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Exploding stars send out powerful bursts of energy − I’m leading a citizen scientist project to classify and learn about these bright flashes

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theconversation.com – Amy Lien, Assistant Professor of Physics, of Tampa – 2024-04-16 07:31:08
Gamma-ray bursts, as shown in this illustration, come from powerful astronomical .
NASA, ESA and M. Kornmesser

Amy Lien, University of Tampa

When faraway explode, they send out flashes of energy called gamma-ray bursts that are bright enough that telescopes back on Earth can detect them. Studying these pulses, which can also come from mergers of some exotic astronomical objects such as black holes and neutron stars, can help astronomers like me understand the history of the universe.

telescopes detect on average one gamma-ray burst per day, adding to thousands of bursts detected throughout the years, and a community of volunteers are making research into these bursts possible.

On Nov. 20, 2004, NASA launched the Neil Gehrels Swift Observatory, also known as Swift. Swift is a multiwavelength space telescope that scientists are using to find out more about these mysterious gamma-ray flashes from the universe.

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Gamma-ray bursts usually last for only a very short time, from a few seconds to a few minutes, and the majority of their emission is in the form of gamma rays, which are part of the light spectrum that our eyes cannot see. Gamma rays contain a lot of energy and can damage human tissues and DNA.

Fortunately, Earth's atmosphere blocks most gamma rays from space, but that also means the only way to observe gamma-ray bursts is through a space telescope like Swift. Throughout its 19 years of observations, Swift has observed over 1,600 gamma-ray bursts. The information it collects from these bursts helps astronomers back on the ground measure the distances to these objects.

A cylindrical spacecraft, with two flat solar panels, one on each side.
NASA's Swift observatory, which detects gamma rays.
NASA E/PO, Sonoma State University/Aurore Simonnet

Looking back in time

The data from Swift and other observatories has taught astronomers that gamma-ray bursts are one of the most powerful explosions in the universe. They're so bright that space telescopes like Swift can detect them from across the entire universe.

In fact, gamma-ray bursts are among one of the farthest astrophysical objects observed by telescopes.

Because light travels at a finite speed, astronomers are effectively looking back in time as they look farther into the universe.

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The farthest gamma-ray burst ever observed occurred so far away that its light took 13 years to reach Earth. So when telescopes took pictures of that gamma-ray burst, they observed the as it looked 13 billion years ago.

Gamma-ray bursts allow astronomers to learn about the history of the universe, how the birth rate and the mass of the stars change over time.

Types of gamma-ray bursts

Astronomers now know that there are basically two kinds of gamma-ray bursts – long and short. They are classified by how long their pulses last. The long gamma-ray bursts have pulses longer than two seconds, and at least some of these events are related to supernovae – exploding stars.

When a massive star, or a star that is at least eight times more massive than our Sun, runs out of fuel, it will explode as a supernova and collapse into either a neutron star or a black hole.

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Both neutron stars and black holes are extremely compact. If you shrank the entire Sun into a diameter of about 12 miles, or the size of Manhattan, it would be as dense as a neutron star.

Some particularly massive stars can also launch jets of light when they explode. These jets are concentrated beams of light powered by structured magnetic fields and charged particles. When these jets are pointed toward Earth, telescopes like Swift will detect a gamma-ray burst.

Gamma-ray burst emission.

On the other hand, short gamma-ray bursts have pulses shorter than two seconds. Astronomers that most of these short bursts happen when either two neutron stars or a neutron star and a black hole merge.

When a neutron star gets too close to another neutron star or a black hole, the two objects will orbit around each other, creeping closer and closer as they lose some of their energy through gravitational waves.

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These objects eventually merge and emit short jets. When the short jets are pointed toward Earth, space telescopes can detect them as short gamma-ray bursts.

Neutron star mergers emit gamma-ray bursts.

Classifying gamma-ray bursts

Classifying bursts as short or long isn't always that simple. In the past few years, astronomers have discovered some peculiar short gamma-ray bursts associated with supernovae instead of the expected mergers. And they've found some long gamma-ray bursts related to mergers instead of supernovae.

These confusing cases show that astronomers do not fully understand how gamma-ray bursts are created. They suggest that astronomers need a better understanding of gamma-ray pulse shapes to better connect the pulses to their origins.

But it's hard to classify pulse shape, which is different than pulse duration, systematically. Pulse shapes can be extremely diverse and complex. So far, even machine learning algorithms haven't been able to correctly recognize all the detailed pulse structures that astronomers are interested in.

