sla 1.3.1

Non-parametric LOSVD analysis for galaxy spectra

Stellar LOSVD Analysis (SLA)

Code for the non-parametrical recovery of stellar Line-Of-Sight Velocity Distributions (LOSVD) from galaxy spectra.

Stellar LOSVD recovery from the observed galaxy spectra is equivalent to a deconvolution and can be solved as a linear inverse problem. To overcome its ill-posed nature we apply smoothing regularization. Here we present a non-parametric fitting technique and show real-world application to MaNGA spectral data cubes and long-slit spectrum of stellar counter-rotating galaxies.

More information about this technique can be found in ADASS XXXI conference proceeding and in examples

Installation

Use the package manager pip to install sla.

pip install sla

The sla package uses the following dependencies, which should be installed when installing through the pip manager:

pip install -r requirements.txt

• numpy
• scipy
• matplotlib
• astropy
• lmfit
• vorbin
• pseudoslit
• glob
• PyPDF2
• tqdm

Usage

SLA usage examples are shown for stellar counter-rotating galaxy PGC 66551 (Gasymov, Katkov et al. in prep.). To run examples, first download test dataset which includes:

• MaNGA spectral cube
• long-slit spectra.

sh ./data/download.sh 

Use case examples

Example 1

Example of stellar LOSVD recovery along pseudoslit spectrum taken from MaNGA spectral cube along the major axis

cd example
python3 example_MaNGA_without_template.py

Example 2

LOSVD is determined from the RSS long slit spectrum using the un-broadened stellar population template (SSP PEGASE.HR), which was constructed by applying in advance NBursts full spectral fitting method.

cd example
python3 example_NBursts_with_template.py

Example 3

The same as in the previous example, but without using NBursts output. The necessary stellar population template is selected from the model grid for given approximate SSP parameters

cd example
python3 example_NBursts_without_template.py

The resulting file fits file PDF figure will be stored in the result folder.

Authors

• Damir Gasymov (Sternberg Astronomical Institute, Lomonosov Moscow State University)
• Ivan Katkov (NYU Abu Dhabi, Lomonosov Moscow State University)