Search for invisible decays of light mesons via $J/\psi \to VP$ $(V=\omega/\phi,P=\eta/\eta')$ decays at STCF

TL;DR

This study assesses the potential to detect invisible decays of light mesons at the proposed STCF, setting upper limits on branching fractions and suggesting machine learning could enhance sensitivity.

Contribution

It provides a feasibility analysis for searching invisible meson decays at STCF and proposes using deep learning to improve detection limits.

Findings

Upper limits on decay branching fractions are projected at 10^{-7} level.

STCF could collect 3.4×10^{12} J/ψ events annually.

Deep learning may further improve sensitivity to light dark matter signals.

Abstract

We present a preliminary feasibility study of searches for invisible decays of light mesons via $J/\psi \to VP$ $(V=\omega/\phi,P=\eta/\eta')$ using a traditional analytical method at the proposed Super $\tau$-Charm facility (STCF) which is expected to accumulate $3.4\times10^{12}$ $J/\psi$ events per year, based on an inclusive Monte Carlo sample of $1.3 \times 10^{9}$ $J/\psi$ events. The upper limits on the invisible decay branching fractions at the 90\% confidence level are set as $\mathcal{B}(\omega \to invisible) < 3.7 \times 10^{-7}$, $\mathcal{B}(\phi \to invisible) < 8.9 \times 10^{-7}$, $\mathcal{B}(\eta \to invisible) < 1.8 \times 10^{-7}$ and $\mathcal{B}(\eta' \to invisible) < 4.1 \times 10^{-7}$, respectively, using a projected toy data corresponding to the expected STCF statistics. By using the machine learning technique such as Deep Learning, the upper limit may be further improved to approach theoretical predictions for light dark matter.