Data

On this page you find a list of the supported data. Please give credit to the relevant references when using any of the external data in a publication. Support for other datasets can be requested by creating an issue on the GitHub page.

Atmospheric models

Model

$T_\mathrm{eff}$ range

$\lambda/\Delta\lambda$

Wavelength range

Reference

AMES-Cond

\([100, 6600]\,\mathrm{K}\)

\(2000\)

\([0.5, 40]\,\mu\mathrm{m}\)

Allard et al. (2001)

AMES-Dusty

\([500, 4000]\,\mathrm{K}\)

\(2000\)

\([0.5, 40]\,\mu\mathrm{m}\)

Allard et al. (2001)

ATMO

\([200, 3000]\,\mathrm{K}\)

\(1000\)

\([0.2, 2000]\,\mu\mathrm{m}\)

Phillips et al. (2020)

ATMO CEQ

\([200, 3000]\,\mathrm{K}\)

\(2000\)

\([0.2, 2000]\,\mu\mathrm{m}\)

Phillips et al. (2020)

ATMO NEQ strong

\([200, 1800]\,\mathrm{K}\)

\(2000\)

\([0.2, 2000]\,\mu\mathrm{m}\)

Phillips et al. (2020)

ATMO NEQ weak

\([200, 1800]\,\mathrm{K}\)

\(2000\)

\([0.2, 2000]\,\mu\mathrm{m}\)

Phillips et al. (2020)

ATMO (Petrus et al. 2023)

\([800, 3000]\,\mathrm{K}\)

\(3000\)

\([0.2, 30]\,\mu\mathrm{m}\)

Petrus et al. (2023)

ATMO (Petrus et al. 2023; high-res)

\([800, 3000]\,\mathrm{K}\)

\(10000\)

\([0.2, 2.5]\,\mu\mathrm{m}\)

Petrus et al. (2023)

Blackbody

\([10, 20000]\,\mathrm{K}\)

\(1000\)

\([0.1, 5000]\,\mu\mathrm{m}\)

Planck (1901)

BT-Cond

\([800, 4000]\,\mathrm{K}\)

\(5000\)

\([0.1, 100]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-Cond

\([2600, 4000]\,\mathrm{K}\)

\(5000\)

\([0.1, 100]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-Dusty

\([1000, 4000]\,\mathrm{K}\)

\(5000\)

\([0.1, 100]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-NextGen

\([2600, 30000]\,\mathrm{K}\)

\(2000\)

\([0.1, 5000]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-NextGen (high-res)

\([2600, 30000]\,\mathrm{K}\)

\(200000\)

\([0.1, 50]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-NextGen (optical)

\([2600, 30000]\,\mathrm{K}\)

\(500000\)

\([0.1, 1.0]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-NextGen (subsolar)

\([2600, 30000]\,\mathrm{K}\)

\(2000\)

\([0.1, 50]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-Settl

\([400, 4000]\,\mathrm{K}\)

\(2000\)

\([0.1, 500]\,\mu\mathrm{m}\)

Allard et al. (2012)

BT-Settl CIFIST

\([1200, 7000]\,\mathrm{K}\)

\(10000\)

\([0.1, 5000]\,\mu\mathrm{m}\)

Allard et al. (2012)

DRIFT-PHOENIX

\([1000, 3000]\,\mathrm{K}\)

\(2000\)

\([0.1, 500]\,\mu\mathrm{m}\)

Helling et al. (2008)

Exo-REM (low-res)

\([400, 2000]\,\mathrm{K}\)

\(500\)

\([0.34, 250.0]\,\mu\mathrm{m}\)

Charnay et al. (2018)

Exo-REM (cloud-free)

\([200, 1950]\,\mathrm{K}\)

\(500\)

\([0.34, 250.0]\,\mu\mathrm{m}\)

Charnay et al. (2018)

Exo-REM (high-res)

\([400, 2000]\,\mathrm{K}\)

\(20000\)

\([0.7, 250.0]\,\mu\mathrm{m}\)

Charnay et al. (2018)

Exo-REM (CO isotopologues)

\([400, 2000]\,\mathrm{K}\)

\(20000\)

\([0.7, 250.0]\,\mu\mathrm{m}\)

Charnay et al. (2018)

Koester WD

\([5000, 80000]\,\mathrm{K}\)

\(1000\)

\([0.1, 3]\,\mu\mathrm{m}\)

Koester (2010)

coolTLUSTY clear EQ

\([200, 600]\,\mathrm{K}\)

\(4000\)

\([0.4, 300]\,\mu\mathrm{m}\)

Lacy & Burrows (2023)

coolTLUSTY clear NEQ

\([200, 600]\,\mathrm{K}\)

\(4000\)

\([0.4, 300]\,\mu\mathrm{m}\)

