ﻻ يوجد ملخص باللغة العربية
We present a comprehensive analysis for the determination of the confusion levels for the current and the next generation of far-infrared surveys assuming three different cosmological evolutionary scenarios. We include an extensive model for diffuse emission from infrared cirrus in order to derive absolute sensitivity levels taking into account the source confusion noise due to point sources, the sky confusion noise due to the diffuse emission, and instrumental noise. We use our derived sensitivities to suggest best survey strategies for the current and the future far-infrared space missions Spitzer, AKARI (ASTRO-F), Herschel, and SPICA. We discuss whether the theoretical estimates are realistic and the competing necessities of reliability and completeness. We find the best estimator for the representation of the source confusion and produce predictions for the source confusion using far-infrared source count models. From these confusion limits considering both source and sky confusions, we obtain the optimal, confusion limited redshift distribution for each mission. Finally, we predict the Cosmic Far-Infrared Background (CFIRB) which includes information about the number and distribution of the contributing sources.
Fluctuations in the brightness of the background radiation can lead to confusion with real point sources. Such background emission confusion will be important for infrared observations with relatively large beam sizes since the amount of fluctuation
We present detailed predictions for the confusion noise due to extragalactic sources in the far-IR/(sub)-millimeter channels of ESA/ISO, NASA/Spitzer, ESA/Herschel and ESA/Planck satellites, including the contribution from clustering of unresolved SC
The Laser Interferometer Space Antenna (LISA) will detect thousands of gravitational wave sources. Many of these sources will be overlapping in the sense that their signals will have a non-zero cross-correlation. Such overlaps lead to source confusio
Gravitational microlensing surveys target very dense stellar fields in the local group. As a consequence the microlensed source stars are often blended with nearby unresolved stars. The presence of `blending is a cause of major uncertainty when deter
Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretraine