Testing a 95 GeV Scalar at the CEPC with Machine Learning

TL;DR

This paper demonstrates that a 210 GeV run at the CEPC, combined with machine learning techniques, can effectively test the hypothesis of a 95 GeV scalar particle suggested by experimental excesses, achieving high sensitivity and precision.

Contribution

It introduces a machine learning-enhanced analysis strategy for detecting a 95 GeV scalar at the CEPC, optimizing energy and luminosity requirements.

Findings

Deep neural networks halve the luminosity needed for discovery.

CEPC at 210 GeV can reach 5σ sensitivity to the scalar.

Efficient testing of the 95 GeV excess hypothesis at future colliders.

Abstract

Several possible excesses around 95 GeV hint at an additional light scalar beyond the Standard Model. We examine the capability of the CEPC to test this hypothesis in the Higgsstrahlung channel $e^{+}e^{-} \to ZS$ with $Z \to\mu^{+}\mu^{-}$ and $S\to\tau^{+}\tau^{-}$. Full detector simulation shows that the optimal center-of-mass energy to study the 95 GeV light scalar is 210 GeV. A deep neural network classifier reduces the luminosity required for discovery by half. At $L = 20~\mathrm{ab}^{-1}$, the CEPC's $5\sigma$ sensitivity to the signal strength $\mu_{\tau\tau}^{ZS}$ reaches 0.016 and 0.020 for $\sqrt{s} =$ 210 GeV and 240 GeV, respectively. The corresponding thresholds for a 5% precision measurement are $\mu_{\tau\tau}^{ZS} > 0.10$ and $>0.12$. At $\sqrt{s}=$ 210 GeV (240 GeV), $5\sigma$ coverage of all N2HDM-Flipped samples with $\chi^2_{h_{95}}<7.82$ requires $L=800\ \mathrm{fb}^{-1}$ (1.22 $\mathrm{ab}^{-1}$). These results establish a 210 GeV run, augmented by machine-learning selection, as the most efficient strategy to confirm or refute the 95 GeV excess at future lepton colliders.