SPLAT: The SpeX Prism Library Analysis Toolkit

Access SPLAT's full documentation at https://splat.physics.ucsd.edu/splat.

Preamble

SPLAT is a python-based spectral access and analysis package designed to interface
with the SpeX Prism Library (SPL), an online repository of over 3,000 low-resolution, near-infrared spectra, primarily of low-temperature stars and brown dwarfs. It is built on common python packages such as astropy, astroquery, emcee, matplotlib, numpy, pandas, scipy, and others.

SPLAT tools allow you to:

• Search the SpeX Prism Library for spectral data and source information;
• Access and analyze publically-available spectra contained in it;
• Analyze your own spectral data from various spectroscopic instruments;
• Perform basic spectral analyses such as type classification, gravity classification, index measurement, spectrophotometry, reddening, blended light analysis, and basic math operations;
• Access atmosphere models and perform fits to spectral data;
• Transform observables to physical parameters using evolutionary models;
• Use published empirical trends between spectral type, absolute magnitudes, colors, luminosities, effective temperatures, and others;
• Access online data repositories through wrappers to [astroquery] (https://astroquery.readthedocs.io/en/latest)
• Simulate very low mass star and brown dwarf populations by combining spatial, evolutionary, and observational properties; and
• Plot, tabulate, and publish your results.

Note: Many features in SPLAT continue to be in development. Help us improve the code by reporting bugs (and solutions!) to our github site, https://github.com/aburgasser/splat.

Installation and Dependencies

NEW SPLAT can now be installed by pip!

Before installing, it is recommended you set up a conda environment.

conda create -n splat python=3.10
conda activate splat
pip install splat --upgrade

You can also install through github

cd _your_python_folder_
git clone https://github.com/aburgasser/splat.git
cd splat
python -m pip install .

SPLAT has core dependencies on the following packages:

• astropy
• astroquery
• matplotlib
• numpy
• pandas
• requests
• scipy
• corner (for model fitting only)
• emcee (for model fitting only)
• bokeh (for experimental SPLAT web interface only)
• flask (for experimental SPLAT web interface only)

Using SPLAT

SPLAT is organized into a series of modules based on core functionalities:

• splat.core: core functionalities, including index measurement, database access and classification
• splat.citations: biblographic/bibtex routines
• splat.database: access the spectral and source databases, as well as online resources through astroquery
• splat.empirical: empirical conversion relations
• splat.evolve: access to evolutionary models
• splat.model: access to spectral models and model-fitting routines
• splat.photometry: spectrophotometry routines and filter access
• splat.plot: plotting and visualization routines
• splat.simulate: population simulation routines
• splat.utilities: additional routines for general analysis
• splat.web: SPLAT's web interface (under development)

SPLAT is regularly tested on Python 3.7 and higher, and works well with ipython or jupyter notebook.

Data and models

The SPLAT package comes with over 3,000 low-resolution near-infrared spectra from the IRTF/SpeX spectrograph, obtained in its low-dispersion Prism mode; these are contained in the resources/Spectra folder of the package.

In addition, a subset of atmosphere models smoothed to the resolution of the SpeX-Prism data are provided for the following models in the resoures/SpectralModels folder:

• btsettl08: BT-Settl models from [Allard et al. (2012) (https://ui.adsabs.harvard.edu/abs/2012RSPTA.370.2765A/abstract)
• burrows06: Models from [Burrows et al. (2006)] (https://ui.adsabs.harvard.edu/abs/2006ApJ...640.1063B)
• dback24: Sonora Diamondback models from [Morley et al. (2024)] (https://ui.adsabs.harvard.edu/abs/2024ApJ...975...59M/abstract)

Additional models can be downloaded from https://spexarchive.coolstarlab.ucsd.edu/splat/ ; see instructions on that page for how to place these in the SPLAT path

Reading in Spectra

The best way to read in a spectrum is to use getSpectrum(), which takes a number of search keywords and returns a list of Spectrum objects:

import splat
splist = splat.getSpectrum(shortname='0415-0935')  

Retrieving 1 file

splist = splat.getSpectrum(name='TWA30A')  

Retrieving 3 files

splist = splat.getSpectrum(opt_spt=['L2','L5'],jmag=[12,13])

Retrieving 5 files

In each case, splist is a list of Spectrum objects, each a container of various aspects of each spectrum and its source properties. For example, selecting the first spectrum,

sp = splist[0]
sp

SPEX-PRISM spectrum of 2MASSW J0036159+182110

The main elements of the Spectrum obejct are:

• sp.wave: wavelength array in default units of micron
• sp.flux: flux array in default units of erg/cm^2/s/micron
• sp.noise: flux uncertainty array in default units of erg/cm^2/s/micron

A summary of the Spectrum object can be accessed using sp.info().

sp.info()

SPEX-PRISM spectrum of 2MASSW J0036159+182110

Airmass = nan

Source designation = J00361617+1821104

Median S/N = 274

SpeX Classification = L2.0

Spectrum key = 10249, Source key = 10068

If you use these data, please cite:

Burgasser, A. J. et al. (2008, Astrophysical Journal, 681, 579-593)

bibcode: 2008ApJ...681..579B

History:

SPEX-PRISM spectrum successfully loaded

You can also read in your own spectrum by passing a filename

sp = splat.Spectrum(file='PATH_TO/myspectrum.fits')

or a URL

sp = splat.Spectrum(file='http://splat.physics.ucsd.edu/splat/spexprism/spectra/spex-prism_SO0253+1625_20040908_BUR08B.txt')

Both fits and ascii (tab or csv) data formats are supported, but files should ideally conform to the following data format standard:

• column 1: wavelength, assumed in microns
• column 2: flux in flambda units
• column 3: (optional) flux uncertainty in flambda units.

