SQGTool DR5 - Production scripts

This repository contains scripts to be run at the Mauá server for the production phase of the star/quasar/galaxy classification for S-PLUS DR5. The training process and performance analyses are in another repository to be released within further notice.

What is new in the latest code release?

v0.3.0 - Sep/2023

• Environment:
  • Python 3.10.12
  • All packages were updated to the latest version for Python 3.10.12 (2021/10/21). Check file environment.yml for details.
• A new script named sqgtool.py was added. This script is the main script to be run on the Mauá server. It parses command-line arguments and calls the other scripts.
• A better logging process was added. Logs are stored in /logs/.
• Multithreading can be used for both crossmatch and classification processes. The number of threads can be set by the user.

What is new in the analyses?

The classification for S-PLUS DR2 and DR3 follows Nakazono et al. 2021. The classification for S-PLUS DR4 follows the same procedure as DR2/DR3 but with updated performance (as seen in the documentation) due to changes in reduction and calibration processes.

The S-PLUS DR5 has further improvements in reduction and calibration processes. We made a few improvements in the classification process, briefly described below:

• [Base model] Re-trained the classification model that uses S-PLUS magnitudes + WISE magnitudes + morphological parameters using the new data
• The model mentioned above also includes the objects without WISE counterparts due to increasing performance in relation to having two separate models for objects with and without WISE counterpart
• Excluded the model_flag column (due to the item above)
• Replaced ALLWISE magnitudes with unWISE magnitudes
• [GAIA model] Trained new classification model that includes GAIA parameters. We included the results in separate columns:
  • CLASS_GAIA, PROB_STAR_GAIA, PROB_QSO_GAIA, PROB_GAL_GAIA
• Included the columns from unWISE and GAIA that were used for the classification
  • W1_MAG, W2_MAG, Gmag, Plx, E(BP/RP), PM

More improvements are expected for the next months and a full documentation with code access (analyses are stored in another GitHub repository) will be prepared.

Column information

Name	Description
ID	S-PLUS ID
RA	Right ascension in degrees
DEC	Declination in degrees
W1_MAG	unWISE W1 magnitude in Vega
W2_MAG	unWISE W2 magnitude in Vega
Gmag	GAIA G-band mean magnitude
Plx	GAIA parallax
E(BP/RP)	GAIA BP/RP excess factor
PM	GAIA proper motion
CLASS	Class (0= QSO, 1=STAR, 2=GALAXY) from [Base model]
PROB_STAR	Probability [0,1] of a source being a star from [Base model]
PROB_QSO	Probability [0,1] of a source being a quasar from [Base model]
PROB_GAL	Probability [0,1] of a source being a galaxy from [Base model]
CLASS_GAIA	Class (0= QSO, 1=STAR, 2=GALAXY) from [GAIA model]
PROB_STAR_GAIA	Probability [0,1] of a source being a star from [GAIA model]
PROB_QSO_GAIA	Probability [0,1] of a source being a quasar from [GAIA model]
PROB_GAL_GAIA	Probability [0,1] of a source being a galaxy from [GAIA model]

Performances

[Base model]

Metric	QSO	STAR	GALAXY
Precision	0.930	0.984	0.972
Recall	0.930	0.975	0.979
F1	0.930	0.979	0.975

F1_weighted = 0.971

[GAIA model]

Metric	QSO	STAR	GALAXY
Precision	0.939	0.989	0.981
Recall	0.940	0.984	0.984
F1	0.939	0.986	0.983

F1_weighted = 0.978

How to run

1. Clone this repository

2. Create a conda environment

With the environment.yml file, you can create a conda environment with all the packages needed to run the scripts.

```
conda env create --name sqg_dr5 -f environment.yml
```

3. Activate the environment

```
conda activate sqg_dr5
```

4. Run the script

```
python ./sqgtool.py --help
```

4.1. Run the script in Mauá server for DR5

```
python ./sqgtool.py --input_folder /storage/splus/Catalogues/iDR5/VAC_features/20231121 --output_folder /storage/splus/Catalogues/VACs/sqg/iDR5 --crossmatch --n_threads 8 --verbose

```

Output:

5. Check the logs

Error logs for the crossmatch and classification processes are stored in /logs/ folders

How to cite

• Please cite the original publication (S-PLUS DR2): https://ui.adsabs.harvard.edu/abs/2021MNRAS.507.5847N/abstract
• Publication for S-PLUS DR5 is in preparation.

Team

This version of the star/quasar/galaxy classification was done in collaboration with (in alphabetical order):

- Gabriel Fabiano de Souza (IAG-USP)
- Gabriel Jacob Perin (IME-USP)
- Pierre Ré (IAG-USP)
- Raquel Valença (IAG-USP)
- Vitor Cernic (IAG-USP)

Acknowledgments

We thank Elismar Losch for providing help with data, and Gustavo Schwarz for helping with codes and server issues

Report an issue

We stress that the [GAIA model] classification is still under tests by our team. Changes/improvements might be expected for this model in the near future. If you find any problem, please open an issue in this repository or contact lilianne.nakazono at gmail dot com