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opencc-by-4.0Jun 2024View details →

Overcoming the Probing-Depth Dilemma in Spectroscopic Analyses of Batteries with Muon-Induced X-ray Emission (MIXE)

Datasets used in the publication "Overcoming the Probing-Depth Dilemma in Spectroscopic Analyses of Batteries with Muon-Induced X-ray Emission (MIXE)". fig_2: MIXE spectra of (a) an empty laminated Al pouch, (b) a Li metal foil in a laminated Al pouch, (c) a NMC622 electrode in a laminated Al pouch fig_3: MIXE spectrum of a NMC811 electrode in a laminated Al pouch, measured at 23.8 MeV/c fig_4b: Muon stopping profile simulated using PHITS for the cell geometry depicted in Figure 4a of the main manuscript fig_4c: Depth-resolved MIXE spectra of a NMC811||graphite Li-ion battery. Raw data at the 11 momenta measured, and table with the integrated peak areas for selected (K-L) lines. Update in version 2: raw datasets now have one energy column for each momentum. The datasets are of different lengths for each momentum and there was and error in copying the data in version 1.    fig_4c: Table with calculated elemental ratios of the different transition metals (Ni, Mn and Co), at the momenta corresponding to implantation in the NMC811 electrode fig_s2: MIXE spectrum of a NMC622 electrode in a laminated Al pouch, measured at 23.0 MeV/c fig_s3: MIXE spectrum of a NMC111 electrode in a laminated Al pouch, measured at 22.8 MeV/c fig_s4_s5_simulations: Raw data of the muon implantation simulations for the NMC811/graphite cell  Update in version 2: added fig_s4_s5_simulations file   fig_s6: Labelled MIXE spectra (all peaks identified) of a NMC811 electrode in a laminated Al pouch, measured at 24.0, 26.0 and 28.0 MeV/c

opencc-by-4.0Jul 2021View details →

Dark matter and Z' masses preferred by muon g-2 and thermal freeze-out

Companion data for the paper "The Simplest and Most Predictive Model of Muon g−2 and Thermal Dark Matter" [<a href="https://arxiv.org/abs/2107.09067">https://arxiv.org/abs/2107.09067</a>]. The data provides constraints on the masses of DM and Z' which solve the muon g-2 anomaly and generate sufficient thermal dark matter. Two such bands are given (on either side of the resonance), each of which has a 'min', 'mid', and 'max' value based on current 1 sigma constraints on the muon g-2 anomaly. All masses are in MeV.

opencc-by-4.0May 2022View details →

Muon spin relaxation in Ce3Al

We have investigated the dynamics of magnetic-field-driven antiferromagnetic-to-paramagnetic quantum  phase transition in monocrystalline Ce3Al via transverse-field muon spin rotation experiments down to temperature of ~80 mK. The idea is to explore the magnetic transition at these temperatures.

opencc-by-4.0Aug 2023View details →

Detecting Lunar and Martian Water via Backscattered Cosmic Particles using Muon Tomography

Introduction The search for water on the Lunar and Martian surfaces is a cornerstone of space exploration, playing a key role in expanding our understanding of the history and evolution of these celestial bodies. Despite its importance, current knowledge about the distribution, concentration, origin, and migration of water on the Moon and Mars is still limited. This study aims to address these gaps by employing a novel approach that leverages cosmic-ray muon detectors and backscattered radiation. Through the use of advanced muon tracking systems and preliminary simulations conducted with GEANT4, the research suggests that muon tomography holds significant promise for improving our understanding of water-ice content on the Lunar and Martian surfaces. Data Description Data and detector models were generated using GEANT4. The simulations include: <ul> <li>Lunar and Martian dry regolith</li> <li>Lunar and Martian regolith with water-ice beneath the surface</li> </ul> Contents This record includes: <ul> <li><code>*.csv</code>: Output raw files from GEANT4, including 5D information, scattering angle, detector plate position, and particle type.</li> <li><code>backscatter_eventselection.py</code>: Python code to filter events and generate a CSV file of selected backscattered events.</li> <li><code>*.tiff</code>: Visualization files depicting Lunar and Martian scenarios, including detector geometry and particle events.</li> <li><code>ml_classifier.py</code>: Python code for machine learning tasks to classify backscattered events.</li> <li><code>OP_Muographers_2023.pdf</code>: Detailed description of chemical composition and simulated scenarios.</li> <li>Tracking_EKF: Performs track reconstruction and computes track lengths using extended Kalman Filter.</li> </ul> Disclaimer The provided datasets are simulated samples suitable for conceptual R&D and performance studies. They have not been calibrated against real data and should not be used for physics projections about the detectors.