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Community science

My colleagues and I have enlisted the help of volunteers through NASA to identify pulse structures. Volunteers learn to identify the pulse structures, then they look at images on their own computers and classify them.

Our preliminary results suggest that these volunteers – also referred to as citizen scientists – can quickly learn and recognize gamma-ray pulses' complex structures. Analyzing this data will help astronomers better understand how these mysterious bursts are created.

Our team hopes to learn about whether more gamma-ray bursts in the sample the previous short and long classification. We'll use the data to more accurately probe the history of the universe through gamma-ray burst observations.

This citizen science , called Burst Chaser, has grown since our preliminary results, and we're actively recruiting new volunteers to join our quest to study the mysterious origins behind these bursts.The Conversation

Amy Lien, Assistant Professor of Physics, University of Tampa

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Black holes are mysterious, yet also deceptively simple − a new space mission may help physicists answer hairy questions about these astronomical objects

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theconversation.com – Gaurav Khanna, Professor of Physics, of Rhode Island – 2024-05-15 07:16:18

An illustration of a supermassive black hole.

NASA/JPL

Gaurav Khanna, University of Rhode Island

Physicists consider black holes one of the most mysterious objects that exist. Ironically, they're also considered one of the simplest. For years, physicists like me have been looking to prove that black holes are more complex than they seem. And a newly approved European space mission called LISA will us with this hunt.

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Research from the 1970s suggests that you can comprehensively describe a black hole using only three physical attributes – their mass, charge and spin. All the other properties of these massive dying , like their detailed composition, density and temperature profiles, disappear as they transform into a black hole. That is how simple they are.

The idea that black holes have only three attributes is called the “no-hair” theorem, implying that they don't have any “hairy” details that make them complicated.

Black holes are massive, mysterious astronomical objects.

Hairy black holes?

For decades, researchers in the astrophysics community have exploited loopholes or work-arounds within the no-hair theorem's assumptions to up with potential hairy black hole scenarios. A hairy black hole has a physical property that scientists can measure – in principle – that's beyond its mass, charge or spin. This property has to be a permanent part of its structure.

About a decade ago, Stefanos Aretakis, a physicist currently at the University of Toronto, showed mathematically that a black hole containing the maximum charge it could hold – called an extremal charged black hole – would develop “hair” at its horizon. A black hole's horizon is the boundary where anything that crosses it, even light, can't escape.

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Aretakis' analysis was more of a thought experiment using a highly simplified physical scenario, so it's not something scientists expect to observe astrophysically. But supercharged black holes might not be the only kind that could have hair.

Since astrophysical objects such as stars and planets are known to spin, scientists expect that black holes would spin as well, based on how they form. Astronomical evidence has shown that black holes do have spin, though researchers don't know what the typical spin value is for an astrophysical black hole.

Using computer simulations, my team has recently discovered similar types of hair in black holes that are spinning at the maximum rate. This hair has to do with the rate of change, or the gradient, of -time's curvature at the horizon. We also discovered that a black hole wouldn't actually have to be maximally spinning to have hair, which is significant because these maximally spinning black holes probably don't form in nature.

Detecting and measuring hair

My team wanted to develop a way to potentially measure this hair – a new fixed property that might characterize a black hole beyond its mass, spin and charge. We started looking into how such a new property might a signature on a gravitational wave emitted from a fast-spinning black hole.

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A gravitational wave is a tiny disturbance in space-time typically caused by violent astrophysical in the universe. The collisions of compact astrophysical objects such as black holes and neutron stars emit strong gravitational waves. An international network of gravitational observatories, the Laser Interferometer Gravitational-wave Observatory in the United States, routinely detects these waves.

Our recent studies suggest that one can measure these hairy attributes from gravitational wave data for fast-spinning black holes. Looking at the gravitational wave data offers an for a signature of sorts that could indicate whether the black hole has this type of hair.

Our ongoing studies and recent progress made by Som Bishoyi, a student on the team, are based on a blend of theoretical and computational models of fast-spinning black holes. Our findings have not been tested in the field yet or observed in real black holes out in space. But we hope that will soon change.

LISA gets a go-ahead

In January 2024, the European Space Agency formally adopted the space-based Laser Interferometer Space Antenna, or LISA, mission. LISA will look for gravitational waves, and the data from the mission could help my team with our hairy black hole questions.

