IceCube_kNN_examples

The repository (IceCubekNN-main.zip file) contains the Python codes, data, and Jupyter notebooks that illustrate the computations of some key quantities and reproduce some of the figures in the paper submitted to The Astrophysical Journal:

Title: High-energy Neutrino Source Cross-correlations with Nearest Neighbor Distributions (arXiv:2406.00796)
Authors: Zhuoyang Zhou, Jessi Cisewski-Kehe, Ke Fang, and Arka Banerjee. 

Please download and unzip the IceCubekNN-main.zip file before running the notebooks.

Note that almost all experiments in the original paper are done with a high throughput computing cluster.
Therefore, we only provide simpler versions of the codes without any parallelization and some computed data (like the cross-correlations at a specific f_astro) obtained from our experiments.

We thank Michael Larson for his help with using the IceCube public ten-year point-source data.
Part of the codes, including utils.py, background_generator.py, and signal_generator.py, used in the paper for astrophysical and atmospheric events and repositories are from the GitHub repository created by Michael Larson: https://github.com/mjlarson/I3PublicDataSampler. Please visit the repository for a more detailed illustration of events generation.

1. IceCube_kNN_CSRxCSR: This is to illustrate how to compute the kNN-CDFs, joint kNN-CDFs, and kNN-CDFs cross-correlation on a group of complete spatial randomness data (CSR data) on the surface of a unit sphere. It will compute and plot the cross-correlations between uncorrelated samples (two different CSR data) and correlated data (two subsamples from the same group of CSR data).

2. IceCube_kNN_Events_Generation: This notebook illustrates the synthetic events generation (including maskings) for atmospheric-only events and a combined atm. + astro. sample with f_astro ~ 0.0374. It contains the plotting for the synthetic astrophysical neutrinos and background-only events with aitoff projection.

3. IceCube_kNN_LRT-MLE: This notebook illustrates how to conduct the likelihood ratio test with the likelihood function defined in the paper and the maximum likelihood estimation procedure. We provide several cross-correlations with different f_astro and normalizations for reproducing Figure 5 and Figure 8 in the paper.

4. IceCube_kNN_Real_Measurement: This notebook reproduces Figure 6 in the paper by computing the cross-correlation between the IceCube 10-year data: 2008 - 2018 and the selected WISE-2MASS sample.