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LHC physics dataset for unsupervised New Physics detection at 40 MHz

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arxiv 2107.02157 v2 pith:IS5RKR6X submitted 2021-07-05 physics.data-an hep-ex

classification physics.data-anhep-ex
keywords dataseteventdataphysicsreal-timesystemcollisiondetection
verification ladder T0 review T1 audit T2 compute T3 formal

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In particle detectors at the Large Hadron Collider, tens of terabytes of data are produced every second from proton-proton collisions occurring at a rate of 40 megahertz. This data rate is reduced to a sustainable level by a real-time event filter processing system which decides whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events which emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, by publishing this dataset we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised New Physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Generative Amplification with Surrogate Monte Carlo

    hep-ph 2026-08 conditional novelty 6.0 of 10

    An amplitude surrogate trained on a few thousand exact LHC amplitude points statistically outperforms the training data, with largest amplification in sparsely populated kinematic tails of Z+g and Z+4g production.

  2. Analysis of Hardware Synthesis Strategies for Machine Learning in Collider Trigger and Data Acquisition

    physics.ins-det 2024-11 conditional novelty 4.0 of 10

    For small-to-medium fully connected VAE trigger encoders on an Alveo U200 FPGA, hls4ml achieves lower latency and SNL achieves lower LUT and FF usage at matched latency.

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