# Data Analysis in Cosmology by Vicent J. Martinez, Enn Saar, Enrique Martinez Gonzales,

By Vicent J. Martinez, Enn Saar, Enrique Martinez Gonzales, Maria Jesus Pons-Borderia

The volume of cosmological information has dramatically elevated some time past many years as a result of an extraordinary improvement of telescopes, detectors and satellites. successfully dealing with and analysing new facts of the order of terabytes in keeping with day calls for not just computing device strength to be processed but in addition the improvement of refined algorithms and pipelines.Aiming at scholars and researchers the lecture notes during this quantity clarify in pedagogical demeanour the simplest thoughts used to extract info from cosmological facts, in addition to trustworthy equipment that are meant to aid us enhance our view of the universe.

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This certainly allows us to construct a hierarchical representation of the data f (x). However, this transform is not in general invertible. 9 Hybrid Approaches There is some advantage to be gained by working on image data in the Fourier domain and doing the wavelet operations there before transforming back to the real data space. 10 Multifractals It was remarked that the wavelet transform of f (x), F (a, b), is dominated by cusps that represent the scaling behaviour of f (x) in local neighbourhoods.

H3 h 4 0 0 0 0 0 . . 0 h 1 h 2 ⎤ ⎡ g1 g2 g3 g4 0 0 0 . . 0 0 0 ⎢ 0 0 g1 g2 g3 g4 0 . . 0 0 0 ⎥ ⎥ G(2N ×N )) = ⎢ (99) ⎣. . . . . . . . . . . . . . ⎦ . g3 g4 0 0 0 0 0 . . 0 g1 g2 Note that by virtue of (94) the rows of G are orthogonal to one another and by virtue of (93) the rows of H are orthogonal to one another. The rows of G are also orthogonal to the rows of H. We have orthogonality everywhere and such wavelet transforms are referred to a “bi-orthogonal”. We can usefully block the two matrices G and H together to form a version of W whose rows have been rearranged: W(2N ×2N ) = H .

8) 2 (117) . Fig. 9. Top panel: the original data consisting of three wave packets, the central one being a mixture of the other two. Middle panel: Mexican Hat scalogram. Bottom panel: the Gabor scalogram The Sea of Wavelets 39 The analysing wavelets are the Mexican Hat wavelet and the complex Gabor wavelet. The Mexican Hat used here is ψ(x) = x 2π 1 − 2π 1/2 w w 2 exp −π(x/w)2 , (118) where w is a parameter that is varied to generate the scalogram. The data is transformed for a series of values of w.