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Astron. Astrophys. 343, L65-L69 (1999) 3. Analysis and results3.1. Detection and photometrySource catalogs from our field in LW2 and LW3 were constructed
using the SExtractor package (Bertin & Arnouts, 1996). The
detection algorithm searches for 15 contiguous pixels, each having a
surface brightness exceeding a threshold (chosen as 1.5
To assess the contribution of noise to our catalogs we ran the
detection algorithm on the negative fluctuations in the maps,
concluding that we have no false detections above 60 µJy
in the central
Finally, to determine the completeness of the sample we added a
template faint source (scaled-down version of a calibration star) to
the maps repeatedly at different positions in the map and estimated
the efficiency of detecting this source as a function of its flux
density. This provided a reliable estimate of the visibility of a
faint compact source in the maps. The estimated 80% and 50%
completeness limits of the catalogs derived from these simulations are
listed in Table 1. These numbers refer to apparent source
brightness before lensing correction. They are similar to the deepest
observations published to date at 7 µm on the Lockman
Hole (Taniguchi et al. (1997) report faintest detections around
30µJy) and at 15 µm on the HDF (where the
faintest sources are around 50 µJy). However, thanks to
the gravitational magnification of a factor
Table 1. MIR source counts in the image plane, N: number of detected sources, Nneg: number of detected sources on the inverted image 3.2. Cluster contaminationThanks to our high-resolution images we have been able to
unambiguously identify more than 90% of the sources with counterparts
in deep NIR and optical (HST/WFPC2 and ground-based) images. The
relative astrometric accuracy is found to be better than
At 7 µm 30 sources are detected. 2 stars and 14 easily
identified cluster-member galaxies (Pelló et al. 1991, Leborgne
et al. 1992, Abraham et al. 1996). The
5-8.5 At 15 µm, 34 sources are detected in the central
3.3. Source countsThe number density of sources is high with respect to the size of
the FWHM ( Due to the non-uniform sensitivity of our maps, because of the
combined effects of observation strategy and the lensing, the object
density per flux bin was computed using magnification-dependant
surface areas derived from the lensing model so dividing the maps into
sub-maps. Only the central
A detailed lensing model of A2390 has been produced by Kneib et al. (1999). The lensing acts in two ways on the background population of galaxies. It causes:
To estimate these factors we used the spectroscopic redshift for 7
objects (Pelló et al. 1991, Bézecourt & Soucail
1997), and for the rest we use the best redshift estimate obtained
with photometric redshift techniques (Pelló, private
communication), and/or lensing inversion techniques (Kneib et al.
1999). By analysing the case with all background galaxies at a mean
redshift By correcting for the lens magnification and surface dilution effects, contamination by cluster galaxies, and non-uniform sensitivity of our maps, we can derive number counts at 15 µm to compare with blank sky counts (e.g. in the Hubble Deep field and Lockman Hole). The 7 µm number counts are more difficult to derive due to the larger contamination by the cluster and because of the small number statistics. ![]() ![]() ![]() ![]() © European Southern Observatory (ESO) 1999 Online publication: March 1, 1999 ![]() |