Energy reconstruction for a hadronic calorimeter using multivariate data analysis methods
The CALICE highly granular Semi-Digital Hadronic CALorimeter (SDHCAL) technological prototype provides rich information on the shape and structure of the hadronic showers. To exploit this information and to improve on the standard energy reconstruction method where only the total number of hits is u...
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| Veröffentlicht in: | Journal of instrumentation 2019-10, Vol.14 (10), p.P10034-P10034 |
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| container_end_page | P10034 |
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| container_issue | 10 |
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| container_title | Journal of instrumentation |
| container_volume | 14 |
| creator | Liu, B. Liu, D. Shen, Q. Zhang, T. Garillot, G. Guo, J. He, X. Hu, J. Lagarde, F. Laktineh, I. Wang, X. Yan, J. Yang, H. Zhang, X. Zhu, Y. |
| description | The CALICE highly granular Semi-Digital Hadronic CALorimeter (SDHCAL) technological prototype provides rich information on the shape and structure of the hadronic showers. To exploit this information and to improve on the standard energy reconstruction method where only the total number of hits is used, we propose to use two methods based on MultiVariate data Analysis (MVA) techniques: the Multi-Layer Perceptron (MLP) and the Boosted Decision Trees with Gradient Boost (BDTG) . The two new methods achieve better energy linearity (ΔE/Ebeam≤2%) with respect to the classic method (ΔE/Ebeam≤5%) and improve on the relative energy resolution. For instance, the MLP method achieves 6–7% relative improvement on the whole energy range when applied on samples of simulated π− events in the SDHCAL. |
| doi_str_mv | 10.1088/1748-0221/14/10/P10034 |
| format | Article |
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To exploit this information and to improve on the standard energy reconstruction method where only the total number of hits is used, we propose to use two methods based on MultiVariate data Analysis (MVA) techniques: the Multi-Layer Perceptron (MLP) and the Boosted Decision Trees with Gradient Boost (BDTG) . The two new methods achieve better energy linearity (ΔE/Ebeam≤2%) with respect to the classic method (ΔE/Ebeam≤5%) and improve on the relative energy resolution. 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To exploit this information and to improve on the standard energy reconstruction method where only the total number of hits is used, we propose to use two methods based on MultiVariate data Analysis (MVA) techniques: the Multi-Layer Perceptron (MLP) and the Boosted Decision Trees with Gradient Boost (BDTG) . The two new methods achieve better energy linearity (ΔE/Ebeam≤2%) with respect to the classic method (ΔE/Ebeam≤5%) and improve on the relative energy resolution. For instance, the MLP method achieves 6–7% relative improvement on the whole energy range when applied on samples of simulated π− events in the SDHCAL.</description><subject>Data analysis</subject><subject>Decision analysis</subject><subject>Decision trees</subject><subject>Energy resolution</subject><subject>High Energy Physics - Experiment</subject><subject>Linearity</subject><subject>Methods</subject><subject>Multilayers</subject><subject>Multivariate analysis</subject><subject>Physics</subject><subject>Reconstruction</subject><issn>1748-0221</issn><issn>1748-0221</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNpNkEtLAzEQgIMoWB9_QQKePKxNdrO76bGUaoWCHvTgKUwe26ZsNzXJFvrvzbJSHBgmmfkYhg-hB0qeKeF8SmvGM5LndErZlJLpByWkYBdoch5c_ntfo5sQdoSUs5KRCfpedsZvTtgb5boQfa-idR1unMeAt6C966zCClrn7d5E43EfbLfB-76N9gjeQjRYQwQMHbSnYANO2NbpcIeuGmiDuf-rt-jrZfm5WGXr99e3xXydqbxiMZNGEiNlU3FlCNNEc8lyyZshZS0lg0qbmmsK6ZdTRlhTaSiNoikYL4tb9DTu3UIrDulK8CfhwIrVfC2GHsmLWUF4daSJfRzZg3c_vQlR7Fzv0-FB5EVZVwVLnhJVjZTyLgRvmvNaSsSgXAw2xWBTUDY0R-XFL23xdbk</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Liu, B.</creator><creator>Liu, D.