activity
19982026
most citedSearch for the Standard Model Higgs boson at LEP in the year 2000

79 citations · 1.1k across the 61 of their papers we have counts for

collaborators

61 papers

hep-ph2026

production as a window to invisible new physics

Rodrigo Capucha, João Lopes, João Bravo Martins +2

We present a phenomenological study where we probe the sensitivity to invisible dark matter (DM) mediators produced in association with a pair at the Large Hadron Collid…

hep-ph2026

Searches for Extra Higgs Bosons using Higgs{} Events within 2HDMs: Direct versus Indirect Probes

Esteban Chalbaud, Stefania De Curtis, Luigi Delle Rose +3

We study the possibility of establishing the production of additional Higgs states in the process , where = , with being CP-even and $H_…

hep-ph2026★ 1 cited

Extracting a Toponium Signal at the LHC with Spin and Quantum Information Tools

Laura Antozzi, Esteban Chalbaud, Frédéric Déliot +6

We investigate near-threshold top-antitop production at the LHC, focusing on the impact of toponium formation on spin correlations and quantum information properties of the final s…

hep-ph2024

Asymmetries in invisible Dark Matter mediator production associated with final states

E. Chalbaud, Rui M. Silva, António Onofre +2

In this paper, we propose two sets of different CP-sensitive observables inspired by the Higgs production in association with the top quark. We employ a Dark Matter simplified mode…

astro-ph.HE2024★ 6 cited

Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae

Solange Nunes, Gabriel Escrig, Osvaldo G. Freitas +4

We test deep-learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter infe…

gr-qc2023★ 4 cited

Comparison of neural network architectures for feature extraction from binary black hole merger waveforms

Osvaldo Gramaxo Freitas, Juan Calderón Bustillo, José A. Font +3

We evaluate several neural-network architectures, both convolutional and recurrent, for gravitational-wave time-series feature extraction by performing point parameter estimation o…