  Motivated by the expectation that new physics may manifest itself in the form
of very heavy new particles, most of the operation time of the LHC is devoted
to $pp$ collisions at the highest achievable energies and collision rates. The
large collision rates imply tight trigger requirements that include high
thresholds on the final-state particles' transverse momenta $p_{T}$ and an
intrinsic background in the form of particle pileup produced by different
collisions occurring during the same bunch crossing. This strategy is
potentially sub-optimal for several well-motivated new physics models where new
particles are not particularly heavy and can escape the online selection
criteria of the multi-purpose LHC experiments due to their light mass and small
coupling. A solution may be offered by complementary datasets that are
routinely collected by the LHC experiments. These include heavy ion collisions,
low-pileup runs for precision physics, and the so-called 'parking' and
'scouting' datasets. While some of them are motivated by other physics goals,
they all have the usage of mild $p_{T}$ thresholds at the trigger-level in
common. In this study, we assess the relative merits of these datasets for a
representative model whose particular clean signature features long-lived
resonances yielding displaced dimuon vertices. We compare the reach across
those datasets for a simple analysis, simulating LHC data in Run 2 and Run 3
conditions with the Delphes simulation. We show that the scouting and parking
datasets, which afford low-$p_{T}$ trigger thresholds by only using partial
detector information and delaying the event reconstruction, respectively, have
a reach comparable to the standard $pp$ dataset with conventional thresholds.
We also show that heavy ion and low-pileup datasets are far less competitive
for this signature.

High Energy Physics - Phenomenology