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Reproduction package for the paper "A Nançay Radio Telescope study of the hyperactive repeating FRB 20220912A"
<p>This is a reproduction package for the paper "A Nançay Radio Telescope study of the hyperactive repeating FRB 20220912A" by Konijn et al., soon to be published in MNRAS, 2024. The package provides Jupyter-notebooks and the accompanying data to reproduce the figures from the paper. </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>
Dataset used in "Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope" (https://arxiv.org/abs/2107.00048)
<p>'x_train.p', 'y_train.p': pickle files for training split containing 50,757 samples</p> <p>'x_val.p', 'y_val.p': pickle file for validation split containing 5,640 samples</p> <p>'x_test.p', 'y_test.p': pickle file for test split containing 6,267 samples</p> <p>'feature_names.p': pickle file containing names of all 119 features</p>
Ray Tracing-Based Delay Model for Compensating Gravitational Deformations of VLBI Radio Telescopes (Data Set)
<p>The precision and the reliability of very long baseline interferometry (VLBI) depend on several factors. Apart from fabrication discrepancies or meteorological effects, gravity-induced deformations of the receiving unit of VLBI radio telescopes are identified as a crucial error source biasing VLBI products and obtained results such as the scale of a realized global geodetic reference frame. Gravity-induced deformations are systematical errors and yield signal path variations (SPVs). In 1988, Clark and Thomsen derived a VLBI delay model, which was adopted by the International VLBI Service for Geodesy and Astrometry (IVS) to reduce these systematic errors. However, the model parametrizes the SPV by a linear substitute function and considers only deformations acting rotationally symmetrically. The aim of this investigation is to derive the signal path variations of a legacy radio telescope and a modern broadband VGOS-specified radio telescope and to study the effect of nonrotationally symmetric deformation patterns. For that purpose, SPVs are obtained from a nonlinear spatial ray tracing approach. For the first time, a tilt and a displacement of the subreflector perpendicular to the optical axis of the feed unit is taken into account. The results prove the commonly used VLBI delay model as a suitable first-order delay model to reduce gravity-induced deformations.</p>
The Impact of Satellite Trails on Hubble Space Telescope Observations: satellite classifications
<p>This repository contains the Hubble Space Telescope (HST) observations with satellite classifications, released in the paper "<a href="https://www.nature.com/articles/s41550-023-01903-3"><em>The Impact of Satellite Trails on Hubble Space Telescope Observations</em></a>" (DOI: 10.1038/s41550-023-01903-3<em>).</em></p> <p>This table contains 114 607 individual HST images taken in the last 19 years and publicly released in the <a href="https://hst.esac.esa.int/">eHST</a> archive by 3 October 2021, with satellite trail classifications made with machine learning and citizen science.</p> <p>We processed the individual images by adding the two ACS/WFC (WFC3/UVIS, respectively) apertures side-by-side, without correcting for geometric distortions and without the gap between the two detectors (hence why the satellite trails can appear discontinuous in the images). Note that these are not original images from the eHST archive, and therefore are not meant for other scientific analysis. The dataset contains 3072 HST images with satellites (2.7% of the dataset) and 3228 satellite trails in total. The classifications were visually inspected and vetted by the authors (images are flagged with the 'satellite' flag). </p> <p>The table contains the following columns:</p> <ul> <li>observation IDs: both individual exposures (simple_id) and composite images, multiple individual exposures processed and stacked (composite_id);</li> <li>instrument: ACS/WFC or WFC3/UVIS;</li> <li>start time and end time of exposure;</li> <li>exposure duration; </li> <li>right ascension (ra) and declination (dec);</li> <li>'satellite' flag [empty otherwise];</li> <li>no_sat: number of satellites in the image;</li> <li>image URL - URL for the individual HST observations used for satellite classification;</li> <li>additional metadata columns, as available in the <a href="https://hst.esac.esa.int/">eHST</a> archive.</li> </ul> <p>The satellites were classified by volunteers on the <a href="http://www.asteroidhunter.org">Hubble Asteroid Hunter</a> citizen science project and with a machine learning classifier. Please cite the paper (<a href="https://www.nature.com/articles/s41550-023-01903-3">Kruk et al.</a>, <a href="https://www.nature.com/articles/s41550-023-01903-3">https://www.nature.com/articles/s41550-023-01903-3</a>) when using the data in this repository.</p>
