RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
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The most precise measurement to date of antineutrino-induced neutral pion production shows agreement with the GENIE model but indicates that other models underestimate the cross section in the Delta(1232) resonance region.
A 1D convolutional neural network reconstructs the dark-matter phase-space distribution from the matter power spectrum with greater accuracy and broader applicability than an earlier empirical formula.
citing papers explorer
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Reweighting Adversarial Networks for Unbinned Unfolding
RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
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Measurement of $\pi^0$ Production in $\bar{\nu}_{\mu}$ Charged-Current Interactions in the NOvA Near Detector
The most precise measurement to date of antineutrino-induced neutral pion production shows agreement with the GENIE model but indicates that other models underestimate the cross section in the Delta(1232) resonance region.
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Machine Learning Does It and Does It Better: Unearthing Primordial Dark-Matter Velocities from the Matter Power Spectrum
A 1D convolutional neural network reconstructs the dark-matter phase-space distribution from the matter power spectrum with greater accuracy and broader applicability than an earlier empirical formula.