Caroline Haslebacher

(2025) Galileo RegMaps with LineaMapper v1.1 and 2.0

We use my deep-learning framework LineaMapper (see below) to extract a lineament's length, width, and relative age through the number of crosscuttings by length. We conduct a statistical analysis on the southern leading hemisphere and compare characteristics of lineaments in chaos region to lineaments in ridged plains. Finally, we find evidence that only the most recently formed linear features remain discernible following the disruption by chaos terrain and hypothesize that younger features are stronger. The code and data (shapefiles etc) is open source.

Video produced in collaboration with Frank Timmes from the AAS Journal Author Series.

(2025) New statistical framework for axial data

How do we know if our observations of axial or directional data show a preferred direction? Sure, a Rose diagram can be a helpful tool, but when we want to assess the best fit to model predictions, we need a solid statistical analysis. This work led by Prof. Dr. Lutz Dümbgen helps with exactly that by providing the formalism for a generalized linear model (GLM) that uses a von Mises-Fisher distribution and local quadratic modeling. Stay tuned for a publication about a tidal stress model parameter search for Europa.

(2024) LineaMapper

In preparation of Europa Clipper, I trained a neural network for the extraction of lineaments in Galileo images of Europa's surface. More work is needed to make the output more robust, but LineaMapper v1.0 is a proof-of-concept for automated lineament mapping. The code is open source.

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(2022) Ground-based observations through the lens of climate change

For my master thesis with Prof. Dr. Brice-Olivier Demory, I investigated how climate change impacts ground-based observations. We found a strong increase in precipitable water vapor and temperature. Read the beautifully illustrated CNET article about our findings.

Press release University of Bern , Europlanet .

Article in the Europlanet Magazine, Issue 4, 2023.

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(Poster) Climate change drives degradation of future observations with ground-based telescopes

EPSC 2022

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(Poster) Mapping linear surface features on Europa using a deep learning framework

EGU 2023 (OSPP contest winner)

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(Poster) Mapping lineaments in the Europa regional mosaics with the deep-learning tool LineaMapper

Workshop on the Origins and Habitability of the Galilean Moons 2023

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