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Three spacecrafts spaced apart sending light beams towards each other while orbiting the Sun

The LISA spacecrafts observing gravitational waves from a distant source while orbiting the Sun.

Simon Barke/Univ. Florida, CC BY

Formal adoption means that the has the go-ahead to move to the construction phase, with a planned 2035 launch. LISA consists of three spacecrafts configured in a perfect equilateral triangle that will trail behind the Earth around the Sun. The spacecrafts will each be 1.6 million miles (2.5 million kilometers) apart, and they will exchange laser beams to measure the distance between each other down to about a billionth of an inch.

LISA will detect gravitational waves from supermassive black holes that are millions or even billions of times more massive than our Sun. It will build a map of the space-time around rotating black holes, which will help physicists understand how gravity works in the close vicinity of black holes to an unprecedented level of accuracy. Physicists hope that LISA will also be able to measure any hairy attributes that black holes might have.

With LIGO making new observations every day and LISA to offer a glimpse into the space-time around black holes, now is one of the most exciting times to be a black hole physicist.The Conversation

Gaurav Khanna, Professor of Physics, University of Rhode Island

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Viruses are doing mysterious things everywhere – AI can help researchers understand what they’re up to in the oceans and in your gut

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theconversation.com – Libusha , Associate Professor of Systems and Computational Biology, Microbiology and Immunology, Albert Einstein College of Medicine – 2024-05-15 07:16:41

Many viral genetic sequences code for proteins that researchers haven't seen before.

KTSDesign/Science Photo Library via Getty Images

Libusha Kelly, Albert Einstein College of Medicine

Viruses are a mysterious and poorly understood force in microbial ecosystems. Researchers know they can infect, kill and manipulate human and bacterial cells in nearly every environment, from the oceans to your gut. But scientists don't yet have a full picture of how viruses affect their surrounding environments in large part because of their extraordinary diversity and ability to rapidly evolve.

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Communities of microbes are difficult to study in a laboratory setting. Many microbes are challenging to cultivate, and their natural has many more features influencing their or failure than scientists can replicate in a lab.

So systems biologists like me often sequence all the DNA present in a sample – for example, a fecal sample from a patient – separate out the viral DNA sequences, then annotate the sections of the viral genome that code for proteins. These notes on the location, structure and other features of genes researchers understand the functions viruses might carry out in the environment and help identify different kinds of viruses. Researchers annotate viruses by matching viral sequences in a sample to previously annotated sequences available in public databases of viral genetic sequences.

However, scientists are identifying viral sequences in DNA collected from the environment at a rate that far outpaces our ability to annotate those genes. This means researchers are publishing findings about viruses in microbial ecosystems using unacceptably small fractions of available data.

To improve researchers' ability to study viruses around the globe, my team and I have developed a novel approach to annotate viral sequences using artificial intelligence. Through protein language models akin to large language models like ChatGPT but specific to proteins, we were able to classify previously unseen viral sequences. This opens the door for researchers to not only learn more about viruses, but also to address biological questions that are difficult to answer with current techniques.

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Annotating viruses with AI

Large language models use relationships between words in large datasets of text to potential answers to questions they are not explicitly “taught” the answer to. When you ask a chatbot “What is the capital of France?” for example, the model is not looking up the answer in a table of capital . Rather, it is using its on huge datasets of documents and information to infer the answer: “The capital of France is Paris.”

Similarly, protein language models are AI algorithms that are trained to recognize relationships between billions of protein sequences from environments around the world. Through this training, they may be able to infer something about the essence of viral proteins and their functions.

We wondered whether protein language models could answer this question: “Given all annotated viral genetic sequences, what is this new sequence's function?”

In our proof of concept, we trained neural networks on previously annotated viral protein sequences in pre-trained protein language models and then used them to predict the annotation of new viral protein sequences. Our approach allows us to probe what the model is “seeing” in a particular viral sequence that to a particular annotation. This helps identify candidate proteins of interest either based on their specific functions or how their genome is arranged, winnowing down the search of vast datasets.

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Microscopy image of spherical bacteria colored bright green

Prochlorococcus is one of the many species of marine bacteria with proteins that researchers haven't seen before.

Anne Thompson/Chisholm Lab, MIT via Flickr

By identifying more distantly related viral gene functions, protein language models can complement current methods to provide new insights into microbiology. For example, my team and I were able to use our model to discover a previously unrecognized integrase – a type of protein that can move genetic information in and out of cells – in the globally abundant marine picocyanobacteria Prochlorococcus and Synechococcus. Notably, this integrase may be able to move genes in and out of these populations of bacteria in the oceans and enable these microbes to better adapt to changing environments.

