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52 results for “shower”
High Granularity Electromagnetic Shower Images
<p>This is a limited subset of the data used for training in <strong>arXiv:2005.05334</strong>. The network architectures and instructions to generate more data are available at <a href="https://github.com/FLC-QU-hep/getting_high">here. </a></p> <p>Electromagnetic calorimeter for the ILD consists of 30 active silicon layers in a tungsten absorber stack with 20 layers of 2.1 mm followed by 10 layers of 4.2 mm thickness respectively. We project the sensors onto a rectangular grid of 30×30×30 cells. Each cell in this grid corresponds to exactly one sensor, resulting in total of 27k channels.</p> <p>The file has the following structure:</p> <ul> <li> Group named <em>30x30</em> <ul> <li> <em>energy</em> : Dataset {1000, 1}</li> <li> <em> layers</em> : Dataset {1000, 30, 30, 30}</li> </ul> </li> </ul> <p>The <em>energy</em> specifies the true energy of the incoming photons in units of GeV, where <em>layers</em> represent the energy deposited (MeV) in 30 layers of the calorimeter in an image data format. This file contains approximately 24.000 showers.</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>
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>
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>
Gemini meteor shower, by Hao Yin, China
<p>Third place in the 2021 IAU OAE Astrophotography Contest, category Meteor showers.</p> <p>As the Earth travels around the Sun, it may cross the path of debris left behind by a comet or, more rarely, by an asteroid. These debris enter the atmosphere at high speed, producing beautiful tracks as they burn in the sky due to friction with the atmosphere. The image captures the Geminid meteor shower, named because the radiant point is located on the sky in the constellation Gemini. The particles composing the meteor shower travel at similar speed and in parallel trajectories, which causes a perspective effect like if the stream radiates from one single point in the sky, which is known as the radiant point. This image, taken in December 2020 in China, clearly shows this perspective. This is a very prolific shower, in such a way that over one hundred meteorites could be seen per hour in recent appearances. This meteor shower is one of the few associated not with a comet, but with an asteroid – 3200 Phaeton, which might be a comet that lost all its volatile material. This image shows the large number of meteors that can be observed in this shower, which always happens in December every year. The image also shows one of the most prominent constellations in the night sky, Orion, easily seen by the three stars in a diagonal making up Orion’s Belt, and the red-orange star Betelgeuse. Right above the dish is a bright point and that is the Sirius, the brightest star in the night sky and part of the constellation Canis Major. The fuzzy bluish smudge at around 2 ‘o’ clock is the Pleiades star cluster.</p> <p>Credit: Hao Yin/IAU OAE</p>
Geminid Meteor Shower from China, by Dai Jianfeng, China
<p>First place in the 2021 IAU OAE Astrophotography Contest, category Meteor showers.</p> <p>A meteor shower occurs when the Earth in its orbit around the Sun, passes through a debris trail left previously by a comet on its approach around the Sun. As the Earth enters this debris (small sand grain sized), they enter the atmosphere at high speeds and on parallel trajectories, burning completely leaving beautiful tracks (streaks) in the sky. These streaks can appear and disappear in the blink of an eye, or last much longer. On rare occasions the debris originates from asteroids, as in the case of the Geminid meteor shower, shown in this image, picturing many streaks of debris captured in the sky of China in 2017. Due to relative motions and perspective, the shower appears to come from one single point, known as the radiant point, beautifully pictured in this image. This is similar to driving in a car on a rainy day without any wind, looking out the front window it seems that the rain is coming directly towards the window, when in fact the rain is falling vertically downwards.</p> <p>Credit: Dai Jianfeng/IAU OAE</p>
Simulated EM showers data
<p>Electromagnetic (EM) showers simulated dataset. The data contains 16,577 showers. The data includes information about the tracklets: position coordinates, direction and shower id, and about the showers: shower id, initial particle position and direction, shower energy. </p> <p>Generation is done using FairShip framework. Showers energy follows gamma distribution with parameters alpha=1.4, beta = 0.5. Polar angle is simulated using log-normal distribution with parameters nu=0,3, sigma=0.7. </p> <p>The data is in csv native format. Total dataset size is 669.2 MB.</p>
Strong enhancement of electromagnetic shower development induced by high-energy photons in a thick oriented tungsten crystal
<p>We have observed a significant enhancement in the energy deposition by 25-100 GeV photons in a 1 cm thick tungsten crystal oriented along its <111> lattice axes (data of Fig. 2). At 100 GeV, this enhancement, with respect to the value observed without axial alignment, is more than twofold. This effect, together with the measured huge increase in secondary particle generation (data of Fig. 3) is ascribed to the acceleration of the electromagnetic shower development by the strong axial electric field. The experimental results have been critically compared with a newly developed Monte Carlo adapted for use with crystals of multi-X_0 thickness (data of Fig.2, 3 and 4). These results may prove to be of significant interest for the development of high-performance photon absorbers and highly compact electromagnetic calorimeters and beam dumps for use at the energy and intensity frontiers.</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>
