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PreprintCC BY 4.0arXiv

νGundam: a unified framework for point-redshift and PDF-aware spatial correlation functions

  • E. Donoso
  • C. A. Lopez Cortez
  • U. Sureshkumar
  • Mariano J. Domínguez Romero
Open at sourcePDFDOICite

Abstract

Context. Photometric-redshift uncertainties redistribute galaxy pairs along the line of sight (LOS), diluting spatial clustering measurements. Propagating full redshift probability density functions (PDFs) through standard pair-count estimators remains computationally demanding. Aims. We present νGundam, a unified framework to propagate radial PDFs at the pair-count level while retaining a common interface for exact, compressed, and stochastic projected-clustering calculations. Methods. The code implements four modes: exact empirical-PDF integration, Gaussian-mixture compression, Monte Carlo (MC) sampling, and a quantile-compression method. All modes accumulate pair counts on a resolved (rp,π) grid, from which ξ(rp,π) and wp(rp;Π) are reconstructed. We validate these with DESI galaxies and CosmoDC2 mocks. Results. The PDF-aware modes produce nearly indistinguishable LOS redistribution kernels, while point-photo-z estimates discard probabilistic pair information. Radial uncertainty mainly redistributes the clustering signal to larger π; the depth required to recover the truth projection scales with the pairwise radial dispersion. For practical settings, MC, quantile, and Gaussian-mixture compression reproduce the exact result with fractional RMS residuals below 5%. Applied to DESI Bright Galaxy Survey samples, MC sampling recovers spectroscopic clustering shapes and relative amplitudes for rp≳0.8 h−1Mpc using sample-dependent recovery depths of ~400 and ~180 h−1Mpc for red and blue galaxies. Conclusions. Projected clustering with redshift PDFs is a pair-redistribution problem. When propagated consistently, the signal is recovered accurately and efficiently. The integration depth is sample dependent and should be part of the measurement definition rather than a fixed choice.

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Cite

BibTeXRIS
BibTeX
@misc{donoso2026νgundam,
  title = {{$ν$Gundam: a unified framework for point-redshift and PDF-aware spatial correlation functions}},
  author = {E. Donoso and C. A. Lopez Cortez and U. Sureshkumar and Mariano J. Domínguez Romero},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.29116},
  eprint = {2610.29116},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.29116},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - νGundam: a unified framework for point-redshift and PDF-aware spatial correlation functions
AU  - E. Donoso
AU  - C. A. Lopez Cortez
AU  - U. Sureshkumar
AU  - Mariano J. Domínguez Romero
PY  - 2026
DA  - 2026/10/21
PB  - arXiv
DO  - 10.48550/arxiv.2610.29116
UR  - https://arxiv.org/abs/2610.29116
AB  - Context. Photometric-redshift uncertainties redistribute galaxy pairs along the line of sight (LOS), diluting spatial clustering measurements. Propagating full redshift probability density functions (PDFs) through standard pair-count estimators remains computationally demanding. Aims. We present νGundam, a unified framework to propagate radial PDFs at the pair-count level while retaining a common interface for exact, compressed, and stochastic projected-clustering calculations. Methods. The code implements four modes: exact empirical-PDF integration, Gaussian-mixture compression, Monte Carlo (MC) sampling, and a quantile-compression method. All modes accumulate pair counts on a resolved (r_p,π) grid, from which ξ(r_p,π) and w_p(r_p;Π) are reconstructed. We validate these with DESI galaxies and CosmoDC2 mocks. Results. The PDF-aware modes produce nearly indistinguishable LOS redistribution kernels, while point-photo-z estimates discard probabilistic pair information. Radial uncertainty mainly redistributes the clustering signal to larger π; the depth required to recover the truth projection scales with the pairwise radial dispersion. For practical settings, MC, quantile, and Gaussian-mixture compression reproduce the exact result with fractional RMS residuals below 5%. Applied to DESI Bright Galaxy Survey samples, MC sampling recovers spectroscopic clustering shapes and relative amplitudes for r_p≳0.8 h^−1Mpc using sample-dependent recovery depths of ~400 and ~180 h^−1Mpc for red and blue galaxies. Conclusions. Projected clustering with redshift PDFs is a pair-redistribution problem. When propagated consistently, the signal is recovered accurately and efficiently. The integration depth is sample dependent and should be part of the measurement definition rather than a fixed choice.
ER  -