The Energy consumption and Carbon Footprint of the LOFAR Telescope V2.0
<p>The LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This article provides an analysis of the energy consumption and the carbon footprint of LOFAR. The approach used is a Life Cycle Analysis (LCA). We find that one year of LOFAR operations requires 3,627 MWh of electricity, 48,714 m3 gas and 135,497 liters of fuel. The associated carbon emission is 2,624 tCO2e/year. Results include the footprint stemming from operations of all LOFAR stations and central processing, but exclude scientific post-processing and activities. The potential recovery of embodied footprint in construction materials at the end of life equals 17%. The electrical energy required for scientific processing is assessed separately. It ranges from 1% (standard The Energy Consumption and Carbon Footprint of the LOFAR Telescope imaging and time-domain), to 40% (wide field long baseline imaging) of the energy consumption for the observation. The outcome provides<br> a transparent baseline in making LOFAR more sustainable and can serve as a blueprint for the analysis of other research infrastructures.</p>
Bending mode data for Rubin Observatory Simonyi Survey Telescope
<p>Bending mode data for Rubin Observatory Simonyi Survey Telescope</p> <p>Bending modes sags are represented as sums of Zernike polynomials and a bicubic spline for each mirror.</p> <p>Source of bending mode data is: https://github.com/bxin/ZEMAX_FEMAP/tree/main</p> <p>To resolve an extrapolation error in previous legacy bending modes, new modes were created by:</p> <ol> <li>Reading original unit load cases</li> <li>Weighting unit load case forces to match previously determined bending mode forces</li> <li>Applying above weights to unit load surface deviations</li> <li>Decomposing into Zernikes + bicubic splines</li> </ol> <p>The corresponding actuator forces for these bending modes can be found in:</p> <ul> <li>https://github.com/bxin/ZEMAX_FEMAP/tree/main/0M1M3Bending/2bendingModes</li> <li>https://github.com/bxin/ZEMAX_FEMAP/tree/main/1M2Bending/2bendingModes</li> </ul> <p>Representative files:</p> <ul> <li> <p><strong>M1M3_bend_zk.fits.gz</strong>: Zernike coefficients (in meters) for M1M3 sag perturbations. 20 modes x 29 coefficients</p> </li> <li> <p><strong>M1M3_bend_forces.fits.gz</strong>: Forces on each actuator for each bending mode. 20 modes x 156 actuators</p> </li> <li> <p><strong>M1M3_actuator_table.fits.gz</strong>: IDs and locations of actuators.</p> </li> <li> <p><strong>M1_bend_coords.fits.gz</strong>: Grid coordinates for bicubic spline nodes on M1. 2 dimensions x 204 points</p> </li> <li> <p><strong>M1_bend_grid.fits.gz</strong>: Bicubic spline coefficients on M1. First dimension is split into z, dz/dx, dz/dy, dz^2/dxdy. Second dimension is 20 bending modes. Third and fourth dimensions are x and y grid locations.</p> </li> <li> <p><strong>bend.yaml</strong>: YAML file indicating which grid coordinates, grid, and Zernikes to use for each mirror. Also indicates inner and outer radii of annuli for Zernike components, and the ordering of modes to use inside of the batoid_rubin package.</p> </li> </ul> <p>Additional files for M2 or M1 and M3 separately follow the same pattern described above.</p>
PTT-subtracted Bending mode data for Rubin Observatory Simonyi Survey Telescope
