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8 results for “lyman-alpha”
Lyman-alpha forest simulations used for the measurement of the smoothing scale of the intergalactic medium
<p>This repository contains data from the hydrodynamic and dark-matter simulations used in Rorai et al.2017 to measure the pressure smoothing scale of the intergalactic medium (IGM) using the lyman alpha forest from close quasar pairs. </p> <p>The data includes :</p> <p>synthetic spectra (at z~2,2.4,3,3.6) of the transmitted lya flux from a grid of hydrodynamic model of the IGM assuming various thermal and reionization histories;</p> <p>Velocity and density sight lines from a dark matter simulation (at the same redshifts), with different values of the smoothing parameters, which can be used to calculate the lyman alpha flux for the desired values of the thermal parameters (as well as of the mean flux);</p> <p>More information can be found in the README file</p> <p> </p>
CLAMATO2017: IGM Lyman-alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p><strong>CLAMATO 2017 Data Release 1 </strong></p> <p>Public release: 2017 October 9</p> <p>Uploaded to Zenodo on 2018 June 19th after acceptance for publication in ApJS</p> <p>By Khee-Gan Lee (kglee@lbl.gov) and collaborators</p> <p>Supporting paper: https://arxiv.org/abs/1710.02894</p> <p>These are data products associated with the first data release of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field.</p> <p>The following is the summary of the main products:<br> - Source catalog (CL2017_VALUEADDED_RELEASE_20171009.TXT)<br> - Reduced spectra, in /spec_v0/ (blue) and /spec_v0_red (red) sub-directories<br> - Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data.bin)<br> - Wiener-reconstructed 3D absorption map (map_2017_v3.bin)</p> <p>Versions:<br> v0 (not public): Initial rough extraction for 2.15<z<2.55 <br> v1 (not public): Extended redshift range to 2.05<z<2.55 <br> v2 (not public): Caught bug that caused wrong [RA,Dec] for ~4-5 objects<br> v3 (released 2017 Oct 9): Fixed bug that caused wrong aspect ratio in output map<br> v4 (released 2018 Mar 29): Fixed bug that caused negative continua in some spectra</p> <p><br> <strong>Redshift Catalog and Spectra</strong> </p> <p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS</p> <p>The source catalog is provided in the ASCII file CL2017_VALUEADDED_RELEASE_20171009.TXT, with the following columns:</p> <p>- BLUE_SPEC: Blue spectrum filename (in /spec_v0/ sub-directory)<br> - TOMO_ID: CLAMATO ID number<br> - GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog<br> - CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894<br> - ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum<br> - QSO: QSO flag (1 if QSO, 0 if non-QSO)<br> - RA: R.A. in degrees (J2000)<br> - DEC: Dec in degrees (J2000)<br> - S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate<br> - S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate<br> - S/N_RED: Estimated S/N over restframe 1250 ang < lambda < 1350 ang, -9.0 denotes no estimate<br> - TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)<br> - EXPTIME: Exposure time on the spectrum, in seconds (aggregate)<br> - RED_SPEC: Red spectrum filename (in /spec_v0_red/ sub-directory), 'NA' if doesn't exist</p> <p>The tarballs spec_v0.tar.gz and spec_v0_red.tar.gz include all the reduced spectra from LRIS-Blue and LRIS-Red, respectively.</p> <p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:<br> - HDU0: Object spectral flux density, in units of 10^{-17} ergs/s/cm^2/angstrom<br> - HDU1: Noise standard deviation<br> - HDU2: Pixel Wavelengths in angstroms</p> <p><strong>Pixel Data </strong></p> <p>The binary file PIXEL_DATA_v4.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p> <p>The first value in the binary is a 32-bit integer specifying the number of pixels (64332), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p> <p>An example python script to read pixel_data is as follows:<br> import numpy as np<br> with open('CLAMATO2017_public/pixel_data_v4.bin','r') as f:<br> npix = np.fromfile(f, dtype=np.int32, count=1)<br> f.seek(4)<br> pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p> <p>LIST_TOMO_INPUT_2017.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p> <p><strong>Tomographic Map</strong></p> <p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the dachshund algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p> <p>The reconstructed map is MAP_2017_V4.BIN, which is a 60x48x876 = 2552880 pixel double-precision binary with. The dimension that changes fastest is the z-dimension (876 pixels per dimension), followed by the y-dimension (48 pixels per dimension) and x-dimension (60 per dimension).</p> <p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p> <p>The file MAP_2017_V4_SM2.0.BIN is the same map, but smoothed with a R=2Mpc/h Gaussian kernel.</p>