opencc-by-4.0Jun 2017View details →

Data: Muon Tomography sites for Colombia volcanoes for generate muon flux trought volcanic structures (arXiv:1705.09884v1)

Data: Muon Tomography sites for Colombia volcanoes (arXiv:1705.09884v1) The MuTe Collaboration Data for generating figure 6 Muon Tomography sites for Colombia volcanoes (arXiv:1705.09884v1) The files in this record contain data from Extensive Atmospheric Shower simulations made by CORSIKA and Magnetocosmic codes, in a muography of Machin Volcano (Colombia) [https://volcano.si.edu/volcano.cfm?vn=351040] for a particular observation point. The objective was to count muons crossing the volcanic structure for a fixed observation point. Muon transport through the volcanic edifice is calculated by using an algorithm taken Corsika-Magnetocosmic output and taking into account the energy losses with the muon stopping power  tables given by Particle Data Group (PDG). This dataset contains: <ul> <li>Six (6) Corsika output files of 4 hours of simulation each using a flat detector (equivalent to 12 hours of simulated cosmic rays).</li> <li>One (1) Corsika output file of 12 hours of simulation using flat detector (equivalent to 6 hours of simulated cosmic rays).</li> <li>One (1) Corsika output file of 48 hours of simulation using flat detector (equivalent to 24 hours of simulated cosmic rays).</li> <li>Two (2) Corsika output files of 24 hours of simulation each using volumetric detector (equivalent to 48 hours of simulated cosmic rays).</li> <li>Everything makes a total simulated time of 3.75 days.</li> </ul> The output files necessary for the determination of the muon flux through the volcanic structure are obtained through the following process: <ul> <li>From the .shw.bz2 files it is possible to obtain an output file with the momentum information of the particle in the x, y, and z directions, and also the total momentum of the muons, essentially a formatted file (px, py, pz, p). This file can be built by typing in a terminal shell (bash code):</li> </ul> > bzcat *.shw.bz2 | awk '{if($1==0006 ||$1==0005){j=sqrt(($2*$2)+($3*$3)+($4*$4));printf "%s %s %s %.s\n",$2, $3, $4, j }}' | sort -n > salida.out <ul> <li>Metadata in the showers file is as this type (for example):</li> </ul> # # # shw # # CURVED mode is ENABLED and observation level is 2750 m a.s.l. # # This is the Secondaries file - CrkTools v3r0 # # 12 column format is: # # CorsikaId px py pz x y z shower_id prm_id prm_energy prm_theta prm_phi 0001 +1.42146e-04 -3.96008e-05 +1.60247e-04 -1.31716e+03 -6.10051e+01 +2.44986e+03 00000001 0703 +1.25065e+02 +43.016 +021.768 0003 +1.80713e-04 +1.89560e-03 +4.49373e-03 -1.32279e+03 -5.62869e+01 +2.44986e+03 00000001 0703 +1.25065e+02 +43.016 +021.768 0003 +9.41713e-03 +2.38845e-03 +1.10020e-02 -1.32098e+03 -5.68323e+01 +2.44986e+03 00000001 0703 +1.25065e+02 +43.016 +021.768 <ul> <li>Concatenate all output files.</li> <li>Then, the muon flux trought rock can be calculated from two python codes, available in https://github.com/AstroparticulasBucaramanga/Propagacion-Muones-en-Roca. This step generates the final files to be graphed with any plotter, in our case, also using python.</li> </ul>

opencc-by-4.0Sep 2022View details →

Data release for "Measurements of muon-antineutrino and muon-neutrino+muon-antineutrino charged-current cross-sections without detected pions nor protons on water and hydrocarbon at mean antineutrino energy of 0.86 GeV"

