Stellar mass galaxy estimator
Project description
Mstar: ML Stellar Mass Estimator
Mstar is a fast and precise stellar mass estimator for the Dark Energy Survey (DES) galaxies. The algorithm consists of a machine learning code based on the Artificial Neural Networks (ANN) architecture. Checkout our github repo.
The estimator was trained on the DES deep fields matched with the COSMOS dataset. Our results were cross validated with a local volume sample, the SDSS sample matched with DES. For more information about the validation process take a look at Esteves et al. 2023
How to use
The input information is simply the galaxy redshift colors and the z-band magnitude.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file mstar-alpha-0.0.2.tar.gz.
File metadata
- Download URL: mstar-alpha-0.0.2.tar.gz
- Upload date:
- Size: 9.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
bf64ec4b37190cb792caf49f061422acaa55196ac9d4d773e708f37f463c40f8
|
|
| MD5 |
d1c289427f2b39c470747f7d139983fe
|
|
| BLAKE2b-256 |
8fedd98a10f780ef70664397e688cbd043a6d3f4380d7cb65663a93b18f9893e
|
File details
Details for the file mstar_alpha-0.0.2-py3-none-any.whl.
File metadata
- Download URL: mstar_alpha-0.0.2-py3-none-any.whl
- Upload date:
- Size: 10.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
82c48100c4600d1ace4a260e8f407d0e4b5c3a1145276abfe030185536398067
|
|
| MD5 |
6f0ac78fd38c7254ecdd1a202d7d500c
|
|
| BLAKE2b-256 |
6f9847175f1e2a16eab728320ee36ade2fe84582622258787016e73281230049
|