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Neutrinos offer a unique window to the distant, high-energy universe. Severalnext-generation instruments are being designed and proposed to characterize theflux of TeV--EeV neutrinos. The projected physics reach of the detectors isoften quantified with simulation studies. However, a complete Monte Carloestimate of detector performance is costly from a computational perspective,restricting the number of detector configurations considered when designing theinstruments. In this paper, we present a new Python-based software framework,toise, which forecasts the performance of a high-energy neutrino detector usingparameterizations of the detector performance, such as the effective areas,angular and energy resolutions, etc. The framework can be used to forecastperformance of a variety of physics analyses, including sensitivities todiffuse fluxes of neutrinos and sensitivity to both transient and steady statepoint sources. This parameterized approach reduces the need for extensivesimulation studies in order to estimate detector performance, and allows theuser to study the influence of single performance metrics, like the angularresolution, in isolation. The framework is designed to allow for multipledetector components, each with different responses and exposure times, andsupports paramterization of both optical- and radio-Cherenkov (Askaryan)neutrino telescopes. In the paper, we describe the mathematical concepts behindtoise and provide detailed instructive examples to introduce the reader to useof the framework.

toise: a framework to describe the performance of high-energy neutrino detectors