The projected 2-point correlation function of the galaxy density field

2003 ◽  
Vol 31 (2) ◽  
pp. 475-481
Author(s):  
A. Pollo
2010 ◽  
Vol 409 (2) ◽  
pp. 867-872 ◽  
Author(s):  
S. De La Torre ◽  
L. Guzzo ◽  
K. Kovač ◽  
C. Porciani ◽  
U. Abbas ◽  
...  

1991 ◽  
Vol 382 ◽  
pp. 32 ◽  
Author(s):  
Lyman W. Neuschaefer ◽  
Rogier A. Windhorst ◽  
Alan Dressler

2008 ◽  
Vol 672 (2) ◽  
pp. 849-860 ◽  
Author(s):  
Felipe A. Marin ◽  
Risa H. Wechsler ◽  
Joshua A. Frieman ◽  
Robert C. Nichol

1978 ◽  
Vol 79 ◽  
pp. 280-280
Author(s):  
S. Phillipps

The two point correlation function w(θ) has been evaluated for the galaxies measured by the COSMOS machine at the Royal Observatory, Edinburgh, in an area of about 2 square degrees on a 2 hour exposure J plate and a 2 hour exposure R plate (Phillipps, S., Fong, R., Ellis, R.S., Fall, S.M. and MacGillivray, H.T., 1977, Mon. Not. R. astr. Soc., in press). in each case w(θ) is found to be in agreement with the form w = Aθ−0.8 found previously by Peebles and coworkers. Since the samples are not magnitude limited the selection function, i.e. the distribution in distance, was determined by using models of the galaxy distribution to fit the observed angular diameter counts. However, when these selection functions are used to scale the amplitudes found for our samples, the amplitudes are found to be lower than those expected from Peebles' results by a factor of about 3. We consider that this is likely to be due to a lack of clusters in the small area of sky which we have studied: analysis of further areas should show whether this is the case.


2019 ◽  
Vol 631 ◽  
pp. A73 ◽  
Author(s):  
E. Keihänen ◽  
H. Kurki-Suonio ◽  
V. Lindholm ◽  
A. Viitanen ◽  
A.-S. Suur-Uski ◽  
...  

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.


2008 ◽  
Vol 78 (4) ◽  
Author(s):  
Fabian Schmidt ◽  
Alberto Vallinotto ◽  
Emiliano Sefusatti ◽  
Scott Dodelson

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