The existence of a new spin-zero particle with a mass below the electroweak scale is predicted by several theoretical models. Searches for resonant production of photon pairs at the LHC are able to probe these models. We present a search for a narrow resonance produced through gluon fusion that decays into a pair of photons with an invariant mass between 10 and 70 GeV, using a proton-proton collision dataset from the CMS experiment. This dataset, corresponding to an integrated luminosity of 54.4 fb − 1 , was recorded in 2018 at a center-of-mass energy of 13 TeV using a newly introduced diphoton trigger that enabled exploration of the low-mass diphoton spectrum. No significant excess above the expected background is observed. Upper limits are set on the product of the gluon fusion production cross section and the branching fraction of the diphoton decay of a narrow resonance. An interpretation of these limits within an effective field theory framework for axionlike particles is also provided.
A search for Higgs boson ( H ) production in association with two vector bosons ( V = W , Z ) via vector-boson scattering (VBS) is presented using proton-proton collision data collected at s = 13 TeV by the CMS experiment, corresponding to an integrated luminosity of 138 fb − 1 . Events containing two forward jets consistent with VBS, a large-radius jet from the decay of a boosted H to a pair of b quarks, and zero, one, or two charged leptons coming from V decays are selected. The process is excluded at 95% confidence level for observed (expected) values of the V V H H coupling modifier κ 2 V outside the interval 0.40 < κ 2 V < 1.60 ( 0.34 < κ 2 V < 1.66 ), assuming standard model values for all other couplings, thus establishing a novel probe of the V V H H interaction. Constraints are also set on the individual κ 2 W and κ 2 Z coupling modifiers and on the allowed region in the κ 2 W − κ 2 Z plane.
A search for supersymmetric electroweakinos is performed using events with a low-momentum (soft) isolated track and large missing transverse momentum, targeting nearly mass-degenerate higgsinolike charginos and neutralinos. For mass splittings of 0.3–1 GeV, the chargino decays to the lightest neutralino and a low-momentum pion, which can produce a soft, potentially displaced track. A parametrized neural network separates signal from background using kinematic and impact parameter information. The analysis uses 138 fb − 1 of proton-proton collision data at a center-of-mass energy of 13 TeV recorded with the CMS detector. No significant excess above the standard model expectation is observed. At 95% confidence level, the considered higgsino model is excluded for mass splittings in the range 0.28–1.15 GeV and for chargino masses up to 185 GeV, setting stringent constraints on natural supersymmetry scenarios.
The electron beams of a laser-plasma accelerator depend non-linearly on the laser and gas settings. This dependence is explored using particle-in-cell (PIC) simulations, and machine-learning models trained on these simulations predict the beam from the settings. Here we address the reverse problem: given a measured beam, which settings produced it? We infer three of them: the gas pressure p_1, the normalised vector potential a_0 and the nitrogen concentration c_N_2. The solution is not guaranteed to be unique. We train an ensemble of mixture density networks that, for each beam, output several possible sets of settings with their probabilities and uncertainties, and test them on simulated beams with realistic measurement noise. On average, the predictions are accurate (R^2 = 0.91), yet for about two-thirds of the beams the inferred distribution has two competing solutions. These share nearly the same value of a_0^2 p_1, which controls how strongly the laser focuses itself in the plasma and, in earlier PALLAS studies, when electrons start to be injected. Keeping this quantity fixed gives beams that are practically indistinguishable with realistic diagnostics. A forward model used as a check removes unphysical solutions but cannot choose between the two plausible ones. We then test two ways to resolve the ambiguity. Adding the full electron energy spectrum improves the mean prediction (R^2 = 0.94), mostly through a_0. It changes the weights of the two solutions but does not move them. A second measurement at a deliberately different plasma density is more effective. In paired PIC simulations, it reduces the error on a_0 by a factor of 2.6, whereas repeating the measurement at the same density brings almost no gain.
In the recent years, high energy physics discoveries have been driven by the increasing of luminosity and/or detector granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process the detector outputs with current methods and algorithms. Graph Neural Networks (GNNs), have been shown to be powerful tools to address these challenges. While GNNs can deal with the non-euclidean nature of the data extracted from these detectors, their deployment presents significant difficulties, mainly from the computational complexity of the algorithms they are based on. We propose to use the known geometries of the detectors to optimise the algorithms used for GNN pipelines, which usually have quadratic algorithmic complexities. We validate the validity of our optimised approach on various tasks, such as particle identification, energy regression and instance segmentation. Finally, we present HIBOU, a generic modular architecture that provides a blueprint for how these GNN blocks are to be implemented within experiment-specific reconstruction frameworks.
