Dr. Eduard Keilmann Research interests | Dr. Eduard Keilmann

Research interests

I am interested in the understanding of how atomic and molecular clouds form in the Milky Way and external galaxies that lead to star formation, and how stellar feedback mechanisms affect the Interstellar Medium (ISM), which in turn affect star formation. I also work with CO and [CII] data from the NASA SOFIA program FEEDBACK at the I. Physikalisches Institut, Universität zu Köln. This encompasses exploring and analyzing astrophysical data, modelling dust dynamics, as well as modelling and studying photo-dissociation regions (PDRs). It also involves developing various techniques in those areas.

During my PhD I have been closely working with Dr. Nicola Schneider, Dr. Volker Ossenkopf-Okada, Dr. Slawa Kabanovic, Dr. Robert Simon, and Prof. Dr. Jürgen Stutzki; and I wrote a code library in Python for the analysis of astrophysical data and models. This collection of code and functions will be published on github in the future.

Some Research Examples

[CII]-deficit in RCW79

In this A&A Letter to the Editor I show that the infamous [CII]-deficit (relative to the far-IR flux) can be explained by [CII] self-absorption without the need for secondary effects using SOFIA/upGREAT observations. We report the discovery that S144, a bubble‑shaped source embedded in the RCW79 HII region, is predominantly “filled” with ionized carbon and excited by a single O7.5–9.5 V/III star, indicating an early evolutionary stage before significant wind‑blown cavities form.

M33 Cloud Matching II. Physical GMC Properties

In this study (again condensed during my PhD), I studied the physical properties of giant molecular clouds (GMCs) in the flocculent spiral galaxy M33 by applying the Dendrogram algorithm to both a novel 2D dust‑derived H₂ column density map at 18.2″ resolution that I have generated as described in the previous paper and archival IRAM 30 m ¹²CO(2–1) data, using a pixel‑by‑pixel \(X_\mathrm{CO}\) conversion factor instead of a constant value. I identified over 300 dust‑traced and nearly 200 CO‑traced GMC structures, measured their projected areas and deconvolved radii (mean ∼58 pc from dust, ∼68 pc from CO), and computed masses by summing H₂ column densities over each structure.

M33 Cloud Matching I. High-Resolution H2 Maps

The work during my PhD condensed in this first first-author paper, in which I studied the distribution of molecular hydrogen in the Local Group galaxy M33 by developing novel methods to generate high-resolution (18.2″, ∼75 pc) hydrogen column density maps from Herschel far‑infrared data. Using continuum observations between 160 μm and 500 μm, I first derived total hydrogen column densities via pixel‑by‑pixel modified‑blackbody SED fits that incorporate spatially variable emissivity index β and dust absorption coefficient \(\kappa_0\). In parallel, I devised a second, more direct approach that translates only the 250 μm map into NH at the same angular resolution using those same variable dust parameters.

Dijets at Tevatron cannot constrain SMEFT four-quark operators

This study is the result of my work during my master thesis and my very first paper, for which I am also the first-author. I studied the sensitivity of Tevatron dijet measurements to heavy new‐physics effects parameterized by four‐quark operators in the Standard Model Effective Field Theory (SMEFT). Focusing on the Warsaw‐basis dimension‑six contact interactions, I calculated their interference with the QCD amplitude in dijet production as a function of the invariant mass and rapidity of the two leading jets. I enforced a consistent truncation of the SMEFT expansion at order 1/Λ² and supplemented our signal prediction with a mathematical consistent theoretical uncertainty estimate accounting for neglected 1/Λ⁴ effects from both squared dimension‑six and unknown dimension‑eight terms.