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16 results for “CLIC”
Simulated datasets for detector and particle flow reconstruction: CLIC detector, machine learning format
<p><strong>Synopsis</strong></p> <p>Machine-learning friendly format of tracks, clusters and target particles in electron-positron events, simulated with the CLIC detector. Ready to be used with <a href="https://zenodo.org/records/14930299">jpata/particleflow:v2.3.0</a>. Derived from the EDM4HEP ROOT files in <a href="https://zenodo.org/record/8260741">https://zenodo.org/record/8260741</a>.</p> <ul> <li>clic_edm_ttbar_pf.zip: e+e- -> ttbar, center of mass energy at 380 GeV</li> <li>clic_edm_qq_pf.zip: e+e- -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>clic_edm_ww_fullhad_pf.zip: e+e- -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>clic-tfds.ipynb: an example notebook on how to load the files</li> </ul> <p><strong>Contents</strong></p> <p>Each .zip file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a>, <a href="https://github.com/google/array_record">array_record</a> format. We have split the full datasets into 10 subsets, due to space considerations on zenodo, two subsets from each dataset are uploaded. Each dataset contains a train and test split of events.</p> <p><strong>Dataset semantics (to be updated)</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library and used in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and clusters</li> <li>ytarget: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/heptfds/clic_pf_edm4hep/utils_edm.py and https://github.com/jpata/particleflow/blob/v2.3.0/mlpf/data/key4hep/postprocessing.py.</p>
Data underlying CLICS Version 1.0
<p>This is the data underlying the original CLICS database. It was assembled from four different sources (see http://clics.lingpy.org for details) and is now deposited here to allow users quick access to the underlying data, since CLICS 1.0 has now been superseded by the newly published CLICS² database (http://clics.clld.org).</p> <p>In this version, we provide the data both in raw wordlist format (following specifications of the LingPy library, http://lingpy.org), and in CLDF format, which makes it possible to conveniently re-use the data in the new API of CLICS².</p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector
<p><strong>Data description</strong></p> <p>Datasets generated using Key4HEP and the CLIC detector model suitable for particle flow reconstruction studies.</p> <p>The datasets contain generator particles, reconstructed tracks and calorimeter hits, reconstructed Pandora PF particles and their respective links in the <a href="https://doi.org/10.5281/zenodo.7221473">EDM4HEP</a> format.</p> <p>The following processes have been simulated with Pythia 8:</p> <ul> <li>p8_ee_tt_ecm380: ee -> ttbar, center of mass energy at 380 GeV</li> <li>p8_ee_qq_ecm380: ee -> Z* -> qqbar, center of mass energy at 380 GeV</li> <li>p8_ee_ZH_Htautau: ee -> ZH -> Higgs decaying to tau leptons, center of mass energy at 380 GeV</li> <li>p8_ee_WW_fullhad: ee -> WW -> W decaying hadronically, center of mass energy at 380 GeV</li> <li>p8_ee_tt_ecm380_PU10: ee -> ttbar with on average 10 Poisson-distributed events from ee->gg overlayed, center of mass energy at 380 GeV</li> </ul> <p>The following single particle gun samples have been generated with ddsim:</p> <ul> <li>e+/e-: single electron with energy between 1 and 100 GeV</li> <li>mu+/mu-: single muon with energy between 1 and 100 GeV</li> <li>kaon0L: single K0L with energy between 1 and 100 GeV</li> <li>neutron: single neutron with energy between 1 and 100 GeV</li> <li>pi+/pi-: single charged pion with energy between 1 and 100 GeV</li> <li>pi0: single neutral pion with energy between 1 and 100 GeV</li> <li>gamma: single photon with energy between 1 and 100 GeV</li> </ul> <p>The detector simulation has been done with Geant4, the reconstruction with Marlin interfaced via Key4HEP which includes PF reconstruction with Pandora, all using publicly available models and code.</p> <p> </p> <p><strong>Contents</strong></p> <p>This record includes the following files:</p> <ul> <li>*_10files.tar: small archives of 10 files for each data sample, suitable for testing</li> <li>dataset_full.txt: the full list of files, hosted at the Julich HPC courtesy of the Raise CoE project, ~2.5TB total</li> <li>*.cmd: the Pythia8 cards</li> <li>pythia.py: the pythia steering code for Key4HEP</li> <li>run_sim.