Confined spaces training prepares scientists for working with SABRE

You won’t see any white lab coats among the scientists taking part in confined spaces training that will enable them to work with, and in, the SABRE vessel.

Instead, the researchers wore harnesses to learn how to safely work on, in and around with the vessel.

The training is just one aspect of the vessel that defies expectations of business as usual for scientists involved with the Stawell Underground Physics Laboratory (SUPL).

The SABRE vessel will be placed in SUPL by the end of the year, where the researchers will make use of the low levels of cosmic background, and controllable radiation levels underground to try to detect dark matter modulations.

The vessel stands at a height of more than two metres and the researchers’ work may call for them to climb into the vessel to make adjustments to the detectors, place the reflective lining or alter the wiring. This is why they have undergone confined spaces training.

Two training programs have occured, most recently for Australian National University and University of Sydney researchers in early 2023.

In 2020, six students and researchers for the University of Melbourne undertook the training, including Masters student Owen Stanley.

Owen said the training centred on safety issues involved both with working in small spaces as well as from a height, as scientists will have to climb on top of the vessel, or into the vessel.

“ We were taught about different safety and procedural aspects, such as the proper use of harnesses and pulleys as well as procedures to eliminate (manage) hazards and risks, while working on top of (and inside of) SABRE,” he said.

“It’s really exciting to be part of this research, building something that could potentially change the way we see the universe. This training is the next step towards working on that research.”

Owen aims to visit the SABRE vessel when it is in SUPL later in the year, before he finishes his Masters in 2022. He is studying signal processing using machine learning in order to reduce background noise in SABRE and identify signal events.