**AAS 199th meeting, Washington, DC, January 2002**

*Session 16. Cosmology and Lensing*

Display, Monday, January 7, 2002, 9:20am-6:30pm, Exhibit Hall
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## [16.04] Parameter Estimation via Neural Networks in mega-pixel data sets

*N. G. Phillips (SSAI/GSFC/NASA), A. Kogut (GSFC/NASA)*

We present a neural net algorithm for parameter estimation
in the context of large cosmological data sets. Cosmological
data sets present a particular challenge to
pattern-recognition algorithms since the input patterns
(galaxy redshift surveys, maps of cosmic microwave
background anisotropy) are not fixed templates overlaid with
random noise, but rather are random realizations whose
information content lies in the correlations between data
points. We train a ``committee'' of neural nets to
distinguish between Monte Carlo simulations at fixed
parameter values. Sampling the trained networks using
additional Monte Carlo simulations generated at intermediate
parameter values allows accurate interpolation to parameter
values for which the networks were never trained. The Monte
Carlo samples automatically provide the probability
distributions and truth tables required for either a
frequentist or Bayseian analysis of the one observable sky.
We demonstrate that neural networks provide unbiased
parameter estimation with comparable precision as
maximum-likelihood algorithms but significant computational
savings. In the context of CMB anisotropies, the
computational cost for parameter estimation via neural
networks scales as N^{3/2}. The results are insensitive to
the noise levels and sampling schemes typical of large
cosmological data sets and provide a desirable tool for the
new generation of large, complex data sets.

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