RESEARCH
BPASS and hoki
Making BPASS available through Python
The BPASS (see below) model data offer a wide array of predictions but using that data and performing some of the necessary pre-processing can be tricky.
Hoki is a python package I created to make the BPASS data easy to use within the python workflow of most astronomers! It is the easiest way to start playing with the models and their results once you have downloaded them. Some of the main functionalities of hoki are:
Loading the BPASS data into “pythonic” data formats (e.g. pandas data frames or bespoke classes)
Easily plotting some of the key BPASS results (e.g. HRDiagrams, which sounds like it would be easy, put it is not)
Essential data processing, such as creating Colour Magnitude Diagrams from the stellar library, creating SED templates to fit your observations with ppxf.
Hoki is an open source package. If you use it please cite our Journal of Open Source Software paper (Stevance et al. 2020a)
Feel free to send me an email or start an issue on the GitHub! Your feedback is the only way to know what is missing in the code or in the tutorials!
Have any questions of issues with hoki and BPASS?
Use-case examples
The papers linked in this section are examples of BPASS-hoki use-cases that have data analysis code published alongside the journal article (see data availability sections). These are an additional resource of code that you can use within your own work.
VFTS 243 as predicted by the BPASS fiducial models
NGC 1850 BH1: To be or not to be a black hole
Binary pathways to SLSNe-I: SN 2017gci
A systematic ageing method I: H II regions D118 and D119 in NGC 300
General Description
The Binary Population And Spectral Synthesis (BPASS - P.I.s Jan Eldridge and Elizabeth Stanway) models provide a wide array of predictions for how stars in the Universe live and what they look like throughout their lifetimes. They allow astronomers to compare observed properties of real stars and astrophysical events to understand the physical properties of these objects and retrace the life they led to give rise to those events (e.g. supernova explosions).
There are three key aspects to the BPASS models:
1) Detailed stellar evolution (using the Cambridge STARS code). Unlike rapid codes, detailed codes integrate over the equations that model the interior of stars. This is much more time consuming but gives us a lot more information about the physical characteristics of stars throughout their lives, and allows us to make fewer assumptions. The BPASS data release includes a stellar library containing over 250,000 stellar systems across 13 different metallicites.
2) Population Synthesis. Each population in BPASS has a single metallicity and was born with 1 million solar masses. The population synthesis information tells you which systems in the stellar library are part of this population and how many such systems you would expect to find per million solar masses. If you have a galaxy that contains several metallicities and 10 billion solar masses you can then combine and scale the population synthesis information given to match! The BPASS models provide population synthesis resutls for 13 metallicities (Z=1e-5, 1e-4, 0.001, 0.002, 0.003, 0.004, 0.006, 0.008, 0.010, 0.014, 0.020, 0.030, 0.040) and 9 Initial Mass Functions (see Table 1 Stanway et al. 2018).
3) Spectral Synthesis. One of the unique features of BPASS is to also provide spectral synthesis information. This comes in two forms within our data releases. First, each population (1 metallicity, 1 million solar masses) comes with its predicted Spectral Energy Distribution (SED). These can be combined to fit the SEDs of galaxies to figure out what they are made of! In addition, each stellar model comes with predictions on what the stars would look like in dozens of filters commonly used by telescope facilities. This allows you to look for individual observed stars within the stellar library.
Resources
The currently used versions of BPASS are v2.2.1 [Stanway et al. 2018; Eldridge et al. 2017] and v2.3 [Byrne et al. 2022] (alpha-enhanced atmospheres in spectral synthesis). In addition to checking the papers I linked, you can visit the websites of Jan Eldridge and Elizabeth Stanway.
Data
The BPASS data is often split between the model outputs (the results of the population synthesis including population SEDs) and the stellar library (the tables summarising the physical characteristics of each of the >250,000 stellar systems). That is mostly because of size: the stellar library (unziped) is roughly 50Gb. Below I have regrouped links to where you can find the data for each of the currently used versions of BPASS:
[v2.2.1] Full Release (stellar library and population synthesis results for all IMFs)
[v2.2.1] Starter kit (stellar library and fiducial population synthesis outputs)
[v2.3] Stellar population synthesis results (all IMFS)
