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316 results for “gamma rays”
WWLLN Datasets for "A Terrestrial Gamma-ray Flash from the 2022 Hunga Tonga–Hunga Ha'apai Volcanic Eruption"
<p>These data files contain data used in the paper "A Terrestrial Gamma-ray Flash from the 2022 Hunga Tonga–Hunga Ha’apai Volcanic Eruption", M. S. Briggs, S. Lesage, C. Schultz, B. Mailyan, R. H. Holzworth, Geophysical Research Letters, 2022.</p> <p>The authors wish to thank the World Wide Lightning Location Network (WWLLN), a collaboration among over 50 universities and institutions, for providing the lightning location data used in these datasets and in the paper. Additional WWLLN data are available at nominal cost from http://wwlln.net.</p> <p>The file named Fig_1.txt contains the data used to generate Figure 1 in the paper.</p> <p>The first two columns list the time ranges for each histogram bin, in UTC on 2022 January 15, while the final column lists the lightning detection rate, in counts per minute, for all WWLLN sferics located within a 400 km radius of the Hunga Tonga–Hunga Ha’apai volcano.</p> <p>The times when Fermi passed within 1000 km of the volcano, shown as grey bars in Figure 1, are:<br> 03:47:58.5 to 03:52:56.2 UTC<br> 05:29:25.1 to 05:33:59.7 UTC<br> 07:11:04.0 to 07:15:18.3 UTC<br> 08:52:04.8 to 08:57:05.1 UTC<br> 10:33:48.1 to 10:37:32.7 UTC</p> <p>The time of the Fermi TGF detection, shown as a red line in Figure 1, is:<br> 08:52:40.011500 UTC</p> <p><br> The file named Fig_2.txt contains the WWLLN sferic data used to generate Figure 2 in the aforementioned paper.</p> <p>This file has the same format as the text files for the WWLLN maps provided in the Fermi GBM TGF catalog, https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/tgf/.</p> <p>Line 1 is the network_name<br> Line 2 is TGF_name<br> Line 3 is the coordinates of Fermi at the time of the TGF (2022-01-15 08:52:40.011500 UTC).<br> Line 4 is the coordinates of the center of the map<br> The second number on line 5 is the number of sferics in a +/- 1 minute interval about the TGF.<br> The remaining 104 lines list the properties of each sferic in columns containing the following information:<br> sequence_number, longitude, latitude, time_separation_between_sferic_and_TGF_corrected_for_light-travel-time</p> <p>The two GLM lightning flashes, shown as magenta dots in Figure 2, have longitude and latitude values:<br> -175.27394, -20.9348<br> -175.29301, -20.8466</p> <p>All of the aforementioned longitudes are East longitudes.<br> </p>
Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning
<p>Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning, consisting of 2 data files and 221 presentations of TGF-Optical emission events observed by ASIM between end of March 2019 and November 2020.</p> <p>See 0_READ_ME for information about the individual files and variables.</p>
Data release associated with ``Search for Coincident Gravitational Wave and Long Gamma-Ray Bursts from 4-OGC and the Fermi-GBM/Swift-BAT Catalog"
<p>This is associated data release for the paper https://arxiv.org/abs/2208.03279. It contains the skymaps from potential gravitational-wave candidates from binary neutron star or neutron star-black hole merger. The notebook showcases how to use it. More information can be found in the github repository: https://github.com/gwastro/gw-longgrb</p> <pre> </pre> <pre> </pre>
Gamma rays from dark matter spikes in EAGLE simulations - IMBH mock catalogue
