An artificial neural network for proton identification in HERMES data

WANG Si-Guang; MAO Ya-Jun; YE Hong-Xue

doi:10.1088/1674-1137/33/3/011

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2024年10月30日

Chinese Physics C> 2009, Vol. 33> Issue(3) : 217-223 DOI: 10.1088/1674-1137/33/3/011

An artificial neural network for proton identification in HERMES data

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Abstract：
The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ⁰ decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π^－ mass) the reconstructed invariant mass lies within the Λ⁰ resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.

The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.

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WANG Si-Guang, MAO Ya-Jun and YE Hong-Xue. An artificial neural network for proton identification in HERMES data[J]. Chinese Physics C, 2009, 33(3): 217-223. doi: 10.1088/1674-1137/33/3/011

WANG Si-Guang, MAO Ya-Jun and YE Hong-Xue. An artificial neural network for proton identification in HERMES data[J]. Chinese Physics C, 2009, 33(3): 217-223. doi: 10.1088/1674-1137/33/3/011 shu

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Received: 2008-07-03
Revised: 2008-07-30

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通讯作者: 陈斌, [email protected]

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沈阳化工大学材料科学与工程学院沈阳 110142

An artificial neural network for proton identification in HERMES data

Corresponding author: WANG Si-Guang,

Received Date: 2008-07-03
Accepted Date: 2008-07-30
Available Online: 2009-03-05

Abstract:

The HERMES time-of-flight (TOF) system is used for proton identification, but must be carefully calibrated for systematic biases in the equipment. This paper presents an artificial neural network (ANN) trained to recognize protons from Λ⁰ decay using only raw event data such as time delay, momentum, and trajectory. To avoid the systematic errors associated with Monte Carlo models, we collect a sample of raw experimental data from the year 2000. We presume that when for a positive hadron (assigned one proton mass) and a negative hadron (assigned one π^－ mass) the reconstructed invariant mass lies within the Λ⁰ resonance, the positive hadron is more likely to be a proton. Such events are assigned an output value of one during the training process; all others were assigned the output value zero.

The trained ANN is capable of identifying protons in independent experimental data, with an efficiency equivalent to the traditional TOF calibration. By modifying the threshold for proton identification, a researcher can trade off between selection efficiency and background rejection power. This simple and convenient method is applicable to similar detection problems in other experiments.