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    Roadmap details how to improve exoplanet exploration using the JWST

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    The launch of NASA’s James Webb Space Telescope (JWST) in 2021 kicked off an exciting new era for exoplanet research, especially for scientists looking at terrestrial planets orbiting stars other than our sun. But three years into the telescope’s mission, some scientists have run into challenges that have slowed down progress.In a recent paper published in Nature Astronomy, the TRAPPIST-1 JWST Community Initiative lays out a step-by-step roadmap to overcome the challenges they faced while studying the TRAPPIST-1 system by improving the efficiency of data gathering to benefit the astronomy community at large.“A whole community of experts came together to tackle these complex cross-disciplinary challenges to design the first multiyear observational strategy to give JWST a fighting chance at identifying habitable worlds over its lifetime,” says Julien de Wit, an associate professor in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS) and one of the lead authors of the paper.Two-for-one dealLocated 41 light years from Earth, the TRAPPIST-1 system with its seven planets presents a unique opportunity to study a large system with multiple planets of different compositions, similar to our own solar system.“It’s a dream target: You have not one, but maybe three, planets in the habitable zone, so you have a way to actually compare in the same system,” says René Doyon from the Université de Montréal, who co-led the study with de Wit. “There are only a handful of well-characterized temperate rocky planets for which we can hope to detect their atmosphere, and most of them are within the TRAPPIST-1 system.”Astronomers like de Wit and Doyon study exoplanet atmospheres through a technique called transmission spectroscopy, where they look at the way starlight passes through a planet’s potential atmosphere to see what elements are present. Transmission spectra are collected when the planet passes in front of its host star.The planets within the TRAPPIST system have short orbital periods. As a result, their transits frequently overlap. Transit observation times are usually allotted in five-hour windows, and when scheduled properly, close to half of these can catch at least two transits. This “two-for-one” saves both time and money while doubling data collection.Stellar contaminationStars are not uniform; their surfaces can vary in temperature, creating spots that can be hotter or cooler. Molecules like water vapor can condense in cool spots and interfere with transmission spectra. Stellar information like this can be difficult to disentangle from the planetary signal and give false indications of a planet’s atmospheric composition, creating what’s known as “stellar contamination.” While it has often been ignored, the improved capabilities of the JWST have revealed the challenges stellar contamination introduces when studying planetary atmospheres.EAPS research scientist Ben Rackham ran into these challenges when they derailed his initial PhD research on small exoplanets using the Magellan Telescopes in Chile. He’s now seeing the same problem he first encountered as a graduate student repeating itself with the new JWST data.“As we predicted from that earlier work with data from ground-based telescopes, the very first spectral signatures we’re getting with JWST don’t really make any sense in terms of a planetary interpretation,” he says. “The features are not what we would expect to see, and they change from transit to transit.”Rackham and David Berardo, a postdoc in EAPS, have been working with de Wit on ways to correct for stellar contamination using two different methods: improving models of stellar spectra and using direct observations to derive corrections.“By observing a star as it rotates, we can use the sensitivity of JWST to get a clearer picture of what its surface looks like, allowing for a more accurate measuring of the atmosphere of planets that transit it,” says Berardo. This, combined with studying back-to-back transits as proposed in the roadmap, collects useful data on the star that can be used to filter out stellar contamination from both future studies and past ones.Beyond TRAPPIST-1The current roadmap was born from the efforts of the TRAPPIST JWST Community Initiative to bring together separate programs focused on individual planets, which prevented them from leveraging the optimal transit observation windows.“We understood early on that this effort would ‘take a village’ to avoid the efficiency traps of small observation programs,” says de Wit. “Our hope now is that a large-scale community effort guided by the roadmap can be initiated to yield deliverables at a timely pace.” De Wit hopes that it could result in identifying habitable, or inhabitable, worlds around TRAPPIST-1 within a decade.Both de Wit and Doyon believe that the TRAPPIST-1 system is the best place for conducting fundamental research on exoplanet atmospheres that will extend to studies in other systems. Doyon thinks that “the TRAPPIST-1 system will be useful not only for TRAPPIST-1 itself, but also to learn how to do very precise correction of stellar activity which will be beneficial to many other transmission spectroscopy programs also affected by stellar activity.”“We have within reach fundamental and transforming answers with a clear roadmap to them,” says de Wit. “We just need to follow it diligently.”  More