There are a few built-in readers for specific data formats.

To flux calibrate a spectrum, use the Spectrum object's built in fluxCalibrate() method:

sp = splat.getSpectrum(shortname='0415-0935')[0]
sp.fluxCalibrate('2MASS J',14.0)

Visualizing Spectra

To display the spectrum, use the Spectrum object's plot() function

sp.plot()

or the splat.plot routine plotSpectrum() :

import splat.plot as splot
splot.plotSpectrum(sp)

You can save your spectrum by adding a filename:

splot.plotSpectrum(sp,file='spectrum.pdf')

You can also compare multiple spectra:

sp1 = splat.getSpectrum(shortname='0415-0935')[0]
sp2 = splat.getSpectrum(shortname='1217-0311')[0]
splot.plotSpectrum(sp1,sp2,colors=['k','r'])

plotSpectrum() and related routines have many extras to label features, plot uncertainties, indicate telluric absorption regions, make multi-panel and multi-page plots of lists of spectra, plot batches of spectra, etc. Be sure to look through the splat.plot subpackage for more details.

Analysis functions

SPLAT's primary purpose is to allow the analysis of ultracool dwarf spectra.

To measure spectral indices, use measureIndex() or measureIndexSet():

sp = splat.getSpectrum(shortname='0415-0935')[0]
value, error = splat.measureIndex(sp,[1.14,1.165],[1.21,1.235],method='integrate')
indices = splat.measureIndexSet(sp,set='testi')

The last line returns a dictionary, whose value,error pair can be accessed by the name of the index:

print(indices['sH2O-J'])		# returns value, error

You can also determine the gravity classification of a source following [Allers & Liu (2013)] (http://adsabs.harvard.edu/abs/2013ApJ...772...79A) using classifyGravity():

sp = splat.getSpectrum(young=True, lucky=True)[0]
print(splat.classifyGravity(sp))   # returned 'VL-G'

To classify a spectrum, use the various classifyByXXX methods:

sp = splat.getSpectrum(shortname='0415-0935')[0]
spt,unc = splat.classifyByIndex(sp,set='burgasser')
spt,unc = splat.classifyByStandard(sp,spt=['T5','T9'])
result = splat.classifyByTemplate(sp,spt=['T6','T9'],nbest=5)

The last line returns a dictionary containing the best 5 template matches.

To compare a spectrum to another spectrum or a model, use compareSpectra():

import splat.model as spmod
mdl = spmod.loadModel(teff=720,logg=4.8,set='btsettl')      # loads a BTSettl08 model 
sp = splat.getSpectrum(shortname='0415-0935')[0]
chi,scale = splat.compareSpectra(sp,mdl)
mdl.scale(scale)
splat.plotSpectrum(sp,mdl,colors=['k','r'],legend=[sp.name,mdl.name])

You can shortcut the last three lines using the plot keyword:

chi,scale = splat.compareSpectra(sp,mdl,plot=True)

There are also codes still in development to fit models directly to spectra: modelFitGrid(), modelFitMCMC(), and modelFitEMCEE():

import splat.model as spmod
sp = splat.getSpectrum(shortname='0415-0935')[0]
sp.fluxCalibrate('2MASS J',14.49,absolute=True)
nbest = 5
result1 = splat.modelFitGrid(sp,set='btsettl')
result2 = splat.modelFitMCMC(sp,set='btsettl',initial_guess=[800,5.0,0.],nsamples=300,step_sizes=[50.,0.5,0.])
result3 = splat.modelFitEMCEE(sp,set='btsettl',initial_guess=[800,5.0,0.],nwalkers=12,nsamples=500)

The outputs of all of these fitting functions is a dictionary or list of dictionaries containing the parameters of the best-fitting models; there are also several diagnostic plots produced depending on the routine. View the model fitting page for more details.

All of these routines have many options worth exploring, and which are (increasingly) documented at https://splat.physics.ucsd.edu/splat.

If there are capabilities you need/desire, please post in the "Issues" link on our [github site] (https://github.com/aburgasser/splat).

Citing SPLAT and its data

If you use SPLAT tools for your research, please cite Burgasser et al. (2017, ASInC 14, 7), bibcode 2017ASInC..14....7B [NASA ADS] (https://ui.adsabs.harvard.edu/abs/2017ASInC..14....7B/abstract).

In addition, if you use data contained in SPLAT or the SpeX Prism Library, please be sure to cite the original spectral data source, which can be accessed from the Spectrum object:

sp = splat.getSpectrum(lucky=True)
sp.citation().data_reference

'2016ApJ...817..112S'

import splat.citations as spcite
spcite.shortRef(sp.data_reference)

Schneider, A. C. et al. (2016, Astrophysical Journal, 817, 112)

Acknowledgements

SPLAT is an collaborative project of research students in the [UCSD Cool Star Lab] (http://www.coolstarlab.org), aimed at developing research through the building of spectral analysis tools. Contributors to SPLAT have included Christian Aganze, Jessica Birky, Daniella Bardalez Gagliuffi, Adam Burgasser (PI), Caleb Choban, Andrew Davis, Ivanna Escala, Joshua Hazlett, Carolina Herrara Hernandez, Elizabeth Moreno Hilario, Aishwarya Iyer, Yuhui Jin, Mike Lopez, Dorsa Majidi, Diego Octavio Talavera Maya, Alex Mendez, Gretel Mercado, Niana Mohammed, Johnny Parra, Maitrayee Sahi, Adrian Suarez, Melisa Tallis, Tomoki Tamiya, Chris Theissen, Russell van Linge, and Joman Wong.

This project has been supported by the National Aeronautics and Space Administration under Grant No. NNX15AI75G.