GEAR model

GEAR’s model are mainly described in Revaz & Jablonka. This model can be selected with the configuration option --with-subgrid=GEAR and run with the option --gear. A few examples exist and can be found in examples/GEAR.

Pressure Floor

In order to avoid the artificial collapse of unresolved clumps, a minimum in pressure is applied to the particles. This additional pressure can be seen as the pressure due to unresolved hydrodynamics turbulence and is given by:

$P_\textrm{Jeans} = \frac{\rho}{\gamma} \frac{4}{\pi} G h^2 \rho N_\textrm{Jeans}^{2/3}$

where $$\rho$$ is the density, $$\gamma$$ the adiabatic index, $$G$$ is the gravitational constant, $$h$$ the kernel support and $$N_\textrm{Jeans}$$ (GEARPressureFloor:jeans_factor in the parameter file) is the number of particle required in order to resolve a clump.

This must be directly implemented into the hydro schemes, therefore only a subset of schemes (Gadget-2, SPHENIX and Pressure-Energy) have the floor available. In order to implement it, you need equation 12 in Hopkins 2013:

$m_i \frac{\mathrm{d}v_i}{\mathrm{d}t} = - \sum_j x_i x_j \left[ \frac{P_i}{y_i^2} f_{ij} \nabla_i W_{ij}(h_i) + \frac{P_j}{y_j^2} f_{ji} \nabla_j W_{ji}(h_j) \right]$

and simply replace the $$P_i, P_j$$ by the pressure with the floor (when the pressure is below the floor). Here the $$x, y$$ are simple weights that should never have the pressure floor included even if they are related to the pressure (e.g. pressure-entropy).

GEARPressureFloor:
jeans_factor: 10.       # Number of particles required to suppose a resolved clump and avoid the pressure floor.


Cooling: Grackle

Grackle is a chemistry and cooling library presented in B. Smith et al. 2017 (do not forget to cite if used). Four different modes are available: equilibrium, 6 species network (H, H$$^+$$, e$$^-$$, He, He$$^+$$ and He$$^{++}$$), 9 species network (adds H$$^-$$, H$$_2$$ and H$$_2^+$$) and 12 species (adds D, D$$^+$$ and HD). Following the same order, the swift cooling options are grackle_0, grackle_1, grackle_2 and grackle_3 (the numbers correspond to the value of primordial_chemistry in Grackle). It also includes some metal cooling (on/off with GrackleCooling:with_metal_cooling), self-shielding methods and UV background (on/off with GrackleCooling:with_UV_background). In order to use the Grackle cooling, you will need to provide a HDF5 table computed by Cloudy (GrackleCooling:cloudy_table).

When starting a simulation without providing the different element fractions in the non equilibrium mode, the code supposes an equilibrium and computes them automatically. The code uses an iterative method in order to find the correct initial composition and this method can be tuned with two parameters. GrackleCooling:max_steps defines the maximal number of steps to reach the convergence and GrackleCooling:convergence_limit defines the tolerance in the relative error.

In order to compile SWIFT with Grackle, you need to provide the options with-chemistry=GEAR and with-grackle=$GRACKLE_ROOT where $GRACKLE_ROOT is the root of the install directory (not the lib).

You will need a Grackle version later than 3.1.1. To compile it, run the following commands from the root directory of Grackle: ./configure; cd src/clib. Update the variables LOCAL_HDF5_INSTALL and MACH_INSTALL_PREFIX in the file src/clib/Make.mach.linux-gnu. Finish with make machine-linux-gnu; make && make install. Note that we require the 64 bit float version of Grackle, which should be the default setting. (The precision can be set while compiling grackle with make precision-64). If you encounter any problem, you can look at the Grackle documentation

You can now provide the path given for MACH_INSTALL_PREFIX to with-grackle.

In the parameters file, a few different parameters are available. GrackleCooling:redshift defines the redshift to use for the UV background (for cosmological simulation, it must be set to -1 in order to use the simulation’s redshift) and GrackleCooling:provide_*_heating_rates can enable the computation of user provided heating rates (such as with the radiative transfer) in either volumetric or specific units.