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      “They can see themselves shaping the world they live in”

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      During the journey from the suburbs to the city, the tree canopy often dwindles down as skyscrapers rise up. A group of New England Innovation Academy students wondered why that is.“Our friend Victoria noticed that where we live in Marlborough there are lots of trees in our own backyards. But if you drive just 30 minutes to Boston, there are almost no trees,” said high school junior Ileana Fournier. “We were struck by that duality.”This inspired Fournier and her classmates Victoria Leeth and Jessie Magenyi to prototype a mobile app that illustrates Massachusetts deforestation trends for Day of AI, a free, hands-on curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative, headquartered in the MIT Media Lab and in collaboration with the MIT Schwarzman College of Computing and MIT Open Learning. They were among a group of 20 students from New England Innovation Academy who shared their projects during the 2024 Day of AI global celebration hosted with the Museum of Science.The Day of AI curriculum introduces K-12 students to artificial intelligence. Now in its third year, Day of AI enables students to improve their communities and collaborate on larger global challenges using AI. Fournier, Leeth, and Magenyi’s TreeSavers app falls under the Telling Climate Stories with Data module, one of four new climate-change-focused lessons.“We want you to be able to express yourselves creatively to use AI to solve problems with critical-thinking skills,” Cynthia Breazeal, director of MIT RAISE, dean for digital learning at MIT Open Learning, and professor of media arts and sciences, said during this year’s Day of AI global celebration at the Museum of Science. “We want you to have an ethical and responsible way to think about this really powerful, cool, and exciting technology.”Moving from understanding to actionDay of AI invites students to examine the intersection of AI and various disciplines, such as history, civics, computer science, math, and climate change. With the curriculum available year-round, more than 10,000 educators across 114 countries have brought Day of AI activities to their classrooms and homes.The curriculum gives students the agency to evaluate local issues and invent meaningful solutions. “We’re thinking about how to create tools that will allow kids to have direct access to data and have a personal connection that intersects with their lived experiences,” Robert Parks, curriculum developer at MIT RAISE, said at the Day of AI global celebration.Before this year, first-year Jeremie Kwapong said he knew very little about AI. “I was very intrigued,” he said. “I started to experiment with ChatGPT to see how it reacts. How close can I get this to human emotion? What is AI’s knowledge compared to a human’s knowledge?”In addition to helping students spark an interest in AI literacy, teachers around the world have told MIT RAISE that they want to use data science lessons to engage students in conversations about climate change. Therefore, Day of AI’s new hands-on projects use weather and climate change to show students why it’s important to develop a critical understanding of dataset design and collection when observing the world around them.“There is a lag between cause and effect in everyday lives,” said Parks. “Our goal is to demystify that, and allow kids to access data so they can see a long view of things.”Tools like MIT App Inventor — which allows anyone to create a mobile application — help students make sense of what they can learn from data. Fournier, Leeth, and Magenyi programmed TreeSavers in App Inventor to chart regional deforestation rates across Massachusetts, identify ongoing trends through statistical models, and predict environmental impact. The students put that “long view” of climate change into practice when developing TreeSavers’ interactive maps. Users can toggle between Massachusetts’s current tree cover, historical data, and future high-risk areas.Although AI provides fast answers, it doesn’t necessarily offer equitable solutions, said David Sittenfeld, director of the Center for the Environment at the Museum of Science. The Day of AI curriculum asks students to make decisions on sourcing data, ensuring unbiased data, and thinking responsibly about how findings could be used.