An official website of the United States government
This file contains data tables representing gravitational wave backgrounds (GWBs) produced by Nambu-Goto cosmic strings evolved under numerical gravitational backreaction. The GWBs were produced using the methodology of "More accurate gravitational wave backgrounds from cosmic strings" [to appear], by the same authors as this dataset. The file is organized in three columns: The base-10 logarithm of the string coupling to gravity, G\mu. The range is from -8 to -22 in steps of -0.1. The frequency in Hz, f. The range is from 10^(-12) Hz to 10^5 Hz in multiplicative steps of 10^(0.02). The critical energy density fraction in gravitational waves scaled by the dimensionless Hubble constant squared, \Omega_{gw} h^2.
more »
« less
Dataset from "Numerical gravitational backreaction on cosmic string loops from simulation"
This file contains a data table representing the average power spectrum, P_n, of Nambu-Goto cosmic strings evolved under numerical gravitational backreaction. The power spectra and the methods used to produce them are reported on in "Numerical gravitational backreaction on cosmic string loops from simulation" [to appear], by the same authors as this dataset. See Fig. 5 of that paper for a visualization. The file is organized in three columns: The fraction of evaporation, chi. The range is from 0.0 to 0.7 in steps of 0.1. The mode number, n. The range is from 2^0 to 2^39 in multiplicative steps of 2. The logarithmically binned elements of the power spectrum, nP_n. Bin edges are 2^i to 2^(i+1)-1 for i from 0 to 39.
more »
« less
- Award ID(s):
- 2412818
- PAR ID:
- 10598472
- Publisher / Repository:
- Zenodo
- Date Published:
- Edition / Version:
- 1.0.0-alpha
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution 4.0 International
- Institution:
- Tufts University
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
{"Abstract":["This is an extracted data product for radar bed reflectivity from Whillans Ice Plain, West Antarctica. The original data are hosted by the Center for Remote Sensing of Ice Sheets (CReSIS; see associated citation below). The files here can be recalculate and are meant to be used within a set of computational notebooks here:https://doi.org/10.5281/zenodo.10859135\n\nThere are two csv files included here, each structured as a Pandas dataframe. You can load them in Python like:df = pd.read_csv('./Picked_Bed_Power.csv')\n\nThe first file, 'Picked_Bed_Power.csv' is the raw, uncorrected power from the radar image at the bed pick provided by CReSIS. There are also other useful variables for georeferencing, flight attributes, etc.\n\nThe second file, 'Processed_Reflectivity.csv' is processed from the first file. Processing includes: 1) a spreading correction; 2) an attenuation correction; and, 3) a power adjustment flight days based on compared power at crossover points. This file also has identifiers for regions including "grounded ice", "ungrounded ice", and "subglacial lakes"."]}more » « less
-
{"Abstract":["MCMC chains for the GWB analyses performed in the paper "The NANOGrav 15 yr Data Set: Search for Signals from New Physics<\/em>". <\/p>\n\nThe data is provided in pickle format. Each file contains a NumPy array with the MCMC chain (with burn-in already removed), and a dictionary with the model parameters' names as keys and their priors as values. You can load them as<\/p>\n\nmore » « less
with open ('path/to/file.pkl', 'rb') as pick:\n temp = pickle.load(pick)\n\n params = temp[0]\n chain = temp[1]<\/code>\n\nThe naming convention for the files is the following:<\/p>\n\nigw<\/strong>: inflationary Gravitational Waves (GWs)<\/li>sigw: scalar-induced GWs\n\tsigw_box<\/strong>: assumes a box-like feature in the primordial power spectrum.<\/li>sigw_delta<\/strong>: assumes a delta-like feature in the primordial power spectrum.<\/li>sigw_gauss<\/strong>: assumes a Gaussian peak feature in the primordial power spectrum.<\/li><\/ul>\n\t<\/li>pt: cosmological phase transitions\n\tpt_bubble<\/strong>: assumes that the dominant contribution to the GW productions comes from bubble collisions.<\/li>pt_sound<\/strong>: assumes that the dominant contribution to the GW productions comes from sound waves.<\/li><\/ul>\n\t<\/li>stable: stable cosmic strings\n\tstable-c<\/strong>: stable strings emitting GWs only in the form of GW bursts from cusps on closed loops.<\/li>stable-k<\/strong>: stable strings emitting GWs only in the form of GW bursts from kinks on closed loops.<\/li>stable<\/strong>-m<\/strong>: stable strings emitting monochromatic GW at the fundamental frequency.<\/li>stable-n<\/strong>: stable strings described by numerical simulations including GWs from cusps and kinks.<\/li><\/ul>\n\t<\/li>meta: metastable cosmic strings\n\tmeta<\/strong>-l<\/strong>: metastable strings with GW emission from loops only.<\/li>meta-ls<\/strong> metastable strings with GW emission from loops and segments.<\/li><\/ul>\n\t<\/li>super<\/strong>: cosmic superstrings.<\/li>dw: domain walls\n\tdw-sm<\/strong>: domain walls decaying into Standard Model particles.<\/li>dw-dr<\/strong>: domain walls decaying into dark radiation.<\/li><\/ul>\n\t<\/li><\/ul>\n\nFor each model, we provide four files. One for the run where the new-physics signal is assumed to be the only GWB source. One for the run where the new-physics signal is superimposed to the signal from Supermassive Black Hole Binaries (SMBHB), for these files "_bhb" will be appended to the model name. Then, for both these scenarios, in the "compare" folder we provide the files for the hypermodel runs that were used to derive the Bayes' factors.