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Shedding light on dark matter at the LHC with machine learning

Ernesto Arganda1,2,*, Martín de los Rios3,4,†, Andres D. Perez2,‡, Subhojit Roy5,§, Rosa M. Sandá Seoane1,2,∥, and Carlos E. M. Wagner5,6,7,8,9,¶

  • *Contact author: ernesto.arganda@uam.es
  • †Contact author: mdelosrios@unc.edu.ar
  • ‡Contact author: andresd.perez@csic.es
  • §Contact author: sroy@anl.gov
  • ∥Contact author: rosa.sanda@uam.es
  • Contact author: cwagner@uchicago.edu
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Phys. Rev. D 113, 095013 – Published 6 May, 2026

DOI: https://doi.org/10.1103/m1cd-1sfb

Abstract

We investigate a weakly interacting massive particle dark-matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the Z3-symmetric next-to-minimal supersymmetric standard model (NMSSM). This framework gives rise to regions of parameter space where DM is obtained via coannihilation with nearby Higgsino-like electroweakinos and DM direct-detection signals are suppressed, the so-called “blind spots.” On the other hand, collider signatures remain promising due to enhanced radiative decay modes of Higgsinos into the singlino-dominated LSP and photons rather than into leptons or hadrons. Compared to MSSM scenarios with light bino- and winolike electroweakinos, the NMSSM allows for final states with multiple photons arising from cascade radiative decays, providing a distinctive collider signature. This motivates searches for radiatively decaying neutralinos; however, these signals face substantial background challenges, as the decay products are typically soft due to the small mass splits (Δm) between the LSP and the Higgsino-like coannihilation partners. We apply a data-driven machine-learning analysis that improves sensitivity to these subtle signals, offering a powerful complement to traditional search strategies to discover a new physics scenario. Using an LHC integrated luminosity of 100  fb−1 at 14 TeV, the method achieves a 5σ discovery reach for Higgsino masses up to 225 GeV with Δm≲12  GeV and a 2σ exclusion up to 285 GeV with Δm≲20  GeV. These results highlight the power of collider searches to probe DM candidates that remain hidden from current direct-detection experiments and provide a motivation for a search by the LHC collaborations using machine-learning methods.

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Outline

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Phys. Rev. D 113, 095013– Published 6 May, 2026
  • Received 19 January 2026
  • Accepted 14 April 2026
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