Virtual seminar on Robust Virtual Diagnostics for Accurate and Confident Beam Properties Prediction
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The LIV.DAT Virtual Seminar Series – Spring 2022 will continue on Tuesday 2nd August 2022 at 17:15 BST.
Since early 2020 LIV.DAT has organised Virtual Seminars on Data Intensive Science with guest speakers from the UK, Europe and the USA. With the new calendar year underway we can now share details of the upcoming talks, including the speakers and topics.
The seminar will be given by Dr Adi Hanuka, senior software engineer at Eikon Therapeutics, who will present “Robust Virtual Diagnostics for Accurate and Confident Beam Properties Prediction”.
Seminars in this series cover R&D outside of the LIV.DAT centre’s core research areas and give an insight into cutting edge research in this area. At the end of the talk there will be a Q&A session with the speaker.
About the talk
Phase space measurement is one of the key diagnostics in particle accelerator machines. Existing beam diagnostics are invasive, and oftentimes cannot operate at the required resolution. In this work we present the Virtual Diagnostic (VD) tool, a computational tool based on deep learning that can be used to predict a diagnostic output. We show how VD accurately predicted the electron beam longitudinal phase space (LPS) for every shot using spectral information collected non-destructively from the radiation of a relativistic electron beam. In addition, we show both experimental and simulated examples where VD helps overcome resolution limitations (e.g. high repetition-rate machine or high-current ultra-short bunch).
We then show how we quantified the uncertainty of the VD prediction, its robustness against out-of-distribution inputs, and the information extracted from the latent space representations.
VDs are especially useful in systems where measuring the output is invasive, limited, costly or runs the risk of altering the output. They can provide confident knowledge while reducing the load on data storage, readout and streaming requirements. In combination with quantified uncertainties, VDs can enable making informed decisions for safety-critical systems such as particle accelerators.
About the speaker
Dr Hanuka is an electrical engineer, particle accelerator physicist, and AI researcher. She works at the intersection of physics and machine learning (ML), focusing on how ML can build better systems, and how physics can help build better ML algorithms.
Adi earned her PhD in Electrical Engineering from the Israel Institute of Technology - in the field of optical particle accelerators, during which time she collaborated with the Fermilab, UCLA and SLAC as part of the “Accelerator on Chip” international program. In 2019, Adi joined SLAC to continue developing the field of ML for particle accelerators. Adi joined Eikon Therapeutics in mid-2021 with the aim of using artificial intelligence to develop new drugs.
Hanuka was named to the Forbes’ Israel “30 under 30” list of promising young scientists, and her scientific contributions were acknowledged by numerous awards including the Schmidt Foundation Award and the Rothschild Fellowship. Adi is the editor of the “Operation Intelligence” research topic in the ``Frontiers Big Data and AI in High Energy Physics" journal. More recently, Adi taught the US Particle Accelerator School (USPAS) course: “Machine Learning and Optimization for Particle Accelerators”.
How to attend
Participation is free, but you need to register to attend this and other webinars in the series. For more information and how to register please follow this link. Once registered, you will receive the Zoom connection details on the morning of the online seminar.
The seminar details
Speaker: Dr Adi Hanuka (Senior software engineer at Eikon Therapeutics)
Seminar title: “Robust Virtual Diagnostics for Accurate and Confident Beam Properties Prediction”
Date/Time: Tuesday 2nd August at 17:15 BST