STAR-XPC: Set Transformer for Estimating X-ray Transmission in Polymer Composites

Key points

• The reference solution with Monte Carlo transport simulation is highly accurate but computationally expensive: each of our reference simulations is capped at $4\times10^9$ photon histories and a 20,000 s (over 5 hours) time limit, while closed-form analytical methods fail to represent such complex scenarios.1
• We present an artificial neural network model that maps the unordered multi-set of molecular constituents, their mass fractions, and four bulk physical descriptors directly to the transmission ratio $I(t)/I0$ in 1-3 milliseconds.1
• In our proposed method, training data are produced by an automated PENELOPE/penEasy pipeline using the radiation source at 30, 80, 100, and 120 kV tube voltages over polyethylene-based shielding composite slabs of thicknesses spanning 0.15-2.5 mm.1
• After preprocessing, the dataset contains $n = 4697$ simulated configurations on which a leakage-free, jointly stratified 10-fold cross-validation is performed, attaining mean squared error MSE = $(1.12 \pm 0.56)\times10^{-4}$, normalized-MSE percentage NMSE% = 0.0294% $\pm$ 0.0147%, and $R^2 = 0.9988$.1

Sources (1)

• [1]STAR-XPC: Set Transformer for Estimating X-ray Transmission in Polymer Composites
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:39 PM

  Evaluating the energy transmission ratio of an X-ray beam through a polymer composite is a challenging task in radiation shielding studies.

  The reference solution with Monte Carlo transport simulation is highly accurate but computationally expensive: each of our reference simulations is capped at $4\times10^9$ photon histories and a 20,000 s (over 5 hours) time limit, while closed-form analytical methods fail to represent such complex scenarios.

Extractive summary: sentences quoted from the sources.