<p>Piston/Tip/Tilt-subtracted bending mode data for Rubin Observatory Simonyi Survey Telescope</p> <p>Bending modes sags are represented as sums of Zernike polynomials and a bicubic spline for each mirror.</p> <p>Piston, Tip, and Tilt terms have been removed from each mirror in this dataset.</p> <p>Source of bending mode data is: https://github.com/bxin/ZEMAX_FEMAP/tree/main</p> <p>To resolve an extrapolation error in previous legacy bending modes, new modes were created by:</p> <ol> <li>Reading original unit load cases</li> <li>Removing piston/tip/tilt from unit load surface deviations</li> <li>Weighting unit load case forces to match previously determined bending mode forces</li> <li>Applying above weights to unit load surface deviations</li> <li>Decomposing into Zernikes + bicubic splines</li> </ol> <p>The corresponding actuator forces for these bending modes can be found in:</p> <ul> <li>https://github.com/bxin/ZEMAX_FEMAP/tree/main/0M1M3Bending/2bendingModes</li> <li>https://github.com/bxin/ZEMAX_FEMAP/tree/main/1M2Bending/2bendingModes</li> </ul> <p>Representative files:</p> <ul> <li> <p><strong>M1M3_bend_zk.fits.gz</strong>: Zernike coefficients (in meters) for M1M3 sag perturbations. 20 modes x 29 coefficients</p> </li> <li> <p><strong>M1M3_bend_forces.fits.gz</strong>: Forces on each actuator for each bending mode. 20 modes x 156 actuators</p> </li> <li> <p><strong>M1M3_actuator_table.fits.gz</strong>: IDs and locations of actuators.</p> </li> <li> <p><strong>M1_bend_coords.fits.gz</strong>: Grid coordinates for bicubic spline nodes on M1. 2 dimensions x 204 points</p> </li> <li> <p><strong>M1_bend_grid.fits.gz</strong>: Bicubic spline coefficients on M1. First dimension is split into z, dz/dx, dz/dy, dz^2/dxdy. Second dimension is 20 bending modes. Third and fourth dimensions are x and y grid locations.</p> </li> <li> <p><strong>bend.yaml</strong>: YAML file indicating which grid coordinates, grid, and Zernikes to use for each mirror. Also indicates inner and outer radii of annuli for Zernike components, and the ordering of modes to use inside of the batoid_rubin package.</p> </li> </ul> <p>Additional files for M2 or M1 and M3 separately follow the same pattern described above.</p>
Finite Element Analysis perturbation files for Rubin Observatory Simonyi Survey Telescope and LSST Camera
<p>## Notes on FEA files</p> <p><br> </p> <p># M1M3 Bending modes</p> <p> </p> <p>M1M3_1um_156_grid.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1um_156_grid.txt</p> <p>- shape = (5256, 159)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- 0th column is M1M3 disambiguator</p> <p>- 1st and 2nd columns are FEA node x and y in M1M3 CS</p> <p>- Last 156 columns are bending modes; the z-displacement of each node for each mode.</p> <p> </p> <p>M1M3_1um_156_force.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1um_156_force.txt</p> <p>- shape = (156, 159)</p> <p>- Each row is one of 156 bending modes.</p> <p>- 0th column is actuator ID</p> <p>- 1st and 2nd columns are actuator x and y in M1M3 CS</p> <p>- Last 156 columns are forces in Newtons for each mode.</p> <p><br> </p> <p># M1M3 print through</p> <p> </p> <p>M1M3_dxdydz_zenith.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_dxdydz_zenith.npy</p> <p>- shape = (5256, 3)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Columns are dx, dy, dz in M1M3 CS.</p> <p>- This is the gravitational "print through" when mirror is zenith pointing</p> <p> </p> <p>M1M3_dxdydz_horizon.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_dxdydz_horizon.npy</p> <p>- shape = (5256, 3)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Columns are dx, dy, dz in M1M3 CS.</p> <p>- This is the gravitational "print through" when mirror is horizon pointing</p> <p> </p> <p>M1M3_force_zenith.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_force_zenith.npy</p> <p>- shape = (256,)</p> <p>- Each row is one of 256 actuators. (So we consider the x and y actuators here too.)</p> <p>- Columns are forces in Newtons.</p> <p>- These are the mirror support forces when the mirror is zenith pointing. (Is this after optimization? Include LUT or not?)</p> <p> </p> <p>M1M3_force_horizon.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_force_horizon.npy</p> <p>- shape = (256,)</p> <p>- Each row is one of 256 actuators. (So we consider the x and y actuators here too.)</p> <p>- Columns are forces in Newtons.</p> <p>- These are the mirror support forces when the mirror is horizon pointing. (Is this after optimization? Include LUT or not?)</p> <p><br> </p> <p># M1M3 Thermal</p> <p> </p> <p>M1M3_thermal_FEA.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_thermal_FEA.npy</p> <p>- shape = (5244, 7)</p> <p>- Each row is one of 5244 FEA nodes. (Why aren't these the same as above? I don't know.)