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41 results for “Calorimeter”
Simulation of an imaging calorimeter to demonstrate GarNet on FPGA
<p>This data set is an output of a simulation of electrons and pions shot at a chunk of an imaging calorimeter. It is used in the case study for GarNet-on-FPGA, documented in <a href="https://arxiv.org/abs/2008.03601">arXiv:2008.03601</a>.</p> <p>Each HDF5 file contains the following arrays:</p> <p> Name | Shape | Description</p> <ul> <li>cluster | (10000, 128, 4) | Samples for training and inference. Outermost dimension is the event (cluster). Each cluster has maximum 128 hits, each of which has four features: x, y, z, and energy. The coordinates of the hits are in cm. The energy is in GeV. The x and y coordinates are relative to the seed hit, while the z coordinate is with respect to the calorimeter front face.</li> <li>size | (10000) | Number of hits in each cluster. The cluster array is zero-padded when the cluster size is below 128.</li> <li>truth_pid | (10000) | Identity of the primary particle (0: electron, 1: pion).</li> <li>truth_energy | (10000) | True energy of the primary particle.</li> <li>raw | (10000, 4375, 2) | Raw data (actual output of the simulation). For each event (outermost dimension), hit energy and primary fraction (innermost dimension indices 0 and 1) are given for each of the 4375 sensors. Energy is in MeV.</li> <li>coordinates | (4375, 3) | The x, y, and z coordinates of the 4375 sensors, to be used to interpret the raw data.</li> </ul> <p>See the paper for the details of the simulation.</p>
Electromagnetic Calorimeter Shower Images of CaloFlow
<p>These are the calorimeter showers that were used to train and evaluate the normalizing flows of "<a href="https://arxiv.org/abs/2106.05285">CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows</a>" and "<a href="https://arxiv.org/abs/2110.11377">CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows</a>". The training and evaluation scripts can be found in <a href="https://gitlab.com/claudius-krause/caloflow">this git repository</a>.</p> <p>The samples were created with the same GEANT4 configuration file as the original CaloGAN samples. Said configuration can be found at the <a href="https://github.com/hep-lbdl/CaloGAN">CaloGAN repository</a>; the original CaloGAN samples are available at <a href="https://doi.org/10.17632/pvn3xc3wy5.1">this DOI</a>.</p> <p>Samples for each particle (eplus, gamma, piplus) are stored in a separate .tar.gz file. Each tarball contains the following files:</p> <ul> <li> <p>train_particle.hdf5: 70,000 events used to train CaloFlow I and II.</p> </li> <li> <p>test_particle.hdf5: 30,000 events used for model selection of CaloFlow I and II.</p> </li> <li> <p>train_cls_particle.hdf5: 60,000 events used to train the evaluation classifier.</p> </li> <li> <p>val_cls_particle.hdf5: 20,000 events used for model selection and calibration of the evaluation classifier.</p> </li> <li> <p>test_cls_particle.hdf5: 20,000 events used for the evaluation run of the evaluation classifier.</p> </li> </ul> <p>Each .hdf5 file has the same structure as the <a href="https://doi.org/10.17632/pvn3xc3wy5.1">original CaloGAN data</a>.</p>
Electron Energy Regression in High-Granularity Calorimeter Prototype
<p>The dataset consists of simulations of calibrated reconstructed hits produced by a positron passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the positrons with energy ranging from 20 to 350 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.</p>
Pion Energy Regression in High-Granularity Calorimeter Prototype
<p>The dataset consists of simulations of calibrated reconstructed hits produced by a pion passing through the HGCAL test beam prototype. For the simulations, Monte Carlo method is used to produce the pions with energy ranging from 10 to as high as 500 GeV. The dataset contains the coordinates of the calibrated reconstructed hits in the prototype along with the calibrated energy in units of MIP. The HDF5 files can be extracted from the gzip files.</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>
Numerical Calculation of the Thermodynamic Properties of Silver Erbium Alloys for Use in Metallic Magnetic Calorimeters - Data
<p>Data from simulations of the specific heat and magnetization of Ag:Er alloys. The parameter range we consider are temperatures between 1mK and 1K, external magnetic fields of up to 20mT, and erbium concentrations of up to 2000ppm.</p>
High Granularity Electromagnetic Calorimeter Shower Images
