The two-point correlation function of the galaxy distribution is a key cosmological observable that allows us to constrain the dynamical and geometrical state of our Universe. To measure the correlation function we need to know both the galaxy positions and the expected galaxy density field. The expected field is commonly specified using a Monte-Carlo sampling of the volume covered by the survey and, to minimize additional sampling errors, this random catalog has to be much larger than the data catalog. Correlation function estimators compare data-data pair counts to data-random and random-random pair counts, where random-random pairs usually dominate the computational cost. Future redshift surveys will deliver spectroscopic catalogs of tens of millions of galaxies. Given the large number of random objects required to guarantee sub-percent accuracy, it is of paramount importance to improve the efficiency of the algorithm without degrading its precision. We show both analytically and numerically that splitting the random catalog into a number of subcatalogs of the same size as the data catalog when calculating random-random pairs and excluding pairs across different subcatalogs provides the optimal error at fixed computational cost. For a random catalog fifty times larger than the data catalog, this reduces the computation time by a factor of more than ten without affecting estimator variance or bias. © 2019 ESO.

Estimating the galaxy two-point correlation function using a split random catalog / Keihanen, E.; Kurki-Suonio, H.; Lindholm, V.; Viitanen, A.; Suur-Uski, A. -S.; Allevato, V.; Branchini, E.; Marulli, F.; Norberg, P.; Tavagnacco, D.; De La Torre, S.; Valiviita, J.; Viel, M.; Bel, J.; Frailis, M.; Sanchez, A. G.. - In: ASTRONOMY & ASTROPHYSICS. - ISSN 0004-6361. - 631(2019), pp. 1-11. [10.1051/0004-6361/201935828]

Estimating the galaxy two-point correlation function using a split random catalog

Viel M.
;
2019

Abstract

The two-point correlation function of the galaxy distribution is a key cosmological observable that allows us to constrain the dynamical and geometrical state of our Universe. To measure the correlation function we need to know both the galaxy positions and the expected galaxy density field. The expected field is commonly specified using a Monte-Carlo sampling of the volume covered by the survey and, to minimize additional sampling errors, this random catalog has to be much larger than the data catalog. Correlation function estimators compare data-data pair counts to data-random and random-random pair counts, where random-random pairs usually dominate the computational cost. Future redshift surveys will deliver spectroscopic catalogs of tens of millions of galaxies. Given the large number of random objects required to guarantee sub-percent accuracy, it is of paramount importance to improve the efficiency of the algorithm without degrading its precision. We show both analytically and numerically that splitting the random catalog into a number of subcatalogs of the same size as the data catalog when calculating random-random pairs and excluding pairs across different subcatalogs provides the optimal error at fixed computational cost. For a random catalog fifty times larger than the data catalog, this reduces the computation time by a factor of more than ten without affecting estimator variance or bias. © 2019 ESO.
631
1
11
A73
https://www.aanda.org/articles/aa/abs/2019/11/aa35828-19/aa35828-19.html
https://ui.adsabs.harvard.edu/abs/2019A&A...631A..73K/abstract
Keihanen, E.; Kurki-Suonio, H.; Lindholm, V.; Viitanen, A.; Suur-Uski, A. -S.; Allevato, V.; Branchini, E.; Marulli, F.; Norberg, P.; Tavagnacco, D.; De La Torre, S.; Valiviita, J.; Viel, M.; Bel, J.; Frailis, M.; Sanchez, A. G.
File in questo prodotto:
File Dimensione Formato  
aa35828-19.pdf

accesso aperto

Tipologia: Versione Editoriale (PDF)
Licenza: Non specificato
Dimensione 380.17 kB
Formato Adobe PDF
380.17 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11767/104294
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 9
social impact