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    Communications system achieves fastest laser link from space yet

    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

    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