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A bstract Most observables at particle colliders involve physics at a wide variety of distance scales. Due to asymptotic freedom of the strong interaction, the physics at short distances can be calculated reliably using perturbative techniques, while long distance physics is non-perturbative in nature. Factorization theorems separate the contributions from different scales, allowing to identify the pieces that can be determined perturbatively from those that require non-perturbative information, and if the non-perturbative pieces can be reliably determined, one can use experimental measurements to extract the short distance effects, sensitive to possible new physics. Without the ability to compute the non-perturbative ingredients from first principles one typically identifies observables for which the non-perturbative information is universal in the sense that it can be extracted from some experimental observables and then used to predict other observables. In this paper we argue that the future ability to use quantum computers to calculate non-perturbative matrix elements from first principles will allow to make predictions for observables with non-universal non-perturbative long-distance physics.

Hydrogen will play a key role in decarbonizing economies. Here, we quantify the costs and environmental impacts of possible large-scale hydrogen economies, using four prospective hydrogen demand scenarios for 2050 ranging from 111–614 megatonne H 2 year −1 . Our findings confirm that renewable (solar photovoltaic and wind) electrolytic hydrogen production generates at least 50–90% fewer greenhouse gas emissions than fossil-fuel-based counterparts without carbon capture and storage. However, electrolytic hydrogen production could still result in considerable environmental burdens, which requires reassessing the concept of green hydrogen. Our global analysis highlights a few salient points: (i) a mismatch between economical hydrogen production and hydrogen demand across continents seems likely; (ii) region-specific limitations are inevitable since possibly more than 60% of large hydrogen production potentials are concentrated in water-scarce regions; and (iii) upscaling electrolytic hydrogen production could be limited by renewable power generation and natural resource potentials. Future hydrogen economies need massive amounts of low-carbon hydrogen. Here, we show that mismatches between economic production and supply locations, water scarcity, and the need for renewable power and materials might limit large-scale hydrogen production.

A bstract We compute the decay spectrum for dark matter (DM) with masses above the scale of electroweak symmetry breaking, all the way to the Planck scale. For an arbitrary hard process involving a decay to the unbroken standard model, we determine the prompt distribution of stable states including photons, neutrinos, positrons, and antiprotons. These spectra are a crucial ingredient in the search for DM via indirect detection at the highest energies as being probed in current and upcoming experiments including IceCube, HAWC, CTA, and LHAASO. Our approach improves considerably on existing methods, for instance, we include all relevant electroweak interactions.

A bstract We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets — a regime that has been suggested for a quantum advantage. We find that Classical Machine Learning (CML) benchmarks outperform standard QML algorithms and are able to automatically identify the presence of anomalous events injected into otherwise background-only datasets.

Key static and dynamic properties of matter — from creation in the Big Bang to evolution into subatomic and astrophysical environments — arise from the underlying fundamental quantum fields of the standard model and their effective descriptions. However, the simulation of these properties lies beyond the capabilities of classical computation alone. Advances in quantum technologies have improved control over quantum entanglement and coherence to the point at which robust simulations of quantum fields are anticipated in the foreseeable future. In this Perspective article, we discuss the emerging area of quantum simulations of standard-model physics, outlining the challenges and opportunities for progress in the context of nuclear and high-energy physics.Quantum simulations of the fundamental particles and forces of nature have a central role in understanding key static and dynamic quantum properties of matter. Motivations, techniques and future challenges for simulations of quantum fields are discussed, highlighting examples of early progress towards the dynamics of high-density, non-equilibrium systems of quarks, gluons and neutrinos.

