In:
The European Physical Journal Plus, Springer Science and Business Media LLC, Vol. 137, No. 7 ( 2022-07)
Abstract:
The $$\text {t}\bar{\text {t}}\text {H}(\text {b}\bar{\text {b}})$$ t t ¯ H ( b b ¯ ) process is an essential channel in revealing the Higgs boson properties; however, its final state has an irreducible background from the $$\text {t}\bar{\text {t}}\text {b}\bar{\text {b}}$$ t t ¯ b b ¯ process, which produces a top quark pair in association with a b quark pair. Therefore, understanding the $$\text {t}\bar{\text {t}}\text {b}\bar{\text {b}}$$ t t ¯ b b ¯ process is crucial for improving the sensitivity of a search for the $$\text {t}\bar{\text {t}}\text {H}(\text {b}\bar{\text {b}})$$ t t ¯ H ( b b ¯ ) process. To this end, when measuring the differential cross section of the $$\text {t}\bar{\text {t}}\text {b}\bar{\text {b}}$$ t t ¯ b b ¯ process, we need to distinguish the b-jets originating from top quark decays and additional b-jets originating from gluon splitting. In this paper, we train deep neural networks that identify the additional b-jets in the $${\text {t}}{\bar{\text {t}}}{\text {b}}{\bar{\text {b}}}$$ t t ¯ b b ¯ events under the supervision of a simulated $$\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$$ t t ¯ b b ¯ event data set in which true additional b-jets are indicated. By exploiting the special structure of the $$\text {t}\bar{\text {t}}\text {b}\bar{\text {b}}$$ t t ¯ b b ¯ event data, several loss functions are proposed and minimized to directly increase matching efficiency, i.e., the accuracy of identifying additional b-jets. We show that, via a proof-of-concept experiment using synthetic data, our method can be more advantageous for improving matching efficiency than the deep learning-based binary classification approach presented in [1]. Based on simulated $$\text {t}\bar{\text {t}}\text {b}\bar{\text {b}}$$ t t ¯ b b ¯ event data in the lepton+jets channel from pp collision at $$\sqrt{s}$$ s = 13 TeV, we then verify that our method can identify additional b-jets more accurately: compared with the approach in [1], the matching efficiency improves from 62.1 $$\%$$ % to 64.5 $$\%$$ % and from 59.9 $$\%$$ % to 61.7 $$\%$$ % for the leading order and the next-to-leading order simulations, respectively.
Type of Medium:
Online Resource
ISSN:
2190-5444
DOI:
10.1140/epjp/s13360-022-03024-8
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2022
detail.hit.zdb_id:
2595693-0
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