“There’s an ethical concern about tracking people’s data,” said Ethan Jorda, a New England Innovation Academy student. His group used open-source data to program an app that helps users track and reduce their carbon footprint.Christine Cunningham, senior vice president of STEM Learning at the Museum of Science, believes students are prepared to use AI responsibly to make the world a better place. “They can see themselves shaping the world they live in,” said Cunningham. “Moving through from understanding to action, kids will never look at a bridge or a piece of plastic lying on the ground in the same way again.”Deepening collaboration on earth and beyondThe 2024 Day of AI speakers emphasized collaborative problem solving at the local, national, and global levels.“Through different ideas and different perspectives, we’re going to get better solutions,” said Cunningham. “How do we start young enough that every child has a chance to both understand the world around them but also to move toward shaping the future?”Presenters from MIT, the Museum of Science, and NASA approached this question with a common goal — expanding STEM education to learners of all ages and backgrounds.“We have been delighted to collaborate with the MIT RAISE team to bring this year’s Day of AI celebration to the Museum of Science,” says Meg Rosenburg, manager of operations at the Museum of Science Centers for Public Science Learning. “This opportunity to highlight the new climate modules for the curriculum not only perfectly aligns with the museum’s goals to focus on climate and active hope throughout our Year of the Earthshot initiative, but it has also allowed us to bring our teams together and grow a relationship that we are very excited to build upon in the future.”Rachel Connolly, systems integration and analysis lead for NASA’s Science Activation Program, showed the power of collaboration with the example of how human comprehension of Saturn’s appearance has evolved. From Galileo’s early telescope to the Cassini space probe, modern imaging of Saturn represents 400 years of science, technology, and math working together to further knowledge.“Technologies, and the engineers who built them, advance the questions we’re able to ask and therefore what we’re able to understand,” said Connolly, research scientist at MIT Media Lab.New England Innovation Academy students saw an opportunity for collaboration a little closer to home. Emmett Buck-Thompson, Jeff Cheng, and Max Hunt envisioned a social media app to connect volunteers with local charities. Their project was inspired by Buck-Thompson’s father’s difficulties finding volunteering opportunities, Hunt’s role as the president of the school’s Community Impact Club, and Cheng’s aspiration to reduce screen time for social media users. Using MIT App Inventor, ​their combined ideas led to a prototype with the potential to make a real-world impact in their community.The Day of AI curriculum teaches the mechanics of AI, ethical considerations and responsible uses, and interdisciplinary applications for different fields. It also empowers students to become creative problem solvers and engaged citizens in their communities and online. From supporting volunteer efforts to encouraging action for the state’s forests to tackling the global challenge of climate change, today’s students are becoming tomorrow’s leaders with Day of AI.“We want to empower you to know that this is a tool you can use to make your community better, to help people around you with this technology,” said Breazeal.Other Day of AI speakers included Tim Ritchie, president of the Museum of Science; Michael Lawrence Evans, program director of the Boston Mayor’s Office of New Urban Mechanics; Dava Newman, director of the MIT Media Lab; and Natalie Lao, executive director of the App Inventor Foundation. More

