How well can we measure and understand foregrounds with 21 cm experiments?

Before it becomes a sensitive probe of the Epoch of Reionization, the Dark Ages, and fundamental physics, 21 cm tomography must successfully contend with the issue of foreground contamination. Broadband foreground sources are expected to be roughly four orders of magnitude larger than any cosmological signals, so precise foreground models will be necessary. Such foreground models often contain a large number of parameters, reflecting the complicated physics that governs foreground sources. In this paper, we concentrate on spectral modeling (neglecting, for instance, bright point source removal from spatial maps) and show that 21 cm tomography experiments will likely not be able to measure these parameters without large degeneracies, simply because the foreground spectra are so featureless and generic. However, we show that this is also an advantage, because it means that the foregrounds can be characterized to a high degree of accuracy once a small number of parameters (likely three or four, depending on one's instrumental specifications) are measured. This provides a simple understanding for why 21 cm foreground subtraction schemes are able to remove most of the contaminants by suppressing just a small handful of simple spectral forms. In addition, this suggests that the foreground modeling process should be relatively simple and will likely not be an impediment to the foreground subtraction schemes that are necessary for a successful 21 cm tomography experiment.Comment: 15 pages, 9 figures, 2 tables; Replaced with accepted MNRAS version (slight quantitative changes to plots and tables, no changes to any conclusions

A new class of wavelet networks for nonlinear system identification

A new class of wavelet networks (WNs) is proposed for nonlinear system identification. In the new networks, the model structure for a high-dimensional system is chosen to be a superimposition of a number of functions with fewer variables. By expanding each function using truncated wavelet decompositions, the multivariate nonlinear networks can be converted into linear-in-the-parameter regressions, which can be solved using least-squares type methods. An efficient model term selection approach based upon a forward orthogonal least squares (OLS) algorithm and the error reduction ratio (ERR) is applied to solve the linear-in-the-parameters problem in the present study. The main advantage of the new WN is that it exploits the attractive features of multiscale wavelet decompositions and the capability of traditional neural networks. By adopting the analysis of variance (ANOVA) expansion, WNs can now handle nonlinear identification problems in high dimensions

On the clustering phase transition in self-gravitating N-body systems

The thermodynamic behaviour of self-gravitating $N$-body systems has been worked out by borrowing a standard method from Molecular Dynamics: the time averages of suitable quantities are numerically computed along the dynamical trajectories to yield thermodynamic observables. The link between dynamics and thermodynamics is made in the microcanonical ensemble of statistical mechanics. The dynamics of self-gravitating $N$-body systems has been computed using two different kinds of regularization of the newtonian interaction: the usual softening and a truncation of the Fourier expansion series of the two-body potential. $N$ particles of equal masses are constrained in a finite three dimensional volume. Through the computation of basic thermodynamic observables and of the equation of state in the $P - V$ plane, new evidence is given of the existence of a second order phase transition from a homogeneous phase to a clustered phase. This corresponds to a crossover from a polytrope of index $n=3$, i.e. $p=K V^{-4/3}$, to a perfect gas law $p=K V^{-1}$, as is shown by the isoenergetic curves on the $P - V$ plane. The dynamical-microcanonical averages are compared to their corresponding canonical ensemble averages, obtained through standard Monte Carlo computations. A major disagreement is found, because the canonical ensemble seems to have completely lost any information about the phase transition. The microcanonical ensemble appears as the only reliable statistical framework to tackle self-gravitating systems. Finally, our results -- obtained in a ``microscopic'' framework -- are compared with some existing theoretical predictions -- obtained in a ``macroscopic'' (thermodynamic) framework: qualitative and quantitative agreement is found, with an interesting exception.Comment: 19 pages, 20 figure