Engineering topics

Gonski, Julia

Publications and source records attributed to Gonski, Julia.

4 records

DOE OSTI2022

Snowmass Early Career

The Snowmass 2021 strategic planning process provided an essential opportunity for the United States high energy physics and astroparticle (HEPA) community to come together and discuss upcoming physics goals and experiments. As this forward-looking perspective on the field often reaches far enough into the future to surpass the timescale of a single career, consideration of the next generation of physicists is crucial. The 2021 Snowmass Early Career (SEC) organization aimed to unite this group, with the purpose of both educating the newest generation of physicists while informing the senior generation of their interests and opinions. SEC is the latest in a series of the previously dubbed "Snowmass Young" organizations, from 2013 and 2001. This iteration has expanded on these efforts to significantly increase involvement and broaden the representation of the early career community in the process. Early career physicists are the future of the field. They will design, build, and operate next-generation experiments, and put in the work to usher in new discoveries. They are also disproportionately involved in work to improve the climate within HEPA. This document summarizes the work of SEC in consolidating a huge variety of physics perspectives and community opinions towards a bright, strategic future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

High-dimensional anomaly detection with radiative return in $\mathcal{e}$⁺$\mathcal{e}$⁻ collisions

Experiments at a future $\mathcal{e}$⁺$\mathcal{e}$⁻ collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods that use imperfect or missing training labels can achieve sensitivity to generic new particle production in radiative return events. In addition to presenting an application of the classification without labels (CWoLa) search method in $\mathcal{e}$⁺$\mathcal{e}$⁻ collisions, our study combines weak supervision with variable-dimensional information by deploying a deep sets neural network architecture. We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

Readout for Calorimetry at Future Colliders: A Snowmass 2021 White Paper

Calorimeters will provide critical measurements at future collider detectors. As the traditional challenge of high dynamic range, high precision, and high readout rates for signal amplitudes is compounded by increasing granularity and precision timing the readout systems will become increasingly complex. This white paper reviews the challenges and opportunities in calorimeter readout at future collider detectors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Furthermore, methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