Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
10
datasets available to search
ShareScore release 0.9.0
Dataset results
10 results for “Neutron detector”
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Jupiter Notebook and example data for "Prospects for a camera-based detector for Neutron Reflectometry"
<p>We report the outcome of a proof-of-principle (IPTS-29165) neutron reflectivity measurement obtained using a neutron scintillator and a Photonis (brand) camera. We were motivated to test this technology because it provides much better spatial resolution and count rate capability than the BL4A <sup>3</sup>He position sensitive detector (Table 1). The report describes the detector setup, challenges encountered, a reflectivity measurement and next steps.</p> <p>Two example measurements are provided and a Jupyter Notebook to create a NumPy binary file consisting of event positions and times.</p>
Fifradina dataset for radiative thermal neutron-capture processes in cryogenic detectors (CRAB)
<p>The emission of <span class="math-tex">\(\gamma\)</span>-rays after a neutron capture in a cryogenic detector can generate mono-energetic nuclear recoils in the sub-keV regime, of direct interest for the calibration of Dark Matter and Coherent Elastic Neutrino Nucleus Scattering experiments. Accurate predictions of the nuclear recoil spectra induced by neutron captures require taking into account the interplay between the development in time of the de-excitation γ-cascade of the target nucleus and that of the associated atomic collisions in matter. Detailed simulations coupling the FIFRELIN code for the description of the γ-cascades and the Iradina code for the modelling of the fast atomic movements in matter have been described in</p> <p>G. Soum-Sidikov and al. (CRAB collaboration), <em>Study of collision and γ-cascade times following neutron-capture processes in cryogenic detectors</em>et al. 2023. doi :10.48550/arXiv.2305.10139. URL<a href="http://https://doi.org/ 10.48550/arXiv.2305.10139"> https://doi.org/<br> 10.48550/arXiv.2305.10139</a></p> <p>This dataset acts as supplemental material to this article. It provides detailed nuclear de-excitation cascades with associated total recoil energies for:</p> <ul> <li>Al (n, <span class="math-tex">\(\gamma\)</span>) in Al2O3</li> <li>Si (n, <span class="math-tex">\(\gamma\)</span>) in Si</li> <li>Ge (n, <span class="math-tex">\(\gamma\)</span>) in Ge</li> <li>W (n, <span class="math-tex">\(\gamma\)</span>) in CaWO4</li> </ul>
Neutron tomography data collected with MCP detector (soil specimen)
<p>Neutron tomography data collected with MCP detector in July 2016 at the ISIS Neutron Spallation Source, UK as part of the scientific commissioning of the neutron imaging beamline, IMAT.</p> <p>Different projection angles were acquired over an angular range of 180° (first 41 projections with angular step of 3° and the last 14 projections with angular step of 4°).</p> <p> </p>
Parameter estimation catalogs for binary neutron star mergers detected with next-generation gravitational wave detectors
<div> <p>Next-generation gravitational wave (GW) observatories, such as the Einstein Telescope (ET) and the Cosmic Explorer, will provide access to the population of binary neutron star (BNS) mergers throughout cosmic history and yield precise parameter estimates. Here, we publish the results of a comprehensive study evaluating BNS merger detection prospects using the ET alone or in a network of current or next-generation detectors up to redshift equal to 1. We publicly release all the parameter estimation for 10 years of observations of BNSs in the form of catalogs. These catalogs are made available to the community for multi-messenger studies, multi-probe cosmology, and nuclear study to constrain the neutron star (NS) equation of state (EOS). They can be used to focus on specific events (for example golden events with high signal-to-noise ratio) or for statistical studies on the BNS populations. </p> <p>Our simulations assessed the perspectives for detecting the optical emission of BNS mergers in the era of next-generation detectors, considering how uncertainties in BNS population properties, NS mass distribution, and the EOS might affect the detection rate and parameter estimation. The study is published in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo, Hazra, Dupletsa, Branchesi et al. 2024</a> arXiv:2411.02342 (submitted to A&A).