The European aviation sector must substantially reduce climate impacts to reach net-zero goals. This reduction, however, must not be limited to flight CO 2 emissions since such a narrow focus leaves up to 80% of climate impacts unaccounted for. Based on rigorous life-cycle assessment and a time-dependent quantification of non-CO 2 climate impacts, here we show that, from a technological standpoint, using electricity-based synthetic jet fuels and compensating climate impacts via direct air carbon capture and storage (DACCS) can enable climate-neutral aviation. However, with a continuous increase in air traffic, synthetic jet fuel produced with electricity from renewables would exert excessive pressure on economic and natural resources. Alternatively, compensating climate impacts of fossil jet fuel via DACCS would require massive CO 2 storage volumes and prolong dependence on fossil fuels. Here, we demonstrate that a European climate-neutral aviation will fly if air traffic is reduced to limit the scale of the climate impacts to mitigate. Europe’s aviation must reduce more than just flight CO 2 emissions to achieve net-zero. Synthetic fuels and carbon capture and storage could help but decreasing air traffic is crucial due to non-CO 2 climate impacts and resource constraints.

E-fuels promise to replace fossil fuels with renewable electricity without the demand-side transformations required for a direct electrification. However, e-fuels’ versatility is counterbalanced by their fragile climate effectiveness, high costs and uncertain availability. E-fuel mitigation costs are €800–1,200 per tCO2. Large-scale deployment could reduce costs to €20–270 per tCO2 until 2050, yet it is unlikely that e-fuels will become cheap and abundant early enough. Neglecting demand-side transformations threatens to lock in a fossil-fuel dependency if e-fuels fall short of expectations. Sensible climate policy supports e-fuel deployment while hedging against the risk of their unavailability at large scale. Policies should be guided by a ‘merit order of end uses’ that prioritizes hydrogen and e-fuels for sectors that are inaccessible to direct electrification.E-fuels—hydrocarbon fuels synthesized from green hydrogen—can replace fossil fuels. This Perspective highlights the opportunities and risks of e-fuels, and concludes that hydrogen and e-fuels should be prioritized for sectors inaccessible to direct electrification.

Emerging data demonstrate that the activity of immune cells can be modulated by microbial molecules. Here, we show that the short-chain fatty acids (SCFAs) pentanoate and butyrate enhance the anti-tumor activity of cytotoxic T lymphocytes (CTLs) and chimeric antigen receptor (CAR) T cells through metabolic and epigenetic reprograming. We show that in vitro treatment of CTLs and CAR T cells with pentanoate and butyrate increases the function of mTOR as a central cellular metabolic sensor, and inhibits class I histone deacetylase activity. This reprogramming results in elevated production of effector molecules such as CD25, IFN-γ and TNF-α, and significantly enhances the anti-tumor activity of antigen-specific CTLs and ROR1-targeting CAR T cells in syngeneic murine melanoma and pancreatic cancer models. Our data shed light onto microbial molecules that may be used for enhancing cellular anti-tumor immunity. Collectively, we identify pentanoate and butyrate as two SCFAs with therapeutic utility in the context of cellular cancer immunotherapy. The activity of immune cells can be regulated by the microbiome. Here, the authors show that the fatty acids pentanoate and butyrate—normally released by the microbiome—increase the anti-tumour activity of cytotoxic T lymphocytes and chimeric antigen receptor T cells through metabolic and epigenetic reprogramming.

A bstract We compute the leading-order evolution of parton distribution functions for all the Standard Model fermions and bosons up to energy scales far above the electroweak scale, where electroweak symmetry is restored. Our results include the 52 PDFs of the unpolarized proton, evolving according to the SU(3), SU(2), U(1), mixed SU(2)×U(1) and Yukawa interactions. We illustrate the numerical effects on parton distributions at large energies, and show that this can lead to important corrections to parton luminosities at a future 100 TeV collider.

A bstract We develop a gauge invariant, Loop-String-Hadron (LSH) based representation of SU(2) Yang-Mills theory defined on a general graph consisting of vertices and half-links. Inspired by weak coupling studies, we apply this technique to maximal tree gauge fixing. This allows us to develop a fully gauge-fixed representation of the theory in terms of LSH quantum numbers. We explicitly show how the quantum numbers in this formulation directly relate to the variables in the magnetic description. In doing so, we will also explain in detail how the Kogut-Susskind formulation, prepotentials, and point splitting work for general graphs. In the appendix of this work, we provide a self-contained exposition of the mathematical details of Hamiltonian pure gauge theories defined on general graphs.