Dataset from the paper "The boring history of GAIA BH3 from isolated binary evolution": IIB - simulation outcomes, different metallicities

This repository contains (a part) of the population synthesis outputs analysed in Iorio et al., 2024. In particular, it contains the simulation sets with different metallicities.

Content

Each tar file contains the outputs of a given simulation set from Iorio et al., 2024. The name of the files corresponds to the name of the set as reported in Table 1 of Iorio et al., 2024. The details of the sets can be found in Section 2.2 and Section 2.3 of Iorio et al., 2024. The outputs stored in this repository contain only the systems that satisfy the following conditions:

The two objects are in a  binary system

An object is a black hole and the other one is a star in any evolutionary phase prior to the ignition of the helium burning in the core

The two objects are not currently interacting except for wind mass transfer (no ongoing Roche-Lobe overflow, common envelope or merger).

The tar files contain three different files in gunzipped parquet format (https://parquet.apache.org):

Python scripts

analyse_sample.py: python script we used to produce the file BHMS.pq.gz and BHMS_ic.pq.gz analysing the SEVN outputs

create_aux.py: python script we used to produce the file BHMS_VSNCE.pq.gz by combining the SEVN log outputs with the BHMS_ic.pq.gz file

BHMS.pq.gz

 File containing all the systems satisfying the above condition.

Columns:

ID: unique id of the binary (Long integer)

name: unique name of the binary (Long integer)

BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) 

BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.

ms: mass of the star in Msun

ks: stellar evolutionary phase following the Hurley+2000 classification (integer)

ks_sevn:  stellar evolutionary phase following the Iorio+2023 classification (integer)

rs: stellar radius in Rsun

Ts: stellar effective temperature in Kelvin

Ls: stellar luminosity in Lsun

mbh: mass of the black hole in Msun

P: binary period in days

BEvent: Event corresponding to the SEVN output (see SEVN user guide)

Eccentricity: Eccentricity of the binary

Semimajor: Semimajor axis of the binary in Rsun

bh3dist: "Relative" Euclidean distance of the system from the Gaia BH3 properties:

                      bh3dist =     sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )

      Notice-1: in this file each system (identified by ID and name) can appear more than once. Indeed this file contains all the outputs in which a system satisfy the condition BH-star condition discussed above.

      Notice-2: the dataframe is sorted based on the bh3dist column, so the first rows contains the objects that are closer to the properties of Gaia BH3 based on the bh3dist statistic.

      Notice-3: The file has been created by analysing the main SEVN outputs using the script analyse_sample.py included in this repository

BHMS_ic.pq.gz

File containing  the initial conditions of all the systems included in the file BHMS.pq.gz

Columns:

Info from BHMS.pq.gz:

these columns report the same information as in the BHMS.pq.gz file, but each system (identified by ID and name) appears just once (the  occurrence from the BHMS.pq.gz  with the minimum bh3dist)

ID: unique id of the binary (long integer)

name: unique name of the binary (long integer)

BWorldtime: simulation time in Myr (all the simulations start at BWorldtime=0) 

BTimestep: timestep of the simulation corresponding to the current binary properties in Myr. In this time range, the binary properties can be considered constant.

ms: mass of the star in Msun

ks: stellar evolutionary phase following the Hurley+2000 classification (integer)

ks_sevn:  stellar evolutionary phase following the Iorio+2023 classification (integer)

rs: stellar radius in Rsun

Ts: stellar effective temperature in Kelvin

Ls: stellar luminoisity in Lsun

mbh: mass of the black hole in Msun

P: binary period in days

BEvent: Event corresponding to the SEVN output (see SEVN user guide)

Eccentricity: Eccentricity of the binary

Semimajor: Semimajor axis of the binary in Rsun

bh3dist: "Relative" Eucledian distance of the system from the Gaia BH3 properties:

              bh3dist =     sqrt( ((ms-ms_GBH3)/(ms_GBH3))**2 + ((mbh-mbh_GBH3)/(mbh_GBH3))**2 + ((P-P_GBH3)/(P_GBH3))**2 + ((ecc-ecc_GBH3)/(ecc_GBH3))**2 )

Initial properties:

mzams_0: Initial (ZAMS) mass of the black hole  in Msun

mzams_1: Initial (ZAMS) mass of the star  in Msun

Semimajor_ini: Initial semimajor axis of the binary in Rsun 

Eccentricity_ini: Initial eccentricity axis of the binary 

seed: Random seed used in the SEVN simulation

P_ini: Intial period of the binary in days 

       Notice: The file has been created by analysing the main SEVN outputs using the script analyse_sample.py included in this repository

BHMS_VSNCE.pq.gz

A file containing additional information about the evolutionary events of the systems stored in BHMS.pq.gz

ID: unique id of the binary (long integer)

name: unique name of the binary (long integer)

time_SN: formation time of the black hole (BH) in Myr (all the simulations start from time=0)

Vcom: Velocity magnitude of the center of mass after the formation of the BH (before the formation Vcom=0 is assumed)

time_CE_preSN: time of the last CE before the BH formation (if empty no CE before BH formation)

time_CE_postSN: time of the first CE after the BH formation (if empty no CE after BH formation)

time_RL_preSN: time of the last Roche-Lobe overflow before the BH formation (if empty no RLO before BH formation)

time_RL_postSN: time of the first Roche-Lobe overflow after the BH formation (if empty no RLO before BH formation)

Note: The file has been created by  combining the file BHMS_ic.pq.gz with the main SEVN outputs using the script create_aux.py included in this repository

How to read the files

The parquet files can be easily read in Python by using the Pandas module:

import pandas as pd

df_bhms=pd.read_parquet("BHMS.pq.gz")
df_bhms_ic=pd.read_parquet("BHMS_ic.pq.gz")
df_bhms_vsnce=pd.read_parquet("BHMS_VSNCE.pq.gz")

Notes

The code used in Iorio et al., 2024 is SEVN (Iorio et al., 2023) version 2.10.1 (commit a4753f11).