</p> </div> <h3>BNS merger rate</h3> <p>As shown in <a href="https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.4877S/abstract" target="_blank" rel="noopener">Santoliquido et al. (2021)</a>, the common envelope ejection efficiency parameter, α, determines one of the main sources of uncertainty for the number of BNS mergers per year. In order to evaluate the impact of the uncertainties of the BNS merger rate normalization on our results, we generate two catalogues of BNS mergers assuming α to be either <strong>0.5</strong> or <strong>1.0</strong>. </p> <h3>NS mass distribution</h3> <p>We draw the component masses of the NS binaries, M_1 and M_2, from two different mass distributions: <strong>Gaussian</strong> and<br><strong>uniform</strong> mass distributions. The Gaussian distribution is centred at 1.33 M⊙ with a standard deviation of 0.09 M⊙. The uniform mass distribution ranges in [1.1 M⊙, M_max], where M_max depends on the selected EOS.</p> <h3>Equation of state (EOS)</h3> <p>Since the NS EOS affects both the GW and EM signals expected from BNS mergers, we consider<br>two different EOSs, namely the <strong>APR4</strong> and <strong>BLh</strong> microscopic EOSs.</p> <h3>Detector configuration</h3> <p>Given the two values of α (0.5 and 1.0), the two mass distributions (uniform and Gaussian), and the two EOSs (BLh and APR4), we have a total of 8 different population sets, which constitute our injections for the gravitational signal analysis. For each of these datasets, we consider the following GW detector configurations:</p> <ul> <li>ET in its triangular design of 10 km arms, located in Sardinia, alone and operating together with (<strong>ET_delta_10_cryo</strong>): <ul> <li>the current ground-based network LIGO-Hanford, LIGO-Livingston, Virgo, KAGRA, LIGO-India (<strong>LVKI</strong>) </li> <li>one L-shaped CE with 40 km arms, located in the USA (<strong>1CE</strong>)</li> <li>2 CEs, both with 40 km arms, one in the USA and one in Australia (<strong>2CE</strong>)</li> </ul> </li> <li>ET in its 2L-shaped interferometer configuration of 15 km arms misaligned at 45 deg (one located in Sardinia and the other in the Netherlands); we consider the same networks as above, using the 2L-configuration instead of the triangular one (<strong>ET_2L_15_cryo_45deg</strong>). </li> </ul> <p>We thus have eight different detector networks giving a total of 64 simulations available in this repository. </p> <h3>Catalog description</h3> <p>The parameter estimation of the injected GW signals by the various detector networks is obtained through the Fisher matrix software <strong>GWFish</strong> (<a href="https://ui.adsabs.harvard.edu/abs/2023A%26C....4200671D/abstract" target="_blank" rel="noopener">Dupletsa et al. 2023</a>). The Fisher analysis method approximates the likelihood with a multivariate Gaussian distribution. All the parameters [M_1, M_2, dL, ι, RA, DEC, Ψ, phase, tc, Λ_1, Λ_2] are considered for the Fisher matrix derivation. The uncertainties on parameters coming from the covariance matrix (the inverse of the Fisher matrix) are given at 1σ. We implement a duty cycle of 85% for each of the L-shaped detectors, and for each of the three nested detectors composing the triangle. </p> <ul> <li><strong>Signals_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the parameters describing a GW event and the corresponding network signal-to-noise ratio (SNR) <ul> <li><strong>mass_1: </strong>primary mass of the binary in [Msol] (in detector frame) (M_1)</li> <li><strong>mass_2:</strong> secondary mass of the binary in [Msol] (in detector frame) (M_2)</li> <li><strong>luminosity_distance:</strong> the luminosity distance of the merger in [Mpc]</li> <li><strong>dec:</strong> declination angle in [rad]. It varies in [−𝜋/2,+𝜋/2]</li> <li><strong>ra:</strong> right ascension in [rad]. It varies in [0,2/𝑝𝑖]</li> <li><strong>theta_jn:</strong> the angle between the line of observation and the total angular momentum (orbital, spin and GR corrections) of the binary [rad] (it reduces to the so-called inclination angle or <strong>iota</strong> if the spin component is absent); it ranges in [0,𝜋]</li> <li><strong>psi:</strong> the polarization angle in [rad]; it ranges in [0,𝜋]</li> <li><strong>geocent_time:</strong> merger time as GPS time in [s]</li> <li><strong>phase:</strong> the initial phase of the merger in [rad]; it ranges in [0,2𝜋]</li> <li><strong>redshift: </strong>the redshift of the merger</li> <li><strong>lambda_1: </strong>dimensionless tidal polarizabilty of primary component</li> <li><strong>lambda_2:</strong> dimensionless tidal polarizabilty of secondary component</li> <li><strong>network_SNR:</strong> network SNR for a the given event</li> </ul> </li> <li><strong>Errors_<em>{BNS_merger_rate}</em>_<em>{EOS}</em>_<em>{NS_mass_distribution}</em>_<em>{Detector_configuration}</em>.txt </strong>contains the <div> <div>parameter errors for each event. The first column is <strong>network_SNR</strong>, the following columns repeat the injected parameters as above and the relative errors <strong>err_<em>{parameter}</em></strong><em>. </em>The last column is the error on sky localisation (<strong>err_sky_location</strong>) at 90% credible interval. </div> </div> </li> </ul> <h3>Further details </h3> <p>Further details on the assumptions we made to produce these catalogs can be found in <a href="https://arxiv.org/abs/2411.02342" target="_blank" rel="noopener">Loffredo et al. 2024</a>, while further details on GWFish can be found on <a href="https://colab.research.google.com/github/janosch314/GWFish/blob/main/gwfish_tutorial.ipynb" target="_blank" rel="noopener">this tutorial</a>. We also provide the jupyter notebook <strong>paper_plots.ipynb</strong>, to reproduce Figs. 10, 11, 13, D.1, D.5, D.6. </p>
Gamma and neutron tagged dataset from CLYC SiPM detector
<p>Tagged dataset of gamma and neutron events from <a href="https://www.iaea.org/publications/15101/nuclear-science-and-instrumentation-newsletter-no-3-february-2022">Deuterium-Deuterium (DD) and Deuterium-Tritium (DT) generators</a>. Data from a calibration Cs-137 gamma-only source are also included. The dataset was recorded at the Neutron Science Facility (NSF) of the Nuclear Science and Instrumentation Laboratory (NSIL), IAEA. The <a href="https://scionix.nl/wp-content/uploads/2017/03/V12.7B30_SIP-E3-CLYC-X.pdf">detector</a> is based on a small CLYC (Cs2LiYCl6:Ce) cylindrical crystal (0.5 in diameter by 30 mm length) coupled to a 4-element SiPM array. The data were sampled at 4 GSPS with 10-bits resolution using a CAEN DT5761 digitizer. Subsampling was performed to allow the dataset to be used in real-time embedded deployments, leading to a 100 MSPS final sampling rate. The detector energy range goes up to ~4.5 MeVee (gamma energy-equivalent units).</p><p>Classification of gamma and neutron events was carried out using a frequency-based pulse shape analysis (FCI), as described in the journal article: </p><p><a href="https://doi.org/10.1016/j.net.2023.11.013">https://doi.org/10.1016/j.net.2023.11.013</a></p><p>Two data files are included: <i>gamma.csv</i> and <i>neutron.csv</i>. The data are stored as follows in both files:</p><ul><li>Each row represents a single recorded event at 100 MSPS</li><li>The first 3001 columns represent the raw pulse samples in ADC units</li><li>Column 3002 is named "Energy" and represents each event's calibrated energy in keVee (kilo electron-Volt energy-equivalent) units</li><li>Column 3003 is named "FCI" and represents the neutron/gamma classification index based on the frequency-domain parameter</li></ul><p>A plot is provided (<i>Dataset Preview.png</i>) to show how the events are classified using FCI against Energy.</p><p>The total gamma and neutron events in this dataset are 10913 and 27696, respectively.</p>
LRO LUNAR EXPLORATION NEUTRON DETECTOR 4/5 RDR V1.0
Calibrated or converted housekeeping and scientific data collected from the Lunar Exploration Neutron Detector aboard the Lunar Reconnaissance Orbiter.
LRO LUNAR EXPLORATION NEUTRON DETECTOR 2 EDR V1.0
Raw, uncalibrated housekeeping and scientific data collected from the Lunar Exploration Neutron Detector (LEND) aboard the Lunar Reconnaissance Orbiter.
LRO LUNAR EXPLORATION NEUTRON DETECTOR 2 EDR V1.0
Raw, uncalibrated housekeeping and scientific data collected from the Lunar Exploration Neutron Detector (LEND) aboard the Lunar Reconnaissance Orbiter.
LRO LUNAR EXPLORATION NEUTRON DETECTOR 4/5 RDR V1.0
Calibrated or converted housekeeping and scientific data collected from the Lunar Exploration Neutron Detector aboard the Lunar Reconnaissance Orbiter.
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.