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We present results of ultra-deep ISOCAM observations through a cluster-lens at 7 and 15 micron with the Infrared Space Observatory (ISO) satellite. These observations reveal a large number of luminous Mid-Infrared (MIR) sources. Cross-identification in the optical and Near-Infrared (NIR) wavebands shows that about half of the 7 micron sources are cluster galaxies. The other 7 micron and almost all 15 micron sources are identified as lensed distant galaxies. Thanks to the gravitational amplification they constitute the faintest MIR detected sources, allowing us to extend the number counts in both the 7 and 15 micron bands. In particular, we find that the 15 micron counts have a steep slope alpha_15 = -1.5 +/- 0.3 and are large, with N_15 (>30 microJy}) = 13 +/- 5 per square arcmin. These numbers rule out non-evolutionary models and favour very strong evolution. Down to our counts limit, we found that the resolved 7 and 15 microns background radiation intensity is respectively (2 +/-0.5) 10^(-9) and (5 +/-1) 10^(-9) W m^(-2) sr^(-1).
We present imaging results and source counts from an ISOCAM deep and ultra-deep cosmological survey through gravitationally lensing clusters of galaxies at 7 and 15 microns. A total area of about 53 sq.arcmin was covered in maps of three clusters. Th
ISOCAM was used to perform a deep survey through three gravitationally lensing clusters of galaxies. Nearly seventy sq. arcmin were covered over the clusters A370, A2218 and A2390. We present maps and photometry at 6.7 & 14.3 microns, showing a total
Gravitational lensing by massive galaxy clusters allows study of the population of intrinsically faint infrared galaxies that lie below the sensitivity and confusion limits of current infrared and submillimeter telescopes. We present ultra-deep PACS
We present imaging results and source counts from a deep ISOCAM cosmological survey at 15 microns, through gravitationally lensing galaxy clusters. We take advantage of the cluster gravitational amplification to increase the sensitivity of our survey
Existing work on understanding deep learning often employs measures that compress all data-dependent information into a few numbers. In this work, we adopt a perspective based on the role of individual examples. We introduce a measure of the computat