<p>DArk Matter SPIkes (DAMSPI) is a fully Python-based software for the analysis of dark matter spikes around Intermediate Mass Black Holes (IMBHs) in the Milky Way. It allows to extract an IMBH catalogue and their corresponding dark matter spike parameters from the EAGLE simulations in order to probe a potential gamma-ray signal from dark matter self-annihilation. </p> <p>The dataset contains the IMBH catalogue including, among others, the coordinates, mass, formation redshift and spike parameters for each individual IMBH. Each column of the catalogue is described in detail in J. Aschersleben et al. (2024). We also provide separate files for which we calculated the gamma-ray fluxes for different dark matter masses and annihilation cross sections. Lastly, we provide a catalogue of our selection of Milky Way like galaxies within EAGLE. The columns of these files are also described in J. Aschersleben et al. (2024).</p> <p>The source code to extract this dataset is publicy available here:</p> <div> <div> <pre><a href="https://doi.org/10.5281/zenodo.11488472">https://doi.org/10.5281/zenodo.11488472</a></pre> </div> </div> <h2>Description of the data files</h2> <p>The imbh_catalogue/imbh/ directory contains the following files:</p> <ol> <li>catalogue_nfw.h5</li> <li>catalogue_cored_gamma_0p3.h5</li> <li>catalogue_cored_gamma_0p9.h5</li> <li>catalogue_cored_gamma_free.h5</li> </ol> <p>They contain the IMBH catalogues, including the coordinates and dark matter spike parameters, calculated assuming the 1.) NFW profile, 2.) cored profile with a fixed core index of 0.0, 3.) cored profile with a fixed core index of 0.4 and 4.) cored profile with the core index as a free fitting parameter.</p> <p>The imbh_catalogue/flux/<channel>/<energy_threshold>/ directory contains the gamma-ray fluxes of the IMBHs for a given annihilation channel, energy threshold, and dark matter mass. E.g. the imbh_catalogue/flux/b_channel/e_th_0.1GeV/m_dm_10.0GeV.h5 file contains the IMBH fluxes assuming the b-channel, an energy threshold of 0.1 GeV and a dark matter mass of 10 GeV. The IMBH fluxes are calculated for a variety of velocity weighted annihilation cross sections. </p> <p>The imbh_catalogue/galaxy/ directory contains the mw_galaxies_catalogue_nfw.h5 file which contains our selection of Milky Way-like galaxies within EAGLE.</p> <p>The HDF files can be opened in Python with:</p> <pre><code>import pandas as pd file_path = "<path_to_file>.h5" df = pd.read_hdf(file_path, key="table") # Printing the first few rows of the DataFrame print(df.head())</code><code> </code></pre>
Data used in the study: Highly dynamic gamma-ray emissions are common in tropical thunderclouds
<p>This repository includes the data presented in the study: Highly dynamic gamma-ray emissions are common in tropical thunderclouds.</p> <p>Detailed description is given in the uploaded pdf document: Data_description.pdf</p>
Complete figure set for 'Radio Morphology of Gamma-ray Sources: Double-Lobed Radio Sources'
<p>This file is the online supplementary material for the data that will be published in the manuscript title "Radio Morphology of Gamma-ray Sources: Double-Lobed Radio Sources" by the Astrophysical Journal.</p>
Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes - data release
