By: Dylan Rankin
February 21, 2024

The Reach of High Energy Physics

The A3D3 institute innovates in AI to enable discovery across science, with high energy physics (HEP) being one of the three research thrusts. Within the field of HEP there are a dizzying array of research topics and the tools physicists must use to study them are equally so. There are experiments in HEP that collide particles traveling near the speed of light and study the aftermath of the collisions to search for hints of new phenomena. Other experiments observe the vast reaches of space to try to understand the forces that shaped our universe. And still others seek to detect and measure incredibly rare interactions of elusive particles with our world that might shed light on the most mysterious known particle, the neutrino. These experiments study our universe from the quantum realm of subatomic particles to the astronomical realm of galaxies and black holes.

The Particle Physics Prioritization Panel Report

Once every decade a group of high energy physicists is charged by the Department of Energy (DOE) with evaluating the projects on the horizon across HEP and charting a fiscally responsible path for the next ten years. This involves a multi-year process and hundreds of studies by the community but allows every group within HEP to make the case for the projects and actions they believe to be the most exciting. This report, called the Particle Physics Project Prioritization Panel (P5) report, was released on December 8th, 2023, and sets out the goals across the field for the next decade. While members of A3D3 participate in many projects outside of HEP, the P5 report is very important in that it helps guide a lot of focus inside HEP for the next 10 years.

A3D3 Members Collaborate on Projects Recognized in Report

One major outcome of the P5 report is the suggestions regarding how best to utilize the funding we expect over the next ten years. While it would be wonderful if there were enough funding for all the great ideas in HEP, in practice some tough decisions must be made. Some projects are deemed more critical or cost-effective than others. Some projects must be prioritized now and others must be delayed for the future. A3D3 members contributed heavily to the planning process and many projects that count A3D3 members as leaders were strongly endorsed in the report, demonstrating major support for our work. These projects include the High-Luminosity Large Hadron Collider upgrade, multiple phases of the Deep Underground Neutrino Experiment, the IceCube experiment, and, implicitly, the Laser interferometer for Gravitational-wave Observation experiment. 

The P5 report is more than just a priority list of HEP projects. It also attempts to take a wide-angle look at the field as a whole and provide guidelines for areas of growth. One of the most overarching callouts in the P5 report is the use of Artificial Intelligence and Machine Learning (AI/ML). AI/ML is obviously a main component of the work in A3D3. Even more so, the ways in which A3D3 is pioneering AI/ML usage were called out as future directions for investment. These include the major A3D3 work on real-time systems like the ATLAS and CMS trigger systems, as well as significant computational work being spearheaded by A3D3 members related to the effective use of emerging hardware. 

The High-Luminosity Large Hadron Collider Upgrade

As the most powerful particle collider ever built the LHC is capable of producing conditions unlike any other machine on earth. It made the discovery of the Higgs boson possible and continues to allow many searches for new particles. But in order to continue to push the boundaries of the so-called energy frontier of HEP, the collider and the detectors need to be upgraded to allow us to collect even more data, or luminosity. This upgrade is called the High-Luminosity Large Hadron Collider, and its successful execution is one of the utmost importance according to the P5 report. The contributions of A3D3 members who work within the ATLAS and CMS experiments will be critical to the success of the HL-LHC upgrade.

The Deep Underground Neutrino Experiment

The Deep Underground Neutrino Experiment (DUNE) at Fermi National Accelerator Laboratory will allow incredibly precise measurements of elusive neutrinos. The detector is comprised of multiple stages, each with its own role to play in helping enable these measurements. Multiple future phases of the Deep Underground Neutrino Experiment (DUNE) were a focus of the report. The endorsement is a testament to the importance of neutrinos for a long time to come in US HEP research.

The IceCube Experiment

The IceCube experiment at the South Pole also seeks to study the properties of neutrinos. However, while the neutrinos at the DUNE detector are produced by the accelerator complex at Fermi National Accelerator Laboratory, those detected by IceCube are produced in space and can have energies higher even than particles produced at the LHC. The current IceCube detector instruments a volume of roughly 1 cubic kilometer, but a proposed IceCube Gen-2 would increase this volume ten-fold to further study of these astrophysical neutrinos. This upgrade is recommended by the P5 report to unlock the wide set of physics it can enable.