For the feedback, it can be made more efficient by turning off the cooling during a few Myr (GrackleCooling:thermal_time_myr) for the particles touched by a supernovae.

The self shielding method is defined by GrackleCooling:self_shielding_method where 0 means no self shielding, > 0 means a method defined in Grackle (see Grackle documentation for more information) and -1 means GEAR’s self shielding that simply turn off the UV background when reaching a given density (GrackleCooling:self_shielding_threshold_atom_per_cm3).

GrackleCooling:
cloudy_table: CloudyData_UVB=HM2012.h5       # Name of the Cloudy Table (available on the grackle bitbucket repository)
with_UV_background: 1                        # Enable or not the UV background
redshift: 0                                  # Redshift to use (-1 means time based redshift)
with_metal_cooling: 1                        # Enable or not the metal cooling
provide_volumetric_heating_rates: 0          # (optional) User provide volumetric heating rates
provide_specific_heating_rates: 0            # (optional) User provide specific heating rates
max_steps: 10000                             # (optional) Max number of step when computing the initial composition
convergence_limit: 1e-2                      # (optional) Convergence threshold (relative) for initial composition
thermal_time_myr: 5                          # (optional) Time (in Myr) for adiabatic cooling after a feedback event.
self_shielding_method: -1                    # (optional) Grackle (1->3 for Grackle's ones, 0 for none and -1 for GEAR)
self_shielding_threshold_atom_per_cm3: 0.007 # Required only with GEAR's self shielding. Density threshold of the self shielding


Star formation

The star formation is done in two steps: first we check if a particle is in the star forming regime and then we use a stochastic approach to transform the gas particles into stars.

A particle is in the star forming regime if:
• The velocity divergence is negative ($$\nabla\cdot v < 0$$),

• The temperature is lower than a threshold ($$T < T_t$$ where $$T_t$$ is defined with GEARStarFormation:maximal_temperature),

• The particle reaches the pressure floor ($$\rho > \frac{\pi}{4 G N_\textrm{Jeans}^{2/3} h^2}\frac{\gamma k_B T}{\mu m_p}$$ where $$N_\textrm{Jeans}$$ is defined in the pressure floor).

A star will be able to form if a randomly drawn number is below $$\frac{m_g}{m_\star}\left(1 - \exp\left(-c_\star \Delta t / t_\textrm{ff}\right)\right)$$ where $$t_\textrm{ff}$$ is the free fall time, $$\Delta t$$ is the time step of the particle and $$c_\star$$ is the star formation coefficient (GEARStarFormation:star_formation_efficiency), $$m_g$$ the mass of the gas particle and $$m_\star$$ the mass of the possible future star. The mass of the star is computed from the average gas mass in the initial conditions divided by the number of possible stars formed per gas particle (GEARStarFormation:n_stars_per_particle). When we cannot have enough mass to form a second star (defined with the fraction of mass GEARStarFormation:min_mass_frac), we fully convert the gas particle into a stellar particle. Once the star is formed, we move it a bit in a random direction and fraction of the smoothing length in order to avoid any division by 0.

Currently, only the following hydro schemes are compatible: SPHENIX and Gadget2. Implementing the other hydro schemes is not complicated but requires some careful thinking about the cosmological terms in the definition of the velocity divergence (comoving vs non comoving coordinates and if the Hubble flow is included or not).

GEARStarFormation:
star_formation_efficiency: 0.01   # star formation efficiency (c_*)
maximal_temperature:  3e4         # Upper limit to the temperature of a star forming particle
n_stars_per_particle: 4           # Number of stars that an hydro particle can generate
min_mass_frac: 0.5                # Minimal mass for a stellar particle as a fraction of the average mass for the stellar particles.


