 Coverage excess from central quantiles at a zero-duration atom

Coverage excess from central quantiles at a zero-duration atom is the additional distributional probability included when a closed quantile interval captures an entire point mass at zero. Here coverage means probability under the stated duration model. For $L=\tau \max (0,X)$ with $X\sim N(\mu ,s^2)$, the zero mass is $p_0=\Phi (-\mu /s)$ and the positive part is continuous. Let $a=\alpha /2$, $b=1-\alpha /2$, and use the lower quantiles from quantiles of a clipped Gaussian acquisition duration.

For the closed central interval $[q_a,q_b]$, its probability isTo verify this, write the probability as $F(q_b)-F(q_a^{-})$. When both endpoints are positive, the lower left limit is $a$ and the upper value is $b$. Once the lower endpoint is zero, its left limit is zero; if the upper endpoint is zero too, the interval contains exactly the zero atom. Endpoint inclusion is therefore part of the probability calculation.

For a nominal 95 percent central construction, zero masses $0.01$, $0.20$ and $0.99$ give actual probabilities $0.95$, $0.975$ and $0.99$ respectively. In the middle case, the excess is $0.025$, not the whole $0.20$ atom: the interval already includes the probability between zero and the upper quantile.

As an original dimensional example, set $\tau =20\mathrm{ns}$, $s=0.02$ and $\mu /s\approx 0.84162$, giving $p_0=0.20$. The nominal central 95 percent interval is approximately $[0,1.1206]\mathrm{ns}$ and contains probability $0.975$. SciPy documents the normal cdf and inverse-cdf ppf used to evaluate these Gaussian-component values.^[ref]

A one-sided upper interval or a separately specified probability construction can target a different included probability. Randomized endpoint inclusion at a zero-duration atom would require an explicit randomization rule, not a change made silently after seeing an endpoint equal zero. NIST explains why interval interpretation requires the input distributions and the chosen construction.^[ref] Distributional probability for this conditional acquisition model is separate from a worst-case optical-energy guarantee or a confidence procedure for an unknown fixed parameter.