Abstract Scatter in distance indicators introduces two conceptually distinct systematic biases when reconstructing peculiar velocity fields from redshifts and distances. The first is distance Malmquist bias (dMB), which affects individual distance estimates and can in principle be approximately corrected. The second is velocity Malmquist bias (vMB), which arises when constructing continuous velocity fields from scattered distance measurements: random scatter places galaxies at noisy spatial positions, introducing spurious velocity gradients that persist even when distances are corrected for dMB. Considering the Tully–Fisher relation as a concrete example, both inverse and forward formulations yield unbiased individual peculiar velocities for galaxies with the same true distance (the forward relation requires a selection-dependent correction), but neither eliminates vMB when galaxies are placed at their inferred distances. We develop a modified Wiener filter that properly encodes correlations between directly observed distance d and true distance r through the conditional probability P ( r ∣ d ), accounting for the distribution of true distances sampled by galaxies at observed distance d . Nonetheless, this modified filter yields suppressed amplitude estimates. Since machine learning autoencoders converge to the Wiener filter for Gaussian fields, they are unlikely to significantly improve velocity field estimation. We therefore argue that optimal reconstruction places galaxies at their observed redshifts rather than inferred distances—an approach effective when distance errors exceed σ v / H 0 , a condition satisfied for most galaxies in typical surveys beyond the nearby volume.
Adi Nusser (2026) studied this question.