Open AccessDetermination of the linear mass power spectrum from the mass function of galaxy clusters
Author(s) -
Sánchez Ariel G.,
Padilla Nelson D.,
Lambas Diego G.
Publication year - 2002
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1046/j.1365-8711.2002.05893.x
Subject(s) - physics , spectral density , galaxy , mass distribution , statistical physics , singular value decomposition , range (aeronautics) , function (biology) , regularization (linguistics) , astrophysics , computational physics , statistics , algorithm , mathematics , computer science , materials science , evolutionary biology , artificial intelligence , composite material , biology
We develop a new method to determine the linear mass power spectrum using the mass function of galaxy clusters. We obtain the rms mass fluctuation σ( M ) using the expression for the mass function in the Press & Schechter, Sheth, Mo & Tormen and Jenkins et al. formalisms. We apply different techniques to recover the adimensional power spectrum Δ 2 ( k ) from σ( M ) namely the k eff approximation, the singular value decomposition and the linear regularization method. The application of these techniques to the τCDM and ΛCDM GIF simulations shows a high efficiency in recovering the theoretical power spectrum over a wide range of scales. We compare our results with those derived from the power spectrum of the spatial distribution of the same sample of clusters in the simulations obtained by application of the classical Feldman, Kaiser & Peacock (FKP) method. We find that the mass function based method presented here can provide a very accurate estimate of the linear power spectrum, particularly for low values of k . This estimate is comparable to, or even better behaved than, the FKP solution. The principal advantage of our method is that it allows the determination of the linear mass power spectrum using the joint information of objects of a wide range of masses without dealing with specific assumptions on the bias relative to the underlying mass distribution.