This data release is associated with the publication "Measurement of charged-current cross-sections on water and hydrocarbon without detected pions nor protons using the T2K anti-neutrino beam at an off-axis angle 1.5 degrees". It is available in <a href="https://doi.org/10.1093/ptep/ptab014">Progress of Theoretical and Experimental Physics</a> and <a href="https://arxiv.org/abs/2004.13989">arXiv:2004.13989 [hep-ex]</a>. The data release contains: <ul> <li>The "histograms.root" file contains several histograms related to the cross-sections. <ul> <li>flux_numubar_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>flux_numu_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>Err_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numubar cross-sections and their uncertainties.</li> <li>Err_numu_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numu+numubar cross-sections and their uncertainties.</li> <li>xsec_numubar_* -> 1D histogram with the predicted flux-integrated numubar cross-sections by NEUT (5.3.3).</li> <li>xsec_numu_numubar_* -> 1D histogram with the predicted flux-integrated numu+numubar cross-sections by NEUT (5.3.3).</li> </ul> </li> <li>The "Covariance_Matrix_Numubar.root" file contains the covariance matrix for the flux-integrated numubar cross-sections, considering all the uncertainties.</li> <li>The "Covariance_Matrix_Numu+Numubar.root" file contains the covariance matrix for the flux-integrated numu+numubar cross-sections, considering all the uncertainties.</li> <li>The "flux" file contains the (anti-)muon neutrino flux prediction at the WAGASCI module or the Proton Module of the T2K experiment</li> </ul>

opencc-by-4.0Jun 2024View details →

Test data for Galaxy IUC `muon` tool

Test data for Galaxy IUC `muon`  tool.  The data is based on published 10x human PBMC 3k multiomics data. The data was filtered for chromosome 21 only.

opencc-by-4.0Mar 2023View details →

Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"

### On-/Off-Axis Data Release #### (Version 1.0.1, dated 2024/08/12) This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below: + `onoffaxis_xsec_data.root` This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README. + `flux_analysis.root` This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix). + `xsec_data_mc.csv` The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row. + `cov_matrix.csv` and `inv_matrix.csv` The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file. + `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv` The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins. + `calc_chisq.cxx` This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`. + `calc_chisq.py` This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv` + ND280 angle bin numbering     - 0: `-1.0 < cos(#theta) < 0.20`     - 1: `0.20 < cos(#theta) < 0.60`     - 2: `0.60 < cos(#theta) < 0.70`     - 3: `0.70 < cos(#theta) < 0.80`     - 4: `0.80 < cos(#theta) < 0.85`     - 5: `0.85 < cos(#theta) < 0.90`     - 6: `0.90 < cos(#theta) < 0.94`     - 7: `0.94 < cos(#theta) < 0.98`     - 8: `0.98 < cos(#theta) < 1.00` + INGRID angle bin numbering     - 0: `0.50 < cos(#theta) < 0.82`     - 1: `0.82 < cos(#theta) < 0.94`     - 2: `0.94 < cos(#theta) < 1.00`      ### Changelog #### v1.0.1 Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.

opencc-by-4.0Oct 2022View details →

Data in support to the manuscript: Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation by Gianessi et al. (2024)

