A new implementation of an optimal filter for the detection of galaxy clusters through weak lensing

Panebianco, Gabriele (2021) A new implementation of an optimal filter for the detection of galaxy clusters through weak lensing. [Laurea magistrale], Università di Bologna, Corso di Studio in Astrofisica e cosmologia [LM-DM270]
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We developed a new version of a C++ code, Get the Halo 2021, that implements the optimal linear matched filter presented in Maturi et al.(2005). Our aim is to detect dark matter haloes of clusters of galaxies through their weak gravitational lensing signatures applying the filter to a catalogue of simulated galaxy ellipticities. The dataset represents typical data that will be available thanks to the Euclid mission, thus we are able to forecast the filter performances on weak lensing data obtained by Euclid. The linear matched filter is optimised to maximise the signal-to-noise ratio (S/N) of the detections and minimise the number of spurious detections caused by superposition of large-scale structures; this is achieved by suppressing those spatial frequencies dominated by the large-scale structure contamination. We compared our detections with the true population of dark matter haloes used to produce the catalogue of ellipticities. We confirmed the expectations on the filter performance raised by Maturi et al.(2005) and Pace et al.(2007). We found that S/N 7 can be considered as a reliable threshold to detect haloes through weak lensing as 83% of our detections with S/N>7 were matched to the haloes; this is consistent with Pace et al.(2007). The purity of our catalogues of detections increases as a function of S/N and reaches 100% at S/N 10.5-11. We also confirmed that the filter selects preferentially haloes with redshift between 0.2 and 0.5, that have an intermediate distance between observer and background sources, condition that maximises the lensing effects. The completeness of our catalogues is a steadily growing function of the mass until 4-5Msun/h, where it reaches values 58-68%. Our algorithm might be used to enhance the reliability of the detections of the AMICO code (Bellagamba et al.2018), the optimal linear matched filter implemented in the Euclid data analysis pipeline to identify galaxy clusters in photometric data (Euclid Collaboration et al.2019).

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Panebianco, Gabriele
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
cosmology: theory,dark matter,gravitational lensing
Data di discussione della Tesi
29 Ottobre 2021

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