Abstract
We study the prospect of constraining invisible branching ratio of the Higgs boson in the vector boson fusion channel using deep learning techniques. Taking advantage of the differing QCD radiation patterns between signal and background, we find that modern machine learning techniques have the capability of significantly outperforming traditional analyses.
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Notes
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It may be possible that the particle may decay to SM particles outside the detector with a long lifetime.
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Ngairangbam, V.S. (2022). Improving Bounds on Invisible Branching Ratio of the Higgs with Deep Learning. In: Mohanty, B., Swain, S.K., Singh, R., Kashyap, V.K.S. (eds) Proceedings of the XXIV DAE-BRNS High Energy Physics Symposium, Jatni, India. Springer Proceedings in Physics, vol 277. Springer, Singapore. https://doi.org/10.1007/978-981-19-2354-8_53
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DOI: https://doi.org/10.1007/978-981-19-2354-8_53
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