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particleflow – AI Model by jpata | AlphaNeural AI

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jpata/
particleflow

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Overview

MLPF focuses on developing full event reconstruction based on computationally scalable and flexible end-to-end ML models.

Multimodal high energy physics event

Models

Please see the linked model cards below for more details.

cld/clusters/v3.1.0
cld/hits/v3.1.0
cms/v2.6.0

Papers

Communications Physics, https://doi.org/10.1038/s42005-024-01599-5
CERN-CMS-DP-2022-061, http://cds.cern.ch/record/2842375
J. Phys. Conf. Ser. 2438 012100, http://dx.doi.org/10.1088/1742-6596/2438/1/012100
CERN-CMS-DP-2021-030, https://cds.cern.ch/record/2792320
EPJC, https://doi.org/10.1140/epjc/s10052-021-09158-w

Datasets

MLPF-CLIC, raw data: https://zenodo.org/records/8260741 or https://www.coe-raise.eu/od-pfr
MLPF-CLIC, processed for ML, tracks and clusters: https://zenodo.org/records/8409592
MLPF-CLIC, processed for ML, tracks and hits: https://zenodo.org/records/8414225
Delphes dataset: https://doi.org/10.5281/zenodo.4559324