A3D3 All-hands meeting

Session

Poster Session / Reception

14 Oct 2024, 17:15

Main hall (Marriot Hall @ Purdue University)

8. Decoding multi-limb trajectories of naturalistic running from calcium imaging using deep learning

Seungbin Park

14/10/2024, 17:15

poster

Decoding neural activity into behaviorally-relevant variables such as speech or movement is an essential step in the development of brain-machine interfaces (BMIs)and can be used to clarify the role of distinct brain areas in relation to behavior. Two-photon (2p) calcium imaging provides access to thousands of neurons withsingle-cell resolution in genetically-defined populations and therefore...

18. Noise or Astrophysical: Developing Machine Learning Classifiers for Characterizing Gravitational Wave Events

Seiya Tsukamoto

14/10/2024, 17:20

poster

The detection of gravitational waves with the Laser Interferometer Gravitational Wave Observatory (LIGO) has provided the tools to probe the furthest reaches of the universe. A rapid follow up to compact binary coalescence (CBC) events and their electromagnetic counterparts is crucial to find short lived transients. After a gravitational wave (GW) detection, another particular challenge is...

32. Detecting the Invisible: Electron Hit Localization in High-Resolution TEM Images Using Deep Learning on FPGAs

Derrick Appiah Osei

14/10/2024, 17:25

poster

Field-Programmable Gate Arrays (FPGAs) are increasingly becoming pivotal in the advancement of artificial intelligence (AI) and deep learning applications. Their unique architecture allows for customizable hardware acceleration, which is instrumental in handling the intensive computational demands of modern AI algorithms.

Transmission Electron Microscopy (TEM) provides exceptional...

13. Estimating the Hubble Constant by combining posteriors from Multi-Messenger Kilonovae Observations

Megan Averill

14/10/2024, 17:30

poster

Multi-Messenger observations of kilonovae can be used to measure the Hubble Constant by combining distance posteriors from the gravitational wave observations with a redshift measurement of the source’s host galaxy. There is a significant discrepancy between two existing, prominent estimates of the Hubble constant: Planck, utilizing cosmic microwave background radiation and lambda cold dark...

27. Real-time compression of CMS detector data with machine learning

Mariel Peczak

14/10/2024, 17:35

poster

The upcoming high-luminosity upgrade to the LHC will involve a dramatic increase in the number of simultaneous collisions delivered to the Compact Muon Solenoid (CMS) experiment. To deal with the increased number of simultaneous interactions per bunch crossing as well as the radiation damage to the current crystal ECAL endcaps, a radiation-hard high-granularity calorimeter (HGCAL) will be...

12. Multi-messenger Astronomy Observations Via Alert Stream Filtering

Kira Nolan (California Institute of Technology)

14/10/2024, 17:40

poster

In this work we show advancements in follow-up methods for detection of electromagnetic counterparts to gravitational wave signals. These multi-messenger observations are important targets for their ability to unlock science including measurement of the Hubble constant, which is a current major effort in cosmology. In this work we include a data-driven heuristic to select anomalous flares...

26. Improving Sensitivity to Neutron Star Gravitational Wave Events using the Qp Transform

Emma de Bruin

14/10/2024, 17:45

poster

Aframe is a gravitational wave search pipeline being constructed at the University of Minnesota Twin Cities. It is a machine learning pipeline designed to look for gravitational wave signals. It performs well for events with a high chirp mass, but could be better for ones with a lower chirp mass. My work is on improving Aframe’s performance for these lower mass events using a new type of Q...

31. Model Logging for FPGA Deployment

Julian Goddy

14/10/2024, 17:50

poster

Deploying lightweight models on FPGAs requires robust workflows for tracking, saving, and transferring model information, and ensuring that this information adheres to FAIR (Findable, Accessible, Interoperable, and Reproducible) principles. We present a Python package that automates the identification and documentation of key metadata for machine learning models developed in PyTorch or...

21. Accelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models

Mostafa Cham (iHARP)

14/10/2024, 17:55

poster

Accurate estimation of subglacial bed topography is crucial for understanding ice sheet dynamics and their responses to climate change. In this study, we employ machine learning models, enhanced with Spark parallelization, to predict subglacial bed elevation using surface attributes such as ice thickness, flow velocity, and surface elevation. Radar track data serves as ground truth for model...

28. Early-time Classification of Astronomical Transients

Alexandra Junell Brown

14/10/2024, 18:00

poster

In time-domain astronomy, rapid classification of astronomical transients is critical for determining candidates for follow-up observations. With the advent of the Vera Rubin Observatory’s Legacy Survey of Space and Time, the backlog of astronomical data will increase by terabytes a night. Machine learning models capable of processing and analyzing large quantities of data can advance the...

25. Finding Binary Black Hole Mergers

Steven Henderson (UMN - Twin Cities)

14/10/2024, 18:05

poster

Binary black hole mergers can be located by collecting and analyzing the unique gravitational wave signals they produce. Deep learning computational models, specifically Aframe, are used to identify and filter gravitational wave signals more accurately and in less time than traditional matched filtering analyses. The current machine learning model that we use, Aframe, was originally developed...

33. Online track reconstruction with graph neural networks on FPGAs for the ATLAS experiment

Jared Burleson (University of Illinois at Urbana-Champaign)

14/10/2024, 18:10

poster

The next phase of high energy particle physics research at CERN will
involve the High-Luminosity Large Hadron Collider (HL-LHC). In preparation for
this phase, the ATLAS Trigger and Data AcQuisition (TDAQ) system will undergo
upgrades to the online software tracking capabilities. Studies are underway to
assess a heterogeneous computing farm deploying GPUs and/or FPGAs, together
with the...

20. Smart Pixels: A Machine Learning Approach Towards Data Reduction in Next-Generation Particle Detectors

David Jiang (Univ. Illinois at Urbana Champaign (US))

14/10/2024, 18:15

poster

Pixel detectors are highly valuable for their precise measurement of charged particle trajectories. However, next-generation detectors will demand even smaller pixel sizes, resulting in extremely high data rates surpassing those at the HL-LHC. This necessitates a “smart” approach for processing incoming data, significantly reducing the data volume for a detector’s trigger system to select...

22. Foundation Model for Real-Time Model Selection and Fitting

Cymberly Tsai

14/10/2024, 18:28

poster

Fitting data to a variety of models is a fundamental challenge in the monitoring and control of dynamical systems across science and manufacturing domains. In this work, we present a compact foundation model designed for adaptive function selection and regression. The proposed architecture utilizes 1D convolutional neural networks (CNNs), augmented by physical constraints, to facilitate the...