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Purpose: <p>This paper presents a 3D fit method for reconstructing electromagnetic showers in high-granularity 3D calorimeters, aiming to improve the energy and angular reconstruction performance, to maximize the scientific potential of the detector.</p> Methods: <p>Electromagnetic shower distribution is parameterized to calculate the expected energy deposition in the calorimeter for perpendicularly incident electromagnetic particles. A likelihood fit is then performed to extract precisely the energy and incidence direction of the particle.</p> Results: <p>The proposed methodology achieves an energy resolution of approximately 1.4% and an angular resolution of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\sim 0.45^{\circ }\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo>∼</mo> <mn>0</mn> <mo>.</mo> <msup> <mn>45</mn> <mo>∘</mo> </msup> </mrow> </math></EquationSource> </InlineEquation> for perpendicularly incident electrons at 200 GeV, compared to 1.5% energy resolution and over <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(1.0^{\circ }\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>1</mn> <mo>.</mo> <msup> <mn>0</mn> <mo>∘</mo> </msup> </mrow> </math></EquationSource> </InlineEquation> angular resolution for the Convolutional Neural Network.</p> Conclusion: <p>The results validate the method’s ability to accurately reconstruct the energy and angular information of perpendicularly incident electrons and significantly improve the angular resolution, which is crucial for high-energy gamma measurements.</p>

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Three-dimensional fitting reconstruction for high-granularity calorimeters

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Mei-Jun Liang,
  • Cheng Zhang,
  • Zhi-Cheng Tang,
  • Shang-Lin Li,
  • Hao Chen,
  • Heng-Yi Cai,
  • Sen-Quan Lu,
  • Ze-Tong Sun,
  • Feng-Ze Zhang,
  • Hao-Tian Yang,
  • Yu-Hang You,
  • Zi-Xuan Yan,
  • Ye Tian,
  • Hong-Qing Wu,
  • Zu-Hao Li

摘要

Purpose:

This paper presents a 3D fit method for reconstructing electromagnetic showers in high-granularity 3D calorimeters, aiming to improve the energy and angular reconstruction performance, to maximize the scientific potential of the detector.

Methods:

Electromagnetic shower distribution is parameterized to calculate the expected energy deposition in the calorimeter for perpendicularly incident electromagnetic particles. A likelihood fit is then performed to extract precisely the energy and incidence direction of the particle.

Results:

The proposed methodology achieves an energy resolution of approximately 1.4% and an angular resolution of \(\sim 0.45^{\circ }\) ∼ 0 . 45 ∘ for perpendicularly incident electrons at 200 GeV, compared to 1.5% energy resolution and over \(1.0^{\circ }\) 1 . 0 ∘ angular resolution for the Convolutional Neural Network.

Conclusion:

The results validate the method’s ability to accurately reconstruct the energy and angular information of perpendicularly incident electrons and significantly improve the angular resolution, which is crucial for high-energy gamma measurements.