University of Minnesota students (from left to right): Katrine Kompanets, Lilia Bouayed, and Emma de Bruin
University of Minnesota students Emma de Bruin, Lilia Bouayed, and Katrine Kompanets attended “Coding The Cosmos: Introduction to Gravitational Waves Summer Workshop”, a week-long workshop hosted by the Missouri University of Science and Technology and the NSF. We learned various techniques for data analysis and processing of gravitational waves. On the last day, we applied all the skills we learned that week to find as many simulated signals as we could be buried in 4000 seconds of data. Not only did we discover 3 signals, but we also determined that they all came from black hole collisions and we successfully identified their masses. We also discovered a noise remnant (known as a ‘glitch’) that almost threw us off. We had a lot of fun and are excited to combine our new data analysis skills with AI expertise from A3D3 to search for gravitational waves!
By Angela Tran May 2024
A3D3 recently engaged in one of the largest U.S. national physics conferences—the American Physical Society’s April meeting in Sacramento. A variety of A3D3 students presented their talks on institute-related activities.
A3D3 member Ethan Marx from The Massachusetts Institute of Technology brought his passion for data’s potential to bring new discoveries. His APS talk was about applying machine learning (ML) techniques to search for gravitational waves in LIGO-Virgo-Kagra (LVK) data. He says, “Specifically, we validated our algorithm on archival data from the third LVK observing run. The end goal of this work is to deploy this algorithm in real-time in order to issue alerts for electromagnetic astronomers to follow-up. This work is a direct implementation of the institute’s aim for real time processing of large datasets.” In his research, he enjoys the challenge of applying new cutting edge statistical/ML techniques. After being part of A3D3 for about three years, he says he likes that the Institute emphasizes the similarities between seemingly distinct fields.
A3D3 member Will Benoit from The University of Minnesota contributed his enjoyment of how his field probes the behavior of the most extreme universe matter. His APS talk was about demonstrating the production-readiness of a custom AI algorithm for the real-time detection of gravitational waves. He says, “Gravitational waves are one of the messengers in multi-messenger astronomy, and the ability to detect them more quickly will allow other instruments to follow-up more quickly.” In his two and a half years with A3D3, he has especially appreciated the interdisciplinary collaboration with fellow students working on similar technical problems, and seeing how results from different fields can apply to one another.A3D3 member Jared Burleson from The University of Illinois at Urbana-Champaign works in Experimental High Energy Particle Physics to answer the big questions about the fundamental laws of the universe. His APS talk was about the use of machine learning and artificial intelligence for track reconstruction for upgrades to the Large Hadron Collider, which he says “will see a drastic increase in the amount of real-time data gathered. My work aims to utilize AI for processing large data in real time with a focus on discovery in high energy physics.” After about a year with A3D3, he says, “I really enjoy being able to connect with other people in my field and outside my field who are interested in data-driven computing solutions to problems.”
Links to some of the talks presented at APS by A3D3 members are listed below.
D14.1 Jared Burleson, Track reconstruction for the ATLAS Phase-II High-Level Trigger using Graph Neural Networks on FPGAs with detector segmentation and regional processing.
G13.1 Will Benoit, A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
D03.2 Ethan Marx, A search for binary mergers in archival LIGO data using aframe, a machine learning detection pipeline
DD03 Yuan-Tang Chou, NSF HDR ML Anomaly Detection Challenge
DD03 Haoran Zhao, Graph Neural Network-based Track finding as a Service with ACTS
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.
Affiliations
MIT LIGO Laboratory, USA
Massachusetts Institute of Technology, USA
University of Minnesota, USA
University of Pennsylvania, USA
Carnegie Mellon University
By: Deep Chatterjee
December 27, 2023
New Orleans, LA – The 37th Conference on Neural Information Processing Systems was held in New Orleans between Dec 10 – 16, 2023. The Machine Learning and the Physical Sciences Workshop at NeurIPS brought together researchers pursuing applications of Machine Learning techniques in Physics, and developing new techniques based on physical concepts like conversation laws and symmetries. There was a total of 250 accepted papers for the workshop poster session – both in-person and remote. While most papers were related to Physics and Astronomy, there were several papers on applications in medical sciences, material science, and earth sciences. A3D3 had a prominent presence at the workshop, with a contributed talk, 4 papers from A3D3 members, and 2 papers from A3D3 Steering Board committee members’ team.
Elham E Khoda from UW Seattle presented on the implementation of Transformers on FPGA using HLS4ML, for low-latency applications like L1 triggers at the LHC and ATLAS, and searches for anomalous signals in gravitational-wave data.
Elham Khoda presents a contributed talk on the implementation of transformers in HLS4ML
Deep Chatterjee from MIT presented a poster on optimizing likelihood-free inference by marginalizing nuisance parameters using self-supervision. [paper link]
Niharika Sravan from Drexel University presented Pythia – a reinforcement learning model that maximizes the search for kilonovae in the presence of several contaminant objects from the Zwicky Transient Facility. [paper link]
Anni Li from UCSD gave a poster on Induced Generative Adversarial Particle Transformers on the use of induced particle-attention blocks to surpass existing particle simulation generative models. [paper link]
Deep Chatterjee (left) and Alex Gagliano (right) present posters.
There were two posters presented from A3D3 steering board members. Ashley Villar (along with Alex Gagliano) gave a poster on convolutional variational autoencoder to estimate the redshift, stellar mass, and star-formation rates of galaxies from multi-band imaging data. [paper link] Nhan Tran (along with C. Xu) have a poster on a Proximal Policy Optimization (PPO) algorithm to uniform proton beam intensity for the Mu2e experiment at Fermilab. [paper link]