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      Study doubles the number of known repeating fast radio bursts

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      Fast radio bursts (FRBs) are repeating flashes of radio waves that remain a source of mystery to astronomers. We do know a few things about them: FRBs originate from far outside the Milky Way, for instance, and they’re probably produced from the cinders of dying stars. While many astronomical radio waves have been observed to have burst only once, some waves have been seen bursting multiple times — a puzzle that has led astronomers to question if these radio waves are similar in nature and origin.

      Now, a large team of astronomers, including several from the MIT Kavli Institute for Astrophysics and Space Research and the MIT Department of Physics, have collaborated on work to decipher the origin and nature of FRBs. Their recent open-access publication in The Astrophysical Journal reports the discovery of 25 new repeating FRB sources, doubling the known number of these phenomena known to scientists to 50. In addition, the team found that many repeating FRBs are inactive, producing less than one burst per week of observing time.

      The Canadian-led Canadian Hydrogen Intensity Mapping Experiment (CHIME) has been instrumental in detecting thousands of FRBs as it scans the entire northern sky. So, astronomers with the CHIME/FRB Collaboration developed a new set of statistics tools to comb through massive sets of data to find every repeating source detected so far. This provided a valuable opportunity for astronomers to observe the same source with different telescopes and study the diversity of emission. “We can now accurately calculate the probability that two or more bursts coming from similar locations are not just a coincidence,” explains Ziggy Pleunis, a Dunlap Postdoctoral Fellow at the Dunlap Institute for Astronomy and Astrophysics and corresponding author of the new work.

      The team also concluded that all FRBs may eventually repeat. They found that radio waves seen to have burst only once differed from those that were seen to have burst multiple times both in terms of duration of bursts and range of frequencies emitted, which solidifies the idea that these radio bursts have indeed different origins.

      MIT postdoc Daniele Michilli and PhD student Kaitlyn Shin, both members of MIT Assistant Professor Kiyoshi Masui’s Synoptic Radio Lab, analyzed signals from CHIME’s 1,024 antennae. The work, Michilli says, “allowed us to unambiguously identify some of the sources as repeaters and to provide other observatories with accurate coordinates for follow-up studies.”

      “Now that we have a much larger sample of repeating FRBs, we’re better equipped to understand why we might observe some FRBs to be repeaters and others to be apparently non-repeating, and what the implications are for better understanding their origins,” says Shin.

      Adds Pleunis, “FRBs are likely produced by the leftovers from explosive stellar deaths. By studying repeating FRB sources in detail, we can study the environments that these explosions occur in and understand better the end stages of a star’s life. We can also learn more about the material that is being expelled before and during the star’s demise, which is then returned to the galaxies that the FRBs live in.”

      In addition to Michilli, Shin, and Masui, MIT contributors to the study include physics graduate students Calvin Leung and Haochen Wang. More

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      Communications system achieves fastest laser link from space yet

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      In May 2022, the TeraByte InfraRed Delivery (TBIRD) payload onboard a small CubeSat satellite was launched into orbit 300 miles above Earth’s surface. Since then, TBIRD has delivered terabytes of data at record-breaking rates of up to 100 gigabits per second — 100 times faster than the fastest internet speeds in most cities — via an optical communication link to a ground-based receiver in California. This data rate is more than 1,000 times higher than that of the radio-frequency links traditionally used for satellite communication and the highest ever achieved by a laser link from space to ground. And these record-setting speeds were all made possible by a communications payload roughly the size of a tissue box.

      MIT Lincoln Laboratory conceptualized the TBIRD mission in 2014 as a means of providing unprecedented capability to science missions at low cost. Science instruments in space today routinely generate more data than can be returned to Earth over typical space-to-ground communications links. With small, low-cost space and ground terminals, TBIRD can enable scientists from around the world to fully take advantage of laser communications to downlink all the data they could ever dream of.

      Designed and built at Lincoln Laboratory, the TBIRD communications payload was integrated onto a CubeSat manufactured by Terran Orbital as part of NASA’s Pathfinder Technology Demonstrator program. NASA Ames Research Center established this program to develop a CubeSat bus (the “vehicle” that powers and steers the payload) for bringing science and technology demonstrators into orbit more quickly and inexpensively. Weighing approximately 25 pounds and the size of two stacked cereal boxes, the CubeSat was launched into low-Earth orbit (LEO) aboard Space X’s Transporter-5 rideshare mission from Cape Canaveral Space Force Station in Florida in May 2022. The optical ground station is located in Table Mountain, California, where most weather takes place below the mountain’s summit, making this part of the sky relatively clear for laser communication. This ground station leverages the one-meter telescope and adaptive optics (to correct for distortions caused by atmospheric turbulence) at the NASA Jet Propulsion Laboratory Optical Communications Telescope Laboratory, with Lincoln Laboratory providing the TBIRD-specific ground communications hardware.

      “We’ve demonstrated a higher data rate than ever before in a smaller package than ever before,” says Jade Wang, the laboratory’s program manager for the TBIRD payload and ground communications and assistant leader of the Optical and Quantum Communications Technology Group. “While sending data from space using lasers may sound futuristic, the same technical concept is behind the fiber-optic internet we use every day. The difference is that the laser transmissions are taking place in the open atmosphere, rather than in contained fibers.”