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380, p8_ee_qq_ecm380, p8_ee_ZH_Htautau, p8_ee_WW_fullhad datasets</li> <li>run_sim_pu.sh: the steering script for generating, simulating and reconstructing a single file of 100 events from the p8_ee_tt_ecm380_PU10 dataset</li> <li>run_sim_gun.sh: the steering script for generating the single-particle gun samples</li> <li>run_sim_gun_np.sh: the steering script for generating multi-particle gun samples (extensive datasets have not yet been generated)</li> <li>check_files.py: the main driver script that configures the full statistics and creates submission scripts for all the simulations</li> <li>PandoraSettings.zip: the settings used for Pandora PF reconstruction</li> <li>main19.cc: the Pythia8+HepMC driver code for generating the events with PU overlay</li> <li>clicRec_e4h_input.py: the steering configuration of the reconstruction modules in Key4HEP</li> <li>clic_steer.py: the steering configuration of the Geant4 simulation modules in Key4HEP</li> <li>clic-visualize.ipynb: an example notebook demonstrating how the dataset can be loaded and events visualized in Python</li> <li>visualization.mp4: an example visualization of the hits and generator particles of a single ttbar event from the dataset</li> </ul> <p> </p> <p><strong>Dataset semantics</strong></p> <p>Each file consists of event records. Each event contains structured branches of the relevant physics data. The branches relevant to particle flow reconstruction include:</p> <ul> <li>MCParticles: the ground truth generator particles</li> <li>ECALBarrel, ECALEndcap, ECALOther, HCALBarrel, HCALEndcap, HCALOther, MUON: reconstructed hits in the various calorimeter subsystems</li> <li>SiTracks_Refitted: the reconstructed tracks</li> <li>PandoraClusters: the calorimeter hits, clustered by Pandora to calorimeter clusters</li> <li>MergedRecoParticles: the reconstructed particles from the Pandora particle flow algorithm</li> <li>CalohitMCTruthLink: the links between MC particles and reconstructed calorimeter hits</li> <li>SiTracksMCTruthLink: the links between MC particles and reconstructed tracks</li> </ul> <p>The full details of the EDM4HEP format are available <a href="https://doi.org/10.5281/zenodo.7221473">here</a>.</p> <p> </p> <p><strong>Dataset characteristics</strong></p> <p>The full dataset in dataset_full.txt consists of 43 tar files of up to 100GB each. The tar files contain in total 58068 files, 2.5TB in the ROOT EDM4HEP format.</p> <p>The subset in *_10files.tar for consists of 150 files, 26GB in the ROOT EDM4HEP format.</p> <p> </p> <p><strong>How can you use these data?</strong></p> <p>The ROOT files can be directly loaded with the <a href="https://zenodo.org/record/8239801">uproot</a> Python library.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>These are simulated samples suitable for conceptual machine learning R&D and software performance studies. They have not been calibrated with respect to real data, and should not be used to derive physics projections about the detectors.</p> <p>Neither CLIC nor CERN endorse any works, scientific or otherwise, produced using these data. All releases will have a unique DOI that you are requested to cite in any applications or publications.</p>
CLIC Calorimeter 3D images: Electron showers at Fixed Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Electrons entering the detector at variable energy and fixed direction (perpendicular to the ECAL inner surface)</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
CLIC Calorimeter 3D images: Photon showers at Fixed Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Photons entering the detector at variable energy and fixed direction (perpendicular to the ECAL inner surface)</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
lexibank/tls: CLICS 2.0
<p>Release of this dataset for CLICS 2.0, where it is included.</p> <p>Data comes from the "Tanzanian Language Survey (TLS)" as hosted at http://www.cbold.ish-lyon.cnrs.fr/Docs/TLSinfo.html and was gathered as part of a lexicostatistical survey of about 100 Tanzanian languages undertaken in the early 1970s by Derek Nurse, Gérard Philippson and a team from the Department of Foreign Languages and Linguistics of the University of Dar es Salaam. The source (paper) documents for these computer files were sets of printed forms, each set containing 1079 entries in parallel columns of Swahili and English. These forms were distributed to native Tanzanians for translation into the target Tanzanian languages.</p>
Simulated datasets for detector and particle flow reconstruction: CLIC detector, hit-based data, machine learning format
<p>Derived from https://zenodo.org/record/8260741, prepared in a machine-learning friendly TFDS format, ready to be used with https://zenodo.org/record/8397954.</p> <ul> <li>clic_edm_ttbar_hits_pf10k.tar: ee -> ttbar, center of mass energy at 380 GeV, 10k events</li> <li>clic_edm_qq_hits_pf10k.tar: ee -> Z* -> qqbar, center of mass energy at 380 GeV, 10k events</li> </ul> <p><strong>Contents</strong></p> <p>Each .tar file contains the dataset in the <a href="https://github.com/tensorflow/datasets">tensorflow-datasets</a> (minimum version v4.9.1), <a href="https://github.com/google/array_record">array_record</a> format.</p> <p><strong>Dataset semantics</strong></p> <p>Each dataset consists of events that can be iterated over using the tensorflow-datasets library in either tensorflow or pytorch. Each event has the following information available:</p> <ul> <li>X: the reconstruction input features, i.e. tracks and calorimeter hits</li> <li>ygen: the ground truth particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> <li>ycand: the baseline Pandora PF particles with the features ["PDG", "charge", "pt", "eta", "sin_phi", "cos_phi", "energy", "jet_idx"], with "jet_idx" corresponding to the gen-jet assignment of this particle</li> </ul> <p>The full semantics, including the list of features for X, are available at https://github.com/jpata/particleflow/blob/v1.6/mlpf/heptfds/clic_pf_edm4hep_hits/utils_edm.py.</p>
