GEOMAX: beyond linear compression for three-point galaxy clustering statistics
GEOMAX: beyond linear compression for three-point galaxy clustering statistics
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DOI：
10.1093/mnras/staa1941
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发表时间：
2019-12
影响因子：
4.8
通讯作者：
D. Gualdi;H. Gil-Marín;M. Manera;B. Joachimi;O. Lahav
中科院分区：
文献类型：
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作者：
D. Gualdi;H. Gil-Marín;M. Manera;B. Joachimi;O. Lahav
我们提出了GEOMAX算法及其Python实现的两步压缩的双谱测量。第一步组双谱的几何性质，他们的参数，第二步，然后最大限度地提高费舍尔信息相对于一组选定的模型参数在每个组。该算法只需要数据向量对参数的导数和少量的模拟数据，从而产生有效的非线性压缩。通过将GEOMAX应用于来自BOSS DR 12 CMASS红移空间星系聚类数据的双谱测量，我们将推断参数（b1，b2，f，σ8）的68%可信区间相对于标准MCMC在完整数据向量上减少了50.4%，56.1%，33.2%和38.3%。我们对100个星系模型的压缩方法进行了分析和比较，以测试改进的统计意义。平均而言，GEOMAX的性能比单独的几何或最大线性压缩高出15%，并且与无损压缩一致。鉴于其灵活性，GEOMAX方法有可能最佳地利用各种宇宙学探测器的三点统计数据，如来自当前和未来宇宙学数据集（如DESI，欧几里得，PFS和SKA）的弱透镜或线强度图。
We present the GEOMAX algorithm and its python implementation for a two-step compression of bispectrum measurements. The first step groups bispectra by the geometric properties of their arguments; the second step then maximizes the Fisher information with respect to a chosen set of model parameters in each group. The algorithm only requires the derivatives of the data vector with respect to the parameters and a small number of mock data, producing an effective, non-linear compression. By applying GEOMAX to bispectrum monopole measurements from BOSS DR12 CMASS redshift-space galaxy clustering data, we reduce the 68 per cent credible intervals for the inferred parameters (b1, b2, f, σ8) by 50.4, 56.1, 33.2, and 38.3 per cent with respect to standard MCMC on the full data vector. We run the analysis and comparison between compression methods over 100 galaxy mocks to test the statistical significance of the improvements. On average, GEOMAX performs ∼15 per cent better than geometrical or maximal linear compression alone and is consistent with being lossless. Given its flexibility, the GEOMAX approach has the potential to optimally exploit three-point statistics of various cosmological probes like weak lensing or line-intensity maps from current and future cosmological data sets such as DESI, Euclid, PFS, and SKA.