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The increasing statistical precision of photometric redshift surveys requiresimproved accuracy of theoretical predictions for large-scale structureobservables to obtain unbiased cosmological constraints. In $\Lambda$CDMcosmologies, massive neutrinos stream freely at small cosmological scales,suppressing the small-scale power spectrum. In massive neutrino cosmologies,galaxy bias modeling needs to accurately relate the scale-dependent growth ofthe underlying matter field to observed galaxy clustering statistics. In thiswork, we implement a computationally efficient approximation of theneutrino-induced scale-dependent bias (NISDB). Through simulated likelihoodanalyses of Dark Energy Survey Year 3 (DESY3) and Legacy Survey of Space andTime Year 1 (LSSTY1) synthetic data that contain an appreciable NISDB, weexamine the impact of linear galaxy bias and neutrino mass modeling choices oncosmological parameter inference. We find model misspecification of the NISDBapproximation and neutrino mass models to decrease the constraining power ofphotometric galaxy surveys and cause parameter biases in the cosmologicalinterpretation of future surveys. We quantify these biases and devisemitigation strategies.

Modeling Neutrino-Induced Scale-Dependent Galaxy Clustering for Photometric Galaxy Surveys