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>
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>
Hadron Shower Simulation Data for Generative Models in Fundamental Physics
<p>Data set containing pion calorimeter showers used to train and evaluate our generative models for our Hadrons, Better, Faster, Stronger publication. Complete dataset consist of three hdf5 files:</p> <ul> <li>pion_train_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to train the models.</li> <li>pion_eval_uniform.hdf5 contains showers originating form pions with a uniformly distributed energy ranging form 10 GeV to 100 GeV. This set was used to evaluate the models.</li> <li>pion_eval_steps20to90.hdf5 contains showers originating form pions with discrete energies ranging form 20 GeV to 90 GeV in steps of 10 GeV. This set was used to evaluate the models.</li> </ul> <p>Each file contains a group called 'hcal_only'. This group has two dataset, 'energy' which contains the energy of the pions in GeV and 'layers' which contains the shower images, projected onto a 48x48x48 grid. For our model training this was reduced to 48x25x25 via slicing. The entries correspond to energy depositions in MeV.</p>
Data from "Towards a sustainable use of shower water: Habits and explanatory factors in southern Spain"
<p>One of the targets of SDG 6 is the efficient use of water resources in all sectors in order to tackle water shortages. In the home, showering is one of the main water consuming activities. How can people make a more sustainable use of shower water? To answer this question, this research analyses showering habits and explanatory factors of shower use. The study is carried out with data from 945 students of the University of Granada, Spain. Significant differences are observed in shower use during the summer and winter months: the average duration is 8.8 and 11.6 minutes, respectively, and shower frequency is greater in summer. Determinants of different shower water use include gender, ideology, pro-environmental actions, inherent values and connectedness to nature, among others. Those variables relate differently to duration and frequency of showers, according to the season, thus highlighting the importance of seasonality. The results show that there is room to achieve a more sustainable use of the shower, in terms of frequency and duration, through awareness measures that are tailored to groups that make a less sustainable use of showers. The main recommendation is that awareness campaigns should be designed on the basis of the user profile as well as the season.</p>
Data from: How does sustainable water consumption in the shower relate to different dimensions of perceived well-being? Empirical evidence from university students
<p>Water scarcity is already a worrying issue and it is predicted to get worse in the future. This creates an imperative to use water efficiently and sustainably. In the domestic sphere, one of the main uses of water is showering, not only for hygiene reasons but also as a wellness activity. In order to gain insight into the implications of sustainable shower use, in this paper we analyse the relationship between subjective well-being and water consumption in the shower. We aim to answer the following questions: 1) How does shower water consumption relate to subjective well-being, 2) Does this relationship differ depending on showering habits (time spent in the shower, and number of showers per week), and 3) Does this relationship differ depending on the season (winter and summer). The dataset contains information on 937 students from different disciplines at the University of Granada, Spain. The different interpretations of subjective well-being considered are life satisfaction, affect, and vitality. Results suggest that there is a negative relationship between water consumption and subjective well-being, in line with the literature that identifies a well-being dividend from green behaviour (being pro-environmental helps the environment and increases happiness). All subjective well-being dimensions are negatively related to time spent in the shower, regardless of the season. In contrast, the frequency of showering is not significantly related to well-being. Therefore, it appears that higher water consumption does not translate into higher perceived well-being, indicating that there is no conflict between efficient shower water use and individual well-being.</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>
Par04 Lead Tungstate Voxel Shower with 10000 Events at 1GeV, ZxRxP = 35x22x16 PbWO4
<p>The dataset contains 10,000 Geant4 voxelized events generated with Par04 [1] with modifications to the original detector geometry. The dataset is made with a single bloc of 30cm of Lead Tungstate (PbWO4) and a cylindrical scoring mesh of Z = 35 , Rho = 22 , Phi = 16. Cylinder dimensions were targeted to conserve 98% of incident energy, intially from a 90° pion collision at 1GeV. </p> <p> </p> <pre>-[1]: https://gitlab.cern.ch/fastsim/par04 </pre>
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>
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