Kavli Affiliate: Carlos E. M. Wagner
| First 5 Authors: Ernesto Arganda, Ernesto Arganda, , ,
| Summary:
We investigate a WIMP dark matter (DM) candidate in the form of a
singlino-dominated lightest supersymmetric particle (LSP) within the
$Z_3$-symmetric Next-to-Minimal Supersymmetric Standard Model. This framework
gives rise to regions of parameter space where DM is obtained via
co-annihilation 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 a photon, rather than
into leptons or hadrons. 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 ($Delta m$)
between the LSP and the higgsino-like coannihilation partners. We apply a
data-driven Machine Learning (ML) 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
$100~mathrmfb^-1$ at $14~mathrmTeV$, the method achieves a $5sigma$
discovery reach for higgsino masses up to $225~mathrmGeV$ with $Delta
m!lesssim!12~mathrmGeV$, and a $2sigma$ exclusion up to
$285~mathrmGeV$ with $Delta m!lesssim!20~mathrmGeV$. 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 ML methods.
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