Multi-Messenger Astronomy and the Laser Interferometer for Gravitational-wave Observation

Although the Laser Interferometer for Gravitational-wave Observation (LIGO) is not funded by the DOE, the physics that it and other similar gravitational-wave experiments enable was strongly supported in the P5 report. Specifically, the report calls out the emerging field of Multi-Messenger Astronomy (MMA) that seeks to observe astronomical sources through both gravitational waves and electromagnetic signals. This field has largely been born out of the success of the LIGO experiment, and the strong endorsement of MMA from the P5 report represents a strong endorsement of the future of LIGO.

A3D3 Shows Promise of Bridging the Gap Between Hardware and Fundamental Science

It is clear that the work represented in A3D3 is strongly aligned not only with the endorsed experiments but also with the modes of discovery. The report comments that “upgraded detectors and advances in software and computing, including artificial intelligence/machine learning (AI/ML), will enable the experiments to detect rare events with higher efficiency and greater purity.” The connections in A3D3 between the hardware and the fundamental science are intended to facilitate the advances that the P5 report notes.

Much of the importance of this work was demonstrated in studies performed by A3D3 members during the planning process. In addition to work on the experiments above, A3D3 members led a review of the community needs, tools, and resources for AI/ML across HEP [], which was a primary resource of its kind. The alignment of the P5 recommendations with the work done by A3D3 members is a strong demonstration of the support in the HEP community for this sort of work.

Finally, one major component of the work in A3D3 is its cross-disciplinary nature. Solutions to problems in HEP are likely to come not only from within the field but through collaboration with other domains. The sharing of tools, problems, and expertise has the potential to unlock solutions across traditional research boundaries. The study of neural computations involved in sensory and motor behavior is seemingly far removed from the trigger systems in CMS and ATLAS. However, both areas of research require solutions for data processing that are capable of extremely high throughput. This connection has been strengthened through A3D3 work to enable ultrafast recurrent neural networks []. The P5 report makes explicit mention of collaborations to take advantage of these sorts of connections and suggests increasing their prevalence. This signals strong support for the sorts of trans-disciplinary connections A3D3 has fostered, both now and in the future.

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

By: Katya Govorkova and Yuan-Tang Chou
November 27, 2023

The “AI and the Uncertainty Challenge in Fundamental Physics” workshop was a dynamic experience filled with experts from different areas of applied AI in science. Experts from fundamental science, computer science, and statistics exchange ideas on how to incorporate uncertainty in AI. The workshop took place at the Sorbonne Center for Artificial Intelligence (SCAI) in Paris and at Institut Pascal Université Paris-Saclay in Orsay, France.

Researcher Mark Neubauer (Faculty at the University of Illinois at Urbana-Champaign, A3D3 co-PI) led the discussion in the Uncertainty Quantification session on Tuesday and emphasized the idea of explainable AI.

The session on Wednesday afternoon was specifically dedicated to the FAIR Universe ML challenge, which addresses uncertainties in physics through AI. A3D3, represented by Yuan-Tang Chou (A3D3 Postdoc at the University of Washington) and Katya Govorkova (A3D3 Postdoc at the Massachusetts Institute of Technology), shared valuable insights from organizing an anomaly detection data challenge, emphasizing the necessity of robust frameworks and collaboration. Lessons learned from Katya’s experiences underscored the crucial role challenges play in advancing the field. She also underlined what can be improved in future challenges. 

Katya Govorkova presenting Exploring Data Challenges and Leveraging Codabench: A Practical Journey with unsupervised New Physics detection at 40 MHz.

Yuan-Tang highlights another ML challenge example for NSF HDR institute beyond particle physics. The challenge utilized the Neuroscience dataset provided by Prof. Dadarlat’s lab ( Purdue University, A3D3), which tried to decode limb trajectories from neuro activity with ML.

