Shedding light on dark matter at the LHC with machine learning

We investigate a weakly interacting massive particle dark-matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:msub> <a:mi>Z</a:mi> <a:mn>3</a:mn> </a:msub> </a:math> -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 ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:mi mathvariant="normal">Δ</c:mi> <c:mi>m</c:mi> </c:math> ) 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 <f:math xmlns:f="http://www.w3.org/1998/Math/MathML" display="inline"> <f:mn>100</f:mn> <f:mtext> </f:mtext> <f:mtext> </f:mtext> <f:msup> <f:mi>fb</f:mi> <f:mrow> <f:mo>−</f:mo> <f:mn>1</f:mn> </f:mrow> </f:msup> </f:math> at 14 TeV, the method achieves a <h:math xmlns:h="http://www.w3.org/1998/Math/MathML" display="inline"> <h:mn>5</h:mn> <h:mi>σ</h:mi> </h:math> discovery reach for Higgsino masses up to 225 GeV with <j:math xmlns:j="http://www.w3.org/1998/Math/MathML" display="inline"> <j:mi mathvariant="normal">Δ</j:mi> <j:mi>m</j:mi> <j:mo>≲</j:mo> <j:mn>12</j:mn> <j:mtext> </j:mtext> <j:mtext> </j:mtext> <j:mi>GeV</j:mi> </j:math> and a <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mn>2</m:mn> <m:mi>σ</m:mi> </m:math> exclusion up to 285 GeV with <o:math xmlns:o="http://www.w3.org/1998/Math/MathML" display="inline"> <o:mi mathvariant="normal">Δ</o:mi> <o:mi>m</o:mi> <o:mo>≲</o:mo> <o:mn>20</o:mn> <o:mtext> </o:mtext> <o:mtext> </o:mtext> <o:mi>GeV</o:mi> </o:math> . 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.