Blazar emission modeling¶

MMDC supports three blazar broadband emission models:

Model	Description	Typical fit time
`SSC`	Synchrotron Self-Compton	10–15 s
`EIC`	External Inverse Compton	about 1 min
`HADRONIC`	Hadronic emission model	about 2 min

Fit times are the run on the server, without any wait in the queue. batch_infer and wait_for_batch return about a second after the fit ends.

Input Data Format¶

All modeling endpoints need a CSV file with frequency, flux and flux_err columns (case-sensitive, lowercase):

frequency,flux,flux_err
1.00e+09,2.50e-14,3.00e-15
4.85e+09,3.10e-14,2.80e-15
...

Columns may come in any order and other columns are ignored. Upper limits (is_ul true, or flag UL) and rows without a positive flux_err are dropped.

An SED from client.sed can be fitted directly: its CSV (freq_hz, nufnu, nufnu_err, is_ul, …) is accepted as well. Keep one period, or the file is refused as too variable:

sed = client.sed.get(ra=166.113808, dec=38.208833, name="Mrk 421")
sed.between(59000, 59030).to_csv("mrk421.csv")
result = client.modeling.batch_infer("mrk421.csv", z=0.031, ebl=True, model_type="SSC")

Validate CSV Before Submitting¶

validation = client.modeling.validate_csv("observations.csv")

print(validation.success)          # True/False
print(validation.data_points)      # Number of valid rows
print(validation.columns)          # ["frequency", "flux", "flux_err"]
print(validation.frequency_range)  # [min, max]
print(validation.flux_range)       # [min, max]

SSC Model Fitting¶

result = client.modeling.batch_infer(
    "observations.csv",
    z=0.158,            # Redshift (0 < z <= 4.99)
    ebl=True,           # EBL absorption correction
    model_type="SSC",
)

print(result.pdf_link)                   # URL to PDF report
print(result.csv_best_parameters_link)   # URL to best-fit parameters CSV
print(result.csv_best_model_link)        # URL to best-fit model CSV
print(result.best_parameters)            # Dict of parameter name -> {value, error}

Fixed Parameters¶

Fix specific model parameters instead of fitting them:

result = client.modeling.batch_infer(
    "observations.csv",
    z=0.158,
    ebl=True,
    model_type="SSC",
    fixed_parameters={
        "log_B": -1.5,
        "lorentz_factor": 20.0,
    },
)

SSC parameters: log_B, log_electron_luminosity, log_gamma_cut, log_gamma_min, log_radius, lorentz_factor, spectral_index

EIC parameters: log_B, log_Ld, log_MBH, log_electron_luminosity, log_gamma_cut, log_gamma_min, log_radius, lorentz_factor, spectral_index, log_nu_BLR, log_nu_DT

HADRONIC parameters: log_B, log_Le, log_gamma_e_cut, log_gamma_e_min, log_gamma_p_cut, log_Lp, log_R, lorentz_factor, pe, pp

EIC Model Fitting¶

result = client.modeling.batch_infer(
    "observations.csv",
    z=0.5,
    ebl=True,
    model_type="EIC",
    fixed_parameters={"log_nu_BLR": 15.0, "log_nu_DT": 13.5},
)

HADRONIC Model with Neutrino Parameters¶

Hadronic models require additional neutrino likelihood parameters. Choose either Poisson or chi-square likelihood:

Poisson likelihood:

result = client.modeling.batch_infer(
    "observations.csv",
    z=1.0,
    ebl=True,
    model_type="HADRONIC",
    likelihood_type="poisson",
    n_icecube=3,       # Number of IceCube neutrino events
    dt=12.0,           # Observation period in months
)

Chi-square likelihood:

result = client.modeling.batch_infer(
    "observations.csv",
    z=1.0,
    ebl=True,
    model_type="HADRONIC",
    likelihood_type="chi2",
    x1=100.0,          # First neutrino energy (TeV)
    x2=200.0,          # Second neutrino energy (TeV)
    y=-12.0,           # Neutrino flux log value
)