Yuan-Tang presenting ML challenge using Neuroscience dataset

The Codabench platform, noted for its versatility, was highlighted as instrumental in organizing public challenges. The public showed a great interest in hosting and participating in challenges. Challenges were recognized as bridges connecting different disciplines, particularly physics and computer science. The event highlighted those challenges.

The participants provided many great ideas and suggestions during the one-week workshop. Elham E Khoda (Postdoc at the University of Washington, A3D3) gave an excellent summary on the last day to discuss the lesson learned during the week and the next step to improve the HiggsML Uncertainty Challenge. “We definitely want not only people outside the particle physics to join the ML Challenge,” Elham said. “We also encourage participation outside of the domain who can think differently and come up with innovative ideas.”

By: Patrick McCormack (Postdoc MIT, A3D3)

October 30, 2023

At the workshop, seven A3D3 trainees gave presentations on their work.  Leading off, Farouk Mokhtar of UCSD and Santosh Parajuli of UIUC presented their work on implementing machine learning models for the LHC experiments CMS and ATLAS, respectively.  Though working on independent efforts, both use graph neural networks to efficiently and scalably reconstruct particles. Alongside the first smatterings of autumn leaves, an international assortment of more than 150 Physicists, Computer Scientists, Engineers (and more) descended upon Imperial College London (ICL) this past week.  There they enjoyed the crisp weather and the fourth iteration of the Fast Machine Learning for Science (FastML) Workshop, which ran from September 25-28.

The FastML workshop series was born in 2019 as a small and informal workshop focused on High Energy Physics (HEP), but it has since grown to include participants from diverse fields, such as medicine, astrophysics, and statistics.  Unsurprisingly, a workshop centered on this multidisciplinary approach to accelerated machine learning drew the participation of several members of A3D3.  And just as A3D3 revolves around mutual support and cross-disciplinary efforts, participants in the workshop were intrigued to see how the same techniques and algorithms were found in diverse applications across different disciplines.

“The workshop brings together researchers from very different specialties who do not typically have a chance to come together and exchange ideas,” said Fermilab’s Nhan Tran, one of the original FastML organizers.  “Despite this, it was so refreshing to see many amazing talks and enthusiastic discussion from all the workshop participants willing to get out of their comfort zones and expand their research.  I really appreciate that spirit and it makes the workshop series very unique and fun.”

Emphasizing the increased scope of the workshop series, Fermilab’s Kevin Pedro said that he attended the workshop “to learn about new cutting-edge computational techniques that are accelerating ML throughout many scientific fields.”

At the workshop, seven A3D3 trainees gave presentations on their work.  Leading off, Farouk Mokhtar of UCSD and Santosh Parajuli of UIUC presented their work on implementing machine learning models for the LHC experiments CMS and ATLAS, respectively.  Though working on independent efforts, both use graph neural networks to efficiently and scalably reconstruct particles.

Jeffrey Krupa presents his work on developing a Sparse Point Voxel CNNSantosh Parajuli presents his work on implementing graph neural networks for Event Filter Tracking in ATLAS.  In keeping with the “Fast” theme of the workshop, he is developing a VHDL implementation of his algorithm to run on FPGAs. for machine-learning-based clustering in hadronic calorimeters.

Next up, Patrick McCormack and Jeffrey Krupa, both of MIT, gave talks about two projects that they both work on.  One is a CMS effort to implement GPU acceleration for ML-methods via an Inference as a Service scheme, and the other is an implementation of a Sparse Point Voxel CNN (SPVCNN) for determining clusters of energy in hadron calorimeters for LHC experiments.  According to Krupa, “the SPVCNN algorithm is a first-time use of HCAL depth segmentation in clustering for CMS, and it removes the latency associated with HCAL clustering from reconstruction workflows.”

Jeffrey Krupa presents his work on developing a Sparse Point Voxel CNN for machine-learning-based clustering in hadronic calorimeters.