<p><strong>Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes - data release</strong></p> <p>The results of gamma-ray observations of the binary system HESS J0632+057 collected during 450 hours over 15 years, between 2004 and 2019, with the H.E.S.S., MAGIC, and VERITAS telescopes are presented in <strong>Observation of the gamma-ray binary HESS J0632+057 with the H.E.S.S., MAGIC, and VERITAS telescopes</strong> (ApJ, to be published).<br> This repository provides access to all processed data presented in the publication in csv and ascii format.<br> For a detailed description of analysis and data processing, see the associated primary publication.</p> <p><strong>Please cite always the following primary reference when using these data: </strong></p> <ul> <li> <p><a href="https://doi.org/10.3847/1538-4357/ac29b7">The Astrophysical Journal, 923:241 (30pp), 2021 December 20</a></p> </li> <li> <p><a href="https://arxiv.org/abs/2109.11894">arXiv:2109.11894</a></p> </li> </ul> <p>Data Publication Year: 2021</p> <p>Citation: The VERITAS, MAGIC, and H.E.S.S. Collaborations (2021). Observation of the gamma-ray binary HESS J0632+057 with the HESS, MAGIC, and VERITAS telescopes - data release. DOI **[DOI to be added]**</p> <p>Additional information on the gamma-ray observatories:<br> - H.E.S.S. (<a href="https://www.mpi-hd.mpg.de/hfm/HESS/">https://www.mpi-hd.mpg.de/hfm/HESS/</a>) and H.E.S.S. Auxiliary Data Page (<a href="https://www.mpi-hd.mpg.de/hfm/HESS/pages/publications/auxiliary/auxinfo_hessj0632_HMVdata.html">https://www.mpi-hd.mpg.de/hfm/HESS/pages/publications/auxiliary/auxinfo_hessj0632_HMVdata.html</a>)<br> - MAGIC (<a href="https://magic.mpp.mpg.de/">https://magic.mpp.mpg.de/</a>) and MAGIC Data Page (<a href="http://vobs.magic.pic.es/fits/">http://vobs.magic.pic.es/fits/</a>)<br> - VERITAS (<a href="https://veritas.sao.arizona.edu/">https://veritas.sao.arizona.edu/</a>) and VERITAS Data Page (<a href="https://github.com/VERITAS-Observatory/VERITAS-VTSCat">https://github.com/VERITAS-Observatory/VERITAS-VTSCat</a>)</p> <p>This data repository is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://opendatacommons.org/licenses/pddl/1.0/</p> <p>## List of data:</p> <p>(best to view with a markdown reader)</p> <p>1. Gamma-ray and X-ray fluxes (Figures 2, 3, and 9):<br> - Gamma-ray integral flux (>350 GeV) from H.E.S.S. observations: [Fig02_03_09/LightCurve-HESS.ecsv](Fig02_03_09/LightCurve-HESS.ecsv)<br> - Gamma-ray integral flux (>350 GeV) from MAGIC observations: [Fig02_03_09/LightCurve-MAGIC.ecsv](Fig02_03_09/LightCurve-MAGIC.ecsv)<br> - Gamma-ray integral flux (>350 GeV) from VERITAS observations: [Fig02_03_09/LightCurve-VERITAS.ecsv](Fig02_03_09/LightCurve-VERITAS.ecsv)<br> - X-ray fluxes (0.3–10 keV) from Swift-XRT, Chandra, XMM, NuSTAR, Suzaku observations: [Fig02_03_09/LightCurve-XRay.ecsv](Fig02_03_09/LightCurve-XRay.ecsv)<br> 2. Halpha observations (Figure 4):<br> - Profile parameters of Halpha observations [Fig04/Halpha.ecsv](Fig04/Halpha.ecsv)<br> 3. Gamma-ray - X-ray correlation (Figure 5):<br> - Contemporaneous gamma-ray (>350 GeV) vs X-ray (0.3–10 keV) integral fluxes: [Fig05/LC-cross-Gamma-XRay.ecsv](Fig05/LC-cross-Gamma-XRay.ecsv)<br> - Discrete cross-correlation function (DCF) between gamma- ray and X-ray data: [Fig05/DCF-cross-Gamma-XRay-HESSJ0632p057.ecsv](Fig05/DCF-cross-Gamma-XRay-HESSJ0632p057.ecsv)<br> 4. Gamma-ray vs Optical and X-ray vs Optical correlations (Figure 6):<br> - Halpha vs gamma-ray observations: [Fig06/Gamma-ray-Optical-Correlation.ecsv](Fig06/Gamma-ray-Optical-Correlation.ecsv)<br> - Halpha vs X-ray observations: [Fig06/X-ray-Optical-Correlation.ecsv](Fig06/X-ray-Optical-Correlation.ecsv)<br> 5. Spectral energy distributions (phase averaged; Figure 7 and 8)<br> - SEDs from H.E.S.S. observations: [Fig07_08/HESS-phaserange04-spectrum.ecsv](Fig07_08/HESS-phaserange04-spectrum.ecsv), [Fig07_08/HESS-phaserange1-spectrum.ecsv](Fig07_08/HESS-phaserange1-spectrum.ecsv), [Fig07_08/HESS-phaserange2-spectrum.ecsv](Fig07_08/HESS-phaserange2-spectrum.ecsv), [Fig07_08/HESS-phaserange3-spectrum.ecsv](Fig07_08/HESS-phaserange3-spectrum.ecsv)<br> - SEDs from MAGIC observations: [Fig07_08/MAGIC-phaserange04-spectrum.ecsv](Fig07_08/MAGIC-phaserange04-spectrum.ecsv), [Fig07_08/MAGIC-phaserange1-spectrum.ecsv](Fig07_08/MAGIC-phaserange1-spectrum.ecsv), [Fig07_08/MAGIC-phaserange2-spectrum.ecsv](Fig07_08/MAGIC-phaserange2-spectrum.ecsv)<br> - SEDs from VERITAS observations: [Fig07_08/VERITAS-phaserange04-spectrum.ecsv](Fig07_08/VERITAS-phaserange04-spectrum.ecsv), [Fig07_08/VERITAS-phaserange1-spectrum.ecsv](Fig07_08/VERITAS-phaserange1-spectrum.ecsv), [Fig07_08/VERITAS-phaserange2-spectrum.ecsv](Fig07_08/VERITAS-phaserange2-spectrum.ecsv), [Fig07_08/VERITAS-phaserange3-spectrum.ecsv](Fig07_08/VERITAS-phaserange3-spectrum.ecsv)<br> - SEDs from Swift-XRT observations: [Fig07_08/XRT-phaserange04-spectrum.ecsv](Fig07_08/XRT-phaserange04-spectrum.ecsv), [Fig07_08/XRT-phaserange1-spectrum.ecsv](Fig07_08/XRT-phaserange1-spectrum.ecsv), [Fig07_08/XRT-phaserange2-spectrum.ecsv](Fig07_08/XRT-phaserange2-spectrum.ecsv), [Fig07_08/XRT-phaserange3-spectrum.ecsv](Fig07_08/XRT-phaserange3-spectrum.ecsv)<br> 6. Spectral energy distributions (orbit 9 and 17; Figure 10):<br> - SEDs from VERITAS observations: [Fig10/VERITAS-MJD55585-55600-spectrum.ecsv](Fig10/VERITAS-MJD55585-55600-spectrum.ecsv), [Fig10/VERITAS-MJD55600-55603-spectrum.ecsv](Fig10/VERITAS-MJD55600-55603-spectrum.ecsv), [Fig10/VERITAS-MJD55614-55623-spectrum.ecsv](Fig10/VERITAS-MJD55614-55623-spectrum.ecsv), [Fig10/VERITAS-MJD55624-55631-spectrum.ecsv](Fig10/VERITAS-MJD55624-55631-spectrum.ecsv), [Fig10/VERITAS-MJD58136-spectrum.ecsv](Fig10/VERITAS-MJD58136-spectrum.ecsv), [Fig10/VERITAS-MJD58141-spectrum.ecsv](Fig10/VERITAS-MJD58141-spectrum.ecsv), [Fig10/VERITAS-MJD58142-spectrum.ecsv](Fig10/VERITAS-MJD58142-spectrum.ecsv), [Fig10/VERITAS-MJD58143-spectrum.ecsv](Fig10/VERITAS-MJD58143-spectrum.ecsv), [Fig10/VERITAS-MJD58153-58154-spectrum.ecsv](Fig10/VERITAS-MJD58153-58154-spectrum.ecsv)<br> - SEDs from MAGIC observations: [Fig10/MAGIC-MJD55585-55600-spectrum.ecsv](Fig10/MAGIC-MJD55585-55600-spectrum.ecsv)<br> - SEDs from Swift-XRT observations: [Fig10/XRT-MJD55585-55600-spectrum.csv](Fig10/XRT-MJD55585-55600-spectrum.csv), [Fig10/XRT-MJD55600-55603-spectrum.csv](Fig10/XRT-MJD55600-55603-spectrum.csv), [Fig10/XRT-MJD55614-55623-spectrum.csv](Fig10/XRT-MJD55614-55623-spectrum.csv), [Fig10/XRT-MJD55624-55631-spectrum.csv](Fig10/XRT-MJD55624-55631-spectrum.csv), [Fig10/XRT-MJD58142-spectrum.csv](Fig10/XRT-MJD58142-spectrum.csv), [Fig10/XRT-MJD58143-spectrum.csv](Fig10/XRT-MJD58143-spectrum.csv), [Fig10/XRT-MJD58152-spectrum.csv](Fig10/XRT-MJD58152-spectrum.csv), [Fig10/XRT-MJD58153-spectrum.csv](Fig10/XRT-MJD58153-spectrum.csv)<br> 7. Contemporaneous X-ray and gamma-ray spectral energy distribution (Appendix D)<br> - SEDs from VERITAS observations: [Auxiliary/VERITAS*](Auxiliary/)</p>
Synthetic urban gamma-ray spectra for training spectral detection and identification models
Open the record for dataset details and reuse information.
