Detection of spatial clustering in the 1000 richest SDSS DR8 redMaPPer clusters with nearest neighbor distributions

Distances to the k-nearest-neighbor (kNN) data points from volume-filling query points are a sensitive probe of spatial clustering. Here, we present the first application of kNN summary statistics to observational clustering measurement, using the 1000 richest redMaPPer clusters (0.1 <= z <= 0.3) from the SDSS DR8 catalog. A clustering signal is defined as a difference in the cumulative distribution functions (CDFs) of kNN distances from fixed query points to the observed clusters versus a set of unclustered random points. We find that the k = 1, 2-NN CDFs of redMaPPer deviate significantly from the randoms' across scales of 35 to 155 Mpc, which is a robust signature of clustering. In addition to kNN, we also measure the two-point correlation function for the same set of redMaPPer clusters versus random points, which shows a noisier and less significant clustering signal within the same radial scales. Quantitatively, the chi(2) distribution for both the kNN-CDFs and the two-point correlation function measured on the randoms peak at chi(2) similar to 50 (null hypothesis), whereas the kNN-CDFs (chi(2) similar to 300, p = 1.54 x 10(-36)) pick up a much more significant clustering signal than the two-point function (chi(2) similar to 100, p = 1.16 x 10(-6)) when measured on redMaPPer. Finally, the measured 3NN and 4NN CDFs deviate from the predicted k = 3, 4-NN CDFs assuming an ideal Gaussian field, indicating a non-Gaussian clustering signal for redMaPPer clusters, although its origin might not be cosmological due to observational systematics. Therefore, kNN serves as a more sensitive probe of clustering complementary to the two point correlation function, providing a novel approach for constraining cosmology and galaxy-halo connection.