BPASS pair-instability supernova tagging

<p>These data contain the tagging of individual models from the BPASS population for different pair-instability supernova prescriptions. The data is stored as a pandas DataFrame with the identifier: `rates`.</p> <pre><code>df = pd.read_hdf(f'22_3_2024_{MET}.h5', 'rates')</code></pre> <p>The following data columns are available:</p> <ul> <li>filenames: the BPASS model name. Note: some merger&nbsp;models in the v2.2 release failed before reaching/avoiding the PISN regime. These have been rerun and often do not lead to a PISN.</li> <li>model_imf: the weight of the model; directly from BPASS</li> <li>types: the type of model (merger, primary, secondary, single, effectively single)</li> <li>mixed_imf:&nbsp;the weight of the model for secondaries; directly from BPASS</li> <li>mixed_age: the start age of a secondary model due to rejuvenation; directly from BPASS</li> <li>total_mass: the total mass of the PISN progenitor model at the end of the model</li> <li>helium_mass: the total helium core mass&nbsp;of the PISN progenitor model at the end of the model</li> <li>co_mass:&nbsp;the total carbon-oxygen core mass of the PISN progenitor model at the end of the model</li> </ul> <p>The remaining columns are "booleans" for the different PISN prescriptions:</p> <ul> <li>standard: The fiducial model used in Briel et al. (2023)</li> <li>old_bpass: The original BPASS tagging used in Briel et al. (2022)</li> <li>CO_only: Tagging with only the CO core &gt;= 60 Msun as a limit</li> <li>merchant: PISN limits from Marchant et al. (2019)</li> <li>noH: standard tagging but no hydrogen present.</li> <li>&nbsp;['0', '1', '2', '3', '4', '5']: different formation channels, shifted one up from the BPASS model identification</li> <li>['R150', 'R175', 'R200', 'R225', 'R250', 'He70', 'He80', 'He90', 'He100', 'He110', 'He120', 'He130']: tagging based on each light curve.</li> </ul> <p>This can be used to get the metallicity bias functions (number of events per Msun over metallicity) or to create new taggings.</p> <p>For example, below we use both the existing tagging with `model_imf` weights to get the rate of PISN at each metallicity and we create a new tagging `shifted_up`, which is a tagging where the PISN limit is shifted upwards.</p> <p>&nbsp;</p> <pre><code>import pandas as pd import numpy as np from hoki.constants import BPASS_METALLICITIES # from Kaasen et al. 2008 paper envelopes = np.array([70.9, 79.4, 84.4, 96.8, 112.3, 0, 0, 0, 0, 0, 0, 0]) helium_cores = np.array([72.0, 84.4, 96.7, 103.5, 124.0, 70, 80, 90, 100, 110, 120, 130]) curve_models = [ 'R150', 'R175', 'R200', 'R225', 'R250', 'He70', 'He80', 'He90', 'He100', 'He110', 'He120', 'He130'] bias_functions = pd.DataFrame({'standard':np.zeros(13), 'standard_old':np.zeros(13), 'CO_only':np.zeros(13), 'marchant':np.zeros(13), 'shift_up':np.zeros(13), 'old_BPASS':np.zeros(13), 'He_only':np.zeros(13), 'primary':np.zeros(13), 'secondary':np.zeros(13), 'QHE':np.zeros(13), 'merger':np.zeros(13), 'single':np.zeros(13), 'R150':np.zeros(13), 'R175':np.zeros(13), 'R200':np.zeros(13), 'R225':np.zeros(13), 'R250':np.zeros(13), 'He70':np.zeros(13), 'He80':np.zeros(13), 'He90':np.zeros(13), 'He100':np.zeros(13), 'He110':np.zeros(13), 'He120':np.zeros(13), 'He130':np.zeros(13)}) bias_functions.index = BPASS_METALLICITIES for MET in BPASS_METALLICITIES[:-4]: print(MET) df = pd.read_hdf(f'PISN_data_{MET}.h5', 'rates') for i in curve_models: mask = (df['co_mass'] &gt;=60) &amp; (df['helium_mass'] &lt; 133) &amp; (df[i] == 1) bias_functions.loc[MET, i] = np.sum(df[mask]['model_imf'])/1e6 mask = (df['co_mass'] &gt;=60) &amp; (df['helium_mass'] &lt; 133) bias_functions.loc[MET, 'standard'] = np.sum(df[mask]['model_imf'])/1e6 bias_functions.loc[MET, 'standard_old'] = np.sum(df[df['standard'] == 1]['model_imf'])/1e6 mask = (df['helium_mass'] &gt;=64) &amp; (df['helium_mass'] &lt; 133) bias_functions.loc[MET, 'old_BPASS'] = np.sum(df[mask]['model_imf'][mask])/1e6 mask = (df['co_mass'] &gt;= 60) bias_functions.loc[MET, 'CO_only'] = np.sum(df[df['CO_only'] == 1]['model_imf'][mask])/1e6 mask = (df['helium_mass'] &gt;=60.8) &amp; (df['helium_mass'] &lt; 124) bias_functions.loc[MET, 'marchant'] = np.sum(df[df['marchant'] == 1]['model_imf'][mask])/1e6 bias_functions.loc[MET, 'noH'] = np.sum(df[df['noH'] == 1]['model_imf'])/1e6 mask = (df['helium_mass'] &gt;=90) &amp; (df['helium_mass'] &lt; 180) bias_functions.loc[MET, 'shift_up'] = np.sum(df[mask]['model_imf'])/1e6 mask = np.isclose(df['total_mass'] - df['helium_mass'], 0, atol=0.1) &amp; (df['helium_mass'] &lt; 133) &amp; (df['co_mass'] &gt;=60) bias_functions.loc[MET, 'He_only'] = np.sum(df[mask]['model_imf'])/1e6 mask = (df['co_mass'] &gt;=60) &amp; (df['helium_mass'] &lt; 133) bias_functions.loc[MET, 'merger'] = np.sum(df[(df['types'] == 0) &amp; mask]['model_imf'])/1e6 bias_functions.loc[MET, 'single'] = np.sum(df[((df['types'] == -1) | (df['types'] == 3)) &amp; mask]['model_imf'])/1e6 bias_functions.loc[MET, 'primary'] = np.sum(df[(df['types'] == 1) &amp; mask]['model_imf'])/1e6 bias_functions.loc[MET, 'secondary'] = np.sum(df[(df['types'] == 2) &amp; mask]['model_imf'])/1e6 bias_functions.loc[MET, 'QHE'] = np.sum(df[(df['types'] == 4) &amp; mask]['model_imf'])/1e6</code></pre>