The files contain data presented and discussed in the manuscript: Testing a novel sensor design to jointly measure cosmic-ray neutrons, muons and gamma rays for non-invasive soil moisture estimation by Gianessi et al. (2024). <div> <div>Gianessi, Stefano, Matteo Polo, Luca Stevanato, Marcello Lunardon, Till Francke, Sascha E. Oswald, Hami Said Ahmed, et al. “Testing a Novel Sensor Design to Jointly Measure Cosmic-Ray Neutrons, Muons and Gamma Rays for Non-Invasive Soil Moisture Estimation.” Geoscientific Instrumentation, Methods and Data Systems 13, no. 1 (January 16, 2024): 9–25. <a href="https://doi.org/10.5194/gi-13-9-2024">https://doi.org/10.5194/gi-13-9-2024</a>.</div> </div>

opencc-by-4.0Mar 2023View details →

Muon Scattering Radiography (MSR) measurements on blocks of ice in laboratory, and on simulated snowpack

Experimental setup (scenario 5): Muon data used in this work has been collected with our muon detection system.  This muon monitoring system is currently in use for both scientific and industrial purposes <a href="https://www.zotero.org/google-docs/?broken=RG5rWA">(Martínez-Ruiz del Árbol et al., 2022)</a>. The particle detectors are composed of four Multi-Wire Proportional Chambers (MWPC) and each chamber has two layers with 224 detection wires, all of them separated by 4 mm. The two layers form a two-dimensional grid of wires which covers an area of 89.6 x 89.6 cm and detects the positions where muons cross it. When a muon event is identified, our system detects four points located in the horizontal two-dimensional grids, two points before the particle goes through the target and another two points after the particle traverses it. With this data, way-in and way-out trajectories can be reconstructed, and muon deviations calculated. Specifically, in the numerical analysis of this work, we utilised the projection of muon deviations in two planes perpendicular to the detection wires. Simulation setup (scenarios 1 to 4): The snowpack was simulated using a one-dimensional snow model forced by surface meteorological data. We have used the SNOWPACK model <a href="https://www.zotero.org/google-docs/?EqYNAT">(Bartelt & Lehning, 2002</a><a href="https://www.zotero.org/google-docs/?LUKxAu">)</a> to realistically simulate the behaviour of the snowpack along two seasons, 2015/2016 (1_Modelling) and 2016/2017 (2_Testing). SNOWPACK was forced by the ERA5-Land surface reanalysis <a href="https://www.zotero.org/google-docs/?QCLBxK">(Muñoz-Sabater et al., 2021)</a>. The simulations were performed in the Pyrenees, using the ERA5-Land cell whose centroid falls closer to the Monte Perdido massif (42.7°N, -0.1°E), at an elevation of 2041m asl. We coupled the SNOWPACK simulations with a full MSR simulation setup that uses the Cosmic RaY generator <a href="https://www.zotero.org/google-docs/?oSIPTu">(Hagmann et al., 2012)</a> to reproduce the atmosphere muon flux and GEANT4 <a href="https://www.zotero.org/google-docs/?3ZDRDN">(Agostinelli et al., 2003)</a> to simulate the muon scattering caused by the snowpack. GEANT4 is a state-of-the-art software designed and maintained at CERN to simulate the interactions of particles and matter in high-energy and nuclear physics. Our simulation framework contains a model of our experimental setup including the muon detectors and their response. This framework has been successfully applied to multiple industrial problems, for instance, to steel-made pipe wear <a href="https://www.zotero.org/google-docs/?F8nYbS">(Martínez-Ruiz del Árbol et al., 2018)</a>. Similar simulation frameworks are typically used to research applications of muography <a href="https://www.zotero.org/google-docs/?115QeU">(Mori et al., 2017)</a>. We expanded the one-dimensional snowpack geometry to a 1m² snow column, assuming homogeneous snow layers in the longitude and latitude dimensions. Then, we propagated and measured muons penetrating the whole snow column, virtually reproducing the detection process using GEANT4. We collected muon deviations and their Root-mean-square (RMS) value for different accumulations of snow during the two simulated seasons.

opencc-by-4.0May 2023View details →

Data release for "Updated T2K measurements of muon neutrino and antineutrino disappearance using 3.6E21 protons on target"

