Join the disputation 
The PhD defence and trial lecture will be hybrid. The room opens for participation just before the disputation starts, and closes for new participants approximately 15 minutes after the defence has begun.
Join the trial lecture - 30th of June at 09:15 (Aud. 209, Svein Rosselands Hus)
"How do new discoveries with JWST regarding the galaxy population in the first billion years change our understanding of the early universe?"
Conferral summary
Avhandlingen bidrar til COMAP-eksperimentet, som har som mål å lage verdens første 3D-kart over stjernedannende karbonmonoksid, eller CO-gass, fra tiden da galakser ble dannet for omtrent 11-12 milliarder år siden. Gjennom utvikling av nye metoder for dataanalyse renses og kalibreres COMAP-dataene, noe som har ført til de mest sensitive 3D CO-himmelkartene som finnes.
From Darkenss to Light – Towards mapping the Faint Star-Forming Universe
Modern cosmology has uncovered many details of the evolution and content of the Universe by studying the cosmic microwave background (CMB) at early times and the large-scale galaxy distribution of the late Universe. However, eras in between, like the Epochs of Reionization (EoR) and Galaxy Assembly (EoGA), remain data-starved. Line intensity mapping (LIM) aims to fill this gap by mapping the large-scale aggregate line emission of all bright and diffuse cosmic sources in 3D.
In this thesis, I contribute to this effort by developing a data analysis pipeline for the CO Mapping Array Project (COMAP) LIM experiment. COMAP aims to map the large-scale distribution of star-forming carbon monoxide gas emitted around the EoR and EoGA. I present a robust method for cleaning and calibrating the data, ultimately generating 3D sky maps of CO emission. My analysis assesses the statistical properties of the maps to extract insight into the physics of the star-forming Universe. This work yields the strongest constraints on the large-scale cosmic carbon monoxide reported in the literature, and COMAP is enroute towards a world’s first detection. Lastly, I propose future improvements to the COMAP pipeline based on the Bayesian sampling techniques originally developed for CMB surveys.
