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Sarah Demers

Publications and source records attributed to Sarah Demers.

5 recordsLinked to original sources
arXiv2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

hep-ex↗
arXiv2025

Weakly Supervised Anomaly Detection in Events with a Higgs Boson and Exotic Physics

We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this method on the final state of a Higgs boson decaying to two photons. The demonstration targets high-dimensional deviations in the region of phase space containing the Higgs mass peak in a fully signal-agnostic manner. A latent-space embedding, learned from event kinematics, enables the use of a large set of potentially sensitive features. Backgrounds are estimated using a hybrid approach that combines machine learning-based generative modelling with traditional simulation, and a discriminator is trained in the latent space to distinguish data from background estimates. After applying a selection on the classifier output, the invariant mass distribution of the diphoton system is examined for localized excesses above the simulated Higgs peak. We benchmark the sensitivity of this strategy using simplified simulated proton-proton collisions corresponding to data recorded during Run 2 of the LHC, and show that the method can provide significant improvements in sensitivity, even for small signal injections that could remain undetected in an inclusive analysis. These results demonstrate that the proposed strategy is a promising and viable approach for future searches and should be applied to recorded collider data.

hep-ex↗
arXiv2022

Public Education and Outreach

This article summarizes recommendations made by the Community Engagement Frontier conveners as part of the 2022 Snowmass process. It suggests that institutions involved in high energy physics (e.g., universities, national laboratories, funding agencies, etc.) add education and outreach as a valuable effort and one that should be considered in hiring and promotion decisions.

hep-ex↗
arXiv2022

The need for structural changes to create impactful public engagement in US particle physics

This Snowmass21 Contributed Paper addresses the structural changes that need to occur in the many groups and organizations that intersect with the US particle physics community to enable impactful public engagement to flourish. The impetus for these changes should come from the particle physics community, which should acknowledge the importance of public engagement and act on the recommendations in this Snowmass contributed paper. Scientists have expressed frustration at the barriers, penalties and lack of support that discourage them from participating in public engagement. In this paper, we provide many ways to create a supportive, enabling atmosphere for public engagement among physicists.

physics.soc-ph↗