Robust measurements based on current large-scale structure surveys requireprecise knowledge of statistical and systematic errors. This can be obtainedfrom large numbers of realistic mock galaxy catalogues that mimic the observeddistribution of galaxies within the survey volume. To this end we present afast, distributed-memory, planar-parallel code, L-PICOLA, which can be used togenerate and evolve a set of initial conditions into a dark matter field muchfaster than a full non-linear N-Body simulation. Additionally, L-PICOLA has theability to include primordial non-Gaussianity in the simulation and simulatethe past lightcone at run-time, with optional replication of the simulationvolume. Through comparisons to fully non-linear N-Body simulations we find thatour code can reproduce the $z=0$ power spectrum and reduced bispectrum of darkmatter to within 2% and 5% respectively on all scales of interest tomeasurements of Baryon Acoustic Oscillations and Redshift Space Distortions,but 3 orders of magnitude faster. The accuracy, speed and scalability of thiscode, alongside the additional features we have implemented, make it extremelyuseful for both current and next generation large-scale structure surveys.L-PICOLA is publicly available at https://cullanhowlett.github.io/l-picola
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机译：基于当前大规模结构调查的稳健测量需要对统计和系统误差的准确了解。这可以从大量现实的模拟星系目录中获得，这些目录模仿了观测体积内观察到的星系分布。为此，我们提出了一种快速的分布式内存平面并行代码L-PICOLA，该代码可用于比暗非线性N-Body模拟更快地生成一组初始条件并将其演化为暗物质场。此外，L-PICOLA能够在模拟中包括原始非高斯性，并在运行时模拟过去的光锥，并且可以选择复制模拟量。通过与完全非线性N体仿真的比较，我们发现我们的代码可以重现所有感兴趣尺度上的$ z = 0 $功率谱并将暗物质双谱分别降低到2％和5％以内，以进行重子声振荡和Redshift测量空间失真，但速度要快3个数量级。该代码的准确性，速度和可扩展性，加上我们已实现的其他功能，使其对于当前和下一代大规模结构勘测极为有用。L-PICOLA可在https://cullanhowlett.github.io/l上公开获得。 -picola
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