Lacy & Burrows (2023)

coolTLUSTY thick clouds EQ

\([200, 450]\,\mathrm{K}\)

\(4000\)

\([0.4, 300]\,\mu\mathrm{m}\)

Lacy & Burrows (2023)

coolTLUSTY thick clouds NEQ

\([200, 450]\,\mathrm{K}\)

\(4000\)

\([0.4, 300]\,\mu\mathrm{m}\)

Lacy & Burrows (2023)

coolTLUSTY thin clouds EQ

\([200, 450]\,\mathrm{K}\)

\(4000\)

\([0.4, 300]\,\mu\mathrm{m}\)

Lacy & Burrows (2023)

coolTLUSTY thin clouds NEQ

\([200, 450]\,\mathrm{K}\)

\(4000\)

\([0.4, 300]\,\mu\mathrm{m}\)

Lacy & Burrows (2023)

Morley T/Y dwarfs

\([400, 1300]\,\mathrm{K}\)

\(6000\)

\([0.6, 30]\,\mu\mathrm{m}\)

Morley et al. (2012)

petitCODE cool clear

\([500, 1000]\,\mathrm{K}\)

\(1000\)

\([0.1, 250]\,\mu\mathrm{m}\)

Mollière et al. (2015)

petitCODE cool cloudy

\([500, 850]\,\mathrm{K}\)

\(1000\)

\([0.1, 250]\,\mu\mathrm{m}\)

Mollière et al. (2015)

petitCODE hot clear

\([1000, 2000]\,\mathrm{K}\)

\(1000\)

\([0.1, 250]\,\mu\mathrm{m}\)

Mollière et al. (2015)

petitCODE hot cloudy

\([1000, 2000]\,\mathrm{K}\)

\(1000\)

\([0.1, 250]\,\mu\mathrm{m}\)

Mollière et al. (2015)

petitCODE clear (Linder et al. 2019)

\([150, 1000]\,\mathrm{K}\)

\(1000\)

\([0.1, 250]\,\mu\mathrm{m}\)

Linder et al. (2019)

petitCODE cloudy (Linder et al. 2019)

\([150, 1000]\,\mathrm{K}\)

\(1000\)

\([0.1, 250]\,\mu\mathrm{m}\)

Linder et al. (2019)

PHOENIX (Husser et al. 2013)

\([2300, 12000]\,\mathrm{K}\)

\(20000\)

\([0.5, 5]\,\mu\mathrm{m}\)

Husser et al. (2013)

Saumon & Marley (2008) clear

\([500, 2400]\,\mathrm{K}\)

\(6000\)

\([0.4, 50]\,\mu\mathrm{m}\)

Saumon & Marley (2008)

Saumon & Marley (2008) cloudy

\([500, 2400]\,\mathrm{K}\)

\(6000\)

\([0.4, 50]\,\mu\mathrm{m}\)

Saumon & Marley (2008)

Sonora Cholla

\([500, 1300]\,\mathrm{K}\)

\(5000\)

\([1, 250]\,\mu\mathrm{m}\)

Karalidi et al. (2021)

Sonora Bobcat

\([200, 2400]\,\mathrm{K}\)

\(5000\)

\([0.4, 50]\,\mu\mathrm{m}\)

Marley et al. (2021)

Sonora Bobcat C/O

\([200, 2400]\,\mathrm{K}\)

\(5000\)

\([0.4, 50]\,\mu\mathrm{m}\)

Marley et al. (2021)

Sonora Elf Owl

\([1300, 2400]\,\mathrm{K}\)

\(60000\)

\([0.6, 15]\,\mu\mathrm{m}\)

Mukherjee et al. (2023)

Sonora Elf Owl

\([575, 1200]\,\mathrm{K}\)

\(60000\)

\([0.6, 15]\,\mu\mathrm{m}\)

Mukherjee et al. (2023)

Sonora Elf Owl

\([275, 550]\,\mathrm{K}\)

\(60000\)

\([0.6, 15]\,\mu\mathrm{m}\)

Mukherjee et al. (2023)

Sonora Diamondback

\([900, 2400]\,\mathrm{K}\)

\(6000\)

\([0.3, 250]\,\mu\mathrm{m}\)

Morley et al. (2024)

Sonora Diamondback (high-res)

\([900, 2400]\,\mathrm{K}\)

\(30000\)

\([0.3, 250]\,\mu\mathrm{m}\)

Morley et al. (2024)

SPHINX

\([2000, 4000]\,\mathrm{K}\)

\(250\)

\([0.1, 20]\,\mu\mathrm{m}\)

Iyer et al. (2023)

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()

Tip

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

Evolutionary models

Spectral libraries

Photometric libraries

Calibration

Dust extinction