Scientific Research

Planetary remnants & white dwarf atmospheres

Using spectroscopy, disk modeling, and computational tools to study what survives when planetary systems die

The research

My research focuses on polluted white dwarfs and the circumstellar material that reveals the composition and evolution of planetary systems after the main-sequence lifetime of their host stars.

I approach these systems as a form of cosmic forensics: using spectroscopy, disk modeling, and numerical workflows to infer the physical and chemical history of material left behind by disrupted planetary bodies.

Core field Polluted white dwarfs, planetary debris, circumstellar disks

Primary tools Spectroscopy, radiative transfer, dust modeling, computational analysis

Scientific goal Connecting observed material around white dwarfs to the chemistry and architecture of planetary systems

Database steele.nimbusno.de ↗

Research themes

The science approached from three angles — the stars, the material around them, and what that material tells us about planetary history.

Artistic representation of a white dwarf with a circumstellar disk from tidal disruption of a planetesimal — An artistic representation of a white dwarf with a disk produced from the tidal disruption of a rogue planetesimal. From spectroscopic observations of the gaseous phase of the sublimated material, we directly learn the composition of the planetesimal.

01

Polluted white dwarfs

White dwarf atmospheres can preserve signatures of accreted planetary material. I use these systems as chemical probes of disrupted exoplanetesimals and planetary debris.
02

Circumstellar disks

Dust and gas disks around white dwarfs connect observed spectra to the physical structure, temperature, and composition of material close to the star.
03

Cosmic forensics

Each polluted white dwarf system is a partial record of a planetary system's history. The scientific challenge is extracting that record from incomplete and complex data.

Methods

Computational and observational, with emphasis on turning physical questions into modelable, testable workflows.

Spectroscopy

Analysis and interpretation of spectral features encoding chemical, thermal, and dynamical information.

Disk modeling

Modeling circumstellar material to connect composition, temperature, geometry, and radiative behavior.

Parameter exploration

Systematic model generation and comparison across large parameter spaces to understand degeneracies and constraints.

Scientific software

Python, shell workflows, databases, and visualization tools for reproducible, searchable research analysis.

Research as technical training

Why the research background transfers to data and software roles.

Scientific skills

Working with incomplete, noisy, and physically constrained data
Connecting observations to models and evaluating competing interpretations
Designing workflows for computationally expensive model comparisons
Communicating uncertainty clearly across technical and nontechnical contexts
Building analysis around evidence rather than convenient assumptions

Technical skills

Python-based scientific computing and automation
Large model grids and structured parameter exploration
Data cleaning, transformation, visualization, and interpretation
SQL-backed organization of model outputs and metadata
Public-facing tools that make complex results easier to search and inspect

Selected research outputs

A curated list of publications, talks, and research products. See CV for a full list.

Polluted white dwarf and circumstellar disk research Add publication title, journal, year, and role.

Modeling and optical constants work Add publication title, journal, year, and role.

Additional selected paper or project Add publication title, journal, year, and role.

View CV → Code & Data →

How I describe the work

I study planetary systems after stellar death by treating polluted white dwarfs as cosmic mass spectrometers. Their atmospheres and surrounding disks offer clues about the composition of rocky material beyond the Solar System.

That research has also shaped how I build technical systems: I am used to ambiguous data, long-running computations, messy intermediate products, and the need to make results traceable, searchable, and interpretable.

Explore database → GitHub →