Contributor info: Dr Philip Harris

Antonio Boveia, Mohamed Berkat, Thomas Y. Chen, Aman Desai, Caterina Doglioni, Alex Drlica-Wagner, Susan Gardner, Stefania Gori, Joshua Greaves, Patrick Harding, Philip C. Harris, W. Hugh Lippincott, Maria Elena Monzani, Katherine Pachal, Chanda Prescod-Weinstein, Gray Rybka, Bibhushan Shakya, Jessie Shelton, Tracy R. Slatyer, Amanda Steinhebel, Philip Tanedo, Natalia Toro, Yun-Tse Tsai, Mike Williams, Lindley Winslow, Jaehoon Yu, Tien-Tien Yu

SciPost Phys. Comm. Rep. 7 (2025) · published 19 May 2025 | Toggle abstract · pdf

The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of experiments and searches while broadening the types of candidates we can pursue. Over the next decade, there is great potential for discoveries that would transform our understanding of dark matter. In the following, we outline a road map for discovery developed in collaboration among the frontiers. A strong portfolio of experiments that delves deep, searches wide, and harnesses the complementarity between techniques is key to tackling this complicated problem, requiring expertise, results, and planning from all Frontiers of the Snowmass 2021 process.

Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris

SciPost Phys. 18, 150 (2025) · published 6 May 2025 | Toggle abstract · pdf

The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics.