<p>We developed a dedicated data analysis framework for silicon strip detector telescopes (SSDTs) of the Compact Spectrometer for Heavy-IoN Experiments (CSHINE) that addresses the challenges of processing complex signals. The framework integrates advanced algorithms for precise calibration, accurate particle identification, and efficient event reconstruction, aiming to account for critical experimental factors such as charge-sharing effects, multi-hit event resolution, and detector response nonuniformity. Its robust performance was demonstrated through the successful analysis of light-charged particles in the 25 MeV/u <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41365_2025_1743_Article_IEq1.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">$^{86}$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>86</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>Kr + <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41365_2025_1743_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$^{124}$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>124</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>Sn experiment conducted at the first Radioactive Ion Beam Line in Lanzhou, allowing for precise extraction of physical observables, including energy, momentum, and particle type. Furthermore, utilizing the reconstructed physical information, such as the number of effective physical events and energy spectra to optimize the track recognition algorithm, the final track recognition efficiencies of approximately 90<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41365_2025_1743_Article_IEq3.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">$\%$</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation> were achieved. This framework establishes a valuable reference methodology for SSDT-based detector systems in heavy-ion reaction experiments, thereby significantly enhancing the accuracy and efficiency of data analysis in nuclear physics research.</p>

Data analysis framework for silicon strip detector in compact spectrometer for heavy-ion experiments

期刊论文
发表日期：

2025年6月1日

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Xiao-Bao Wei,
Yu-Hao Qin,
Sheng Xiao,
Da-Wei Si,
Dong Guo,
Zhi Qin,
Fen-Hai Guan,
Xin-Yue Diao,
Bo-Yuan Zhang,
Bai-Ting Tian,
Jun-Huai Xu,
Tian-Ren Zhuo,
Yi-Bo Hao,
Zeng-Xiang Wang,
Shi-Tao Wang,
Chun-Wang Ma,
Yi-Jie Wang,
Zhi-Gang Xiao

摘要

We developed a dedicated data analysis framework for silicon strip detector telescopes (SSDTs) of the Compact Spectrometer for Heavy-IoN Experiments (CSHINE) that addresses the challenges of processing complex signals. The framework integrates advanced algorithms for precise calibration, accurate particle identification, and efficient event reconstruction, aiming to account for critical experimental factors such as charge-sharing effects, multi-hit event resolution, and detector response nonuniformity. Its robust performance was demonstrated through the successful analysis of light-charged particles in the 25 MeV/u $^{86}$ $^{86}$ Kr + $^{124}$ $^{124}$ Sn experiment conducted at the first Radioactive Ion Beam Line in Lanzhou, allowing for precise extraction of physical observables, including energy, momentum, and particle type. Furthermore, utilizing the reconstructed physical information, such as the number of effective physical events and energy spectra to optimize the track recognition algorithm, the final track recognition efficiencies of approximately 90 $\%$ $%$ were achieved. This framework establishes a valuable reference methodology for SSDT-based detector systems in heavy-ion reaction experiments, thereby significantly enhancing the accuracy and efficiency of data analysis in nuclear physics research.