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Collisional damping in debris discs: Only significant if collision velocities are low

<p>This dataset contains results of numerical simulations of debris discs. We used a numerical model of collisional evolution of debris discs to explore the effect of collisional damping on the average eccentricity and inclination of debris particles with collisional outcomes that include fragmentation and cratering processes (Jankovic, Wyatt &amp; Lohne, A&amp;A, 691 (2024) A302, DOI: https://doi.org/10.1051/0004-6361/202451080, available with open access as of 2024 and available as a pre-print on arxiv). Here we provide the key results of our simulations in machine readable form.</p> <p>The ZIP archive contains directories for each of the models presented in the main text of our paper. Model IDs consist of a number (2, 3, 4 or 5) and a letter (a or b). Letters correspond to initial conditions of the simulation with respect to assumed level of excitation in the disc. Letter 'a' is for high-excitation discs with initial maximum eccentricity of 0.2 and letter 'b' is for low-excitation discs with initial maximum eccentricity of 0.01. Numbers in the model IDs correspond to the assumed collisional properties of colliders. Number 2 is for models in which particles are indestructible (inelastic bouncing only), while the other models include fragmentation and cratering with different specific critical energy prescriptions (Q_D^*). Numbers 3, 4 and 5 in model IDs correspond to Q_D^* prescriptions 1, 2 and 3 in Table 1 in the paper, respectively.</p> <p>Each of those directories contains 3 text files: postproc_int_grid.txt (basic data on time points and size grid used in the kinetic model), postproc_int_avgecc.txt (average eccentricity as function of size (columns) and time (rows)) and postproc_int_avginc.txt (average inclination as function of size (columns) and time (rows)). Also supplied is a python script (plot.py) which reads the files and produces a figure for each model. The script can be executed from the command line by providing the name of the model directory, e.g.:<br>python plot.py model_2_a</p>

ShareScore

32/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
8
Engagement
0

Dataset details

Repository
zenodo
Published
November 20, 2024
License
cc-by-4.0
DOI
10.5281/zenodo.14196398

Scored with release 0.9.0

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