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Purpose: <p>Amplitude analysis is a pivotal tool in hadron spectroscopy, fundamentally involving a series of likelihood fits to multi-dimensional experimental distributions. While robust goodness-of-fit tests exist for low-dimensional scenarios, evaluating goodness-of-fit in amplitude analysis remains challenging.</p> Methods: <p>We propose a machine-learning approach using anomaly detection for goodness-of-fit assessment in amplitude analysis. Our method employs a classifier to identify discrepancies between data and fit results in multi-dimensional phase space.</p> Results and conclusion: <p>Using Monte Carlo simulations of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(J/\psi \rightarrow \gamma \pi ^+\pi ^-\pi ^0\pi ^0\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi>J</mi> <mo stretchy="false">/</mo> <mi>ψ</mi> <mo stretchy="false">→</mo> <mi>γ</mi> <msup> <mi>π</mi> <mo>+</mo> </msup> <msup> <mi>π</mi> <mo>-</mo> </msup> <msup> <mi>π</mi> <mn>0</mn> </msup> <msup> <mi>π</mi> <mn>0</mn> </msup> </mrow> </math></EquationSource> </InlineEquation> decays, we demonstrate that this method detects contributions from an additional resonance with a signal strength of 1%. The detection power is sufficient for practical amplitude analyses, where contributions with fit fractions larger than 1% are typically included in the nominal fit. This approach shows promise for amplitude analyses of multi-body processes.</p>

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Machine-learning-based method for goodness-of-fit test in amplitude analysis

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Guoyi Hou,
  • Beijiang Liu

摘要

Purpose:

Amplitude analysis is a pivotal tool in hadron spectroscopy, fundamentally involving a series of likelihood fits to multi-dimensional experimental distributions. While robust goodness-of-fit tests exist for low-dimensional scenarios, evaluating goodness-of-fit in amplitude analysis remains challenging.

Methods:

We propose a machine-learning approach using anomaly detection for goodness-of-fit assessment in amplitude analysis. Our method employs a classifier to identify discrepancies between data and fit results in multi-dimensional phase space.

Results and conclusion:

Using Monte Carlo simulations of \(J/\psi \rightarrow \gamma \pi ^+\pi ^-\pi ^0\pi ^0\) J / ψ → γ π + π - π 0 π 0 decays, we demonstrate that this method detects contributions from an additional resonance with a signal strength of 1%. The detection power is sufficient for practical amplitude analyses, where contributions with fit fractions larger than 1% are typically included in the nominal fit. This approach shows promise for amplitude analyses of multi-body processes.