<p>Each HDF5 file contains energy deposits (shower images) created by <strong>electrons</strong> in one of the two calorimeters, for a specific incident angle of particles. Each HDF5 file has a structure of datasets, where each dataset represents energy deposits for a specific particle energy (in GeV). Particle energies are ranging from <strong>1</strong> to <strong>1024 GeV </strong>in powers of 2 and particle angles are ranging from <strong>50</strong> to <strong>90 degrees</strong> in a step of 10 (angle of 90 degrees indicates a particle entering the detector perpendicularly). Each dataset has the following structure<strong> {number of events,18,50,45}</strong>, with <strong>18x50x45</strong> being the granularity of a shower image.</p> <p>The calorimeter used to produce those data is a setup of concentric cylinders (layers). Each layer consists of active and passive material. The <strong>SiW</strong> geometry has 90 layers of 1.4 mm of tungsten as passive absorber and 0.3 mm of silicon as active material. The <strong>SciPb</strong> geometry has 45 layers of 4.4 mm of lead and 1.2 mm of scintillator. The number of readout cells is <strong>RxPxZ=18x50x45=40500</strong>, representing the cylindrical segmentation (rho,phi,z). The size of a single cell has been chosen to correspond to (approximately) 0.25 Moliere radius along the R axis and 0.5 radiation length along Z axis.</p> <p><br> The samples were created with the <strong><a href="https://gitlab.cern.ch/geant4/geant4/-/tree/master/examples/extended/parameterisations/Par04">Par04</a> </strong>Geant4 example, which demonstrates how to use Machine Learning inference to create energy deposits as a fast simulation model using LWTNN and ONNX runtime.</p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 2
<p>This is dataset 2 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of two files with 100k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector has a concentric cylinder geometry with 45 layers, where each layer consists of active (silicon) and passive (tungesten) material. Each layer has 144 readout cells, 9 in radial and 16 in angular direction, yielding a total of 9x16x45 = 6480 voxels.</p> <p>dataset_2_1.hdf5 should be used for training, dataset_2_2.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 3
<p>This is dataset 3 of the “Fast Calorimeter Simulation Challenge 2022”. It consists of four files with 50k GEANT4-simulated showers each of electrons with energies sampled from a log-uniform distribution ranging from 1 GeV to 1 TeV. The detector geometry is similar to dataset 2, but has a much higher granularity. Each of the 45 layer has now 18 radial and 50 angular bins, totalling 18x50x45=40500 voxels. This dataset was produced using the <a href="https://gitlab.cern.ch/geant4/geant4/-/tree/master/examples/extended/parameterisations/Par04">Par04 Geant4 example</a>.</p> <p>dataset_3_1.hdf5 and dataset_3_2.hdf5 should be used for training, dataset_3_3.hdf5 and dataset_3_4.hdf5 can be used as reference in the evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></p>
Comparison of black and transparent PMMA in the cone calorimeter
<p>This data set is a supplementary resource for the article "Comparison of Black and Transparent PMMA in the Cone Calorimeter", which will be published by ASTM international for the ASTM 42nd symposium on Obtaining Data for Fire Growth models. </p> <p>The information is structured into multiple *.zip folders. The directory named 'black' contains the data-files for the Black PMMA samples. The directory called 'transparent' contains the data-files for the Transparent PMMA samples. </p> <p>The files are structured in the following way: </p> <ul> <li>Name: Material_color_measurement_heatflux_repetition (e.g.: PMMA_Black_HRR_25kWm2_R1).</li> <li>Each HRR file contains the following columns: Time in seconds, CO concentration in Vol%, CO2 concentration in Vol%, O2 concentration in Vol%, heat release rate per unit area in kW/m2. </li> <li>Time t=0s corresponds to opening the shutter.</li> </ul> <p>These files are not the raw data. CO, CO2 and O2 concentrations are measured as a voltage signal. This voltage signal is converted to a volume percentage using the calibration of the gas analyzer. The gas analyzer was calibrated at the beginning of every day. The analyzer showed a small drift over the time of the experiment. Therefore, a drift correction was done on the measured values. The concentrations in the data files have been corrected for this drift. The corrected concentrations were used to calculate the heat release rate. A surface area of 0.0088m2 was used to calculate the HRR per unit area.</p>
Photon Showers in a High Granularity Calorimeter with Varying Incident Energy and Angle
