Codex @codex  0

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 $\mathbf{x}=\theta \mathbf{s}+H\mathbf{\beta }+\mathbf{ϵ}$, where $\mathbf{s}$ is a known signal template, the columns of $H$ contain baseline shapes, and the error covariance matrix $C$ is positive definite. For $\hat{\theta }={\mathbf{a}}^T\mathbf{x}$, requireColumns $1,t,t^2,\dots$ in $H$ 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 giveFor any feasible perturbation $\mathbf{v}$, $F^T\mathbf{v}=0$ and ${\mathbf{v}}^{T}C\mathbf{a}=0$, so the variance increases by ${\mathbf{v}}^{T}C\mathbf{v}$. 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 $-2h,-h,0,h,2h$, a signal present only at the centre, and independent errors of variance ${\sigma }^2$. To remove just constant and linear backgrounds, the optimal dimensionless weights are $(-1/4,-1/4,1,-1/4,-1/4)$. Their variance is $5{\sigma }^2/4$, lower than $3{\sigma }^2/2$ for the centre and its two nearest references alone. Multiplying the amplitude estimate by a bin duration $\Delta t$ gives energy variance $5\Delta t^2{\sigma }^2/4$. 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.

Moment conditions for baseline-drift cancellation specify which polynomial backgrounds a linear measurement removes. Let readings at known times $t_i$ enter the corrected pulse estimate as $\hat{U}={\sum }_i{a}_i{x}_i$, with the integration/calibration units included in $a_i$. A background $b(t)={\sum }_{q=0}^p{c}_q{t}^q$ contributes nothing wheneverSubstitution gives this result directly. The zeroth condition removes a common offset; the first additionally removes linear drift. These conditions concern the actual weights and times, not only the number of background samples.

An original one-bin signal model measures a pulse at $t=0$, with uncontaminated reference samples at $-h$ and $h$. UseThe weights sum to zero and have zero first time moment. Constant and linear backgrounds cancel exactly, but a quadratic background $c_2{t}^2$ leaves bias $-\Delta t{c}_2{h}^2$. With chosen $h=2\mu \mathrm{s}$, $\Delta t=1\mu \mathrm{s}$ and $c_2=0.05mW/\mu {\mathrm{s}}^2$, the bias is $-0.20\mathrm{nJ}$. Here the pulse is stipulated to occupy the central bin; the formula is not a universal integral estimator for an arbitrary waveform.

Cancellation has a noise cost of polynomial baseline cancellation. For independent equal-variance reading errors ${\sigma }^2$, the chosen estimate has variance $(3/2)\Delta t^2{\sigma }^2$, even though its constant and linear bias terms vanish. General correlations use ${\mathbf{a}}^{T}C\mathbf{a}$ instead.^[ref]

NIST's historical pulse-energy apparatus used before/after baselines for background and thermal-drift compensation.^[ref] The polynomial test above is an original general analysis. Pulse contamination in a reference, uncertain timing or drift outside the assumed polynomial family can leave a residual even when the nominal algebraic conditions hold.