Lyman-alpha Transmission Curves
<p>We provide a large set of Lyman-alpha transmission curves around star-forming halos above <span class="math-tex">\(5\cdot 10^9 \textrm{M}_\odot\)</span>in the IllustrisTNG100 simulation at redshifts 0.0, 1.0, 2.0, 3.0, 4.0 and 5.0. The transmission curves are provided as optical depth <span class="math-tex">\(\tau\)</span> as a function of injected wavelength offset <span class="math-tex">\(\Delta\lambda = \lambda - \lambda_\mathrm{Lya}\)</span>evaluated at the source's systemic redshift with integration starting at <span class="math-tex">\(r=1.5\cdot r_\mathrm{vir}\)</span> We provide the transmission curves along 6 lines of sight roughly aligned with the basis vectors of the Cartesian coordinate system. Further information is made available in a referencing publication that will be linked here.</p> <p>The catalog itself is provided as hdf5 file with the following structure: On the base level, the data set "los_vectors" contains the direction vectors of the 6 lines of sight and the data set "dlambda_bins" provides the wavelength offsets which we evaluate the optical depths at. Most importantly, the transmission curves are provided in the group "tau" in which we provide separate data sets for each simulated redshift. These data sets are three dimensional with the first dimension indexing the line of sight, the second dimension the wavelength bin and the third dimension the targeted halo. For further study, we also provide the IllustrisTNG100 halo identifiers in the group "haloIDs", which again are provided as separate data set for each redshift. </p> <p>Note that an extended data set is available upon request, with 1000 lines of sight per emitter drawn from an equally spaced Fibonacci sphere, which could not be provided here due to the file size.</p>
The Lyman Alpha Reference Sample. XVI. Global 21cm HI properties of Lyman-alpha emitting galaxies.
<p>21cm maps and spectra of galaxies in the Lyman Alpha Reference Samples (LARS and eLARS). The field of view has been tailored to show the full extent of the main SoFIA 2 detection. In each FIgure, the top left panel shows DECaLS optical composite image with HI column density contours at level $1.0\times2^n \times10^{19}\,\textrm{cm}^{-2}$, with $n=0,1,2,...,5$ overlaid. The blue solid line shows the regions with SNR>3. Contours fully included in the SNR>3 mask are shown in white, low signal-to-noise contours are shown in gray. A gray cross indicates the position of the galaxy according to optical coordinates, a synthesized beam shaped aperture centred on these coordinates was used to extract HI properties in the center. The top right panel shows column density maps with the same contours as on the previous panel overlaid. The middle left pannel shows the moment-1 map. The middle right panel shows the linewidth map. The bottom pannel shows the total 21cm spectrum (gray) and beam-extracted 21cm spectrum (black). Velocity centroids are indicated by a vertical solid line, and velocity at half width on either side of the peaks by dashed lines, in either gray or black for the total or beam extraction respectively. In the Figure for eLARS05, the object on the bottom left of the top left panel is a separate detection by SoFIA-2.</p>
CLAMATO DR2: IGM Lyman-Alpha Forest Tomography Survey Public Data Release of Spectra and Maps
<p> </p><p><strong>CLAMATO Data Release 2</strong></p><p>Updated 2023 Jan 11th by Khee-Gan Lee (<a href="mailto:kglee@ipmu.jp">kglee@ipmu.jp</a>)</p><p>Supporting paper has been published in <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..263...27H/abstract">ApJS</a> (arXiv:2109.09660)</p><p>These are data products associated with the second data release (DR2) of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field. This is an updated version of the original DR2 repository (https://doi.org/10.5281/zenodo.5842842), which had accidentally left out several files. The other files that was in that repository are duplicated here.