      From radio waves to laser light

      Whether video conferencing, gaming, or streaming movies in high definition, you are using high-data-rate links that run across optical fibers made of glass (or sometimes plastic). About the diameter of a strand of human hair, these fibers are bundled into cables, which transmit data via fast-traveling pulses of light from a laser or other source. Fiber-optic communications are paramount to the internet age, in which large amounts of data must be quickly and reliably distributed across the globe every day.

      For satellites, however, a high-speed internet based on laser communications does not yet exist. Since the beginning of spaceflight in the 1950s, missions have relied on radio frequencies to send data to and from space. Compared to radio waves, the infrared light employed in laser communications has a much higher frequency (or shorter wavelength), which allows more data to be packed into each transmission. Laser communications will enable scientists to send 100 to 1,000 times more data than today’s radio-frequency systems — akin to our terrestrial switch from dial-up to high-speed internet.

      From Earth observation to space exploration, many science missions will benefit from this speedup, especially as instrument capabilities advance to capture larger troves of high-resolution data, experiments involve more remote control, and spacecraft voyage further from Earth into deep space.  

      However, laser-based space communication comes with several engineering challenges. Unlike radio waves, laser light forms a narrow beam. For successful data transmission, this narrow beam must be pointed precisely toward a receiver (e.g., telescope) located on the ground. And though laser light can travel long distances in space, laser beams can be distorted because of atmospheric effects and weather conditions. This distortion causes the beam to experience power loss, which can result in data loss.

      For the past 40 years, Lincoln Laboratory been tackling these and related challenges through various programs. At this point, these challenges have been reliably solved, and laser communications is rapidly becoming widely adopted. Industry has begun a proliferation of LEO cross-links using laser communications, with the intent to enhance the existing terrestrial backbone, as well as to provide a potential internet backbone to serve users in rural locations. Last year, NASA launched the Laser Communications Relay Demonstration (LCRD), a two-way optical communications system based on a laboratory design. In upcoming missions, a laboratory-developed laser communications terminal will be launched to the International Space Station, where the terminal will “talk” to LCRD, and support Artemis II, a crewed program that will fly by the moon in advance of a future crewed lunar landing.

      “With the expanding interest and development in space-based laser communications, Lincoln Laboratory continues to push the envelope of what is possible,” says Wang. “TBIRD heralds a new approach with the potential to further increase data rate capabilities; shrink size, weight, and power; and reduce lasercom mission costs.”

      One way that TBIRD aims to reduce these costs is by utilizing commercial off-the-shelf components originally developed for terrestrial fiber-optic networks. However, terrestrial components are not designed to survive the rigors of space, and their operation can be impacted by atmospheric effects. With TBIRD, the laboratory developed solutions to both challenges.

      Commercial components adapted for space

      The TBIRD payload integrates three key commercial off-the-shelf components: a high-rate optical modem, a large high-speed storage drive, and an optical signal amplifier.

      All these hardware components underwent shock and vibration, thermal-vacuum, and radiation testing to inform how the hardware might fare in space, where it would be subject to powerful forces, extreme temperatures, and high radiation levels. When the team first tested the amplifier through a thermal test simulating the space environment, the fibers melted. As Wang explains, in vacuum, no atmosphere exists, so heat gets trapped and cannot be released by convection. The team worked with the vendor to modify the amplifier to release heat through conduction instead.

      To deal with data loss from atmospheric effects, the laboratory developed its own version of Automatic Repeat Request (ARQ), a protocol for controlling errors in data transmission over a communications link. With ARQ, the receiver (in this case, the ground terminal) alerts the sender (satellite) through a low-rate uplink signal to re-transmit any block of data (frame) that has been lost or damaged.

      “If the signal drops out, data can be re-transmitted, but if done inefficiently — meaning you spend all your time sending repeat data instead of new data — you can lose a lot of throughput,” explains TBIRD system engineer Curt Schieler, a technical staff member in Wang’s group. “With our ARQ protocol, the receiver tells the payload which frames it received correctly, so the payload knows which ones to re-transmit.”

