Kathmandu, Nepal — The BCVSPIN-2024 Masterclass, held from 4-6 December 2024 at Tribhuvan University, Nepal, brought together 34 undergraduate and graduate students for an immersive learning experience in particle physics, high-energy physics (HEP) experiments, and machine learning (ML). Spearheaded by Dr. Santosh Parajuli (UIUC), Dr. Prajita Bhattarai (SLAC), and their colleagues, this program bridged theoretical concepts with practical applications, offering expert-led lectures, hands-on projects, and career development opportunities. Participants tackled advanced topics like Higgs boson decay analysis and large-radius jet tagging, employing cutting-edge machine learning techniques such as Deep Neural Networks (DNN) and Boosted Decision Trees (BDT) on real-world physics datasets. They also took part in the Harnessing the Data Revolution (HDR) ML Challenge, sponsored by the National Science Foundation (NSF), further honing their machine learning skills on scientific datasets.
Dr. Claire David (African Institute of Mathematical Sciences, South Africa) delivered an inspiring talk on the transformative role of artificial intelligence in physics research, while students gained hands-on experience using ATLAS Open Data, which enhanced their understanding of high-energy physics analysis. The masterclass offered free participation, resources, and meals, ensuring accessibility and inclusivity for all selected participants. Feedback from Google forms reflected the program’s success, with students commending its depth and impact while suggesting longer durations and more advanced content for future editions. This impactful outreach event was made possible by the generous support of US ATLAS, the International Union of Pure and Applied Physics (IUPAP), Washington College, and A3D3. By fostering academic growth and equipping students with cutting-edge skills, the BCVSPIN-2024 Masterclass has inspired the next generation of physicists in Nepal.
By: Miles Cochran-Branson July 26, 2024
Following the three-day US-ATLAS conference held at the University of Washington (UW) Seattle campus, students met for a brief tutorial on tools available for US-ATLAS members. After introductions in computing resources, Yuan-Tang Chou—a postdoc at UW and member of the A3D3 team—gave a presentation on GPU resources and utilization with a focus on new applications to particle-physics workflows.
Chou gives a brief presentation on applications of the NVIDIA Triton server for deploying models as-a-service.
The presentation focused on deploying models on accelerators such as GPUs, as-a-service (aaS) using the NVIDIA Triton Inference Server. Chou discussed the merits of heterogeneous computing—the most straightforward way to deploy algorithms where the CPU and GPU both are connected on a single node. He noted that for many physics processes such as Graph-Neural-Network (GNN) based tracking, flavor tagging, and detector simulation, heterogeneous computing could be “inefficient and very expensive to scale.” Hence, offloading expensive tasks to a GPU server could streamline the deployment of large models important for ATLAS physics.
Sample architecture of as-a-service model deployment from CPU-only client nodes or CPU / GPU client nodes to a GPU server.
After motivating why deploying models as-a-service could be beneficial in physics analysis, Chou gave a brief demo on deploying GNN-tracking aaS. This was followed by a hands-on tutorial deploying the resnet50 image recognition deep neural network as-a-service on computing resources at CERN. The tutorial material focused on building the proper model repository structure and configuration for image detection on a GPU server. Students set-up a work environment, deployed a backend on the server, and sent an image to the server to be classified.
Students work on deploying a backend on a server and sending images to the server to be classified.
By the end of the tutorial, students had successfully deployed a backend and received image classifications back from the image recognition model. Interested students were connected with experts currently working in ongoing development of aaS tools in algorithm development of ATLAS algorithms.
Written by Miles Cochran-Branson, PhD student at University of Washington
arXiv: 2309.11537 By: Ryan Raikman1,5, Eric A. Moreno2, Ekaterina Govorkova2, Ethan J Marx1,2, Alec Gunny1,2, William Benoit3, Deep Chatterjee1,2, Rafia Omer3, Muhammed Saleem3, Dylan S Rankin4, Michael W Coughlin3, Philip C Harris2, Erik Katsavounidis1,2
April 19, 2024
Credit: Ingrid Bourgault, Illustration for the Announcement of new Results from the LIGO-Virgo-Kagra Collaboration
The Laser Interferometer Gravitational-Wave Observatory (LIGO) represents a pinnacle in the quest to detect gravitational waves, ripples in the fabric of spacetime predicted by Albert Einstein’s general theory of relativity. Gravitational wave detectors like LIGO are marvels of precision engineering designed to observe the most cataclysmic events in the universe, such as the collisions of black holes and neutron stars. Consisting of two 4-kilometer long interferometers in Hanford, Washington and Livingston, Louisiana, LIGO operates by measuring infinitesimally small disturbances in spacetime caused by passing gravitational-waves. Our goal is to increase the search capabilities of these detectors to never-before-seen phenomena like supernovae explosions, cosmic strings, and gravitational bremsstrahlung, just to name a few.
GWAK: New Methods of Anomaly Evaluation on the Horizon
The detection of gravitational wave (GW) signals is pivotal for unraveling the mysteries of the cosmos, relying heavily on accurately modeled templates of GW emissions. However, the possibility of unmodeled transients poses a significant challenge. This study proposes a novel approach utilizing deep recurrent autoencoders and a semi-supervised strategy called Gravitational-Wave Anomalous Knowledge (GWAK, reads: guac) to broaden the search for potential anomalies beyond conventional templates.
Key Points
GWAK Methodology
The GWAK method introduces alternative signal priors, capturing essential features of new physics signatures, thereby extending sensitivity beyond pre-computed templates.
Despite a potential decrease in accuracy compared to supervised techniques, GWAK offers qualitative advantages by generalizing experimental sensitivity.
Construction of GWAK Space
The authors construct a low-dimensional embedded space using the GWAK method, which delineates distinct physical signatures of signals along each axis.
This embedded space facilitates the identification of binaries, detector glitches, and exploration of hypothesized astrophysical sources emitting GWs in the interferometer frequency band.
Fig. 1: A 3-dimensional GWAK space with different types of signatures lying in different regions of the GWAK space. Different selection regions can be made to further isolate specific signals, such as Selection Region 2 [green] completely isolating Core-Collapse Supernovae [avocado]
Detection of Anomalies
Five unsupervised autoencoders were trained on datasets comprising background noise, glitches, and three simulated signals representing potential new physics signatures.
The five autoencoders create a 5-dimensional embedding for each incoming event. This allows a final classifier to easily select regions in this space corresponding to anomalous GW events.
The GWAK method efficiently detected anomalies in GW datasets, including unmodeled sources like Core-Collapse Supernovae and White Noise Bursts, while distinguishing signal-like anomalies from detector glitches.
The Future of the GWAK Method Promises Enhanced Efficiency
The GWAK method emerges as a potent tool for anomaly detection in gravitational wave datasets, showcasing its potential to augment existing detection systems. By leveraging deep recurrent autoencoders and alternative signal priors, this approach offers a promising avenue for uncovering hidden astrophysical phenomena and refining our understanding of the universe. Overall, this study marks a significant stride towards enhancing the efficacy and sensitivity of GW detection, opening new horizons for gravitational wave astronomy. The GWAK method doesn’t just improve our existing capabilities; it invites us to imagine what lies beyond the current frontiers of our knowledge.
