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7 results for “lsst”
Stellar Evolution Models from "Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST"
<p>These data consist of all runs from the Modules for Experiments in Stellar Astrophysics (MESA; Paxton et al. 2011, 2013, 2015, 2018, 2019) code, used to construct radius priors for modeling supernova precursor emission in<em> <a href="https://arxiv.org/abs/2408.13314">Finding the Fuse: Prospects for the Detection and Characterization of Hydrogen-Rich Core-Collapse 5 Supernova Precursor Emission with the LSST</a></em> (Gagliano+2024, submitted). </p> <p>The contents of the data files are detailed in the file <strong>ReadmeMESA.txt</strong>. Additional detail concerning the simulations can be found in Section 2.2 of the linked publication. </p>
The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document v1 Released Data Products
<p>This tarball includes software and data products associated with the DESC Science Requirements Document (SRD) v1. See the "Executive Summary and User Guide" in the enclosed PDF of the DESC SRD for instructions on how to use and cite those products. The DESC SRD is described on <a href="https://arxiv.org/abs/1809.01669">arXiv</a> as follows:</p> <p>The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. The Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment.</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>
Random catalog of stars from LSST Simulations
<p>A random catalog of stars drawn from the LSST Simulations database. Columns are:</p> <p>RA (degrees)</p> <p>Dec (degrees)</p> <p>u (extincted magnitudes)</p> <p>g (extincted magnitudes)</p> <p>r (extincted magnitudes)</p> <p>i (extincted magnitudes)</p> <p>z (extincted magnitudes)</p> <p>y (extincted magnitudes)</p> <p>E(B-V) (magnitudes)</p> <p> </p> <p>Note: because this simulation was done for LSST, there are no stars north of Dec~35 degrees</p>
Engineering the docs at LSST
<p>Documentation plays a quiet, but critical, role in our field. Sooner or later, all of us have to write docs, whether we want to or not, and whether we have formal training or not. Docs always seem to take longer to make, and are harder to maintain, than we expect. In this talk, I will walk through several case studies of how we approach documentation production at the Large Synoptic Survey Telescope from my perspective as the documentation engineer for the Data Management subsystem. I'll show how we democratized information sharing with our Developer Guide and technical notes platform; how a Slack bot and templates make everyone on the team work like professional documentarians; how implementing continuous delivery for documentation stopped endless email chains of PDF and Word files; and even how we're helping the commissioning team communicate their investigations through Jupyter notebooks. I'll talk candidly about both the benefits and costs of our engineering-forward approach to LSST documentation and hopefully, will inspire you to take a fresh look at writing the docs for your own projects.</p>
Data products for Unresolved lensed SNe Ia in LSST (arxiv: 2404.15389)
<p>This data release is a part of arxiv 2404.15389. If needed, the ipython notebooks to analyse these datasets are availble at <br><a href="https://github.com/deltasata/Unresolved_LSNeIa_in_LSST">https://github.com/deltasata/Unresolved_LSNeIa_in_LSST</a></p> <p>There are two zip files stemming from two directories.</p> <p><strong>1. unresolved_catalog: </strong>contains the catalogs of lensed type-Ia supernovae (LSNe Ia) -- all and unresolved ones, separately.<strong> </strong>The notebook at the following path can provides statistics of the unresolved systems (please place the notebook in the same directory).<br><a href="https://github.com/deltasata/Unresolved_LSNeIa_in_LSST/tree/main/unresolved_stat">https://github.com/deltasata/Unresolved_LSNeIa_in_LSST/tree/main/unresolved_stat</a></p> <p>There are two subdirectories: <br><strong>(i) catalog data:</strong> contains two catalogs - one listing all LSNe Ia expected over 10 effective years of LSST observations, and another specifically detailing the unresolved systems. See the "unresolved_catalog/catalog data/read_me.txt" for more details. There is also a subdirectory called "down_sampled_data" which contains details of the downsampled unresolved LSNe Ia systems. It is needed for running the ipython notebook.<br><strong>(ii) save_micro_mag_res:</strong> needed for running the ipython notebook.</p> <p><strong>2. build_blended_lc:</strong> contains necessary data files to create blended light curves for the unresolved systems. The subdirectory, "ML_downsampled_data" contains microlensed light curves for the downsampled unresolved LSNeIa. The file "Good_cadence_systems_fiveSigmaDepth_riyz.pkl" contains multi-band cadence distributions from the LSST baseline3.2 observing strategy.<br>The required codes are availble at:<br><a href="https://github.com/deltasata/Unresolved_LSNeIa_in_LSST/tree/main/build_blended_lc">https://github.com/deltasata/Unresolved_LSNeIa_in_LSST/tree/main/build_blended_lc</a><br>please put all the codes from the above in the same directory (build_blended_lc).</p> <p> </p> <h3>References:</h3> <ol> <li><a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240415389B/abstract">Bag, S. et al 2024, arxiv:2404.15389</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2010MNRAS.405.2579O/abstract">Oguri, M. & Marshall, P. J. 2010, MNRAS, 405, 2579</a></li> <li><a href="https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.3842O/abstract">Oguri, M. 2018, MNRAS, 480, 3842</a></li> </ol>
Catalog of Lensed SNe Ia in 10 year of LSST run
<p>This data release is a part of arxiv 2404.15389. The catalog provides all lensed SNe Ia to be present in 10-year LSST run. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.