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Fermionic Monte Carlo calculations with continuous auxiliary fields oftenencounter infinite variance problem from fermionic observables. This issuerenders the estimation of observables unreliable, even with an infinite numberof samples. In this work, we show that the infinite variance problem stems fromthe fermionic determinant. Also, we propose an approach to address this problemby employing a reweighting method that utilizes the distribution from an extratime-slice. Two strategies to compute the reweighting factor are explored: oneinvolves truncating and analytically calculating the reweighting factor, whilethe other employs a secondary Monte Carlo estimation. With Hubbard model as atestbed, we demonstrate that utilizing the sub-Monte Carlo estimation, coupledwith an unbiased estimator, offers a solution that effectively mitigates theinfinite variance problem at a minimal additional cost.

A solution for infinite variance problem of fermionic observables