</p><p>The following is the summary of the main products:</p><ul><li>Source catalog (cl2020_valueadded_release_20200602.txt)</li><li>Reduced spectra, in spec_v0 .tar.gz tarball</li><li>Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data_v0.bin)</li><li>Wiener-reconstructed 3D absorption map (map_2020_v0.bin)</li><li>Reconstructed 3D matter density field using the TARDIS-II algorithm (TARDIS_CLAMATO_DR2_v0.4_smoothed.npy)</li><li>Eigenvalues of the pseudo-deformation tensor calculated from the density field (TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy)</li></ul><p><strong> Redshift Catalog and Spectra</strong></p><p>We provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS-Blue.</p><p>The source catalog is provided in the ASCII file cl2020_valueadded_release_20200602.txt, with the following columns:</p><ul><li>SPECFIL: Filename of spectrum (within spec_v0.tar.gz)</li><li>TOMO_ID: CLAMATO ID number</li><li>GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog</li><li>CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894</li><li>ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum</li><li>QSO: QSO flag (1 if QSO, 0 if non-QSO)</li><li>RA: R.A. in degrees (J2000)</li><li>DEC: Dec in degrees (J2000)</li><li>S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate</li><li>S/N_RED: Estimated S/N over restframe 1250 Å < \(\lambda\) < 1350 Å, -9.0 denotes no estimate</li><li>TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)</li><li>EXPTIME: Exposure time on the spectrum, in seconds (aggregate)</li></ul><p>The tarball spec_v0.tar.gz include all the reduced spectra from LRIS-Blue, with the respective filename indicated by the first column of the catalog. We decided not to make available the LRIS-Red spectra.</p><p>The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:</p><ul><li>HDU0: Object spectral flux density, in units of \(10^{-17}\,\mathrm{erg\,s^{-1}\,cm^{-2}\,angstrom^{-1}}\)</li><li>HDU1: Noise standard deviation</li><li>HDU2: Pixel Wavelengths in angstroms</li></ul><p><strong>Pixel Data</strong></p><p>The binary file PIXEL_DATA_v0.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. </p><p>The first value in the binary is a 32-bit integer specifying the number of pixels (84608), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.</p><p>An example python script to read pixel_data is as follows:</p><p>import numpy as np with open('pixel_data_v0.bin','r') as f: npix = np.fromfile(f, dtype=np.int32, count=1) f.seek(4) pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))</p><p>LIST_TOMO_INPUT_2020.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.</p><p>The origin of the map coordinates is at [RA=149.89150 deg, Dec=2.0915050 deg], where x is increasing in the R.A. dimension and y is increasing in the Dec.</p><p><strong>Tomographic Map</strong></p><p>The Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the "dachshund" algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). </p><p>The reconstructed map is MAP_2020_V0.BIN, which is a 68x56x876 = 3335808 pixel double-precision binary file. The dimension that changes fastest is the z-dimension along the line-of-sight (876 pixels per dimension), followed by the y-dimension in increasing Declination (56 pixels per dimension) and x-dimension along the direction of increasing R.A. (68 pixels per dimension).</p><p>Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].</p><p>Note that no additional smoothing has been applied in this binary, whereas most of the visualizations in the paper have had Gaussian smoothing applied.</p><p><strong>Reconstructed Density Field and Cosmic Web</strong></p><p>We also release the underlying matter density field in the CLAMATO volume, estimated using the TARDIS-II algorithm (https://arxiv.org/abs/2007.15994). This is in numpy format, and can be read directly into a 3D array using Python as follows:</p><p>>>> import numpy as np >>> den=np.load('TARDIS_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(den) (34, 28, 438) >>> eigen = np.load('TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(eigen) (34, 28, 438, 3)</p><p>The 3 dimensions correspond to the R.A., Declination, and line-of-sight directions, respectively. </p><p>Note that unlike the Wiener-filtered absorption map<i>, the TARDIS-II reconstruction outputs are in 1Mpc/h comoving voxels. </i>The coordinate zero-point sand line-of-sight comoving distance-redshift relationship are otherwise the same as the Wiener map. </p><p>The eigenvalues of the pseudo-deformation tensor are in TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy, with the same array shape as the matter density, but with an additional array dimension storing the 3 sorted eigenvalues at each point in the volume. </p><p>For reference, we have included an iPython notebook (Tomographic_Maps.ipynb) that plots the Wiener-filtered absorption alongside the TARDIS-II densities and eigenvalues (e.g. Figure 8 of the main paper).</p>