</creator><creator>Shen, Q.</creator><creator>Zhang, T.</creator><creator>Garillot, G.</creator><creator>Guo, J.</creator><creator>He, X.</creator><creator>Hu, J.</creator><creator>Lagarde, F.</creator><creator>Laktineh, I.</creator><creator>Wang, X.</creator><creator>Yan, J.</creator><creator>Yang, H.</creator><creator>Zhang, X.</creator><creator>Zhu, Y.</creator><general>IOP Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>1XC</scope></search><sort><creationdate>20191001</creationdate><title>Energy reconstruction for a hadronic calorimeter using multivariate data analysis methods</title><author>Liu, B. ; Liu, D. ; Shen, Q. ; Zhang, T. ; Garillot, G. ; Guo, J. ; He, X. ; Hu, J. ; Lagarde, F. ; Laktineh, I. ; Wang, X. ; Yan, J. ; Yang, H. ; Zhang, X. ; Zhu, Y.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c264t-beb0ebbf68ce04d0d8b42b8f2b8fb7bb4a6de78d1ab7b21404f6da5ec11114853</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Data analysis</topic><topic>Decision analysis</topic><topic>Decision trees</topic><topic>Energy resolution</topic><topic>High Energy Physics - Experiment</topic><topic>Linearity</topic><topic>Methods</topic><topic>Multilayers</topic><topic>Multivariate analysis</topic><topic>Physics</topic><topic>Reconstruction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, B.</creatorcontrib><creatorcontrib>Liu, D.</creatorcontrib><creatorcontrib>Shen, Q.</creatorcontrib><creatorcontrib>Zhang, T.</creatorcontrib><creatorcontrib>Garillot, G.</creatorcontrib><creatorcontrib>Guo, J.</creatorcontrib><creatorcontrib>He, X.</creatorcontrib><creatorcontrib>Hu, J.</creatorcontrib><creatorcontrib>Lagarde, F.</creatorcontrib><creatorcontrib>Laktineh, I.</creatorcontrib><creatorcontrib>Wang, X.</creatorcontrib><creatorcontrib>Yan, J.</creatorcontrib><creatorcontrib>Yang, H.</creatorcontrib><creatorcontrib>Zhang, X.</creatorcontrib><creatorcontrib>Zhu, Y.</creatorcontrib><collection>CrossRef</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Journal of instrumentation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, B.</au><au>Liu, D.</au><au>Shen, Q.</au><au>Zhang, T.</au><au>Garillot, G.</au><au>Guo, J.</au><au>He, X.</au><au>Hu, J.</au><au>Lagarde, F.</au><au>Laktineh, I.</au><au>Wang, X.</au><au>Yan, J.</au><au>Yang, H.</au><au>Zhang, X.</au><au>Zhu, Y.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Energy reconstruction for a hadronic calorimeter using multivariate data analysis methods</atitle><jtitle>Journal of instrumentation</jtitle><date>2019-10-01</date><risdate>2019</risdate><volume>14</volume><issue>10</issue><spage>P10034</spage><epage>P10034</epage><pages>P10034-P10034</pages><issn>1748-0221</issn><eissn>1748-0221</eissn><abstract>The CALICE highly granular Semi-Digital Hadronic CALorimeter (SDHCAL) technological prototype provides rich information on the shape and structure of the hadronic showers. To exploit this information and to improve on the standard energy reconstruction method where only the total number of hits is used, we propose to use two methods based on MultiVariate data Analysis (MVA) techniques: the Multi-Layer Perceptron (MLP) and the Boosted Decision Trees with Gradient Boost (BDTG) . The two new methods achieve better energy linearity (ΔE/Ebeam≤2%) with respect to the classic method (ΔE/Ebeam≤5%) and improve on the relative energy resolution. For instance, the MLP method achieves 6–7% relative improvement on the whole energy range when applied on samples of simulated π− events in the SDHCAL.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1748-0221/14/10/P10034</doi></addata></record> |
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| subjects | Data analysis Decision analysis Decision trees Energy resolution High Energy Physics - Experiment Linearity Methods Multilayers Multivariate analysis Physics Reconstruction |
| title | Energy reconstruction for a hadronic calorimeter using multivariate data analysis methods |
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