Lumière (Latest Upgraded Methodologies for the Investigation of X-Ray Emitting sources)

Dendrogram clustering and quantum extreme learning for the study of supernova remnants images and optically-thin X-ray spectra

13 janv. 2026, 11:45

15m

Institut Pascal

Orateur

Marco Miceli (Department of Physics and Chemistry E. Segrè, University of Palermo)

Description

I report on preliminary results from a collaborative project between the Supernova Remnants and Cosmic Ray Group, the Observatory of Complex Systems Group, and the Quantum Theory Group at the University of Palermo.
By adopting different dendrogram-based techniques, we developed a tool to quantitatively measure the similarities among different narrow-band images of a supernova remnant and of different supernova remnants. We tested our tool on Chandra observations of Cassiopeia A, Tycho's SNR, Kepler's SNR, W49B, and G292.0+01.8. We identified clusters of different chemical elements in Type Ia and core-collapse remnants and obtained interesting clues on the origin of W49B.
I also present the methodology we developed to adopt Quantum Extreme Learning Machines for the retrieval of the best-fit parameters of optically-thin X-ray spectra. I will show preliminary results obtained by using a real quantum computer as a reservoir for the training of the deep learning algorithm.