<p>Dataset of photon showers in the Si-W ILD Electromagnetic calorimeter, consisting of 30 layers<br> of active silicon sensors sandwiched between tungsten absorber layers. The incident energies vary uniformly in the range of 10-100 GeV, along with the incident angle which varies in the range of 90-30 degrees from the axis orthogonal to the calorimeter face. The incident point to the calorimeter face is fixed.</p> <p>The cells are projected to a regular grid of shape (z, x, y) = (30, 30, 60), where the z axis points into the calorimeter face, giving a total of 54k channels.</p> <p>In total, the file contains approximately 500k showers. The structure of the file is as follows:</p> <ul> <li>Group name <em><strong>ecal</strong></em> <ul> <li><em><strong>energy</strong></em> : Dataset{500k, 1}</li> <li><strong><em>theta</em> </strong>: Dataset{500k, 1}</li> <li><em><strong>layers</strong></em> : Dataset{500k, 30, 30, 60}</li> </ul> </li> </ul> <p>The <strong><em>energy</em></strong> is the energy of the initial incident photon in units of GeV, <strong><em>theta</em></strong> is the incident angle of the incoming photon in units of radians and <strong><em>layers</em></strong> is the energy deposited in each cell in units of MeV</p> <p> </p> <p> </p>
Fast Calorimeter Simulation Challenge 2022 - Dataset 1
<p>This is dataset 1 of the “Fast Calorimeter Simulation Challenge 2022”. It is based on the ATLAS GEANT4 open datasets that were published <a href="http://opendata-qa.cern.ch/record/15012">here</a>. There are four files, two for photons and two for charged pions. Each dataset contains the voxelised shower information obtained from single particles produced at the calorimeter surface in the η range (0.2-0.25) and simulated in the ATLAS detector. Each file contains "incident_energies" of shape (num_showers, 1) and "showers" of shape (num_showers, num_voxels). There are 15 incident energies from 256 MeV up to 4 TeV produced in powers of two. 10k events are available in each sample with the exception of those at higher energies that have a lower statistics. These samples were used to train the corresponding two GANs presented in the AtlFast3 paper <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/SIMU-2018-04/">SIMU-2018-04</a> and in the FastCaloGAN note <a href="https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-SOFT-PUB-2020-006/">ATL-SOFT-PUB-2020-006</a>. The number of radial and angular bins varies from layer to layer and is also different for photons and pions, resulting in 368 voxels for photons and 533 for pions.</p> <p>dataset_1_photons_1.hdf5 and dataset_1_pions_1.hdf5 should be used for training, dataset_1_photons_2.hdf5 and dataset_1_pions_2.hdf5 for evaluation.</p> <p>More details, in particular helper scripts to parse the data and calculate and visualize basic high-level physics features, are available at <a href="https://calochallenge.github.io/homepage/">https://calochallenge.github.io/homepage/</a></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>
Magnetic micro-calorimeter raw data
<p>Raw micro-calorimeter data used to produce figure 1 a) of the publication "Measurement of the Th-229 isomer energy with a magnetic micro-calorimeter" (<a href="https://arxiv.org/pdf/2005.13340.pdf">https://arxiv.org/pdf/2005.13340.pdf</a>). The data represents raw pulse amplitudes U for individual gamma impact events, no energy callibration has been applied yet. All micro-calorimeter pixels are added into a single datafile. The data has undergone some selection/filtering and temperature correction. Please contact the authors of the above publication for details or discussion. </p>
Electromagnetic Sampling Calorimeter Shower Images
<p>We include two files: "gamma_1.hdf5" and "gamma_2.hdf5". The first file was used to train CaloFlow and the second file was used during evaluation. Each file has the following structure:</p> <p>energy Dataset {100000, 1}<br>layer_0 Dataset {100000, 3, 96}<br>layer_1 Dataset {100000, 12, 12}<br>layer_2 Dataset {100000, 12, 6}<br>overflow Dataset {100000, 3}</p> <p>Each file is contains 100,000 calorimeter showers originating from incoming photons with incident energies uniformly distributed in the range [1,100] GeV.</p> <p>The sampling calorimeter we built is segmented longitudinally into three layer with different depths and granularities. In units of mm, the three layers have the following (eta, phi, z) dimensions:<br>Layer 0: (5, 160, 90) | Layer 1: (40, 40, 347) | Layer 2: (80, 40, 43)</p> <p>In the hdf5 files, the `energy' entry specifies the incident energy of the incoming photon in units of GeV. `layer_0', `layer_1', and `layer_2' represents the energy deposited in each layer of the calorimeter in an image data format. Given the segmentation of each calorimeter layer, these images have dimensions 3x96 (in layer 0), 12x12 (in layer 1), and 12x6 (in layer 3). The `overflow` contains the amount of energy that was deposited outside of the calorimeter section we are considering.</p> <p>We also include the files containing signal showers used in the paper "Anomaly detection with flow-based fast calorimeter simulators". The signal showers originating from chi particles decaying at fixed displacements are included in "files_fixed_disp.zip", and the signal showers originating from chi particles with fixed decay lifetimes are included in "files_fixed_lifetime.zip".</p>
