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Context: When selecting a classifier to use for a supernova Ia (SN Ia)cosmological analysis, it is common to make decisions based on metrics ofclassification performance, i.e. contamination within the photometricallyclassified SN Ia sample, rather than a measure of cosmological constrainingpower. If the former is an appropriate proxy for the latter, this practicewould save those designing an analysis pipeline from the computational expenseof a full cosmology forecast. Aims: This study tests the assumption thatclassification metrics are an appropriate proxy for cosmology metrics. Methods:We emulate photometric SN Ia cosmology samples with controlled contaminationrates of individual contaminant classes and evaluate each of them under a setof classification metrics. We then derive cosmological parameter constraintsfrom all samples under two common analysis approaches and quantify the impactof contamination by each contaminant class on the resulting cosmologicalparameter estimates. Results: We observe that cosmology metrics are sensitiveto both the contamination rate and the class of the contaminating population,whereas the classification metrics are insensitive to the latter. Conclusions:We therefore discourage exclusive reliance on classification-based metrics forcosmological analysis design decisions, e.g. classifier choice, and insteadrecommend optimizing using a metric of cosmological parameter constrainingpower.

Are classification metrics good proxies for SN Ia cosmological constraining power?