A3D3 All-hands meeting

Session

Afternoon Plenary

14 Oct 2024, 13:45

Innovation Room (Convergence Center @ Purdue University)

Presentation materials

A3D3 All-Hands 2024 Town Hall Notes.pdf

A3D3_TraineeSurvey_2024_SeungbinPark.pdf

42. Postbac research experience: light from black holes and other multi-messenger astronomy

Kira Nolan

14/10/2024, 13:45

talk

In astronomy, the successful identification of electromagnetic counterparts to gravitational wave signals unlocks unique science that is otherwise impossible with siloed observations. Efforts in multi-messenger astronomy stand to become increasingly fruitful
but also more complex over the next decade as new instruments provide exponentially larger data streams. Not only does this work...

43. Attention at Silicon Speed: Towards Efficient Transformers on FPGAs

Rian Flynn (Purdue University (US))

14/10/2024, 13:55

talk

The High-Luminosity Large Hadron Collider (HL-LHC), anticipated to begin operations in 2029, will generate data at an astounding rate on the order of 100 terabits per second. To efficiently process and filter these data, the Compact Muon Solenoid (CMS) experiment
relies on the extremely low-latency Level-1 trigger, which uses Field-Programmable Gate Arrays (FPGAs). My project focuses on...

36. Postbac Research Experience: Multimodal Classification of Astronomical Transients

Alexandra Junell Brown

14/10/2024, 14:05

The Zwicky Transient Facility is capable of triggering hundreds of thousands of alerts a night for potential astronomical transients. Quickly classifying these objects is critical for determining candidates for follow up observations. Machine learning already plays a role in the transient classification pipeline, and has shown success training on image time series and photometric time series....

9. Implementation of `traccc` as-a-service

Miles Cochran-Branson (University of Washington (US))

14/10/2024, 14:15

talk

Particle tracking at Large Hadron Collider (LHC) experiments is a crucial component of particle reconstruction, yet it remains one of the most computationally challenging tasks in this process. As we approach the High-Luminosity LHC era, the complexity of tracking is expected to increase significantly. Leveraging coprocessors such as GPUs presents a promising solution to the rising...

29. Channel tagging: identifying events with supernova pointing information

Janina Dorin Hakenmueller (Duke University)

14/10/2024, 14:28

talk

Core collapse supernova explosions offer a rich potential of physics to explore. The emitted neutrinos are the first signals to reach the earth. Detecting these neutrinos and their direction can provide valuable information to optical detection systems in a multi messenger astronomy approach.
In liquid argon time projection chambers such as DUNE the charge interactions are the most abundant...

17. Combining Neural Networks for IceCube DeepCore Reconstruction and Classification Tasks

Josh Peterson

14/10/2024, 14:41

talk

IceCube DeepCore is an infill of the IceCube Neutrino Observatory designed to study neutrinos with energies as low as 5 GeV. Reconstruction and classification tasks near the lower energy threshold of IceCube DeepCore are especially difficult due to the low number of detected photons per neutrino event. Many neural networks have been developed for these tasks, and there are many ways we could...

37. Real-time Anomaly Detection at the CMS L1 Trigger

Melissa Quinnan (Univ. of California San Diego (US))

14/10/2024, 14:54

19. Neural Architecture Codesign for Fast Physics Applications

Jason Weitz (UCSD)

14/10/2024, 15:30

talk

We develop an automated pipeline to streamline neural architecture codesign for physics applications, to reduce the need for ML expertise when designing models for a novel task. Our method employs a two-stage neural architecture search (NAS) design to enhance these models, including hardware costs, leading to the discovery of more hardware-efficient neural architectures. The global search...

24. ConNAS4ML: Gradient-Based NAS Framework with Hardware Constraints for hls4ml

ChiJui Chen

14/10/2024, 15:43

talk

In software-hardware co-design, balancing performance with hardware constraints is critical, especially when using FPGAs for real-time applications in scientific fields with hls4ml. Limited resources and stringent latency requirements exacerbate this challenge. Existing frameworks such as AutoQKeras use Bayesian optimization to balance model size/energy, and accuracy, but they are...

41. Autoencoders for Anomaly Detection and Output Reduction on the Edge (AADORE)

Alexander Yue

14/10/2024, 15:56

talk

Detectors at next-generation high-energy physics experiments face several daunting requirements: high data rates, damaging radiation exposure, and stringent constraints on power, space, and latency. To address these challenges, machine learning (ML) in readout
electronics can be leveraged for smart detector designs, enabling intelligent inference and data reduction at-source. Autoencoders...

11. Closed-loop Brain Stimulation in NHPs using hardware-accelerated Machine Learning

Rajeev Botadra

14/10/2024, 16:09

talk

Non-Human Primates (NHPs) are central to neuroscience research due to their complex behavioral interactions and physiological similarities to the human brain. A principal motivation behind the NHP research in the aoLab at the University of Washington is to understand and model neural circuits, which can be translated for practical applications for humans. However, the nonlinear...

39. Real-time Anomaly Detection at the CMS L1 Trigger

Melissa Quinnan (Univ. of California San Diego (US))

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