Fermi edge correction

Use a measured reference spectrum with known temperature and energy units. The reference and the data to correct must use the same analyzer settings and the same detector-coordinate definition. Restrict the correction to the detector-coordinate range covered by the reference fit.

For the corresponding Manager workflow, see Fermi edge correction.

Curved Fermi edge correction

Fit the edge over verified angle and energy ranges:

import erlab.analysis as era

edge_fit = era.gold.poly(
    gold_reference,
    angle_range=(-15, 15),
    eV_range=(-0.2, 0.2),
    temp=sample_temperature,
    vary_temp=False,
    bkg_slope=False,
    degree=2,
    plot=True,
)

Inspect the fitted edge centers, their uncertainties, and the polynomial before you apply the correction. Correct the reference or compatible sample data without overwriting the original array:

corrected = era.gold.correct_with_edge(data, edge_fit)

(Source code)

By default, erlab.analysis.gold.correct_with_edge() can change the eV coordinate and its length to retain the shifted spectra. The other dimensions remain in their original order. Set shift_coords=False only when the output must keep the original energy grid and shape. This setting can discard intensity at the grid boundaries.

Use use_step_edge=True and a unit-checked resolution estimate when the reference temperature is missing or unreliable. Do not derive an angle-dependent correction from sample-band positions.

See erlab.analysis.gold.poly() and erlab.analysis.gold.correct_with_edge() for accepted inputs and fit output.

Separate EDC fit ranges

Use a separate fit range for each EDC when the edge position changes substantially across a measured reference or when other spectral features make one fixed range unreliable. Set one outer energy range that contains the edge and usable background for every EDC:

import erlab.analysis as era

edge_energy_range = (-0.35, 0.20)
edge_fit = era.gold.poly(
    gold_reference,
    angle_range=(-15, 15),
    eV_range=edge_energy_range,
    adaptive=True,
    temp=sample_temperature,
    resolution=energy_resolution,
    vary_temp=False,
    degree=2,
    plot=True,
)

Inspect a difficult EDC and its estimated range when you need to check the selection:

selected_angle = 0.0
selected_edc = gold_reference.sel(alpha=selected_angle, method="nearest")
selected_edc = selected_edc.where(
    (selected_edc.eV >= min(edge_energy_range))
    & (selected_edc.eV <= max(edge_energy_range)),
    drop=True,
)
estimated_range = era.gold.guess_edge_fit_range(
    selected_edc,
    temp=sample_temperature,
    resolution=energy_resolution,
)
estimated_edc = selected_edc.where(
    (selected_edc.eV >= estimated_range[0]) & (selected_edc.eV <= estimated_range[1]),
    drop=True,
)
estimated_edc.plot()

(Source code)

Supply the measured temperature and an energy-resolution estimate in electronvolts. Use use_step_edge=True when the temperature is missing or unreliable. Adaptive range selection detects a falling edge. Do not use it to derive a reference correction from sample-band positions.

The outer eV_range limits the data available to every fit. With adaptive=True, ERLabPy estimates a separate interval inside that limit for each EDC. The estimate uses the expected thermal and instrumental edge width. It changes the fit region, not the edge model.

See erlab.analysis.gold.guess_edge_fit_range() for the estimator requirements.