Lyman-alpha tomographic map of the large-scale matter distribution using the eBOSS - Stripe 82 data
<p><strong>Lyman-alpha tomography map using DR16 eBOSS data</strong></p> <p>This data release contains products associated to the Lyman-alpha large-scale tomographic map realized with the 16<sup>th</sup> data release of SDSS-eBOSS. The densest Lyman-alpha forest set of eBOSS in the Stripe 82 field is used : 220 deg<sup>2</sup> wide field with a 37 deg<sup>-2</sup> density. A Lyman-alpha flux contrast map over a volume of 0.94 h<sup>-3</sup>Gpc<sup>3</sup> is obtained. Voids and protoclusters are detected in this portion of the sky.</p> <p>For more details : see https://arxiv.org/abs/2004.01448</p> <p>All the data detailed below can be opened by using the short python script read_data_release.py.</p> <p><strong>Pixels and map data</strong></p> <p>The pixel file pixels_lya_tomography_stripe82.bin was used to create the Lya tomographic map map_lya_tomography_stripe82.bin by using the dachshund algorithm (https://github.com/caseywstark/dachshund).</p> <p>Pixel and map file are in the binary numpy format. Pixels are created from 8999 Lya forests positioned from -43° to +45° in the RA(J2000) direction, from -1.25° to +1.25° in the DEC(J2000) direction and from z=2.1 to z=3.2. The (RA,DEC,z) coordinates are converted to (X,Y,Z) coordinates in Mpc.h<sup>-1</sup>. The pixel file shares the same origin than the map. Each pixel is defined by a set of five floats corresponding to (X, Y, Z, σ, 𝛿) where 𝛿 is the Lyman-alpha flux contrast and σ its associated error based on pipeline noise.</p> <p>The map is also a binary numpy file. It contains a (6354,181,834) Mpc.h<sup>-1</sup> cube of Lyman-alpha flux contrast with a pixel shape (2928,90,417). The origin of the map (0,0,0) corresponds to the coordinates RA=-43°, DEC=-1.25° and z=2.1 and in the cube coordinate to (X,Y,Z)=(0,0,0) Mpc.h<sup>-1</sup>.</p> <p>Note that to obtain this map, the pixel file was first separated to parallelize the tomographic procedure. In comparison to the map detailed in the linked article, this map is rebinned to be less voluminous. Furthermore, a mask is applied to the map where the distance to the nearest line-of-sight of the pixel file is below 20 Mpc.h<sup>-1</sup>. At these locations, map flux contrast is put to 0.</p> <p> </p> <p><strong>Derived catalog data</strong></p> <p>Voids and proto-cluster searches are applied to the tomographic map. The results of this procedure, along with additional catalog cut is given in this data release. Catalogs of voids (catalog_voids_lya_stripe82_*.fits) and proto-clusters (catalog_protoclusters_lya_stripe82_*.fits) are given in the (X,Y,Z) and (RA,DEC,z) coordinates of the map, sharing the same origin. The catalogs are in the FITS format which can be opened with the fitsio python library.</p>
SORCE SOLSTICE Level 3 Lyman-alpha Irradiance As-Measured Cadence V018 (SOR3SOLS_LA_018) at GES DISC
The SORCE SOLSTICE Level 3 Lyman-alpha Irradiance As-Measured Cadence product consists of all measurements of the Lyman-alpha irradiance from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. A complete solar spectrum is made in about 30 minutes, a quick scan mode in about 5 minutes. The spectral resolution of SOLSTICE is 0.1 nm.The Lyman-alpha data are stored in netCDF files containing a full year of. Each days measurements are in a separate netCDF group. Each group contains variables for irradiance, uncertainty, repeatability, long x-ray flux, short x-ray flux, spacecraft height, latitude, longitude, time, wavelength and target zenith angle, with overr 3000 measurements.
CAMEX-4 NOAA LYMAN-ALPHA HYGROMETER V1
The CAMEX-4 NOAA Lyman-Alpha Hygrometer dataset was collected by the NOAA Lyman-alpha Total Water Hygrometer, which was flown during the fourth field campaign in the CAMEX series (CAMEX-4). CAMEX-4 ran from 16 August to 24 September 2001 and was based out of Jacksonville Naval Air Station, Florida, and included missions in the Gulf of America, Caribbean and Western Atlantic. The experiment focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using both NASA-funded aircraft and surface remote sensing instrumentation.
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