CLIC Calorimeter 3D images: Photon showers at Random Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Photons entering the detector at variable energy and direction</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
MLPF results on the simulated CLIC dataset
<p>Updates over the previous version:</p> <ul> <li>updated validation outputs for the cluster-based model <ul> <li>fixed a bug with how the PF candidates were stored</li> <li>added single particle gun samples to validation</li> </ul> </li> <li>added new timing runs <ul> <li>for the baseline algo, included memory information</li> <li>run the GNN model up to ~10k inputs</li> </ul> </li> <li>added hypertuning summary tables</li> </ul> <p>Trained models and evaluation results for the upcoming paper "Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors", https://doi.org/10.48550/arXiv.2309.06782.</p> <p>The archive contains the following subfolders:</p> <ul> <li><strong>clusters_best_tuned_gnn_clic_v130</strong> <ul> <li>MLPF GNN model configs and weight files after hypertuning</li> <li>the inputs are reconstructed tracks and Pandora clusters</li> <li>the outputs are reconstructed PF candidates</li> <li>trained on tt and qq v1.3.0 (1M events each)</li> </ul> </li> <li><strong>hits</strong> <ul> <li>MLPF GNN model configs and weight files</li> <li>inputs are reconstructed tracks and calorimeter hits</li> <li>outputs are reconstructed PF candidates</li> <li>trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0</li> <li>training was restarted several times from previous checkpoints</li> </ul> </li> <li><strong>hypertuning</strong> <ul> <li>GNN and transformer model before and after hypertuning</li> <li>summary tables of the hypertuning runs</li> </ul> </li> <li><strong>timing</strong> <ul> <li>scaling study of baseline PF with number of gun particles on CPU</li> <li>scaling study of GNN model with number of input elements on GPU</li> </ul> </li> <li><strong>gpu_scaling</strong> <ul> <li>the scaling study of model training on multiple accelerator cards</li> </ul> </li> </ul> <p> </p> <p>The training dataset is available at</p> <ul> <li>Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741</li> </ul>
CLIC Calorimeter 3D images: Electron showers at Random Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Electrons entering the detector at variable energy and direction</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
CLIC Calorimeter 3D images: Neutral Pion showers at Random Angle
<p>Energy deposits from single-particle showers in the ECAL+HCAL calorimeters of the CLIC detector</p> <p>Simulation performed with GEANT4 (https://geant4.web.cern.ch) and DD4HEP software (https://dd4hep.web.cern.ch/dd4hep/)</p> <p>Neutral Pions entering the detector at variable energy and direction</p> <p>See https://arxiv.org/abs/1912.06794 for details</p>
The BAU Scenario Clic Sand , R.E Scenario Clic Sand and an integrated BAU and R.E Visualization Results for Energy Policy for Uganda Using OSeMOSYS
<p>Business As Usual (BAU) Scenario , Renewable Energy (R.E) Scenario Clic Sand are used as data sets to run the model. The integrated BAU and R.E. visualization result templates contain the findings of the models. For instance, power generation graphs, installed energy capacity graphs, annual capital cost graphs, annual carbon dioxide emissions graphs , capital cost and any other parameters of interest related to the model can be found in the result visualization template.</p>
CLIC-1901 for the Treatment of Patients With Relapsed/Refractory CD19 Positive Hematologic Malignancies
ClinicalTrials.gov study NCT03765177. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
CLIC database of best practices
<p>CLIC Database of best practices of cultural heritage adaptive reuse. Available at: http://140.164.42.97/clicapp/</p>
GPNMB-targeting Chimeric Antigen Receptor T-Cell Therapy (GCAR1) for a Patient With Alveolar Soft Part Sarcoma (CLIC-YYC-GPNMB-02)
ClinicalTrials.gov study NCT06789081. IPD Sharing: NO. Countries: 0. Publications: 0.
CLIC-2201 for the Treatment of Relapsed/Refractory B Cell Malignancies
ClinicalTrials.gov study NCT06208735. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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