Binary population synthesis - arsenal-popsynth/arsenal_gear GitHub Wiki

References about binary population synthesis models go here. Currently, many codes including binaries are based on the observations compiled by Moe & di Stefano (2017). There is a detailed discussion in Stanway & Elridge (2023) of the stellar mass above which a population fully samples a distribution of binaries; this may be useful to decide/recommend when pre-computed populations should be used versus when an IMF and distribution of binaries should be sampled.

Editorial note (Claude): Looking over the list of codes, I think our default/recommended choice should be BPASS for a fully sampled population and POSYDON for populations where we evolve a list of stars/systems.

POSYDON

Output optimized for compact objects and gravitational waves
Reference paper: Fragos et al. (2023), Andrews et al. (2024, pre-print) (in preparation) includes different metallicities
User manual: POSYDON v1.1.0

Single star model: Interpolation from grid of MESA models

Pre-computed SSP: The population from the code release paper nominally available (see website) but the page is under construction. POSYDON can also be used to generate our own population from the available models. The default parameters are described below.
IMF: Kroupa (2001) IMF between 7 and 150 MSun for the primary
Binary fraction: N/A (it may implicitly be set to 100%, to check if we use available data)
Mass ratio distribution: Uniform from 0 to 1 (down to 0.35 MSun)
Period distribution: Power-law over P [days] = 0.7 - 6105 scaling as P-0.45 (Sana et al. 2013)
Eccentricity distribution: Circular orbits only

Luminosity: Bolometric luminosity from the stars objects
Mass loss rate: From change in mass (?)

  • Use the MESA Dutch scheme for stars initially more massive than 8 MSun or hotter than 12,000 K: De Jager et al. (1988) for Teff < 10,000 K, Vink et al. (2001) for Teff > 11,000 K, and interpolating between

SN rate: Can get SNe from events of the binary objects. It may be possible to get them for a full population.
Yields: Surface and centre H, He, 12C, 14N and 16O abundances. It may be possible to get more yields from the MESA models, TBD.

BPASS

Output optimized for SEDs and stellar populations
Reference paper: Eldridge, Stanway et al. (2017), Stanway & Eldridge (2018) for v2.2 (which incorporates the mass-dependent binary fraction and properties from Moe & di Stefano 2017), and Byrne et al. (2022) for v2.3
Webiste: https://bpass.auckland.ac.nz/index.html
User manual: BPASS v2.1
BPASS data can be accessed directly from Python with Hoki

Single star model: STARS, originally based on the 1D models by Eggleton 1971. The version used in BPASS is from Elridge, Izzard & Tout (2008) (I will add details - Claude)
During the evolution of the primary, the companion is evolved with the rapid single star stellar evolution models of Hurley et al. (2002).

Pre-computed stellar population: Yes
See overview in table below, taken from the BPASS v2.1 reference paper. For each initial Z, X and Y are calculated from X = 0.75 − 2.5Z and Y = 0.25 + 1.5Z.

Screenshot 2024-11-03 at 13 22 07

IMF: See above, new IMFs also available from v2.2 onwards (see [Stanway & Eldridge 2018])
Binary fraction: Using the mass-dependent binary fraction and properties from Moe & di Stefano (2017) from v2.2 onwards; for previous versions, uniform binary fraction of 100% that could be diluted with a single star population.
Mass ratio distribution: Mass-dependent q distribution (only up to 0.9, with excess twin fraction at 0.9)
Period distribution: Mass-dependent P distribution over log (P [days]) = 0 - 4
Eccentricity distribution: Circular orbits only

Luminosity: SED, ionizing flux, and broadband colours

  • For SED, returns total flux in each wavelength bin (gridded from 1 to 100 000 A in 1 A bins) in units of LSun, for stellar populations of ages 1 Myr to 100 Gyr in increments of log (age/yr) = 0.1, assuming a co-eval stellar population of total mass 10^6 MSun (from v2.2 paper)