Downsampled Calorimeter Shower Images to 8 Pixels
<p>Energy depositions from single particle showers in ECAL calorimeters of the CLIC detector. </p> <p>The data set is preprocessed and down-sampled to 8 pixels along the longitudinal shower direction. It contains a training and a test set with around 1000 image samples.</p> <p>It is applied for simplified quantum image generation simulations.</p> <p>The original data set can be found here: https://doi.org/10.5281/zenodo.3603122</p> <p> </p>
Electromagnetic + Hadronic Sampling Calorimeter Shower Images
<p>We include multiple hdf5 files:</p> <ol> <li>The files 'piplus_1.hdf5' and 'piplus_2.hdf5' each contains 100,000 calorimeter showers originating from incoming charged pions with incident energies <strong>uniformly</strong> distributed in the range [1,100] GeV. The file 'piplus_1.hdf5' was used to train our models. The second file 'piplus_2.hdf5' was used in evaluation of generation performance.</li> <li>The file 'piplus_log.hdf5' contains 100,000 calorimeter showers originating from incoming charged pions with incident energies <strong>log-uniformly</strong> distributed in the range [1,100] GeV.</li> <li>The files with names 'piplus_{E_true}.hdf5' each contain 100,000 calorimeter showers originating from incoming charged pions with fixed incident energy at E_true = {10, 20, 30, 40, 50, 60, 70, 80, 90} GeV. These files were used for evaluation of calibration performance.</li> </ol> <p>Each file has the following structure:</p> <p>energy Dataset {100000, 1}</p> <p>layer_0 Dataset {100000, 3, 96}</p> <p>layer_1 Dataset {100000, 12, 12}</p> <p>layer_2 Dataset {100000, 12, 6}</p> <p>layer_3 Dataset {100000, 3, 96}</p> <p>layer_4 Dataset {100000, 12, 12}</p> <p>layer_5 Dataset {100000, 12, 6}</p> <p>overflow Dataset {100000, 6}</p> <p>The sampling calorimeter we built is segmented longitudinally into six layers with different depths and granularities. In units of mm, the six layers have the following (eta, phi, z) dimensions:<br>Layer 0: (5, 160, 90) | Layer 1: (40, 40, 347) | Layer 2: (80, 40, 43) | Layer 3: (20.83, 666.67, 375) | Layer 4: (166.67, 166.67, 667) | Layer 5: (333.33, 166.67, 958)</p>
L2LFlows: Generating High-Fidelity 3D Calorimeter Images
<p>This upload contains the datasets used in <a href="https://arxiv.org/pdf/2302.11594.pdf">arXiv:2302.11594</a>. The file <em>g4-showers_950k_10x10_train_val_test.pt</em> contains the <strong>760k training</strong>, <strong>95k validation</strong> and <strong>95k test</strong> showers as well as their incident energies. It should be loaded as follows: </p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p><em>import torch </em></p> <p><em>list_tensors = torch.load(args.file_path)</em></p> <p><em>for (idx, tensor) in enumerate(list_tensors):</em></p> <p><em> [showers_train, showers_val, showers_test, inc_energies_train, inc_energies_val, inc_energies_test] = list_tensors</em></p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p>The file <em>g4-showers_665k_10x10_test.pt</em> contains 665k additional showers that were used for the classifier scaling studies, in addition to the 95k test showers from the file <em>g4-showers_950k_10x10_train_val_test.pt</em>. It should be loaded as follows: </p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p><em>import torch</em></p> <p><em>list_tensors = torch.load("g4-showers_950k_10x10_train_val_test.pt") </em></p> <p><em>[showers_geant, inc_energies_geant] = list_tensors</em></p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p>A detailed description of how the datasets were simulated can be found in the paper. </p>
CMS High Granularity Calorimeter Trigger Cell Simulated Dataset (Part 1)
<p>The dataset consists of simulated events of electron-positron pairs (<em>e</em><sup>+</sup><em>e</em><sup>−</sup>) with flat transverse momentum <em>p</em><sub>T</sub> distribution <em>p</em><sub>T</sub> ∈ [1,200] GeV, with Phase 2 conditions, 200 pileup, V11 geometry, HLT TDR Summer20 campaign The <a href="https://cmsweb.cern.ch/das/request?input=dataset%3D%2FDoubleElectron_FlatPt-1To100%2FPhase2HLTTDRSummer20ReRECOMiniAOD-PU200_111X_mcRun4_realistic_T15_v1-v2%2FGEN-SIM-DIGI-RAW-MINIAOD&instance=prod/global">original dataset (CMS-internal)</a>.</p> <p>This derived dataset in ROOT format contains generator-level particle and simulated detector information. More information about how the dataset is derived is available at this <a href="https://twiki.cern.ch/twiki/bin/viewauth/CMS/HGCALTriggerPrimitivesSimulation">TWiki (CMS-internal)</a>.</p> <p>A description of each variable is below.