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Data · dataset · 2026

A synthetic GAGG-like waveform dataset for amplitude-dominated gamma-source classification

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This dataset contains 250,000 analytically generated GAGG:Ce-like waveforms for amplitude-dominated gamma-source classification, with 50,000 events per class for Cs137, Co60, Na22, Am241 and Ba133.

Description

Each event contains 2,000 float32 waveform samples, a source-category label and a synthetic energy annotation in MeV.The nominal record window is 400 ns at 5 GHz. The endpoint-inclusive generation grid has an actual spacing of 400/1999 ns.

All classes share one nominal decay shape, with source-dependent energy distributions, Poisson amplitude fluctuations and amplitude-dependent Gaussian noise. The stored records have truncated leading portions and exact trailing-zero padding. They are analytic synthetic data, not experimental measurements or particle-transport simulation output.The supporting archive includes a data dictionary, generation protocol, fixed training/validation/test indices, aligned event metadata, quality-control reports, analysis code, fitted scalar classifiers, predictions and figure source data.

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Version 1.0 is identified by a SHA-256 checksum. Because the original energy-sampling random states were not retained, bitwise regeneration from the historical generator is not claimed; reproducible analyses use the frozen archive.The dataset supports controlled computational studies of amplitude dependence, preprocessing and waveform classification, rather than claims of calibrated detector performance. Data and documentation are licensed under CC BY 4.0.

Software in the code and provenance directories is separately licensed under MIT.

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Catalogue records · 1

Topics

Inferred from text
Simulation 75%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
ScienceDB10.57760/sciencedb.012yi11 d agoJSON v1
FieldAssertionExtractorEvidence
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