University > Physics and Astronomy > ITP > Caroline Heneka

Research projects

ADS | arXiv

Brightness temperature simulation

Evolution of a coeval cube, simulated in the redshift range z= 5 - 25. The dimensions of the box are 200x200 Mpc and the resolution is 100x100 pixels. The brightness temperature is the characteristic temperature associated with the 21 cm signal, a spectral line arising from the spin-flip transition of neutral hydrogen. It is expressed in terms of the deviation from the 21 cm signal from the cosmic microwave background. Since hydrogen is abundant and traces the neutral IGM, this 21 cm signal serves as a key probe for studying structure formation and reionization.

Projects

Line Intensity Mapping
as a probe of Astrophysics and the Epoch of Reionization

I work on simulations tailored for multi-line intensity mapping, to then employ them for parameter, calibration and cross-correlation studies, as well as the creation of realistic mock observations.
[For a first release of simulations see arxiv: 1611.09682; update: 2004.10097, 2104.12739]

Probing Cosmology
with Line Intensity Mapping

Besides multi-line simulations for astrophysics, I also simulate intensity mapping, especially the 21cm line during reionization and cosmic dawn, in general gravity scenarios to explore this novel observable and forecast the feasibility to detect deviations from LCDM.
[See e.g. arXiv: 1805.03629, 1910.02763]

Future Missions

With my simulation-derived realistic mock observations for intensity mapping of multiple lines (Lyman-alpha, H-alpha, 21cm, CII, CO, ..), I provide detectabilty studies and forecasting for upcoming probes like SPHEREx and the Cosmic Dawn Intensity Mapper (CDIM).
[See arXiv:1903.03144, 1903.03629, 1903.11744, 2104.12739]

Development of new Methods

I am also interested in advanced statistical and machine learning tools for astronomy and cosmology. I so far worked on machine learning for cosmological inference, classification of spectra, galaxy deblending, as well as statistical tests of bias with Bayasian methods and genetic algorithms.
[See arXiv: 1310.8435, 1407.2531, 1905.01324, 2201.07587, 2201.02202]