</p> <p>- Columns are:</p> <p>- 0: Unit-Normalized FEA x</p> <p>- 1: Unit-Normalized FEA y</p> <p>- 2: Bulk temperature dz coefficient</p> <p>- 3: x temperature gradient dz coefficient</p> <p>- 3: y temperature gradient dz coefficient</p> <p>- 3: z temperature gradient dz coefficient</p> <p>- 3: r temperature gradient dz coefficient</p> <p><br> </p> <p># M1M3 Miscellany</p> <p> </p> <p>M1M3_influence_256.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_influence_256.npy</p> <p>- shape = (5256, 256)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Each column is one of 256 actuators.</p> <p>- Values are dz/dF for each actuator/node.</p> <p> </p> <p>M1M3_LUT.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_LUT.txt</p> <p>- shape = (257, 91)</p> <p>- First column is index in degrees (0-90 inclusive). Last 256 columns are forces in Newtons.</p> <p>- Each column is LUT for one value of the elevation index.</p> <p> </p> <p>M1M3_1000N_UL_shape_156.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1000N_UL_shape_156.npy</p> <p>- shape = (5256, 156)</p> <p>- Rows must be FEA nodes, columns must be bending modes.</p> <p>- Not sure what the purpose is of this one.</p> <p><br> </p> <p># M2 Bending modes</p> <p> </p> <p>M2_1um_grid.fits.gz</p> <p>- source = IM/data/M2/M2_1um_grid.DAT</p> <p>- shape = (15984, 75)</p> <p>- Each row is one of 15984 FEA nodes.</p> <p>- 0th column is node index ?</p> <p>- 1st and 2nd columns are FEA node x and y in M2 CS</p> <p>- Last 72 columns are bending modes; the z-displacement of each node for each mode.</p> <p> </p> <p>M2_1um_force.fits.gz</p> <p>- source = IM/data/M2/M2_1um_force.DAT</p> <p>- shape = (72, 75)</p> <p>- Each row is one of 72 bending modes.</p> <p>- 0th column is actuator ID</p> <p>- 1st and 2nd columns are actuator x and y in M2 CS</p> <p>- Last 72 columns are forces in Newtons for each mode.</p> <p> </p> <p># M2 print through / thermal</p> <p> </p> <p>M2_GT_FEA.fits.gz</p> <p>- source = IM/data/M2/M2_GT_FEA.txt</p> <p>- shape = (9084, 6)</p> <p>- Each row is one of 9084 FEA nodes. (Why aren't these the same as above? I don't know.)</p> <p>- Columns are:</p> <p>- 0: Unit-Normalized FEA x</p> <p>- 1: Unit-Normalized FEA y</p> <p>- 2: Zenith print through dz coefficient</p> <p>- 3: Horizon print through dz coefficient</p> <p>- 4: z temperature gradient dz coefficient</p> <p>- 5: r temperature gradient dz coefficient</p> <p><br> </p>
Legacy bending mode data for Rubin Observatory Simonyi Survey Telescope
<p>Legacy bending mode data for Rubin Observatory Simonyi Survey Telescope</p> <p>Bending modes sags are represented as sums of Zernike polynomials and a bicubic spline for each mirror.</p> <p>Source of bending mode data is: https://github.com/bxin/ZEMAX_FEMAP/tree/main/2senM/0bendingModeGridFiles</p> <p>The corresponding actuator forces for these bending modes can be found in:</p> <ul> <li>https://github.com/bxin/ZEMAX_FEMAP/tree/main/0M1M3Bending/2bendingModes</li> <li>https://github.com/bxin/ZEMAX_FEMAP/tree/main/1M2Bending/2bendingModes</li> </ul> <p>Representative files:</p> <ul> <li> <p><strong>M1M3_bend_zk.fits.gz</strong>: Zernike coefficients (in meters) for M1M3 sag perturbations. 20 modes x 29 coefficients</p> </li> <li> <p><strong>M1M3_bend_forces.fits.gz</strong>: Forces on each actuator for each bending mode. 20 modes x 156 actuators</p> </li> <li> <p><strong>M1M3_actuator_table.fits.gz</strong>: IDs and locations of actuators.</p> </li> <li> <p><strong>M1_bend_coords.fits.gz</strong>: Grid coordinates for bicubic spline nodes on M1. 2 dimensions x 204 points</p> </li> <li> <p><strong>M1_bend_grid.fits.gz</strong>: Bicubic spline coefficients on M1. First dimension is split into z, dz/dx, dz/dy, dz^2/dxdy. Second dimension is 20 bending modes. Third and fourth dimensions are x and y grid locations.</p> </li> <li> <p><strong>bend.yaml</strong>: YAML file indicating which grid coordinates, grid, and Zernikes to use for each mirror. Also indicates inner and outer radii of annuli for Zernike components, and the ordering of modes to use inside of the batoid_rubin package.</p> </li> </ul> <p>Additional files for M2 or M1 and M3 separately follow the same pattern described above.</p>
Data from: Evolution of cranial telescoping in echolocating whales (Cetacea: Odontoceti)
