Probing submillimeter number counts below the confusion limit: extreme-value statistics of the P(D) distribution and its modulation by gravitational lensing

Abstract: The shape of the submillimeter galaxy number counts below the confusion limit is a key record of cosmic star formation but is accessible only statistically, through the one-point distribution of map surface brightness, $P(D)$. Classical $P(D)$ analysis compresses the counts into flux-integrated constraints and requires a full instrument forward model. We introduce an extreme-value-theory analysis of the confusion $P(D)$ tail: the peaks-over-threshold formalism, in which exceedances above a threshold $u$ follow a generalized Pareto distribution (GPD). The GPD shape parameter $ξ(u)$ is a flux-resolved, normalization-free readout of the local logarithmic slope of the counts, and its gravitational-lensing modulation $Δξ(u)$ probes their local curvature. We derive analytic relations for both, validate them with end-to-end simulations, and apply the method to Planck 857 GHz maps and the Herschel/SPIRE 350 $μ$m map of GAMA-09. At Planck's 5' resolution the tail reflects the bright, clustered sky rather than the faint counts, though the background alone excludes the single power-law count model. At SPIRE resolution $ξ$ rises markedly with threshold, consistent with the strongly lensed bright population (a first detection of lensing in a $P(D)$ tail), and the bright-masked map favors the Schechter model. Behind galaxy clusters we set the first calibrated upper limits on $Δξ(u)$. CCAT/FYST should separate the count models directly, but a cluster-lensing detection needs more $10<sup>{15}\,M_\odot$ clusters than the sky contains. The GPD tail statistic thus discriminates the functional form of the counts at fluxes of order the threshold, below the detection limit, invariant to map mean, gain and count normalization, and robust to clustering; lensing supplies a calibrated ruler for count features, whose detection awaits deep, high-resolution surveys of massive clusters.