Supporting information for a rapid gamma-ray flux reduction paper
<p>Supporting data and information needed to reproduce the described in the paper results and conclusions.</p>
Figure data for the paper: "Spectral Observations of Optical Emissions Associated with Terrestrial Gamma-Ray Flashes"
<p>This repository contains 13 files with data which were used to produce the figures in the paper "Spectral Observations of Optical Emissions Associated with Terrestrial Gamma-Ray Flashes" by Heumesser et al. The description of the different files is included in the supporting information of the paper and additionally uploaded here, see "2020GL090700_OpticalEmissionsAssociatedwithTGFs_SupportingInformation".</p>
Dataset for neutron and gamma-ray pulse shape discrimination: radiation pulse signals and discrimination methodologies
<p>This dataset provides neutron and gamma-ray pulse signals for pulse shape discrimination experiments. Serval traditional and recently proposed pulse shape discrimination algorithms are utilized to conduct pulse shape discrimination under raw pulse signals and noise-enhanced datasets. These algorithms include zero-crossing (ZC), charge comparison (CC), falling edge percentage slope (FEPS), frequency gradient analysis (FGA), pulse-coupled neural network (PCNN), ladder gradient (LG), and heterogeneous quasi-continuous spiking cortical model (HQC-SCM). This dataset also provides the source code of all these pulse shape discrimination methods, together with the source code of schematic pulse shape discrimination performance evaluation and anti-noise performance evaluation. Detailed descriptions of this dataset can be found at: https://doi.org/10.48550/arXiv.2305.18242.</p>
Supporting Information for "First Results of Terrestrial Gamma-ray Flash observations by Insight-HXMT"
<p>The compressed file contains data for 282 HXMT Terrestrial Gamma-ray Flashes (TGFs), spanning from T0-1 second to T0+1 second. Below is a description of the data format included in the file.</p> <p>[HXMT 1K Science Data Description]</p> <p>1. The "Time" column represents the Mission Elapsed Time (MET) in seconds for HXMT, starting from 2012-01-01T00:00:00.</p> <p>2. The "Det_ID" column indicates the detector index for the 18 HXMT/HE NaI/CsI detectors, ranging from 00 to 17.</p> <p>3. The "Channel" column refers to the energy channel for HXMT/HE NaI/CsI detectors, which spans from 0 to 255. Data filtering often uses "Channel>30" to exclude lower energy events.</p> <p>4. The "Pulse_Width" column measures the pulse width of the detector signal in milliseconds. For HXMT/HE data, a Pulse Width value of N translates to N/48 ms. To filter the data, "Pulse_Width>75" is commonly used.</p> <p>5. The "ACD" column presents data from the anticoincidence detectors of the HXMT/HE Anti-Coincidence Detector (ACD). This data consists of an array of 18 boolean values, each corresponding to a specific ACD. For instance, if the first value in the array is 1, it indicates that the event was simultaneously detected by ACD-01; conversely, a 0 in the second position signifies that ACD-02 did not detect the event at the same time. Typically, events detected by any ACD are excluded from data analysis. Conversely, events are included in data analysis when all ACD values are 0.</p> <p>6. The "Event_Type" column describes the operational modes of the detector: 0 for normal-gain mode and 1 for low-gain mode.</p> <p>Details on the NaI/CsI and ACD detector installation positions and the relationship between energy and channel for both normal-gain and low-gain modes can be found in Song et al. 2022.</p>
Natural Gamma Ray (GR-K-Th-U)
<p>Natural Gamma Ray dataset from Sergipe Basin (Core SER-03).</p> <p>GR (cps); K (%), Th (ppm) and U (ppm)</p>
Production of Terrestrial Gamma-ray Flashes During the Early Stages of Lightning Flashes