Mass loss rate: Wind mass loss rates for OB and WR winds, no AGB winds

SN rate: Core-collapse SN rate; also provides rates from type Ia SNe but state that they are not the most precise

  • Get energy injected and yields from SN for each age bin
  • CCSNe take place if the CO core mass is above 1.38 MSun and the total stellar mass above 1.5 MSun
  • Remnant mass calculated from the amount of mass than can be unbound with 10^51 ergs of energy; NSs are assumed to have a mass of 1.4 MSun and BHs to have a mass above 3 MSun
  • If the He core mass is between 64 and 133 MSun, a PISN SNe is assumed and there is no remnant (see Heger & Woolsey 2002)

Yields: X, Y , Z yields from SN and winds (not sure what model used for yields - to check)

COMPAS

Output optimized for compact objects and gravitational waves
Reference paper: Team COMPAS: J. Riley et al. (2022)
User manual: COMPAS

Single star model: Based on SSE (Hurley et al. 2000), and extrapolating to larger masses

Pre-computed SSP: Yes (?) - Single system pre-computed models
*Model defaults are described in Table 1 of the reference paper
IMF: Kroupa (2001) IMF between 7 and 150 MSun for the primary
Binary fraction: 100% (single stars also available in the non-default, can be used to dilute)
Mass ratio distribution: Uniform from 0 to 1 (down to 0.1 MSun)
Period distribution: Log-uniform in semi-major axis, over a [au] = 0.01-1000
Eccentricity distribution: Initially circular orbits only for default, other distributions available

"COMPAS calculates and records properties of the stars and/or binary systems such as the ages, masses, stellar radii, effective temperatures, velocities, eccentricities, and separations. The user can specify which properties are recorded and when during the simulation they are reported."
Luminosity: Bolometric luminosity
Mass loss rate: For SSE, from change in mass. For BSE, from output file.

  • Wind mass loss rates from Vink et al. (2000, 2001)

SN rate: Can be inferred from evolved population
Yields: None

binary_c

Output optimized for yields
Reference paper: Izzard et al. 2004, Izzard et al. 2006
User manual: binary_c
binary_c can now be accessed through Python

Single star model: Based on a modified version of SSE (Hurley et al. 2000)

Pre-computed SSP: No (?), but uses SSE/BSE, and should be fast to run

Luminosity: Bolometric luminosity
Mass loss rate: From change in mass
SN rate: -
Yields: Full nucleosynthesis

SeBa

Reference paper: Official references are Portegies Zwart & Verbunt (1996) and Toonen, Nelemans & Portegies Zwart (2013), however there are several more recent additions to the code
User manual: README

Single star model: Based on Eggleton’s models

Pre-computed SSP: No

Luminosity: Bolometric luminosity
Mass loss rate: From change in mass

  • Need to parse source code for underlying prescription; for example, the Vink et al. (2000) prescription is used for the winds, but with a pre-factor of 1/3 to account for wind-clumping, following Björklund et al. 2020 SN rate: From change in stellar type
    Yields: None

COSMIC

Output optimized for compact objects and gravitational waves
Reference paper: Breivik et al. (2020)
User manual: Github
COSMIC is a python package

Single star model: SSE (Hurley et al. 2000), with a modified version of BSE (Hurley et al. 2002) for binary evolution

Pre-computed SSP: No

Luminosity: Bolometric luminosity for each star
Mass loss rate: From change in mass (?)
SN rate: Can get SN for each system from log
Yields: None

METISSE

Output optimized for compact objects and gravitational waves
Reference paper: Agrawal et al. 2023
User manual: Website
METISSE is not a binary evolution/binary population synthesis code, but a framework to use modern stellar tracks with BSE-based codes (it appears to work with BSE and COSMIC so far)

Single star model: Agrawal et al. 2020

Pre-computed SSP: No

BSE

Reference paper: Hurley et al. 2002
Website: Download from Jarrod HUrley's website I couldn't find any official user guide, but here is a quickstart guide by Michela Mapelli

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