Pyxel 1.0: an open source Python framework for detector and end-to-end instrument simulation

Abstract

Detector modeling is becoming more and more critical for the development of new instruments in scientific space missions and ground-based experiments. Modeling tools are often developed from scratch by each individual project and not necessarily shared for reuse by a wider community. To foster knowledge transfer, reusability, and reliability in the instrumentation community, we developed Pyxel, a framework for the simulation of scientific detectors and instruments. Pyxel is an open-source and collaborative project, based on Python, developed as an easy-to-use tool that can host and pipeline any kind of detector effect model. Recently, Pyxel has achieved a new milestone: the public release and launch of version 1.0, which simplified third-party contributions and improved ease of use even further. Since its launch, Pyxel has been experiencing a growing user community and is being used to simulate a variety of detectors. We give a tour of Pyxel's version 1.0 changes and new features, including a new interface, parallel computing, and new detectors and models. We continue with an example of using Pyxel as a tool for model optimization and calibration. Finally, we describe an example of how Pyxel and its features can be used to develop a full-scale end-to-end instrument simulator.

Infrared detectors for first generation extremely large telescope instruments and their characterization program

“…In order to understand the impact of some of the detector effects, discussed in section 5, on the science it is important to understand the detector behavior through thorough characterization in the labs to the accuracies required. The detector effects can be implemented as models using the characterization data set and evaluate their impact on science using the Pyxel simulator Arko et al (2022) or other instrument‐specific simulators. As each instrument will have its own requirements for its data reduction pipeline software, a test plan with the predefined acquisition of required data products, data analysis, and test reports would be required.…”

Hybrid propagation physics for the design and modeling of astronomical observatories: a coronagraphic example

“…The front-end model computes the complex field distribution at the coronagraph mask and then propagates the field past the mask to the image plane. The field is taken by a model of the detector (e.g., EMCCD Detect 36 and Pyxel 37 ) to create a simulated raw science image that can be postprocessed (e.g., PyKLIP 38 and NMF imaging 39 ).…”