      Another aspect of TBIRD that is new is its lack of a gimbal, a mechanism for pointing the narrow laser beam. Instead, TBIRD relies on a laboratory-developed error-signaling concept for precision body pointing of the spacecraft. Error signals are provided to the CubeSat bus so it knows how exactly to point the body of the entire satellite toward the ground station. Without a gimbal, the payload can be even further miniaturized.

      “We intended to demonstrate a low-cost technology capable of quickly downlinking a large volume of data from LEO to Earth, in support of science missions,” says Wang. “In just a few weeks of operations, we have already accomplished this goal, achieving unprecedented transmission rates of up to 100 gigabits per second. Next, we plan to exercise additional features of the TBIRD system, including increasing rates to 200 gigabits per second, enabling the downlink of more than 2 terabytes of data — equivalent to 1,000 high-definition movies — in a single five-minute pass over a ground station.”

      Lincoln Laboratory developed the TBIRD mission and technology in partnership with NASA Goddard Space Flight Center. More

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      Taming the data deluge

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      An oncoming tsunami of data threatens to overwhelm huge data-rich research projects on such areas that range from the tiny neutrino to an exploding supernova, as well as the mysteries deep within the brain. 

      When LIGO picks up a gravitational-wave signal from a distant collision of black holes and neutron stars, a clock starts ticking for capturing the earliest possible light that may accompany them: time is of the essence in this race. Data collected from electrical sensors monitoring brain activity are outpacing computing capacity. Information from the Large Hadron Collider (LHC)’s smashed particle beams will soon exceed 1 petabit per second. 

      To tackle this approaching data bottleneck in real-time, a team of researchers from nine institutions led by the University of Washington, including MIT, has received $15 million in funding to establish the Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute. From MIT, the research team includes Philip Harris, assistant professor of physics, who will serve as the deputy director of the A3D3 Institute; Song Han, assistant professor of electrical engineering and computer science, who will serve as the A3D3’s co-PI; and Erik Katsavounidis, senior research scientist with the MIT Kavli Institute for Astrophysics and Space Research.

      Infused with this five-year Harnessing the Data Revolution Big Idea grant, and jointly funded by the Office of Advanced Cyberinfrastructure, A3D3 will focus on three data-rich fields: multi-messenger astrophysics, high-energy particle physics, and brain imaging neuroscience. By enriching AI algorithms with new processors, A3D3 seeks to speed up AI algorithms for solving fundamental problems in collider physics, neutrino physics, astronomy, gravitational-wave physics, computer science, and neuroscience. 

      “I am very excited about the new Institute’s opportunities for research in nuclear and particle physics,” says Laboratory for Nuclear Science Director Boleslaw Wyslouch. “Modern particle detectors produce an enormous amount of data, and we are looking for extraordinarily rare signatures. The application of extremely fast processors to sift through these mountains of data will make a huge difference in what we will measure and discover.”

      The seeds of A3D3 were planted in 2017, when Harris and his colleagues at Fermilab and CERN decided to integrate real-time AI algorithms to process the incredible rates of data at the LHC. Through email correspondence with Han, Harris’ team built a compiler, HLS4ML, that could run an AI algorithm in nanoseconds.

      “Before the development of HLS4ML, the fastest processing that we knew of was roughly a millisecond per AI inference, maybe a little faster,” says Harris. “We realized all the AI algorithms were designed to solve much slower problems, such as image and voice recognition. To get to nanosecond inference timescales, we recognized we could make smaller algorithms and rely on custom implementations with Field Programmable Gate Array (FPGA) processors in an approach that was largely different from what others were doing.”

      A few months later, Harris presented their research at a physics faculty meeting, where Katsavounidis became intrigued. Over coffee in Building 7, they discussed combining Harris’ FPGA with Katsavounidis’s use of machine learning for finding gravitational waves. FPGAs and other new processor types, such as graphics processing units (GPUs), accelerate AI algorithms to more quickly analyze huge amounts of data.

