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4 results for “observing scenarios”
DeepRainForest Output Data : Simulated daily rainfall output (2001-2020) under observed tree cover and no deforestation scenarios in South America
<p>This dataset deposited contains simulation data related to the analysis of forest-rainfall relationships and the impact of historical deforestation on rainfall patterns in South America. The data includes outputs from a spatiotemporal neural network model, DeepRainForest, developed to simulate rainfall based on vegetation and climate inputs in South America. This dataset is the data necessary to recreate the figures that appear in an accepted (but yet to be published manuscript) in Global Change Biology titled "Assessing the impact of past and ongoing deforestation on rainfall patterns in South America". When the manuscript is accepted then the article will be linked from here.</p> <p><strong><em>DeepRainForest_daily_rainfall_with_observed_treecover.nc:</em></strong> contains simulated daily rainfall data spanning from 2001 to 2020, considering the observed tree cover. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_2000_treecover.nc: </strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 2000 onwards. </p> <p><em><strong>DeepRainForest_daily_rainfall_with_1982_treecover.nc:</strong></em>contains simulated daily rainfall output for the same time period (2001-2020) but assumes no deforestation from 1982 onwards.</p>
Realistic LIGO/Virgo/KAGRA observing scenarios based on O3 public alerts
<p>Efforts to search for electromagnetic counterparts of gravitational-wave sources have intensified dramatically since the 2017 discovery a binary neutron star merger with an associated gamma-ray burst, optical/infrared kilonova, and panchromatic afterglow. Now, one LIGO/Virgo observing run later, there has not yet been a second secure identification of electromagnetic counterpart. This is not unexpected, and can be mostly explained by the localization uncertainty of events from LIGO and Virgo’s most recent, third observing run (“O3”). The official LIGO/Virgo observing scenarios fail to account for improvements in data analysis that allow LIGO/Virgo to detect fainter and hence worse-localized gravitational- wave sources, which increases the number of detections while decreasing the proportion of well-localized “gold-plated” events. Realistic forecasting of gravitational-wave localization performance is paramount because electromagnetic counterpart searches require large commitments of telescope time. We present simulations of the next several LIGO/Virgo/KAGRA observing runs that are based on the statistics of O3 public alerts.</p>
Observing scenarios simulations for HLVK-Configuration for O4 Runs, using 20 million injections. This simulation led to 17,009 BNS useful for training Parameter Estimations of EM counterparts of GW. (July 2024 edition).
<p>We have conducted a simulation of the HLVK-configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>Due to the large file sizes, we have split them into three parts and uploaded them to Zenodo with the following DOIs:<br><br></p> <ol> <li><strong>The first files is located :</strong> <code>runs_part_aa</code> and <code>runs_part_ab</code> : <a title="https://zenodo.org/doi/10.5281/zenodo.12693652" href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a></li> <li><strong>The second files is located : </strong><code>runs_part_ac</code> and <code>runs_part_ad</code> : <a href="../doi/10.5281/zenodo.12694779">https://zenodo.org/doi/10.5281/zenodo.12694779</a></li> <li><strong>The third files is located : </strong><code>runs_part_ae</code> and <code>runs_part_af</code> : <a href="../doi/10.5281/zenodo.12696695">https://zenodo.org/doi/10.5281/zenodo.12696695</a></li> </ol> <blockquote> <p>Download them or use this Python script from GitHub to download all of them by running the script: </p> <p> <a href="https://github.com/weizmannk/ObservingScenariosInsights/blob/main/chunk-xml/zenodo-process/download_split_chunk_data.py">hchunk-files-downloader</a>.</p> </blockquote> <p>After downloading them (6 files ), you will need to combine them in a single file using the following process:</p> <blockquote> <p>1.Combine the parts:<br><code>cat runs_part_* > runs.zip</code></p> </blockquote> <blockquote> <p>2.Verify the combined file:<br><code>ls -lh runs.zip</code><br><code>file runs.zip</code></p> </blockquote> <blockquote> <p>3.Unzip the combined file:<br><code>unzip runs.zip</code></p> </blockquote> <p> </p> <p>In the <code>runs</code> folder, we have three subfolders:</p> <ul> <li><code>O4</code>: This contains the <code>.fits</code> files for skymap localization and all GW parameters of the CBCs that passed the threshold cutoff of 8.</li> <li><code>statistics_results</code>: This contains the summary results of the statistical predictions of GW detections.</li> <li><code>subpopulations</code>: This folder is the split of BNS, NSBH, and BBH events. It is useful for those who need quick parameters of BNS and NSBH for their lightcurve simulations or EM counterpart statistical estimation.</li> </ul> <p> </p> <p>For more information, visit: <a href="https://github.com/lpsinger/observing-scenarios-simulations" target="_new" rel="noreferrer">Observing Scenarios Simulations</a></p> <p>Contact: <a rel="noreferrer">weizmann.kiendrebeogo@oca.eu</a> or <a rel="noreferrer">kiend.weizman7@gmail.com</a></p>
Observational Study That Will Evaluate Treatment Patterns in the Management of Hypertension in Various Scenarios in the Public Primary Care System
ClinicalTrials.gov study NCT07361276. IPD Sharing: YES. Countries: 0. Publications: 0.
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