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Detecting faint stuff in astrophysical datasets
Event details of Amaël Ellien
Date
12 May 2022
Time
12:00 -13:00

In this talk, I will try to justify this provocatively vague title by introducing the two very different fields I am working in. The first one is low surface brightness (LSB) astronomy in optical wavelengths, e.g., the detection and study of very faint and diffuse sources such as intracluster light in galaxy clusters, faint galaxy halos or again ultra-diffuse galaxies. These LSB sources are not only harder to see, but often present unique challenges with complex structures and morphologies. Upcoming missions such as Euclid or the Vera Rubin LSST will provide an unprecedented number of deep images to process, and new analysis tools are needed to extract and characterize the physics from these future datasets. With this goal in mind, I developed a Detection Algorithm with Wavelets for Intracluster light Surveys (DAWIS), which makes use of advanced image analysis concepts to detect and model LSB sources in images. The second topic will be the analysis of X-ray synchrotron dominated spectra in the vicinity of supernova remnants (SNR) shock waves. While a faint thermal signature left by shocked interstellar medium should be found in these spectra, proof for such an emission in Tycho'SNR has been lacking. I will talk about how the way spectra are analyzed is partially responsible for this, and how switching from traditional fitting methods to more complete Bayesian analysis allows for new results, even when using the same archival data.