Odontocete (echolocating whale) skulls exhibit extreme posterior displacement and overlapping of facial bones, here referred to as retrograde cranial telescoping. To examine retrograde cranial telescoping across 40 million years of whale evolution, we collected 3D scans of whale skulls spanning odontocete evolution. We used a sliding semilandmark morphometric approach with Procrustes superimposition and PCA to capture and describe the morphological variation present in the facial region, followed by Ancestral Character State Reconstruction (ACSR) and evolutionary model fitting on significant components to determine how retrograde cranial telescoping evolved. The first PC score explains the majority of variation associated with telescoping and reflects the posterior migration of the external nares and premaxilla alongside expansion of the maxilla and frontal. The earliest diverging fossil odontocetes were found to exhibit a lesser degree of cranial telescoping than later diverging but contemporary whale taxa. Major shifts in PC scores and centroid size are identified at the base of Odontoceti, and early burst and punctuated equilibrium models best fit the evolution of retrograde telescoping. This indicates that the Oligocene was a period of unusually high diversity and evolution in whale skull morphology, with little subsequent evolution in telescoping.
Map Board & Telescope
A cartography board with a map, telescope, and mounted compass to go with it. Works perfectly if you're trying to make an adventure game! Source: Objaverse 1.0 / Sketchfab
Military telescope
Creator: Adam Prażmowski Time and place of creation: 1882, Paris Jagiellonian University Museum Collegium Maius Inventory number: 17434; 2342/V https://muzea.malopolska.pl/en/objects-list/2794 Source: Objaverse 1.0 / Sketchfab
The Big Dipper with the Sardinia Radio Telescope SRT
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Time-lapses of rotation of Big Dipper or Southern Cross: The Big Dipper with the Sardinia Radio Telescope SRT, by Antonio Finazzi.</p> <p>This time-lapse captures the movement of the stars alongside the majestic 64-metre Sardinia Radio Telescope (SRT) from the National Institute of Astrophysics (INAF), with special attention to the renowned Big Dipper against the backdrop of the celestial sphere. The camera pans as the famous asterism sinks in the sky while planes fly past and the radio telescope rotates. The harmonious interplay between the stellar pathways and the colossal dish of the radio telescope creates a mesmerising visual ode to the cosmic ballet taken in September 2019, earning it an honourable mention in the category of Time-lapses of rotation of Big Dipper or Southern Cross.</p> <p><strong>Credit</strong>: Antonio Finazzi/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Gregorian reflecting telescope (1700s)
Full name: **Astronomical telescope with a Gregorian mirror system** This type of telescope was first described in 1663 by James Gregory (1638-1675), a Scottish mathematician and astronomer, professor at the University of Edinburgh and St. Andrews University in Scotland. Inventor of the first reflecting telescope. He proposed a method of determining an astronomical unit based on observations of the transit of Venus in front of the disc of the Sun. This particular telescope was engineered by Jesse Ramsden (1735–1800), optician, maker of scientific instruments, mainly astronomical and navigational ones, Fellow of the Royal Society in London. **For more images and further information, visit:** https://muzea.malopolska.pl/en/objects-list/2778 Inventory number: 4044; 42/V Localisation of the physical object: Jagiellonian University Museum Collegium Maius, Kraków, Poland **Digitalisation: Regional Digitalisation Lab, Małopolska Institute of Culture in Kraków, Poland; "Virtual Museums of Małopolska" project** Source: Objaverse 1.0 / Sketchfab
Data for: "A partial near infrared guide star catalog for Thirty Meter Telescope Operations"
