Speaker
Description
The LHC delivers an unprecedented number of proton-proton collisions
to its experiments. In kinematic regimes first studied by earlier
generations of collider experiments, the limiting factor to more
deeply probing for new physics can be the online and offline
computing, and offline storage, requirements for the recording and
analysis of this data. In this contribution, we describe a strategy
that the ATLAS experiment employs to overcome these limitations and
make the most of LHC data during Run-2 - a compact data stream
involving trigger-level jets, recorded at a far higher rate than is
possible for full event data. We discuss the implementation of this
stream and outline its technical challenges, as well as developments
to further streamline it for more demanding 2018 conditions.
Additionally, the results of an analysis of this dataset are shown to
highlight the competitiveness and complementarity with traditional
data streams.