The last A3D3 talk from the workshop’s first day was given by Duc Hoang, also from MIT, who presented his work on algorithms for the CMS Layer-1 Trigger. These algorithms must be able to produce inferences at 40 MHz, such that one must balance algorithmic complexity with speed.  Thanks to the efforts of Duc and his collaborators, the algorithms that they have implemented on FPGAs for both bottom quark and tau lepton identification will increase the efficiency of the CMS trigger system for rare processes with these particles.

During the second day of the workshop, the focus shifted away from the LHC.  The gravitational waves side of A3D3 was represented by Katya Govorkova and Eric Moreno of MIT.  Katya presented her work on the development of Gravitational Wave Anomalous Knowledge (GWAK), a method for tagging gravitational waves from anomalous sources.  This algorithm is related to the Quasi-Anomalous Knowledge (QUAK) technique that was developed by A3D3 members for application in LHC contexts.  The GWAK algorithm has since evolved and can be used in real time to help distinguish between truly anomalous gravitational waves and meaningless detector glitches.

Katya Govorkova presents “Gravitation Wave Anomalous Knowledge”, or GWAK.  As she was giving the first talk about gravitational waves at the workshop, she covered the basic physics behind the LIGO experiment.

Eric’s talk focused on the Machine Learning for Gravitational Waves (ML4GW) package, which is a suite of tools enabling real-time machine learning for gravitational wave (GW) experiments, such as LIGO.  These tools have accelerated GW-detection, parameter estimation for events, noise regression, and anomaly detection via GWAK.

The workshop’s concluding remarks were given by A3D3’s deputy director and MIT professor Phil Harris.  He exhorted the audience with this playful paradox: “In order to go fast, we have to go slow.  By which I mean that designing an algorithm or workflow for the fastest possible performance takes time and careful consideration.”  In his 20 minute talk, he pointed out many of the similarities and common tools being used across disciplines, such as sparsification and quantization of neural networks, hardware-based acceleration using GPUs and FPGAs, and deep learning architectures such as transformers and graph neural networks.  A complete summary of the topics covered in the workshop would be far too long for this article, but the workshop’s timetable, along with links to most presentations can be found here.

“I really enjoyed that the workshop has a very diverse group of participants and talks that I found very inspiring,” said Professor Mia Liu from Purdue reflecting on the workshop.  “I am learning and thinking of new ways of accelerating science by developing appropriate algorithms and learning methods, in addition to my current research in real-time ML in low latency and high throughput systems.  For example, robust learning methods for embedding of scientific data, that can account for the variance due to the nature of the physical object and the measurement methods etc, is challenging but crucial for broader and long lasting impact of ML on scientific discoveries.”

The workshop also included tutorials on state-of-the-art deployment techniques, such as the hls4ml package for creating firmware implementations of machine learning algorithms, Intel AI Suite for deploying algorithms on Intel FPGAs, and the use of Intelligence Processing Units (IPUs) from Graphcore.  There were also informal tours of several of the labs at ICL.

On a lighter note, some workshop participants found time to explore the sights and sounds of London.  A3D3 members Eric and Duc also put together a public lecture from rapper Lupe Fiasco (Wasalu Jaco), who is currently a visiting professor at MIT.  He discussed some of his work with Google on creating TextFX, which is a large language model for exploring relationships between words and generating phonetically, syntactically, or semantically linked phrases.  They also managed to bring in Irving Finkel of the British Museum, who discussed the history of the game of Ur, which is a shared love with Lupe, Eric, and Duc.

Duc, Eric, Irving Finkel, and Lupe Fiasco discussed the game of Ur in a panel after Lupe’s public lecture on the relationship between rap and large language models.

The workshop proved to be a valuable experience for A3D3 attendees, and I suspect that future iterations will be well attended by our members.  “Attending the workshop was an incredible experience for me,” said Santosh Parajuli.  “The FastML workshop provided a unique platform to learn from experts, exchange ideas, and explore the latest advancements in different fields.  Additionally, I had a chance to share our exciting work on using advanced technology and machine learning to improve how we track particles in high-energy physics, which could help us make big discoveries in the future!”