 Pulse-contrast denominator acceptance rule

A pulse-contrast denominator acceptance rule decides which shots may be normalized by their measured central signal. For background-corrected side signal $S$ and central signal $C$, considerThe rule prevents normalization by an arbitrarily small accepted denominator. It also changes the estimand: averaging accepted ratios estimates $\mathbb{E}[R\mid A]$, a conditional expectation. An unconditional expected value is a different target and need not exist. The pulse-contrast ratio denominator noise explains why even the unfiltered ratio mean can differ from the ratio of mean signals.

The rule provides a precise stability guarantee when its moment assumptions hold. Interpret the indicator-weighted integrand below as zero outside $A$. If $p_A=\mathbb{P}(A)>0$ and $\mathbb{E}{S}^2<\infty$, thenThis proves finite ratio moments under a positive denominator cutoff. The accepted ratio has finite variance and finite first absolute moment, the latter by the Cauchy-Schwarz inequality. No independence of $S$ and $C$ was used. The bound concerns mathematical stability, not whether accepted shots represent the full pulse population.

For an original brightness-selection example, suppose half the shots have $(C,S)=(2,0.02)$ and half have $(10,0.02)$. The two ratios are $0.01$ and $0.002$, so their expected value over all shots is $0.006$. A cutoff $c_{\min }=5$ retains only the bright group, giving accepted conditional expectation $0.002$. Meanwhile the ratio of overall mean signals is $0.02/6\approx 0.003333$. These three quantities answer different questions. Physical dependence between brightness and temporal shape can make the choice consequential even without readout noise.

Selection bias from a noisy denominator threshold arises even at fixed true brightness. Consider another original model with constant $S=s>0$ and measured $C\sim N(\mu ,{\sigma }^2)$, where $\mu >0$. Write $z=(c_{\min }-\mu )/\sigma$, with $\varphi$ the standard normal density and $\Phi$ the standard normal distribution function. The accepted central measurements have a truncated normal distribution, withTo derive the second identity, standardize $C$ and use ${\varphi }^{\prime }(x)=-x\varphi (x)$, so ${\int }_z^{\infty }x\varphi (x)dx=\varphi (z)$. The density-to-tail quotient is an inverse Mills ratio.

Choose $\mu =5$, $\sigma =1$, $c_{\min }=6$ and $s=0.05$ in arbitrary signal units. The true-signal ratio is $s/\mu =0.01$. Only $15.87\%$ of measured shots pass, and the conditional expectation of their measured denominator is $6.5251$. Since $c\mapsto 1/c$ is a convex function for $c>0$, Jensen's inequality and the cutoff implyThe accepted ratios are necessarily biased downward relative to this model's true-signal ratio. The cutoff selects upward central-readout errors; increasing the accepted-shot count does not remove that conditioning.

In the historical single-shot correlator, nonlinear mixing encoded delays spatially and an imaging lens recorded the third-harmonic signal on a filtered CCD detector.^[ref] Selecting a trace by its central pixels conditions subsequent normalization on the same recorded data. Preserve the threshold, background treatment, number attempted and number accepted alongside the shot-resolved pulse-contrast estimator. For independent shots sharing the same acceptance probability, the accepted count has a binomial distribution, carrying information that disappears if only normalized accepted traces are retained.