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published