Climate impacts of particle physics

Bloom, K.; Boisvert, V.; Britzger, D.; Buuck, M.; Eichhorn, Astrid; Headley, Michael; Lohwasser, K.; Merkel, P.

doi:10.48550/arxiv.2203.12389

Cited by 7 publications

(10 citation statements)

References 26 publications

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“…[26], extrapolating to 250-300 kt CO 2 for the FCC tunnel, assuming that the footprint scales with the tunnel length and the square of its diameter. In a more recent contribution to Snowmass'21 [27], a footprint of 221 kt CO 2 is predicted from a bottom-up calculation driven by the tunnel parameters. Both estimates are consistent, and are comparable to the 170 kt CO 2 eq.…”

Section: Discussionmentioning

confidence: 99%

The carbon footprint of proposed $${\mathrm{e}}^+{\mathrm{e}}^-$$ Higgs factories

Janot

Blondel

2022

Eur. Phys. J. Plus

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The energy consumption of any of the $${\mathrm{e}}^+{\mathrm{e}}^-$$ e + e - Higgs factory projects that can credibly operate immediately after the end of LHC, namely three linear colliders (CLIC, operating at $$\sqrt{s}\,=\,380$$ s = 380 GeV; and ILC and $${\mathrm{C}}^3$$ C 3 , operating at $$\sqrt{s}\,=\,250$$ s = 250 GeV) and two circular colliders (CEPC and FCC-ee, operating at $$\sqrt{s}\,=\,240$$ s = 240 GeV), will be everything but negligible. Future Higgs boson studies may therefore have a significant environmental impact. This note proposes to include the carbon footprint for a given physics performance as a top-level gauge for the design optimisation and, eventually, the choice of the future facility. The projected footprints per Higgs boson produced, evaluated using the 2021 carbon emission of available electricity, are found to vary by a factor 100 depending on the considered Higgs factory project.

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Section: Discussionmentioning

confidence: 99%

The carbon footprint of proposed $${\mathrm{e}}^+{\mathrm{e}}^-$$ Higgs factories

Janot

Blondel

2022

Eur. Phys. J. Plus

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“…The impacts of facility construction are evaluated in ref. [46] for the proposed Future Circular Collider (FCC). The analysis in [46] estimates a carbon impact of 237 ktons of CO2 being released for the 97.7 km tunnel alone.…”

Section: Broader Impacts 91 Sustainabilitymentioning

confidence: 99%

“…[46] for the proposed Future Circular Collider (FCC). The analysis in [46] estimates a carbon impact of 237 ktons of CO2 being released for the 97.7 km tunnel alone. This is derived from the 7 million cubic meters of spoil, a mixture of marls and sandstone, that would need to be excavated, for the main tunnel as well as many bypass tunnels, access shafts, large experimental caverns, and new surface sites that are planned.…”

Section: Broader Impacts 91 Sustainabilitymentioning

confidence: 99%

A “Cool” route to the Higgs boson and beyond. The Cool Copper Collider

et al. 2023

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Construction of an e + e - Higgs factory has been identified as a major goal for particle physics. Such a collider will offer precise measurements of the Higgs bosons couplings to other particles. A Higgs factory extendable in energy can also establish the Higgs self-coupling, measure the Higgs coupling to the top quark, and expand the reach to probe new phenomena. We propose a strategy for an energy-extendable Higgs factory based on a new linear accelerator technology. This strategy offers a compact and cost-effective design that could be realized as an accelerator project in the US. The core technologies to be developed have broad applications to accelerators for medicine and for X-ray science. The challenge of realizing these technologies will offer unique and exciting opportunities to young scientists.

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“…Potential solutions to this problem fall in to three categories: methods to make faster simulations, methods to improve computational performance of the networks and methods to reduce the number of simulated events required. However, the use of GPUs in deep learning can carry its own computational burden, and this resource intensity is becoming of increasing importance in the light of high energy costs and increased focus on the carbon footprint of research activities [5] and we must therefore ensure we use such resources as effectively and efficiently as possible. Methods to make faster simulations often use a generative model, typically a Generative Adversarial Network, to approximate the simulation to produce events much more quickly (see, for example, Chapter 6 of Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Fig 5. The accuracy for the three classes (CC ν μ , CC ν e and NC) for the transfer learning all weights and Kaiming-initialised networks.…”

mentioning

confidence: 99%

Application of transfer learning to neutrino interaction classification

Chappell

Whitehead

2022

Eur. Phys. J. C

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Training deep neural networks using simulations typically requires very large numbers of simulated events. This can be a large computational burden and a limitation in the performance of the deep learning algorithm when insufficient numbers of events can be produced. We investigate the use of transfer learning, where a set of simulated images are used to fine tune a model trained on generic image recognition tasks, to the specific use case of neutrino interaction classification in a liquid argon time projection chamber. A ResNet18, pre-trained on photographic images, was fine-tuned using simulated neutrino images and when trained with one hundred thousand training events reached an F1 score of 0.896$$\,\pm \,$$ ± 0.002 compared to 0.836$$\,\pm \,$$ ± 0.004 from a randomly-initialised network trained with the same training sample. The transfer-learned networks also demonstrate lower bias as a function of energy and more balanced performance across different interaction types.

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Climate impacts of particle physics

Cited by 7 publications

References 26 publications

The carbon footprint of proposed $${\mathrm{e}}^+{\mathrm{e}}^-$$ Higgs factories

The carbon footprint of proposed $${\mathrm{e}}^+{\mathrm{e}}^-$$ Higgs factories

A “Cool” route to the Higgs boson and beyond. The Cool Copper Collider

Application of transfer learning to neutrino interaction classification

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