<p>Supporting data to the paper "Production of Terrestrial Gamma-ray Flashes During the Early Stages of Lightning Flashes", Lindanger et al., 2022, JGR</p>
Data relating to "Millisecond Pulsars from Accretion Induced Collapse as the Origin of the Galactic Centre Gamma-ray Excess Signal"
<p>Data describe the evolution of a population of millisecond pulsars (MSPs) born from Accretion Induced Collapse.</p> <p>Data entries are comma-separated.</p> <p>The formation and subsequent evolution of 9194 MSPs have been modelled with code based on the BSE Code [see Hurley, J. R., Pols, O. R. & Tout, C. A. Comprehensive analytic formulae for stellar evolution as a function of mass and metallicity. Mon. Not. Roy. Astron. Soc. 315, 543–569 (2000)].</p> <p>For each MSP, the first row describes the magnetic field B (in Gauss) and the inclination angle (i, in radians) between magnetic & rotational axes.</p> <p>These quantities (B,i) do not evolve.</p> <p>Thus, every row containing only two entries indicates the beginning of the data covering a separate MSP.<br> For subsequent rows, there are seven entries in each row.</p> <p>The first entry in each row is the (discretised) time in units of Gyr since the star formation event when the MSP is formed. 0.1 is the minimum time possible time for which an MSP can be born. Subsequent discrete time steps are 0.1 Gyr. The last row for every MSP is for a time of 15.9 Gyr. </p> <p>Subsequent entries show (as a function of time)</p> <p>period (in s)</p> <p>period derivative dP/dt (s/s)</p> <p>NS mass (in units of solar masses)</p> <p>secondary mass (in units of solar masses)</p> <p>secondary type (for an explanation of secondary type label see Hurley, J. R., Pols, O. R. & Tout, C. A. Comprehensive analytic formulae for stellar evolution as a function of mass and metallicity. Mon. Not. Roy. Astron. Soc. 315, 543–569 (2000).</p> <p>orbital separation</p>
Supplementary material: Multi-wavelength view of the close-by GRB~190829A sheds light on gamma-ray burst physics
<p>This repository contains supplementary data regarding the article "Multi-wavelength view of the close-by GRB~190829A sheds light on gamma-ray burst physics" published by the Astrophysical Journal Letters.</p> <p>In particular, the repository contains:</p> <ul> <li>Markov Chain Monte Carlo samples for both the afterglow modelling and the circular gaussian fits to VLBI data</li> <li>clean radio images, residuals and UV coverage plots for all our VLBI epochs</li> </ul> <p>Data formats should be self-explanatory. Do not hesitate to contact us at omsharan.salafia@gmail.com for any question.</p>
X-ray diffraction data of twinned gamma-form of o-nitroaniline
<p>The diffraction data are of the gamma-form of o-Nitroaniline, C<sub>6</sub>H<sub>6</sub>N<sub>2</sub>O<sub>3</sub>. This compound is known to be polymorphic; the alpha-form is probably amorphous, while the beta- and gamma-forms are crystalline. Difficulties with the unit-cell determination of the gamma-form were reported as a consequence of twinning. These newly recorded diffraction data are of a twinned crystal.</p> <p>The raw data and processing with EVAL are described in details in IUCrData as a Raw Data Letter [Lutz & Kroon-Batenburg, IUCrData (2022].</p> <p>The data were recorded on a Bruker ApexII diffractometer and stored as .sfrm files. They were also converted with Bruker imagesum.py script as part of the APEXII software to full .cbf files.</p>
Data files for "Curvature in the very-high energy gamma-ray spectrum of M87"