This data release accompanies the results of T2K's analysis of muon neutrino and antineutrino oscillation data collected between 2010 and 2020. The file format is ROOT and contains the best-fit point and the 68% and 90% confidence level contours in the oscillation parameters space investigated by the analysis. The results for both mass ordering are included. Each entry in the file is a TGraph described in DataReleaseNuMuAntiNuMuDis.pdf. This is in <a href="https://doi.org/10.1103/PhysRevD.108.072011">Physical Review D </a>and available on the <a href="https://arxiv.org/abs/2305.09916">arXiv:2305.09916 [hep-ex]</a>.

opencc-by-4.0Aug 2023View details →

Data release for "Measurements of the muon-neutrino and muon-antineutrino-induced coherent charged pion production cross sections on Carbon-12 by the T2K experiment"

The T2K experiment reports the measurement of the flux averaged charged current coherent pion production cross section for neutrino and anti-neutrino scattering from  a Carbon nucleus.  These results are at a mean (anti)neutrino energy of 0.85~GeV in a restricted final state kinematic phase space. The neutrino measurement is an update to a previous result with systematic uncertainties reduced by a half. The antineutrino measurement is the first measurement of this cross section to be made at these energies. We find that the neutrino and antineutrino cross sections are consistent, as expected from theory, and that both agree with the current theoretical models, the Rein-Sehgal and Berger-Sehgal models. The data release contains a summary of these results as well as neutrino and antineutrino flux histograms with which the reader can make their own flux averaged cross section calculation. The paper is published in <a href="https://doi.org/10.1103/PhysRevD.108.092009">Physical Review D</a> and is available on the <a href="https://arxiv.org/abs/2308.16606">arXiv:2308.16606 [hep-ex]</a>.

opencc-by-sa-4.0Nov 2024View details →

The Muon Space GNSS-R Surface Soil Moisture Product

Muon Space is building a constellation of small satellites, many of which will carry global navigation satellite system (GNSS) reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R soil moisture (SM) retrievals using data from NASA’s Cyclone GNSS (CYGNSS) mission. This record includes the complete set of these retrievals from August 1, 2018 until September 26, 2024 in the form of individual "tracks" aggregated into daily, per receiver, files (Level 2 data), along with hourly and daily gridded files (Level 3 data) from the entire constellation. The Muon Space CYGNSS SM product achieves improvements in spatial resolution over SMAP with comparable performance in many regions. An ubRMSE of 0.032 cm3 cm-3 for in situ SM observations from SMAP core validation sites has been demonstrated, though performance is lower than SMAP in forests and/or mountainous terrain. The Muon Space product outperforms the official v1.0 CYGNSS SM product in many measures. This initial release serves as the foundation of the Muon Space operational SM product, which soon will additionally include data from Muon Space satellites. A manuscript describing both the processes involved in generating these SM retrievals, as well as the product files in detail, is available <a title="The Muon Space GNSS-R Surface Soil Moisture Product " href="https://arxiv.org/abs/2412.00072" target="_blank" rel="noopener">here</a>. A code repository demonstrating simple visualization of these data files is available <a title="muon-gnssr-soil-moisture-product-intro git" href="https://github.com/Muon-Space/muon-gnssr-soil-moisture-product-intro" target="_blank" rel="noopener">here</a>. Access to an operational version of the SM datasets will be available from this landing page in Q1 of 2025.

opencc-by-4.0Nov 2024View details →

Negative Muon Elemental Spectroscopy Data

Training and test data associated with the DNN exercise notebook from: <a href="https://github.com/mdi-group/iisc-ml-school">https://github.com/mdi-group/iisc-ml-school</a> This work was supported by EPSRC project EP/Y000552/1 and Royal Society project IES\R3\223036.

opencc-by-4.0Feb 2023View details →

Muon Detectors Data (Raw counts and pressure corrected)

Raw counts and Pressure corrected counts from two muon detectors namely muon000 and muon002.