Tutorial : Computing Lyman-alpha escape fractions¶
In this tutorial you will, hopefully, learn how to compute Lyman-alpha escape fractions with zELDA. Note that this part of the code compres directly from FLaREON ( https://github.com/sidgurun/FLaREON , Gurung-lopez et al. 2019b).
Default computation of escape fractions¶
Let’s move to one of the most powerful products of FLaREON: predicting huge amounts of Lyman alpha escape fractions.
However, zELDA implements several gas geometries and is optimized to obtain large amount of escape fractions with only one line of code, so let us expand this a little bit more. If we want to compute the escape fraction in a thin shell outflow with the configurations { V , logNH , ta } , { 200 , 19.5 , 0.1 }, { 300 , 20.0 , 0.01 } and { 400 , 20.5 , 0.001 } we could do
>>> import Lya_zelda as Lya
>>> your_grids_location = '/This/Folder/Contains/The/Grids/'
>>> Lya.funcs.Data_location = your_grids_location
>>> Geometry = 'Thin_Shell'
>>> # Other options: 'Galactic Wind' or 'Bicone_X_Slab_In' or 'Bicone_X_Slab_Out'
>>> # Expansion velocity array in km/s
>>> V_Arr = [ 200 , 300 , 400 ]
>>> # Logarithmic of column densities array in cm**-2
>>> logNH_Arr = [ 19.5 , 20.0 , 20.5 ]
>>> # Dust optical depth Array
>>> ta_Arr = [ 0.1 , 0.01 , 0.001 ]
Where Geometry indicates the gas distribution that is being used. ‘Bicone_X_Slab_In’ indicates the bicone geometry look through the outflow, while ‘Bicone_X_Slab_In’ is looking through the optically thick gas. The ‘Thin_Shell_Cont’ model does not support escape fractions yet.
Now let’s compute the escape fraction for this configurations:
>>> f_esc_Arr = Lya.RT_f_esc( Geometry , V_Arr , logNH_Arr , ta_Arr )
The variable f_esc_Arr is an Array of 1 dimension and length 3 that encloses the escape fractions for the configurations. In particular f_esc_Arr[i] is computed using V_Arr[i] , logNH_Arr[i] and ta_Arr[i].
Deeper options on predicting the escape fraction¶
There are many algorithms implemented to compute f_esc_Arr. By default FLaREON uses a machine learning decision tree regressor and a parametric equation for the escape fraction as function of the dust optical depth (Go to the FLaREON presentation paper Gurung-Lopez et al. in prep for more information). These settings were chosen as default since they give the best performance. However the user might want to change the computing algorithm so here leave a guide with all the available options.
MODE variable refers to mode in which the escape fraction is computed. There are 3 ways in which FLaREON can compute this. i) ‘Raw’ Using the raw data from the RTMC (Orsi et al. 2012). ii) ‘Parametrization’ Assume a parametric equation between the escape fraction and the dust optical depth that allows to extend calculations outside the grid with the highest accuracy (in FLaREON). iii) ‘Analytic’ Use of the recalibrated analytic equations presented by Gurung-Lopez et al. 2018. Note that the analytic mode is not enabled in the bicone geometry although it is in the ‘Thin_Shel’ and ‘Galactic_Wind’
Algorithm variable determines the technique used. This can be i) ‘Intrepolation’: lineal interpolation is used. ii) ‘Machine_Learning’ machine learning is usd. To determine which machine learning algorithm you would like to use please, provide the variable Machine_Learning_Algorithm. The machine learning algorithms implemented are Decision tree regressor (‘Tree’), Random forest regressor (‘Forest’) and KN regressor (‘KN’). The machine learning is implemented by Sci-kit-learn, please, visit their webside for more information (http://scikit-learn.org/stable/).
Finally, any combination of MODE , Algorithm and Machine_Learning_Algorithm is allowed. However, note that the variable Machine_Learning_Algorithm is useles s if Algorithm=’Intrepolation’.