Talks.cam will close on 1 July 2026, further information is available on the UIS Help Site
 

University of Cambridge > Talks.cam > Institute of Astronomy Seminars > ECHO21: A tool for modelling global 21-cm signal from dark ages to reionization

ECHO21: A tool for modelling global 21-cm signal from dark ages to reionization

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Xander Byrne.

I will introduce a Python package called ECHO21 for modelling the global 21-cm signal from the dark ages through cosmic dawn to the end of reionization. Leveraging its analytical framework, ECHO21 generates a single model in O(1) s, allowing a large number of signals to be generated efficiently by distributing models across multiple cores. Thus, it is ideal for performing astrophysical or cosmological inference from a given 21-cm dataset. We offer six astrophysical parameters that control the Lyman-α emissivity, X-ray emissivity, emissivity of ionizing photons, and star formation rate. Beyond its efficiency, some of the attractive and novel features in ECHO21 relative to previously published codes are the inclusion of Lyα heating, the ability to vary the standard cosmological parameters as easily as the astrophysical parameters, different models of star formation rate density (physically-motivated, a semi-empirical, and an empirically-motivated), and modelling the global signal for a Coulomb-like interacting DM (IDM) framework. This IDM model incorporates cooling of baryons as well as a delay in star formation. With several 21-cm experiments soon to provide cosmic dawn 21-cm data, ECHO21 is a flexible and extensible new open-source package for making quick and sufficiently realistic astrophysical inferences.

This talk is part of the Institute of Astronomy Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity