Minimum-noise weights under baseline moment constraints

ID: minimum-noise-weights-under-baseline-moment-constraints

Minimum-noise weights under baseline moment constraints choose a linear pulse estimator with the smallest variance among those that preserve a specified signal and remove specified backgrounds. Write the reading vector as , where is a known signal template, the columns of contain baseline shapes, and the error covariance matrix is positive definite. For , require
Columns in implement the moment conditions for baseline-drift cancellation. The noise cost of polynomial baseline cancellation depends on which feasible weights are chosen.
If the constraint columns are independent, Lagrange multipliers give
For any feasible perturbation , and , so the variance increases by . This proves the minimum and connects the estimator to generalized least squares. The covariance propagation is the full quadratic form, not a sum that discards correlations.[ref]
For an original one-bin pulse example, use five equally spaced times , a signal present only at the centre, and independent errors of variance . To remove just constant and linear backgrounds, the optimal dimensionless weights are . Their variance is , lower than for the centre and its two nearest references alone. Multiplying the amplitude estimate by a bin duration gives energy variance . Extra uncontaminated references can reduce noise when the cancelled background family stays the same.
The inverse requires genuinely independent constraints. Constraint redundancy in baseline-noise minimization calls for removing redundant columns and checking feasibility; a signal template inside the nuisance span cannot be preserved while that span is rejected. NIST's before/after pulse baselines provide the physical measurement context.[ref] The five-point design is an original idealization and assumes no pulse signal in its reference samples.

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