1 Introduction

Following the detection of ultra-luminous infrared galaxies (ULIRG's) by the IRAS satellite (Houck et al. 1984; Houck et al. 1985; Soifer et al. 1984a; Soifer et al. 1984b), it is not clear whether such objects, which are very bright but not numerous in the nearby universe, could be representative of a more common phase in the evolution of normal galaxies. In other words, could we expect the lack of detection of primeval galaxies to be due to dust extinction in systems emitting more than 90% of their light in the infrared, as in local ULIRG's Djorgovski & Thompson 1992).

Several programs were devoted to this search using ISOCAM (Cesarsky et al. 1996), one of the four instruments on board of the ISO (Infrared Space Observatory) spacecraft (Kessler et al. 1996) which ended its life in May, 1998. The present paper is devoted to reduction of data obtained with the long wavelength (LW) detector of ISOCAM, a ($32\times 32$) pixel array of SiGa. The LW detector operates in the range 4 to 18 $\mu$m, with a sensitivity four orders of magnitude better than IRAS and a spatial resolution sixty times better. This channel of ISOCAM was suited for the search of MIR dust emission in galaxies of redshifts typically below z=1.5 (Elbaz et al. 1998). In this wavelength range, we find UIBs (Unidentified Infrared Bands) from 6.2 to 12.7 $\mu$m and Very Small Grains above 10 $\mu$m (Vigroux et al. 1998). However, because the $32\times 32$ pixels of the LW detector were both thick and cold, they were very sensitive to the presence of cosmic rays, and slow to react to changes in fluxes. Therefore, for faint source detection with ISOCAM, it is necessary to discriminate non-Gaussian fluctuations of the signal from Gaussian ones, and to separate cosmic rays, i.e. glitches, from real sources. The method we developed for this purpose relies on the fact that these signal components, when measured by a given pixel, show different signatures in their temporal evolution, and can be identified using a multiscale transform, which separates the various frequencies in the signal. Once the "bad" components (i.e. glitches) are identified, they can be extracted from the temporal signal. The glitch-free signal can then used to build the final image.

The detection of faint sources is then performed on this final image using again a wavelet transform of the signal, but this time spatially instead of temporally. We called this tool PRETI (Pattern REcognition Technique for Isocam data) because we use a temporal signature to recognize each signal component, which appears as a pattern in wavelet space.

In the first part of the paper, we describe the PRETI procedure. Then, we focus on the validation of this technique using Monte-Carlo simulations. These simulations were performed on a data set void of real sources in which we introduced simulated sources with random fluxes and positions, in order to estimate the following characteristics of an observation:

1.

the sensitivity limit: the flux of the faintest detected source.

2.

the photometric accuracy.

3.

the completeness limit: the faintest flux for which all sources are detected or at least an established and important fraction of the total number of sources.

4.

the false detection rate: the number of false sources due to glitches wrongly interpreted as sources, as a function of source strength.

A first set of simulations was already used in Aussel et al. (1999), based on this technique, but the used data set was not void of real sources, so that the fourth point above could not be addressed.