5 papers
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
Zixin Ding, Shaghayegh Emami, Giovanna Salvi +7
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and…
Bridging the divide: axion searches and axino phenomenology at colliders
Gabe Hoshino, Kristin Dona, Keisuke Harigaya +4
We discuss a phenomenological model that extends the minimal supersymmetric standard model to contain axions and their supersymmetric partner, the axino. In the supersymmetric DFSZ…
End-to-end optimisation of HEP triggers
Noah Clarke Hall, Ioannis Xiotidis, Nikos Konstantinidis +1
High-energy physics experiments face extreme data rates, requiring real-time trigger systems to reduce event throughput while preserving sensitivity to rare processes. Trigger syst…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Towards a Self-Driving Trigger at the LHC: Adaptive Response in Real Time
Shaghayegh Emami, Cecilia Tosciri, Giovanna Salvi +7
Real-time data filtering and selection -- or trigger -- systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider (LHC) must process ext…