Description
As observational frontiers push into new territory, we must make apples-to-apples comparisons when comparing theory to observation to ensure robust conclusions are drawn. This means transforming models into the observer frame, where we can pinpoint observational biases, explore uncertain parameter spaces, and demystify what “missing physics” really means (if anything). This process requires expensive computations bringing together theory from all corners of astrophysics. For this process to be viable, we must make the most of the hardware at our disposal.
Our newly developed tool, Synthesizer, streamlines this process. This HPC-ready, C-accelerated Python package is built on the principles of usability, efficiency, and flexibility. By simplifying the conversion of simulated and parametric models into observables, Synthesizer empowers researchers to forge robust connections between theory and data. In this talk, I will outline Synthesizer’s capabilities as we approach its v1.0.0 release.
