Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910746204438528 |
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| author | Backes, Mathias Butter, Anja Dunford, Monica Malaescu, Bogdan |
| author_facet | Backes, Mathias Butter, Anja Dunford, Monica Malaescu, Bogdan |
| contents | The unfolding of detector effects is a key aspect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been developed to provide novel features, e.g. high dimensionality or a probabilistic single-event unfolding based on generative neural networks. Traditionally, many analyses unfold detector effects using transfer-matrix--based algorithms, which are well established in low-dimensional unfolding. They yield an unfolded distribution of the total spectrum, together with its covariance matrix. This paper proposes a method to obtain probabilistic single-event unfolded distributions, together with their uncertainties and correlations, for the transfer-matrix--based unfolding. The algorithm is first validated on a toy model and then applied to pseudo-data for the $pp\rightarrow Zγγ$ process. In both examples the performance is compared to the Machine-Learning--based single-event unfolding using an iterative approach with conditional invertible neural networks (IcINN). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17037 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods Backes, Mathias Butter, Anja Dunford, Monica Malaescu, Bogdan Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology The unfolding of detector effects is a key aspect of comparing experimental data with theoretical predictions. In recent years, different Machine-Learning methods have been developed to provide novel features, e.g. high dimensionality or a probabilistic single-event unfolding based on generative neural networks. Traditionally, many analyses unfold detector effects using transfer-matrix--based algorithms, which are well established in low-dimensional unfolding. They yield an unfolded distribution of the total spectrum, together with its covariance matrix. This paper proposes a method to obtain probabilistic single-event unfolded distributions, together with their uncertainties and correlations, for the transfer-matrix--based unfolding. The algorithm is first validated on a toy model and then applied to pseudo-data for the $pp\rightarrow Zγγ$ process. In both examples the performance is compared to the Machine-Learning--based single-event unfolding using an iterative approach with conditional invertible neural networks (IcINN). |
| title | Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods |
| topic | Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2310.17037 |