Overview

Introduction

species provides a coherent framework for spectral and photometric analysis of directly imaged exoplanets and brown dwarfs. This page contains a overview of the various data that are supported and some of the tools and features that are available.

Supported data

The toolkit benefits from publicly available data resources such as atmospheric model spectra, photometric and spectral libraries, evolutionary tracks, and photometry of directly imaged companions. The relevant data are automatically downloaded and added to the HDF5 database, which acts as the central data storage for a workflow. All data are stored in a fixed format such that the analysis and plotting tools can easily access and process the data.

The following data and models are currently supported:

Atmospheric models

• AMES-Cond

• AMES-Dusty

• ATMO (CEQ, NEQ weak, NEQ strong)

• BT-Cond

• BT-Dusty

• BT-NextGen

• BT-Settl

• BT-Settl-CIFIST

• coolTLUSTY Y dwarf

• DRIFT-PHOENIX

• Exo-REM

• Morley et al. (2012) T/Y dwarf spectra

• petitCODE

• petitRADTRANS

• PHOENIX Husser et al. (2013)

• Saumon & Marley (2008)

• Sonora Bobcat

• Sonora Cholla

• Sonora Diamondback

• Sonora Elf Owl L-type

• Sonora Elf Owl T-type

• Sonora Elf Owl Y-type

Tip

It is also possible to add your own custom grid of model spectra with add_custom_model().

Tip

The available_models() method of the Database class can be used for printing a detailed overview of all available model grids:

from species import SpeciesInit
from species.data.database import Database

SpeciesInit()

database = Database()
database.available_models()

Evolutionary models

• AMES-Cond isochrones

• AMES-Dusty isochrones

• ATMO isochrones (CEQ, NEQ weak, NEQ strong)

• Baraffe et al. (2015) isochrones

• Linder et al. (2019) isochrones

• Saumon & Marley (2008) isochrones

• Sonora Bobcat isochrones

• All isochrones from the Phoenix grids

Spectral libraries

• IRTF Spectral Library

• SpeX Prism Spectral Libraries

• SDSS spectra by Kesseli et al. (2017)

• NIR spectra of young M/L dwarfs by Allers & Liu (2013)

• NIR spectra of young M/L dwarfs by Bonnefoy et al. (2014)

• Spectra of directly imaged planets and brown dwarfs

Photometric libraries

• Database of Ultracool Parallaxes

• Photometry from S. Leggett

• Magnitudes, stellar properties, and other parameters of directly imaged planets and brown dwarfs

• Parallaxes, photometry, and spectra from the SIMPLE database

Calibration

• All filters from the Filter Profile Service

• Latest flux-calibrated spectrum of Vega

Dust extinction

• Extinction models from dust-extinction

• Dust cross sections computed with PyMieScatt

• Optical constants adopted from Mollière et al. (2019)

Please give credit to the relevant references when using any of the external data in a publication. More information is available on the respective websites. Support for other datasets can be requested by creating an issue on the Github page.

Analysis tools

After adding the relevant data to the database, the user can take advantage of the suite of tools that have been implemented for spectral and photometric analysis. Here is an incomplete list of available features and tools:

• Converting between fluxes and magnitudes (see SyntheticPhotometry).

• Calculating synthetic photometry spectra (see SyntheticPhotometry).

• Interpolating and plotting model spectra (see ReadModel and plot_spectrum()).

• Wrapper for generating spectra with petitRADTRANS using various parameterizations for P-T structures, abundances, and clouds (see ReadRadtrans).

• Grid retrievals with Bayesian inference (see FitModel and plot_mcmc).

• Comparing a spectrum with a full grid of model spectra (see compare_model()).

• Free retrievals with a frontend for petitRADTRANS (see AtmosphericRetrieval).

• Creating color-magnitude diagrams (see ReadColorMagnitude and plot_color_magnitude).

• Creating color-color diagrams (see ReadColorColor and plot_color_color).

• Computing synthetic fluxes from isochrones and model spectra (see ReadIsochrone)

• Flux calibration of photometric and spectroscopic data (see ReadCalibration, FitModel, and FitSpectrum).

• Empirical comparison of spectra to infer the spectral type (see spectral_type()).

• Analyzing emission lines from accreting planets (see EmissionLine).