<h1>Summary</h1> <p>In this repository, we provide some auxiliary material in connection to our paper “Curvature in the very-high energy gamma-ray spectrum of M87" accepted for publication in the Astronomy and Astrophysics (A&A) Journal and available on Arxiv through the ID <a href="https://arxiv.org/abs/2402.13330" target="_blank" rel="noopener">arXiv:2402.13330</a>. For the full list of authors, please refer to the paper.</p> <p>In the publication, we study the very-high energy gamma-ray spectrum of a stacked high emission state of M87 using H.E.S.S. observations. We detect a curvature in the spectrum that is not related to the EBL absorption. In addition to that, we show that the gamma-gamma absorption by star light from the galaxy is weak to explain the measured curvature and that it is unlikely that different high states with similar spectral distribution could be able to explain the same curvature.</p> <h1>Data and example code</h1> <h2>ECSV tables</h2> <p>The ecsv tables provide the means to reproduce the figures found in the paper. They can be opened with `astropy.QTable` as demonstrated below.</p> <pre><code>from astropy.table import QTable table = QTable.read('Fig1_lightcurve_table.ecsv') print(table)</code></pre> <p>The following example shows how to reproduce Fig. A2 from the paper (the modules imported are needed in the loaded enviroment):</p> <pre><code>from astropy.table import QTable from scipy.stats import gmean %matplotlib inline import matplotlib.pyplot as plt import numpy as np import seaborn as sns from ebltable.ebl_from_model import EBL cmap = sns.color_palette("colorblind", as_cmap=True) colors = sns.color_palette("colorblind", 6) ebl = {} for m in ["finke2022", "kneiske", "dominguez-upper"]: ebl[m] = EBL.readmodel(m) lmu = np.logspace(-1,3.,100) z = 0.0042 nuInu = {} for m, e in ebl.items(): nuInu[m] = e.ebl_array(z,lmu) nuInu table = QTable.read('FigA2_EBL_ULs.ecsv') wavelengths = table["Wavelength"].value wavelengths = wavelengths counter = 0 for m in ["finke2022", "kneiske", "dominguez-upper"]: plt.loglog(lmu,nuInu[m], lw = 2.,label=f"{m} UL", color=colors[counter] ) ULs = table[f"{m} UL"][(wavelengths>12.4)*(wavelengths<40)].value plt.loglog(wavelengths[(wavelengths>12.4)*(wavelengths<40)],ULs, lw = 2., label = f"UL (this work)", ls='dashed', color=colors[counter]) plt.arrow(gmean(wavelengths[(wavelengths>12.4)*(wavelengths<40)]), np.median(ULs), 0, -0.2*np.median(ULs), head_width=5 ,head_length=0.1*np.median(ULs), alpha=0.5, color=colors[counter]) counter+=1 plt.gca().set_xlabel('Wavelength ($\mu$m)',size = 'x-large') plt.gca().set_ylabel(r'$\nu I_\nu (\mathrm{nW}\,\mathrm{sr}^{-1}\mathrm{m}^{-2})$',size = 'x-large') plt.legend(loc = 'lower center', ncol = 2) plt.tight_layout() plt.show()</code></pre> <h2>Text files</h2> <p>The text files provide the gammapy fit results for the various analyses in the main text of the paper. For a complete definition of the models, we refer the reader to the paper. The name of the file is given by <em>fit_stacked_M87_<strong>MODEL</strong>_flux_90perc_<strong>ENERGYRANGE</strong>_90perc.txt</em>, where <strong>MODEL</strong> is the spectral model fitted (e.g., <em>PLxEBLfinke2022</em> or <em>PLxEBLfinke2022-free</em> in case the EBL intensity alpha_norm is a free parameter) and <strong>ENERGYRANGE</strong> is the energy range of the reduced dataset (e.g., <em>0.3_31.6TeV</em>).</p> <p> </p>
Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks
<p>Data used to train multiclass and binary neural networks to analyse SiPM (Silicon Photomultiplier) signals in a multiplexed array of 16 detectors and detect the signal detector origin. Data acquired using an oscilloscope. Results compared with previous anger logic methods. </p> <p>Dataset used in the publication</p> <p>"Precise positioning of gamma ray interactions in multiplexed pixelated scintillators using artificial neural networks"</p> <p>https://doi.org/10.1088/2057-1976/ad4f73</p>
Supporting information for the paper "Evidence of a new population of weak Terrestrial Gamma-ray Flashes observed from aircraft altitude" by I. Bjørge-Engeland et al.
<p>Supporting data for the paper "Evidence of a new population of weak Terrestrial Gamma-ray Flashes observed from aircraft altitude" by I. Bjørge-Engeland et al. </p>
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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.