Paper detail

Evaluation of the uncertainty in calculating nanodosimetric quantities due to the use of different interaction cross sections in Monte Carlo track structure codes

This study evaluates the uncertainty in nanodosimetric calculations caused by variations in interaction cross sections within Monte Carlo Track Structure (MCTS) simulation codes. Nanodosimetry relies on accurately simulating particle interactions at the molecular scale. Different MCTS codes employ distinct physical models and datasets for electron interactions in liquid water, a surrogate for biological tissues. The paper focuses on the Ionization Cluster Size Distribution (ICSD) generated by electrons of varying energies in nanometric volumes. Seven MCTS codes were tested using their native cross sections and a common dataset derived from averaging data used in the participating codes. The results reveal significant discrepancies among the codes in ICSDs and derived biologically relevant nanodosimetric quantities such as mean ionization numbers (M1) and probabilities of obtaining two or more ionizations (F2). The largest variations were observed for low-energy electrons, where the contribution from interaction cross sections dominates the overall uncertainties. For instance, M1 values for ICSDs of electron of 20 eV can differ by around 45 % (RSD) and 34 % (RSD) was found for F2 values of ICSDs of electrons of 50 eV. Using common cross sections substantially reduced the discrepancies, suggesting that cross section datasets are the primary source of variability. Finally, estimates of deoxyribonucleic acid (DNA) damage using the PARTRAC code highlight tht cross section variations have a non-negligible impact simulated biological outcomes, particularly for double-strand breaks (DSBs) Indeed, despite the fact that many other parameters in the simulation that can greatly differ from one code to another, the different interaction cross-sections studied in this work can lead to differences in the number of DSBs calculated with the PARTRAC code of up to 15%.

preprint2025arXivOpen access

Signal facts

What is known right now

Open access14 authors1 topic

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.