# Integrating sustainability in the design of the Wide-field Spectroscopic Telescope’s data processing and storage

- Institution: CERN
- Speaker: Laurane Fréour
- Year: 2026
- Language: English
- Subjects: Mathematics, Art, architecture and design
- Source: [CERN](https://videos.cern.ch/record/3025560)
- Licence: [CERN](https://copyright.web.cern)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

Astronomers have a unique perspective on the Earth, its fragility, and the absence of a 'planet B'. Yet the carbon footprint of their activities and instruments remains substantial. The amount of data generated by these increasingly precise technical instruments, and the subsequent computing infrastructure needed to process and store it, poses a key challenge to sustainability.

The Wide-field Spectroscopic Telescope (WST) is a 12-meter spectroscopic facility currently under development and potentially operational in Chile in 2040. One of its key science cases is the so-called 'time domain astrophysics', which requires rapid follow-up observations of transient sources in the night sky. The WST will be equipped with a provisional number of ~600 detectors, which will gather data continuously every night over an expected lifetime of 50 years. The expected data volume ranges from 1 to 3 PB per year.

In this talk, we will present the first estimates of the carbon footprint associated with the data reduction pipeline and storage infrastructures. We will highlight the WST's ongoing effort to incorporate sustainability considerations into hardware selection for data processing and storage, and explore how location affects the carbon footprint of the different solutions. We will also discuss how integrating sustainability early in the design process of research infrastructures can effectively mitigate the environmental impact.

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## Lectures

1. [Integrating sustainability in the design of the Wide-field Spectroscopic Telescope’s data processing and storage](https://invisiblenetwork.ai/learn/cern/3025560/7cce4674e560), 31 min (CERN's player)