      “I had worked with the first FPGAs that were out in the market in the early ’90s and have witnessed first-hand how they revolutionized front-end electronics and data acquisition in big high-energy physics experiments I was working on back then,” recalls Katsavounidis. “The ability to have them crunch gravitational-wave data has been in the back of my mind since joining LIGO over 20 years ago.”

      Two years ago they received their first grant, and the University of Washington’s Shih-Chieh Hsu joined in. The team initiated the Fast Machine Lab, published about 40 papers on the subject, built the group to about 50 researchers, and “launched a whole industry of how to explore a region of AI that has not been explored in the past,” says Harris. “We basically started this without any funding. We’ve been getting small grants for various projects over the years. A3D3 represents our first large grant to support this effort.”  

      “What makes A3D3 so special and suited to MIT is its exploration of a technical frontier, where AI is implemented not in high-level software, but rather in lower-level firmware, reconfiguring individual gates to address the scientific question at hand,” says Rob Simcoe, director of MIT Kavli Institute for Astrophysics and Space Research and the Francis Friedman Professor of Physics. “We are in an era where experiments generate torrents of data. The acceleration gained from tailoring reprogrammable, bespoke computers at the processor level can advance real-time analysis of these data to new levels of speed and sophistication.”

      The Huge Data from the Large Hadron Collider 

      With data rates already exceeding 500 terabits per second, the LHC processes more data than any other scientific instrument on earth. Its future aggregate data rates will soon exceed 1 petabit per second, the biggest data rate in the world. 

      “Through the use of AI, A3D3 aims to perform advanced analyses, such as anomaly detection, and particle reconstruction on all collisions happening 40 million times per second,” says Harris.

      The goal is to find within all of this data a way to identify the few collisions out of the 3.2 billion collisions per second that could reveal new forces, explain how dark matter is formed, and complete the picture of how fundamental forces interact with matter. Processing all of this information requires a customized computing system capable of interpreting the collider information within ultra-low latencies.  

      “The challenge of running this on all of the 100s of terabits per second in real-time is daunting and requires a complete overhaul of how we design and implement AI algorithms,” says Harris. “With large increases in the detector resolution leading to data rates that are even larger the challenge of finding the one collision, among many, will become even more daunting.” 

      The Brain and the Universe

      Thanks to advances in techniques such as medical imaging and electrical recordings from implanted electrodes, neuroscience is also gathering larger amounts of data on how the brain’s neural networks process responses to stimuli and perform motor information. A3D3 plans to develop and implement high-throughput and low-latency AI algorithms to process, organize, and analyze massive neural datasets in real time, to probe brain function in order to enable new experiments and therapies.   

      With Multi-Messenger Astrophysics (MMA), A3D3 aims to quickly identify astronomical events by efficiently processing data from gravitational waves, gamma-ray bursts, and neutrinos picked up by telescopes and detectors. 

      The A3D3 researchers also include a multi-disciplinary group of 15 other researchers, including project lead the University of Washington, along with Caltech, Duke University, Purdue University, UC San Diego, University of Illinois Urbana-Champaign, University of Minnesota, and the University of Wisconsin-Madison. It will include neutrinos research at Icecube and DUNE, and visible astronomy at Zwicky Transient Facility, and will organize deep-learning workshops and boot camps to train students and researchers on how to contribute to the framework and widen the use of fast AI strategies.

      “We have reached a point where detector network growth will be transformative, both in terms of event rates and in terms of astrophysical reach and ultimately, discoveries,” says Katsavounidis. “‘Fast’ and ‘efficient’ is the only way to fight the ‘faint’ and ‘fuzzy’ that is out there in the universe, and the path for getting the most out of our detectors. A3D3 on one hand is going to bring production-scale AI to gravitational-wave physics and multi-messenger astronomy; but on the other hand, we aspire to go beyond our immediate domains and become the go-to place across the country for applications of accelerated AI to data-driven disciplines.” More