Chemistry

In the chemistry, we are using the smoothed metallicity scheme that consists in using the SPH to smooth the metallicity of each particle over the neighbors. It is worth to point the fact that we are not exchanging any metals but only smoothing it. The parameter GEARChemistry:initial_metallicity set the (non smoothed) initial mass fraction of each element for all the particles and GEARChemistry:scale_initial_metallicity use the feedback table to scale the initial metallicity of each element according the Sun’s composition.

GEARChemistry:
initial_metallicity: 1         # Initial metallicity of the gas (mass fraction)
scale_initial_metallicity: 1   # Should we scale the initial metallicity with the solar one?


Feedback

The feedback is composed of a few different models:
• The initial mass function (IMF) defines the quantity of each type of stars,

• The lifetime of a star defines when a star will explode (or simply die),

• The supernovae of type II (SNII) defines the rates and yields,

• The supernovae of type Ia (SNIa) defines the rates and yields,

• The energy injection that defines how to inject the energy / metals into the particles.

Most of the parameters are defined inside a table (GEARFeedback:yields_table) but can be override with some parameters in the YAML file. I will not describe theses parameters more than providing them at the end of this section. Two different models exist for the supernovae (GEARFeedback:discrete_yields). In the continuous mode, we integrate the quantities over the IMF and then explodes a floating point number of stars (can be below 1 in some cases). In the discrete mode, we avoid the problem of floating points by rounding the number of supernovae (using a floor and randomly adding a supernovae depending on the fractional part) and then compute the properties for a single star at a time.

Initial mass function

GEAR is using the IMF model from Kroupa (2001). We have a difference of 1 in the exponent due to the usage of IMF in mass and not in number. We also restrict the mass of the stars to be inside $$[0.05, 50] M_\odot$$. Here is the default model used, but it can be easily adapted through the initial mass function parameters:

$\begin{split}\xi(m) \propto m^{-\alpha_i}\, \textrm{where}\, \begin{cases} \alpha_0 = 0.3,\, & 0.01 \leq m / M_\odot < 0.08, \\ \alpha_1 = 1.3,\, & 0.08 \leq m / M_\odot < 0.50, \\ \alpha_2 = 2.3,\, & 0.50 \leq m / M_\odot < 1.00, \\ \alpha_3 = 2.3,\, & 1.00 \leq m / M_\odot, \end{cases}\end{split}$

The lifetime of a star in GEAR depends only on two parameters: first its mass and then its metallicity.

$\begin{split}\log(\tau(m)) = a(Z) \log^2(m) + b(Z) \log(m) + c(Z) \\ \\ a(Z) = -40.110 Z^2 + 5.509 Z + 0.7824 \\ b(Z) = 141.929 Z^2 - 15.889 Z - 3.2557 \\ c(Z) = -261.365 Z^2 + 17.073 Z + 9.8661\end{split}$

where $$\tau$$ is the lifetime in years, $$m$$ is the mass of the star (in solar mass) and Z the metallicity of the star. The parameters previously given are the default ones, they can be modified in the parameters file.

Supernovae II

The supernovae rate is simply given by the number of stars massive enough that end their life at the required time.

$\dot{N}_\textrm{SNII}(t) = \int_{M_l}^{M_u} \delta(t - \tau(m)) \frac{\phi(m)}{m} \mathrm{d}m$

where $$M_l$$ and $$M_u$$ are the lower and upper mass limits for a star exploding in SNII, $$\delta$$ is the Dirac function and $$\phi$$ is the initial mass function (in mass).

The yields for SNII cannot be written in an analytical form, they depend on a few different tables that are based on the work of Kobayashi et al. (2000) and Tsujimoto et al. (1995).

Supernovae Ia

The supernovae Ia are a bit more complicated as they involve two different stars.

$\dot{N}_\textrm{SNIa}(t) = \left( \int_{M_{p,l}}^{M_{p,u}} \frac{\phi(m)}{m} \mathrm{d}m \right) \sum_i b_i \int_{M_{d,l,i}}^{M_{d,u,i}} \delta(t-\tau(m)) \frac{\phi_d(m)}{m}\mathrm{d}m$
$\phi_d(m) \propto m^{-0.35}$

where $$M_{p,l}$$ and $$M_{p,u}$$ are the mass limits for a progenitor of a white dwarf, $$b_i$$ is the probability to have a companion and $$M_{d,l,i}$$ and $$M_{d,u,i}$$ are the mass limits for each type of companion. The first parenthesis represents the number of white dwarfs and the second one the probability to form a binary.

