Contributor info: Mr Ibrahim Chahrour

LHCb Collaboration

SciPost Phys. 18, 071 (2025) · published 26 February 2025 | Toggle abstract · pdf

Measurements are presented of the cross-section for the central exclusive production of $J/\psi\to\mu^+\mu^-$ and $\psi(2S)\to\mu^+\mu^-$ processes in proton-proton collisions at $\sqrt{s} = 13 \ \mathrm{TeV}$ with 2016–2018 data. They are performed by requiring both muons to be in the LHCb acceptance (with pseudorapidity $2<\eta_{\mu^{±}} < 4.5$) and mesons in the rapidity range $2.0 < y < 4.5$. The integrated cross-section results are \begin{align*} \sigma_{J/\psi\to\mu^+\mu^-}(2.0<y_{J/\psi}<4.5,2.0<\eta_{\mu^{±}} < 4.5) &= 400 ± 2 ± 5 ± 12 \ \mathrm{pb}\,,\\ \sigma_{\psi(2S)\to\mu^+\mu^-}(2.0<y_{\psi(2S)}<4.5,2.0<\eta_{\mu^{±}} < 4.5) &= 9.40 ± 0.15 ± 0.13 ± 0.27 \ \mathrm{pb}\,, \end{align*} where the uncertainties are statistical, systematic and due to the luminosity determination. In addition, a measurement of the ratio of $\psi(2S)$ and $J/\psi$ cross-sections, at an average photon-proton centre-of-mass energy of $1\ \mathrm{TeV}$, is performed, giving \begin{equation*} \frac{\sigma_{\psi(2S)}}{\sigma_{J/\psi}} = 0.1763 ± 0.0029 ± 0.0008 ± 0.0039 \,, \end{equation*} where the first uncertainty is statistical, the second systematic and the third due to the knowledge of the involved branching fractions. For the first time, the dependence of the $J/\psi$ and $\psi(2S)$ cross-sections on the total transverse momentum transfer is determined in $pp$ collisions and is found consistent with the behaviour observed in electron-proton collisions.

Ibrahim Chahrour, James D. Wells

SciPost Phys. 12, 187 (2022) · published 8 June 2022 | Toggle abstract · pdf

The need to approximate functions is ubiquitous in science, either due to empirical constraints or high computational cost of accessing the function. In high-energy physics, the precise computation of the scattering cross-section of a process requires the evaluation of computationally intensive integrals. A wide variety of methods in machine learning have been used to tackle this problem, but often the motivation of using one method over another is lacking. Comparing these methods is typically highly dependent on the problem at hand, so we specify to the case where we can evaluate the function a large number of times, after which quick and accurate evaluation can take place. We consider four interpolation and three machine learning techniques and compare their performance on three toy functions, the four-point scalar Passarino-Veltman $D_0$ function, and the two-loop self-energy master integral $M$. We find that in low dimensions ($d = 3$), traditional interpolation techniques like the Radial Basis Function perform very well, but in higher dimensions ($d=5, 6, 9$) we find that multi-layer perceptrons (a.k.a neural networks) do not suffer as much from the curse of dimensionality and provide the fastest and most accurate predictions.