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4 results for “SPheno”
1 million cMSSM parameter space points with low-energy predictions from SPheno and MicrOMEGAs
<p>This dataset was produced and used in the paper <a href="https://arxiv.org/abs/2405.18471">Symbolically Regressing Beyond the Standard Model Physics</a>. The code used to generate and to analyse these data can be found <a href="https://gitlab.com/miguel.romao/symbolic-regression-bsm">here</a>.</p> <p>The dataset specifications:</p> <ul> <li>Randomly sampled 1 million points of the cMSSM parameter space and respective low-energy observables.</li> <li>Low-energy observables computed using using `SPheno` and `MicrOMEGAs`. <ul> <li>Only points that produced `SPheno` output and neutral LSP are processed by `MicrOMEGAs`.</li> <li>The dataset includes all points, even if they are "unphysical", i.e. points without `SPheno` output or neutral LSP. In the paper, this was used to train a classifier to filter out "unphysical" points.</li> </ul> </li> <li>The columns are <ul> <li>'m0', 'm12', 'A0', 'tanb': the four physical parameters of the theory sampled in the priori <ul> <li>'m0': [0, 10] TeV</li> <li>'m12': [0, 10] TeV</li> <li>'A0': [-60,60] TeV</li> <li>'tanb': [1.5,50]</li> <li>The sign of the 'mu' parameter was fixed to positive (+1)</li> </ul> </li> <li>'idx': an utility identifier used during generation, can/should be ignored</li> <li>Flattened `SPheno` outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns 'MINPAR_1', 'MINPAR_2', 'MINPAR_3', 'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.</li> <li>`MicrOMEGAs` outputs. These inlcude: 'dm_Omega', 'dm_spin', 'dm_candidate`, `mo_output`, `dm_c_{bino,wino,higgsino1,higgsino2}`, which are, respectively: dark matter relic density value, dark matter candidate spin, dark matter candidate, the whole `MicrOMEGAs` output, and the coefficient of `{bino,wino,higgsino1,higgsino2}` components of the dark matter state.</li> </ul> </li> </ul> <p>Versions:</p> <ul> <li>SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found <a href="https://gitlab.com/lip_ml/blackboxbsm">here</a>.</li> <li>MicrOMEGAs 5.3.41, with the MSSM model adapted for low-scale slha inputs.</li> </ul> <p>The datasets are provided in <a href="https://parquet.apache.org/">Apache `parquet`</a> format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use <a href="https://arrow.apache.org/docs/python/index.html">`pyarrow`</a>.</p> <p> </p>
cMSSM parameter space points generated with SPheno and micrOMEGAS
<p>These two datasets were produced to be used in two lectures on Machine Learning for SUSY Model Building taught in <a href="https://indico.cern.ch/event/1214657/">pre-SUSY 2023 summer school</a> in Southampton. The code used to generate and to analyse these data can be found <a href="https://gitlab.com/miguel.romao/ml-for-model-building-susy-2023">here</a>.</p> <p>The datasets are as following:</p> <ul> <li>1 million points generated using SPheno only (so no Dark Matter relic density) for the cMSSM with the physical parameters randomly sampled from the table bellow. The columns are <ul> <li>'m0', 'm12', 'A0', 'tanb': the four physical parameters of the theory</li> <li>'idx': an utility identifier used during generation, can/should be ignored</li> <li>The flattened SPheno outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns 'MINPAR_1', 'MINPAR_2', 'MINPAR_3', 'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.</li> </ul> </li> <li>10 thousand points generated using SPheno, and which spectrum outputs was then fed to micrOMEGAS (MSSM model configured to accept low-scale slha files as input), with the physical parameters randomly sampled from the same table bellow. The columns are: <ul> <li>The same as above, in addition to</li> <li> 'Omega', 'dm_spin', 'dm_mass' obtained from the micrOMEGAS output, representing Dark Matter relic density, Dark Matter spin, Dark Matter mass, respectively.</li> </ul> </li> </ul> <p>The full list of columns can be seen in `column_names.txt` file.</p> <p>Versions:</p> <ul> <li>SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found <a href="https://gitlab.com/lip_ml/blackboxbsm">here</a>.</li> <li>micrOMEGAS 5.3.41, with the MSSM model adapted for low-scale slha inputs.</li> </ul> <p>The datasets are provided in <a href="https://parquet.apache.org/">Apache `parquet`</a> format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use <a href="https://arrow.apache.org/docs/python/index.html">`pyarrow`</a>.</p> <p> </p>
Infiltration in the Region of the Palatine Ganglion Spheno by Ropivacaine on Postoperative Pain
ClinicalTrials.gov study NCT02821169. IPD Sharing: NO. Countries: 1. Publications: 1.
Correlation Between the SPhENo-Cardiograph™, a Seismocardiograph Device, and GE Vivid Q, an Echocardiograph, for Known Systemic Timing Intervals (STI)
ClinicalTrials.gov study NCT02636023. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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