<\/p>\n\nIn addition to chains for the stochastic models, we also provide data for the two deterministic models considered in the paper (ULDM and DM substructures). For the ULDM model, the naming convention of the files is the following (all the ULDM signals are superimposed to the SMBHB signal, see the discussion in the paper for more details)<\/p>\n\nuldm_e<\/strong>: ULDM Earth signal.<\/li>uldm_p: ULDM pulsar signal\n\tuldm_p_cor<\/strong>: correlated limit<\/li>uldm_p_unc<\/strong>: uncorrelated limit<\/li><\/ul>\n\t<\/li>uldm_c: ULDM combined Earth + pulsar signal direct coupling \n\tuldm_c_cor<\/strong>: correlated limit<\/li>uldm_c_unc<\/strong>: uncorrelated limit<\/li><\/ul>\n\t<\/li>uldm_vecB: vector ULDM coupled to the baryon number\n\tuldm_vecB_cor:<\/strong> correlated limit<\/li>uldm_vecB_unc<\/strong>: uncorrelated limit <\/li><\/ul>\n\t<\/li>uldm_vecBL: vector ULDM coupled to B-L\n\tuldm_vecBL_cor:<\/strong> correlated limit<\/li>uldm_vecBL_unc<\/strong>: uncorrelated limit<\/li><\/ul>\n\t<\/li>uldm_c_grav: ULDM combined Earth + pulsar signal for gravitational-only coupling\n\tuldm_c_grav_cor: correlated limit\n\t\tuldm_c_cor_grav_low<\/strong>: low mass region <\/li>uldm_c_cor_grav_mon<\/strong>: monopole region<\/li>uldm_c_cor_grav_low<\/strong>: high mass region<\/li><\/ul>\n\t\t<\/li>uldm_c_unc<\/strong>: uncorrelated limit\n\t\tuldm_c_unc_grav_low<\/strong>: low mass region <\/li>uldm_c_unc_grav_mon<\/strong>: monopole region<\/li>uldm_c_unc_grav_low<\/strong>: high mass region<\/li><\/ul>\n\t\t<\/li><\/ul>\n\t<\/li><\/ul>\n\nFor the substructure (static) model, we provide the chain for the marginalized distribution (as for the ULDM signal, the substructure signal is always superimposed to the SMBHB signal)<\/p>"]} -
{"Abstract":["Data description \nThis dataset presents the raw and augmented data that were used to train the machine learning (ML) models for classification of printing outcome in projection two-photon lithography (P-TPL). P-TPL is an additive manufacturing technique for the fabrication of cm-scale complex 3D structures with features smaller than 200 nm. The P-TPL process is further described in this article: \u201cSaha, S. K., Wang, D., Nguyen, V. H., Chang, Y., Oakdale, J. S., and Chen, S.-C., 2019, "Scalable submicrometer additive manufacturing," Science, 366(6461), pp. 105-109.\u201d This specific dataset refers to the case wherein a set of five line features were projected and the printing outcome was classified into three classes: \u2018no printing\u2019, \u2018printing\u2019, \u2018overprinting\u2019. \n \nEach datapoint comprises a set of ten inputs (i.e., attributes) and one output (i.e., target) corresponding to these inputs. The inputs are: optical power (P), polymerization rate constant at the beginning of polymer conversion (kp-0), radical quenching rate constant (kq), termination rate constant at the beginning of polymer conversion (kt-0), number of optical pulses, (N), kp exponential function shape parameter (A), kt exponential function shape parameter (B), quantum yield of photoinitiator (QY), initial photoinitiator concentration (PIo), and the threshold degree of conversion (DOCth). The output variable is \u2018Class\u2019 which can take these three values: -1 for the class \u2018no printing\u2019, 0 for the class \u2018printing\u2019, and 1 for the class \u2018overprinting\u2019. \n\nThe raw data (i.e., the non-augmented data) refers to the data generated from finite element simulations of P-TPL. The augmented data was obtained from the raw data by (1) changing the DOCth and re-processing a solved finite element model or (2) by applying physics-based prior process knowledge. For example, it is known that if a given set of parameters failed to print, then decreasing the parameters that are positively correlated with printing (e.g. kp-0, power), while keeping the other parameters constant would also lead to no printing. Here, positive correlation means that individually increasing the input parameter will lead to an increase in the amount of printing. Similarly, increasing the parameters that are negatively correlated with printing (e.g. kq, kt-0), while keeping the other parameters constant would also lead to no printing. The converse is true for those datapoints that resulted in overprinting. \n\nThe 'Raw.csv' file contains the datapoints generated from finite element simulations, the 'Augmented.csv' file contains the datapoints generated via augmentation, and the 'Combined.csv' file contains the datapoints from both files. The ML models were trained on the combined dataset that included both raw and augmented data."]}more » « less
-
This dataset contains the data used in the paper (arXiv:2301.02398) on the estimation and subtraction of glitches in gravitational wave data using an adaptive spline fitting method called SHAPES . Each .zip file corresponds to one of the glitches considered in the paper. The name of the class to which the glitch belongs (e.g., "Blip") is included in the name of the corresponding .zip file (e.g., BLIP_SHAPESRun_20221229T125928.zip). When uncompressed, each .zip file expands to a folder containing the following. An HDF5 file containing the Whitened gravitational wave (GW) strain data in which the glitch appeared. The data has been whitened using a proprietary code. The original (unwhitened) strain data file is available from gwosc.org. The name of the original data file is the part preceding the token '__dtrndWhtnBndpss' in the name of the file.A JSON file containing information pertinent to the glitch that was analyzed (e.g., start and stop indices in the whitened data time series).A set of .mat files containing segmented estimates of the glitch as described in the paper. A MATLAB script, plotglitch.m, has been provided that plots, for a given glitch folder name, the data segment that was analyzed in the paper. Another script, plotshapesestimate.m, plots the estimated glitch. These scripts require the JSONLab package.more » « less
