A deep-learning algorithm to disentangle self-interacting dark matter and AGN feedback models


"This method represents a way to analyse data from upcoming telescopes that are an order of magnitude more precise and many orders faster than current methods, enabling us to explore the properties of dark matter like never before.

"In the idealized case, my algorithm is 80% accurate at identifying whether a galaxy cluster harbours 
  • Collisionless dark matter; 
  • Dark matter with a self interaction cross-section, σDM/m = 0.1 cm2 g−1; or 
  • Dark matter with σDM/m = 1 cm2 g−1 .
"It is found that weak-lensing information primarily differentiates self-interacting dark matter, whereas X-ray information disentangles different models of astrophysical feedback. 

"The data are forward modelled to imitate observations from Euclid and Chandra, and it is found that the model has a statistical error of σDM/m < 0.01 cm2 g−1 and is insensitive to shape-measurement bias and photometric-redshift errors." 



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