MOSAIC Best Practices

MOSAIC Best Practices#

MOSAIC mode allows ForMoSA to fit multiple datasets simultaneously, each with its own likelihood, while sharing a common set of physical parameters. This page explains how MOSAIC works, when to use it, and how to avoid common pitfalls.

What MOSAIC does#

In standard mode, ForMoSA evaluates a single log-likelihood against one observation. In MOSAIC mode, it evaluates one log-likelihood per observation and combines them into a meta-likelihood:

\[\ln \mathcal{L}_\text{total} = \sum_{i=0}^{N-1} \ln \mathcal{L}_i\]

Each observation can have its own wavelength range (wav_fit_i), likelihood type (logL_type_i), and intercalibration factor (alpha_i). Physical parameters like T_eff, log g, and radius are shared across all observations.

Setting up MOSAIC#

Provide one .fits file per instrument in observation_path:

from ForMoSA.config.global_config import ConfigPath, ConfigInversion, ConfigParameters

config_path = ConfigPath(
    observation_path = [
        "data/sphere_yjh.fits",   # index 0
        "data/gravity_k.fits",    # index 1
        "data/nircam_photo.fits", # index 2
    ],
    adapt_store_path = "adapted_grid/",
    result_path      = "results/",
    model_path       = "atm_grids/ExoREM.nc",
)

Use per-instrument suffixes for local parameters:

config_parameters = ConfigParameters(
    par1    = ["uniform", "800",  "2000"],   # Teff — shared
    par2    = ["uniform", "3.0",  "5.5"],    # log g — shared
    r       = ["uniform", "0.5",  "3.0"],    # radius — shared
    d       = ["constant", "27.7"],           # distance — shared
    alpha_0 = ["uniform", "0.5", "2.0"],     # intercal. for SPHERE
    alpha_1 = ["uniform", "0.5", "2.0"],     # intercal. for GRAVITY
    alpha_2 = ["uniform", "0.5", "2.0"],     # intercal. for NIRCam
)

Per-instrument logL_type and wav_fit in ConfigInversion:

config_inversion = ConfigInversion(
    ns_algo   = "pymultinest",
    npoints   = 300,
    logL_type = ["chi2", "chi2", "chi2"],
    wav_fit   = ["0.95, 1.65", "2.0, 2.45", "2.0, 5.0"],
)

When to use MOSAIC#

  • Heterogeneous datasets — different instruments with different resolutions and calibration histories.

  • Broadband SED coverage — combining optical photometry, near-IR spectroscopy, and mid-IR photometry.

  • Mitigating calibration biases — per-instrument alpha parameters absorb systematic flux offsets without corrupting the shared physical posteriors.

Note

See Ravet et al. (2025) for a detailed study of how MOSAIC handles biases in heterogeneous datasets for \(\beta\) Pic b.

Common pitfalls#

Too many alpha parameters : If every observation has a free alpha, the likelihood surface can become degenerate — especially when observations overlap in wavelength. As a rule of thumb, fix alpha for your most reliably calibrated dataset and let it float for the others.

Resolution mismatch across datasets : The target_res_mod in ConfigAdapt must be set consistently. If one instrument sees the grid at R=4000 and another at R=100, the adapted sub-grids will have different wavelength samplings — which is correct — but make sure wav_fit for each observation only covers wavelengths where that instrument actually has data.

Unequal dataset weights : A high-resolution spectrum with 2000 wavelength points will dominate the meta-likelihood over a 3-point photometric SED. Consider whether this is physically justified, or whether a noise-scaling likelihood (chi2_noisescaling) is more appropriate for the spectroscopic observation.

MOSAIC vs independent runs : Running ForMoSA separately on each instrument and then comparing the posteriors is not equivalent to MOSAIC. MOSAIC enforces a shared physical model during sampling; independent runs cannot enforce that constraint.