fastGRAHSP 0.1.0

Fast neural network simulator for fluxes of galaxies and quasars.

Fast neural network simulator for fluxes of galaxies and quasars.

Generates observed fluxes for common photometric filters based on intrinsic galaxy properties.

fastGRAHSP can be used for simulating surveys extremely rapidly.

Usage

You need to prepare a model parameter array (see example below):

• log10 tau: time-scale of starformation in Myr

• log10 age: age of oldest stars in Myr

• log10 AFeII: amplitude of lines

• log10 Alines: amplitude of lines

• linewidth: width of broad emission lines

• Si: strength of 12µm silicate feature (positive: emission, negative: absorption)

• fcov: covering factor, based on the ratio of 12µm to 5100A luminosity ratio

• COOLlam: central wavelength in µm of cold torus component

• COOLwidth: width (in dex) of cold torus component

• log10 HOTfcov: peak luminosity ratio of hot to cold torus component

• HOTlam: central wavelength in µm of hot torus component

• HOTwidth: width (in dex) of hot torus component

• plslope: AGN power law slope

• plbendloc: wavelength of UV bend.

• log10 plbendwidth: width of UV bend (in dex).

• log10 EBV: E(B-V) attenuation coefficient of entire system.

• log10 EBV_AGN: E(B-V) attenuation coefficient of nucleus.

• alpha: dust slope of Dale+ model

• z: redshift

• M: log10 of galaxy stellar mass in solar masses

• L5100A: log10 of AGN luminosity at 5100A in erg/s.

For example, you can simulate the fluxes of 10000 galaxies like so:

N = 10000
df = dict(
    tau=np.random.uniform(1, 10000, size=N),
    age=np.random.uniform(1, 10000, size=N),
    AFeII=10**np.random.uniform(-1, 1, size=N),
    Alines=10**np.random.uniform(-1, 1, size=N),
    linewidth=np.random.uniform(1000, 30000, size=N),
    Si=np.random.uniform(-5, 5, size=N),
    fcov=np.random.uniform(0, 1, size=N),
    COOLlam=np.random.uniform(12, 28, size=N),
    COOLwidth=np.random.uniform(0.3, 0.9, size=N),
    HOTfcov=np.random.uniform(0, 10, size=N),
    HOTlam=np.random.uniform(1.1, 4.3, size=N),
    HOTwidth=np.random.uniform(0.3, 0.9, size=N),
    plslope=np.random.uniform(-2.6, -1.3, size=N),
    plbendloc=10**np.random.uniform(1.7, 2.3, size=N),
    plbendwidth=10**np.random.uniform(-2, 0, size=N),
    uvslope = np.zeros(N),
    EBV=10**np.random.uniform(-2, -1, size=N),
    EBV_AGN=10**np.random.uniform(-2, 1, size=N),
    alpha=np.random.uniform(1.2, 2.7, size=N),
    z=np.clip(np.random.normal(size=N)**2, 0, 6),
    M=10**np.random.uniform(7, 12, size=N),
    L5100A=10**np.random.uniform(38, 46, size=N),
)

emulator_args = np.transpose([
    np.log10(df['tau']),
    np.log10(df['age']),
    np.log10(df['AFeII']),
    np.log10(df['Alines']),
    df['linewidth'],
    df['Si'],
    df['fcov'],
    df['COOLlam'],
    df['COOLwidth'],
    df['HOTlam'],
    df['HOTwidth'],
    np.log10(df['HOTfcov']),
    df['plslope'],
    df['plbendloc'],
    np.log10(df['plbendwidth']),
    df['uvslope'],
    np.log10(df['EBV']),
    np.log10(df['EBV_AGN']),
    df['alpha'],
    df['M'],
    df['L5100A'],
    df['z'],
    df['EBV'] + df['EBV_AGN'],
])
results = predict_fluxes(emulator_args)
total_fluxes, total_columns, GAL_fluxes, GAL_columns, AGN_fluxes, AGN_columns = results
i = total_columns.index('WISE1')
print(total_fluxes[:, i])  # WISE1 flux in mJy

Licence

GPLv3 (see LICENCE file). If you require another license, please contact me.

Release Notes

0.1.0 (2025-06-10)

• First version