</p> <table> <thead> <tr> <th>Variable</th> <th>Description</th> <th>Type</th> </tr> </thead> <tbody> <tr> <td><code>run</code></td> <td>Run number</td> <td><code>int</code></td> </tr> <tr> <td><code>event</code></td> <td>Event number</td> <td><code>int</code></td> </tr> <tr> <td><code>lumi</code></td> <td>Luminosity section</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_n</code></td> <td>Number of primary generated particles</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_PUNumInt</code></td> <td>Number of pileup interactions</td> <td><code>int</code></td> </tr> <tr> <td><code>gen_TrueNumInt</code></td> <td>Number of true interactions</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_x</code></td> <td>Simulated primary vertex <em>x</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_y</code></td> <td>Simulated primary vertex <em>y</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>vtx_z</code></td> <td>Simulated primary vertex <em>z</em> position in cm</td> <td><code>float</code></td> </tr> <tr> <td><code>gen_eta</code></td> <td>Primary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_phi</code></td> <td>Primary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_pt</code></td> <td>Primary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_energy</code></td> <td>Primary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>gen_charge</code></td> <td>Initial generated particle charge</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_pdgid</code></td> <td>Primary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_status</code></td> <td>Primary generated particle generator status</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>gen_daughters</code></td> <td>Primary generated particle daughters (empty)</td> <td><code>vector<vector<int>></code></td> </tr> <tr> <td><code>genpart_eta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_phi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pt</code></td> <td>Primary and secondary generated particle transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_energy</code></td> <td>Primary and secondary generated particle energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_dvz</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovy</code></td> <td>Primary and secondary generated particle original vertex <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_ovz</code></td> <td>Primary and secondary generated particle original vertex <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_mother</code></td> <td>Primary and secondary generated particle parent particle index (-1 indicates no parent)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_exphi</code></td> <td>Primary and secondary generated particle azimuthal angle <em>ϕ</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exeta</code></td> <td>Primary and secondary generated particle pseudorapidity <em>η</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exx</code></td> <td>Primary and secondary generated particle decay vertex <em>x</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_exy</code></td> <td>Primary and secondary generated particle decay vertex <em>y</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_fbrem</code></td> <td>Primary and secondary generated particle decay vertex <em>z</em> extrapolated to the corresponding HGCAL coordinate</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>genpart_pid</code></td> <td>Primary and secondary generated particle PDG ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_gen</code></td> <td>Index of associated primary generated particle</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_reachedEE</code></td> <td>Primary and secondary generated particle flag: <code>2</code> indicates that the particle reached the HGCAL, <code>1</code> indicates the particle reached the barrel calorimeter, and <code>0</code> indicates other cases</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>genpart_fromBeamPipe</code></td> <td>Deprecated variable, always true</td> <td><code>vector<bool></code></td> </tr> <tr> <td><code>genpart_posx</code></td> <td>Primary and secondary generated particle position <em>x</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posy</code></td> <td>Primary and secondary generated particle position <em>y</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>genpart_posz</code></td> <td>Primary and secondary generated particle position <em>z</em> coordinate in cm</td> <td><code>vector<vector<float>></code></td> </tr> <tr> <td><code>ts_n</code></td> <td>Number of trigger sums</td> <td><code>int</code></td> </tr> <tr> <td><code>ts_id</code></td> <td>Trigger sum ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_subdet</code></td> <td>Trigger sum