Companion

$$M_{d,l,i}$$

$$M_{d,u,i}$$

$$b_i$$

Red giant

0.9

1.5

0.02

Main sequence

1.8

2.5

0.05

The yields are based on the same papers than the SNII.

Energy injection

All the supernovae (type II and Ia) inject the same amount of energy into the surrounding gas (GEARFeedback:supernovae_energy_erg) and distribute it according to the hydro kernel. The same is done with the metals and the mass.

Generating a new table

The feedback table is an HDF5 file with the following structure:

where the solid (dashed) squares represent a group (a dataset) with the name of the object underlined and the attributes written below. Everything is in solar mass or without units (e.g. mass fraction or unitless constant). In Data, the attribute elts is an array of string with the element names (the last should be Metals, it corresponds to the sum of all the elements), MeanWDMass is the mass of the white dwarfs and SolarMassAbundances is an array of float containing the mass fraction of the different element in the sun. In IMF, n + 1 is the number of part in the IMF, as are the exponent (n+1 elements), ms are the mass limits between each part (n elements) and Mmin (Mmax) is the minimal (maximal) mass of a star. In LifeTimes, the coefficient are given in the form of a single table (coeff_z with a 3x3 shape). In SNIa, a is the exponent of the distribution of binaries, bb1 and bb2 are the coefficient $$b_i$$ and the other attributes follow the same names than in the SNIa formulas. The Metals group from the SNIa contains the name of each elements (elts) and the metal mass fraction ejected by each supernovae (data) in the same order. They must contain the same elements than in Data. Finally for the SNII, the mass limits are given by Mmin and Mmax. For the yields, the datasets required are Ej (mass fraction ejected [processed]), Ejnp (mass fraction ejected [non processed]) and one dataset for each element present in elts. The datasets should all have the same size, be uniformly sampled in log and contains the attributes min (mass in log for the first element) and step (difference of mass in log between two elements).

GEARFeedback:
supernovae_energy_erg: 0.1e51                            # Energy released by a single supernovae.
yields_table: chemistry-AGB+OMgSFeZnSrYBaEu-16072013.h5  # Table containing the yields.
discrete_yields: 0                                       # Should we use discrete yields or the IMF integrated one?
GEARInitialMassFunction:
number_function_part:  4                       # Number of different part in the IMF
exponents:  [0.7, -0.8, -1.7, -1.3]            # Exponents of each part of the IMF
mass_limits_msun:  [0.05, 0.08, 0.5, 1, 50]    # Limits in mass between each part of the IMF
linear:  [141.93, -15.8895, -3.25578]      # Linear terms in the fit
constant:  [-261.366, 17.0735, 9.86606]    # Constant terms in the fit
GEARSupernovaeIa:
exponent:  -0.35                      # Exponent for the distribution of companions
min_mass_white_dwarf_progenitor:  3   # Minimal mass of a progenitor of white dwarf
max_mass_white_dwarf_progenitor:  8   # Maximal mass of a progenitor of white dwarf
max_mass_red_giant:  1.5              # Maximal mass for a red giant
min_mass_red_giant:  0.9              # Minimal mass for a red giant
coef_red_giant:  0.02                 # Coefficient for the distribution of red giants companions
max_mass_main_sequence:  2.6          # Maximal mass for a main sequence star
min_mass_main_sequence:  1.8          # Minimal mass for a main sequence star
coef_main_sequence:  0.05             # Coefficient for the distribution of main sequence companions
white_dwarf_mass:  1.38               # Mass of a white dwarf
GEARSupernovaeII:
interpolation_size:  200                # Number of elements for the interpolation of the data