<p>This .zip file contains the infrared guide star catalogs (IRGSC) generated using optical data (in g, r, i, z, and y bands) from the PANSTARRS DR2 for twenty test fields selected across the TMT's observable sky. The IRGSC is generated by computing the near infrared (J, H and K band) magnitudes of the stellar sources using the Kurucz and Phoenix stellar atmospheric models. Each source in the IRGSC is likely to be stellar and contains the respective PANSTARRS and GAIA object identifiers , their astrometric information from Gaia DR3, the optical and computed NIR magnitudes, the model parameters, the scale factor and the metric d_dev that determines the goodness-of-fit of the model to the data. In addition, the test fields also contain the infrared guide star catalog validated using the UKIDSS DR11 data and plots that show the comparison between the computed NIR magnitudes and observed UKIDSS NIR magnitudes.</p> <p>Inside this .zip file, there is also another directory that contains the IRGSC generated for additional PANSTARRS Medium Deep Survey fields with readily available PANSTARRS 3pi survey data. Some plots are included that show the efficiency of star-galaxy classification and completeness of the 3-pi survey data for these fields.</p> <p>More details and the code to generate IRGSC can be found <strong><a href="https://github.com/sshah1502/irgsc/tree/main">here</a></strong></p>
Light curves included in "Broad-Line Region Characterization in Dozens of Active Galactic Nuclei Using Small-Aperture Telescopes"
<p> </p> <p><strong>This repository is for the paper "Broad-Line Region Characterization in Dozens of Active Galactic Nuclei Using Small-Aperture Telescopes" by Catalina Sobrino Figaredo. It is associated with the manuscript number AAS557954.</strong></p> <p><strong>Table Information:<br></strong><strong>EXTNAME</strong>: Name as a combination of the object name and the year of observation<br>(e.g., HE2128-0221_YEAR=2016)<br><strong>Columns</strong>:<br><strong>MJD</strong>: Modified Julian Date<br><strong>FLUX</strong>: Flux in mJy<br><strong>ERRFLUX</strong>: Flux uncertainty in mJy<br><strong>FILTER</strong>: The observation filter (B_j, V_j, r_s, R_j, 670, 680, 690, SII)</p>
Data and Software for: Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances
<p><strong>12/17/24 Update: I was made aware that the MRT files had the J-band and H-band column header labels incorrectly switched (this occured when converting the CSV files to MRT). I have fixed this mistake. The CSV files were correct the entire time. No data have been changed. Apologies for any inconvinence this may have caused. </strong></p> <p> </p> <p>Data for Resolved Near-infrared Stellar Photometry from the Magellan Telescope for 13 Nearby Galaxies: JAGB Method Distances</p> <p>The files are provided in two formats. A CSV format with a single line header, and a MRT files (zipped) written in the machine-readable format used by AAS & CDS: <a href="https://journals.aas.org/mrt-overview/" target="_blank" rel="noopener noreferrer">https://journals.aas.org/mrt-overview/</a></p> <p>Column descriptions in photometry catalogs:</p> <p>X/Y: Image coordinates from Fourstar camera</p> <p>J, J_err: J-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>H, H_er: H-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>K, K_err: K-band magnitudes and photometric errors returned from DAOPHOT/ALLFRAME</p> <p>chi: chi value returned from DAOPHOT/ALLFRAME</p> <p>sharp: sharpness value returned from DAOPHOT/ALLFRAME</p> <p>ra/dec: RA/Decl </p> <p>RGC: semi-major axis distance</p> <p> </p> <p>Notes on versions: all catalogs from all versions contain the same information, style was just adjusted to adhere to AAS standards. </p> <p> </p>
Multiple same-level and telescoping nesting in GFDL's dynamical core (Grids, ICs)
<p>Grids and ICs of the Geoscientific Model Development technical paper: Multiple same-level and telescoping nesting in GFDL's dynamical core.<br> This paper studies the landfall of hurricane Laura (2020) and an Atmospheric river hitting California (2021) using global and regional domains with multiple same level and telescoping nested grids.</p> <p>Files are for the cases shown in this study:<br> 1- Global C48 + nests<br> 2- Global C768 + nests<br> 3- Regional R192 + nests</p>
Telescope & Box
I renovated one of my very first models of a telescope and wooden box. Old time pirate-like telescope with a small jewelry box. Source: Objaverse 1.0 / Sketchfab
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Allen Brain Atlas
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OpenNeuro
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