subdetector</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_zside</code></td> <td>Trigger sum endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_layer</code></td> <td>Trigger sum layer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafer</code></td> <td>Trigger sum wafer ID</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_wafertype</code></td> <td>Trigger sum wafer type: 0 indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, 1 indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and 2 indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>ts_data</code></td> <td>Trigger sum ADC value</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>ts_pt</code></td> <td>Trigger sum transverse momentum in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_mipPt</code></td> <td>Trigger sum energy in units of transverse MIP</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_energy</code></td> <td>Trigger sum energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_eta</code></td> <td>Trigger sum pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_phi</code></td> <td>Trigger sum azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_x</code></td> <td>Trigger sum <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_y</code></td> <td>Trigger sum <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>ts_z</code></td> <td>Trigger sum <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_n</code></td> <td>Number of trigger cells</td> <td><code>int</code></td> </tr> <tr> <td><code>tc_id</code></td> <td>Trigger cell unique ID</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_subdet</code></td> <td>Trigger cell subdetector ID (EE, EH silicon, or EH scintillator)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_zside</code></td> <td>Trigger cell endcap (plus or minus endcap)</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_layer</code></td> <td>Trigger cell layer number</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferu</code></td> <td>Trigger cell wafer <em>u</em> coordinate; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_waferv</code></td> <td>Trigger cell wafer <em>v</em> coordinate; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_wafertype</code></td> <td>Trigger cell wafer type: <code>0</code> indicates fine divisions of wafer with 120 <em>μ</em>m thick silicon, <code>1</code> indicates coarse divisions of wafer with 200 <em>μ</em>m thick silicon, and <code>2</code> indicates coarse divisions of wafer with 300 <em>μ</em>m thick silicon)</td> <td> </td> </tr> <tr> <td><code>tc_cellu</code></td> <td>Trigger cell <em>u</em> coordinate within wafer; <em>u</em>-axis points along − <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_cellv</code></td> <td>Trigger cell <em>v</em> coordinate within wafer; <em>v</em>-axis points at 60 degrees with respect to <em>x</em>-axis</td> <td><code>vector<int></code></td> </tr> <tr> <td><code>tc_data</code></td> <td>Trigger cell ADC data at 21-bit precision after decoding from 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_uncompressedCharge</code></td> <td>Trigger cell ADC data at full precision before compression</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_compressedCharge</code></td> <td>Trigger cell ADC data compressed into 7-bit encoding</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_pt</code></td> <td>Trigger cell transverse momentum <em>p</em><sub>T</sub> in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_mipPt</code></td> <td>Trigger cell energy in units of transverse MIPs</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_energy</code></td> <td>Trigger cell energy in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_simenergy</code></td> <td>Trigger cell energy from simulated particles in GeV</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_eta</code></td> <td>Trigger cell pseudorapidity <em>η</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_phi</code></td> <td>Trigger cell azimuthal angle <em>ϕ</em></td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_x</code></td> <td>Trigger cell <em>x</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_y</code></td> <td>Trigger cell <em>y</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_z</code></td> <td>Trigger cell <em>z</em> position in cm</td> <td><code>vector<float></code></td> </tr> <tr> <td><code>tc_cluster_id</code></td> <td>ID of the 2D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_id</code></td> <td>ID of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<uint></code></td> </tr> <tr> <td><code>tc_multicluster_pt</code></td> <td>Transverse momentum <em>p</em><sub>T</sub> in GeV of the 3D cluster in which the trigger cell is clustered</td> <td><code>vector<float></code></td> </tr> </tbody> </table>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.