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A<sc>bstract</sc> The Electron-Ion Collider (EIC), a forthcoming powerful high-luminosity facility, represents an exciting opportunity to explore new physics. In this article, we study the potential of the EIC to probe the coupling between axion-like particles (ALPs) and photons in coherent scattering. The ALPs can be produced via photon fusion and decay back to two photons inside the EIC detector. In a prompt-decay search, we find that the EIC can set the most stringent bound form_a≲ 20 GeV and probe the effective scales Λ ≲ 10⁵GeV. In a displaced-vertex search, which requires adopting an EM calorimeter technology that provides directionality, the EIC could probe ALPs withm_a≲ 1 GeV at effective scales Λ ≲ 10⁷GeV. Combining the two search strategies, the EIC can probe a significant portion of unexplored parameter space in the 0.2 <m_a< 20 GeV mass range. 

ABSTRACT In this research, we bridge knowledge systems and perspectives from Indigenous and rural fishers alongside state and federal managers and biologists regarding the state of salmon management and research processes in the Kuskokwim Region of southwestern Alaska. The key objectives were to identify strategies to improve salmon management, document perspectives on Alaska Native inclusion in current management and research processes, and illustrate ways to develop more inclusive management processes and organizations. We also identify key opportunities and barriers to relationship building between Tribes and management agencies. Lastly, we explore perceptions of equity and equality and how research and management account for these dimensions. This was a two‐component research project, with one component being primarily Indigenous‐led and community‐engaged, and the second component involving agency management and research staff. We carried out 28 semi‐directed interviews with 45 Indigenous and community knowledge holders across five different communities from June 2019 to May 2022, in addition to 12 interviews with state and federal managers and researchers in 2023. Our study revealed both key differences and shared understandings between state, federal, and community perspectives regarding salmon management and research and around agency inclusion of Indigenous Knowledge systems and Tribal governments. Shared visions and solutions for improving salmon management in southwestern Alaska and elsewhere reflect a greater need for community and Indigenous empowerment and inclusion in fisheries management and research, in addition to increased relationship building and agency time spent in communities. A key recommendation arising from this study is that trust and respect are precursors to meaningfully bridging knowledge systems. Our team encourages further investigation of current power and resource disparities that prohibit equitable knowledge sharing in fisheries management and research, while identifying broad solutions for improving the current salmon management system given diverse sharing across Indigenous, federal, and state experts. 

This Indigenous-led project aims to better understand historical and contemporary ways in which Alaska Native Peoples steward salmon and the values connected to salmon stewardship. Indigenous Peoples have stewarded Alaska lands and waters for thousands of years yet have been largely excluded from western science and management systems. In this project, we utilize a participatory approach that allows for the equitable valuing of ideas and knowledges to document the breadth and depth of Yup'ik and Athabascan knowledge and governance systems in southwestern Alaska. We reshape research methodologies by centering Indigenous frameworks and methodologies, including circle dialogues and multi-generational interviews led by Indigenous scholars and students in their home communities and regions. In this paper, we share the Yup'ik and Athabascan values, knowledge, management, and governance mechanisms that can improve the long-term sustainability and equity of Alaska salmon systems. This research elevates the voices of Alaska Native salmon stewards and experts from the Kuskokwim Bay and the Kuskokwim River. We elaborate on five key themes that emerged from this research, including traditional Indigenous ways of life, Indigenous stewardship, self-determination, food and livelihood sovereignty, and ecosystem changes, and identify a more equitable and sustainable path forward for salmon and people in Alaska. 

Abstract The Lipschitz constant of the map between the input and output space represented by a neural network is a natural metric for assessing the robustness of the model. We present a new method to constrain the Lipschitz constant of dense deep learning models that can also be generalized to other architectures. The method relies on a simple weight normalization scheme during training that ensures the Lipschitz constant of every layer is below an upper limit specified by the analyst. A simple monotonic residual connection can then be used to make the model monotonic in any subset of its inputs, which is useful in scenarios where domain knowledge dictates such dependence. Examples can be found in algorithmic fairness requirements or, as presented here, in the classification of the decays of subatomic particles produced at the CERN Large Hadron Collider. Our normalization is minimally constraining and allows the underlying architecture to maintain higher expressiveness compared to other techniques which aim to either control the Lipschitz constant of the model or ensure its monotonicity. We show how the algorithm was used to train a powerful, robust, and interpretable discriminator for heavy-flavor-quark decays, which has been adopted for use as the primary data-selection algorithm in the LHCb real-time data-processing system in the current LHC data-taking period known as Run 3. In addition, our algorithm has also achieved state-of-the-art performance on benchmarks in medicine, finance, and other applications. 

A bstract In this work, we explore new spin-1 states with axial couplings to the standard model fermions. We develop a data-driven method to estimate their hadronic decay rates based on data from τ decays and using SU(3) flavor symmetry. We derive the current and future experimental constraints for several benchmark models. Our framework is generic and can be used for models with arbitrary vectorial and axial couplings to quarks. We have made our calculations publicly available by incorporating them into the D ark C ast package, see https://gitlab.com/darkcast/releases . 

Dark matter particles may interact with other dark matter particles via a new force mediated by a dark photon, A′, which would be the dark-sector analog to the ordinary photon of electromagnetism. The dark photon can obtain a highly suppressed mixing-induced coupling to the electromagnetic current, providing a portal through which dark photons can interact with ordinary matter. This review focuses on A′ scenarios that are potentially accessible to accelerator-based experiments. We summarize the existing constraints placed by such experiments on dark photons, highlight what could be observed in the near future, and discuss the major experimental challenges that must be overcome to improve sensitivities. 

A key challenge in searches for resonant new physics is that classifiers trained to enhance potential signals must not induce localized structures. Such structures could result in a false signal when the background is estimated from data using sideband methods. A variety of techniques have been developed to construct classifiers which are independent from the resonant feature (often a mass). Such strategies are sufficient to avoid localized structures, but are not necessary. We develop a new set of tools using a novel moment loss function (Moment Decomposition or MoDe) which relax the assumption of independence without creating structures in the background. By allowing classifiers to be more flexible, we enhance the sensitivity to new physics without compromising the fidelity of the background estimation. 

The locations of proton-proton collision points in LHC experiments are called primary vertices (PVs). Preliminary results of a hybrid deep learning algorithm for identifying and locating these, targeting the Run 3 incarnation of LHCb, have been described at conferences in 2019 and 2020. In the past year we have made significant progress in a variety of related areas. Using two newer Kernel Density Estimators (KDEs) as input feature sets improves the fidelity of the models, as does using full LHCb simulation rather than the “toy Monte Carlo” originally (and still) used to develop models. We have also built a deep learning model to calculate the KDEs from track information. Connecting a tracks-to-KDE model to a KDE-to-hists model used to find PVs provides a proof-of-concept that a single deep learning model can use track information to find PVs with high efficiency and high fidelity. We have studied a variety of models systematically to understand how variations in their architectures affect performance. While the studies reported here are specific to the LHCb geometry and operating conditions, the results suggest that the same approach could be used by the ATLAS and CMS experiments.