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We examine the possibility of applying the baryonic acoustic oscillation reconstruction method to improve the neutrino mass Σm ν constraint. Thanks to the Gaussianization of the process, we demonstrate that the reconstruction algorithm could improve the measurement accuracy by roughly a factor of two. On the other hand, the reconstruction process itself becomes a source of systematic error. While the algorithm is supposed to produce the displacement field from a density distribution, various approximations cause the reconstructed output to deviate on intermediate scales. Nevertheless, it is still possible to benefit from this Gaussianized field, given that we can carefully calibrate the “transfer function” between the reconstruction output and theoretical displacement divergence from simulations. The limitation of this approach is then set by the numerical stability of this transfer function. With an ensemble of simulations, we show that such systematic error could become comparable to statistical uncertainties for a DESI-like survey and be safely neglected for other less ambitious surveys.

Constraining neutrino mass remains an elusive challenge in modern physics. Precision measurements are expected from several upcoming cosmological probes of large-scale structure. Achieving this goal relies on an equal level of precision from theoretical predictions of neutrino clustering. Numerical simulations of the non-linear evolution of cold dark matter and neutrinos play a pivotal role in this process. We incorporate neutrinos into the cosmological N-body code CUBEP3M and discuss the challenges associated with pushing to the extreme scales demanded by the neutrino problem. We highlight code optimizations made to exploit modern high performance computing architectures and present a novel method of data compression that reduces the phase-space particle footprint from 24 bytes in single precision to roughly 9 bytes. We scale the neutrino problem to the Tianhe-2 supercomputer and provide details of our production run, named TianNu, which uses 86% of the machine (13,824 compute nodes). With a total of 2.97 trillion particles, TianNu is currently the world's largest cosmological N-body simulation and improves upon previous neutrino simulations by two orders of magnitude in scale. We finish with a discussion of the unanticipated computational challenges that were encountered during the TianNu runtime., Comment: Accepted for publication in Research in Astronomy and Astrophysics