The Maunakea Spectroscopic Explorer (MSE) project will provide multi-object spectroscopy in the optical and near-infrared bands using an 11.25-m aperture telescope, repurposing the original Canada–France–Hawaii Telescope site. MSE will observe 4332 objects per single exposure with a field of view of 1.5 square degrees, utilizing two spectrographs with low-moderate (R∼3000, 6000) and high (R≈30,000) spectral resolution. In general, an exposure time calculator (ETC) is used to estimate the performance of an observing system by calculating the signal- to-noise ratio (S/N) and exposure time. We present the design of the MSE ETC, which has four calculation modes (S/N, exposure time, S/N trend with wavelength, and S/N trend with magnitude) and incorporates the MSE system requirements as specified in the conceptual design. The MSE ETC currently allows for user-defined inputs of the target AB magnitude, water vapor, air mass, and sky brightness AB magnitude (additional user inputs can be provided depending on the computational mode). The ETC is built using Python 3.7 and features a graphical user interface that allows for cross-platform use. The development process of the ETC software follows an Agile methodology and utilizes the unified modeling language diagrams to visualize the software architecture. We also describe the testing and verification of the MSE ETC.
KEYWORDS: Spectrographs, Control software, Software development, Charge-coupled devices, Camera shutters, Design and modelling, Control systems, Computer architecture, Data acquisition, Switches
Local Volume Mapper Spectrograph Control Package (LVMSCP) is the software that controls three spectrographs to acquire science spectral data cubes automatically. The software architecture design based on Python 3.9 follows a hierarchical structure of Actors, the unit that controls each piece of hardware. We used the software framework Codified Likeness Utility to implement each Actor. The Actors communicate with each other through RabbitMQ, which implements the Advanced Message Queuing Protocol. The Actor applies asynchronous programming with non-blocking procedures as the three spectrographs should operate simultaneously. For the requirement of incremental code change and management in the collaboration of the developers, we adopted the SDSS Github Action, which supports continuous integration/continuous deployment. As a result, unit testing with Pytest tested the individual components of the software, respectively, and lab testing with LVMSCP provided the spectra data for the spectrograph calibration. The LVMSCP provides the application programming interface to the Robotic Observation Package to fulfill the required scientific survey execution for the spectrographs.
The Local Volume Mapper (LVM) project in the fifth iteration of the Sloan Digital Sky Survey (SDSS-Ⅴ) will produce large integral-field spectroscopic survey data to understand the physical conditions of the interstellar medium in the Milky Way, the Magellanic Clouds, and other local-volume galaxies. We developed the Local Volume Mapper Spectrograph Control Package (LVMSCP) which controls the instruments for the operation of the spectrograph. We use the new SDSS message passing protocol CLU (Codified Likeness Utility) for the interaction, based on the RabbitMQ that implemented the Advanced Message Queuing Protocol (AMQP). Also, asynchronous programming with non-blocking procedures is applied for the package since three spectrographs should be operated simultaneously. The software is implemented based on Python 3.9, and will provide the Application Programming Interface (API) to the Robotic Observation Package (ROP) for the integrated observation.
The Maunakea Spectroscopic Explorer (MSE) will convert the 3.6-m Canada-France-Hawaii Telescope (CFHT) into an 11.25-m primary aperture telescope with a 1.5 square degrees field-of-view at the prime focus. It will produce multi-object spectroscopy with a suite of low (R∼3,000), moderate (R∼6,000), and high (R∼40,000) spectral resolution spectrographs in optical and near-infrared bands that are capable of detecting over 4,000 objects per pointing. Generally, an exposure time calculator (ETC) should simulate a system performance by computing a signal-to-noise ratio (SNR) and exposure time based on parameters such as a target magnitude, a total throughput of the system, and sky conditions, etc. The ETC that we have developed for MSE has individual computation modes for SNR, exposure time, SNR as a function of AB magnitude, and SNR as a function of wavelength. The code is based on an agile development methodology and allows for a variety of user input. Users must select either LR, MR, or HR spectral resolution settings in order to pull the associated MSE instrument parameters. Additionally, users must specify the target and background sky magnitudes (and have the ability to alter the default airmass and water vapor values). The software is developed with Python 3.7, and Tkinter graphical user interface is implemented to facilitate cross-platform use. In this paper, we present the logic structure and various functionalities of our MSE-ETC, including a software design and a demonstration.
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