Publications

Below are selected publications and manuscripts. For the most up-to-date list, see my Google Scholar profile:

Refereed journal articles

Kurzner, M.M., Côté, P., et al. (2025)
The Next Generation Virgo Cluster Survey (NGVS) XL: The Morphological Properties of Virgo Cluster Galaxies
The Astronomical Journal, 170(6), 363
Links: arXiv: https://arxiv.org/abs/2510.20944
Summary: A large-scale morphological census of Virgo galaxies combining human visual classifications with non-parametric structural measurements and wide-field catalogue properties across heterogeneous imaging data.

Cantiello, M., et al. (including Kurzner, M.M.) (2024)
The Next Generation Virgo Cluster Survey (NGVS) III: A Catalog of Surface Brightness Fluctuation (SBF) Distances and the Three-Dimensional Distribution of Galaxies in the Virgo Cluster
The Astrophysical Journal, 966(1), 145
Links: ADS: https://ui.adsabs.harvard.edu/abs/2024ApJ…966..145C/abstract
Summary: Distance measurements for Virgo galaxies using a consistent inference framework, enabling 3D structure studies of the cluster.

Slivan, S.M., Hosek Jr., M.W., Kurzner, M.M., et al. (2023)
Spin vectors in the Koronis family: IV. Completing the sample of its largest members after 35 years of study
Icarus, 394, 115397
Links: ADS: https://ui.adsabs.harvard.edu/abs/2023Icar..39415397S/abstract
Summary: Long-baseline time-series analysis combining archival and new observations to refine physical parameters for asteroid family members.

Manuscripts in preparation / preprints

Kurzner, M.M., Spengler, C., Côté, P., et al. (in preparation)
The Next Generation Virgo Cluster Survey (NGVS) XXIX: Analysis of the Complete Nuclear Star Cluster Sample
Summary: Statistical modelling and uncertainty-aware inference of nuclear star cluster population properties from heterogeneous measurements across the Virgo cluster.

Kurzner, M.M., Côté, P., Fabbro, S., et al. (in preparation)
Deep Learning and NGVS Morphology: A Practical Application of ZooBot
Summary: Evaluation of CNN-based transfer learning and fine-tuning strategies under domain shift and limited supervision, emphasizing label reliability, subgroup performance, and confidence-stratified failure modes.

Notes

If you’re interested in the machine learning side of my work, I also maintain curated code examples here:

• GitHub: https://github.com/mkurzner
• ZooBot fine-tuning + evaluation example: https://github.com/mkurzner/zoobot-ngvs-finetune