Our language model also identified a novel viral capsid protein that is widespread in the global oceans. We produced the first picture of how its genes are arranged, showing it can contain different sets of genes that we believe indicates this virus serves different functions in its environment.

These preliminary findings represent only two of thousands of annotations our approach has provided.

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Analyzing the unknown

Most of the hundreds of thousands of newly discovered viruses remain unclassified. Many viral genetic sequences match protein families with no known function or have never been seen before. Our work shows that similar protein language models could help study the threat and promise of our planet's many uncharacterized viruses.

While our study focused on viruses in the global oceans, improved annotation of viral proteins is critical for better understanding the role viruses play in and disease in the human body. We and other researchers have hypothesized that viral activity in the human gut microbiome might be altered when you're sick. This means that viruses may help identify stress in microbial communities.

However, our approach is also limited because it requires high-quality annotations. Researchers are developing newer protein language models that incorporate other “tasks” as part of their training, particularly predicting protein structures to detect similar proteins, to make them more powerful.

Making all AI tools available via FAIR Data Principles – data that is findable, accessible, interoperable and reusable – can help researchers at large realize the potential of these new ways of annotating protein sequences leading to discoveries that benefit human health.The Conversation

Libusha Kelly, Associate Professor of Systems and Computational Biology, Microbiology and Immunology, Albert Einstein College of Medicine

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Human differences in judgment lead to problems for AI

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theconversation.com – Mayank Kejriwal, Research Assistant Professor of Industrial & Engineering, of Southern California – 2024-05-14 07:14:06

Bias isn't the only human imperfection turning up in AI.

Emrah Turudu/Photodisc via Getty Images

Mayank Kejriwal, University of Southern California

Many people understand the concept of bias at some intuitive level. In society, and in artificial intelligence systems, racial and gender biases are well documented.

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If society could somehow bias, would all problems go away? The late Nobel laureate Daniel Kahneman, who was a key figure in the field of behavioral economics, argued in his last book that bias is just one side of the coin. Errors in judgments can be attributed to two sources: bias and noise.

Bias and noise both play important roles in fields such as law, medicine and financial forecasting, where human judgments are central. In our work as computer and information scientists, my colleagues and I have found that noise also plays a role in AI.

Statistical noise

Noise in this context means variation in how people make judgments of the same problem or situation. The problem of noise is more pervasive than initially meets the eye. A seminal work, dating back all the way to the Great Depression, has found that different judges gave different sentences for similar cases.

Worryingly, sentencing in court cases can depend on things such as the temperature and whether the local football team won. Such factors, at least in part, contribute to the perception that the justice system is not just biased but also arbitrary at times.

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Other examples: Insurance adjusters might give different estimates for similar claims, reflecting noise in their judgments. Noise is likely present in all manner of contests, ranging from wine tastings to local beauty pageants to college admissions.

Behavioral economist Daniel Kahneman explains the concept of noise in human judgment.

Noise in the data

On the surface, it doesn't seem likely that noise could affect the performance of AI systems. After all, machines aren't affected by weather or football teams, so why would they make judgments that vary with circumstance? On the other hand, researchers know that bias affects AI, because it is reflected in the data that the AI is trained on.

For the new spate of AI models like ChatGPT, the gold standard is human performance on general intelligence problems such as common sense. ChatGPT and its peers are measured against human-labeled commonsense datasets.

Put simply, researchers and developers can ask the machine a commonsense question and compare it with human answers: “If I place a heavy rock on a paper table, will it collapse? Yes or No.” If there is high agreement between the two – in the best case, perfect agreement – the machine is approaching human-level common sense, according to the test.

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So where would noise in? The commonsense question above seems simple, and most humans would likely agree on its answer, but there are many questions where there is more disagreement or uncertainty: “Is the sentence plausible or implausible? My dog plays volleyball.” In other words, there is potential for noise. It is not surprising that interesting commonsense questions would have some noise.

But the issue is that most AI tests don't account for this noise in experiments. Intuitively, questions generating human answers that tend to agree with one another should be weighted higher than if the answers diverge – in other words, where there is noise. Researchers still don't know whether or how to weigh AI's answers in that situation, but a first step is acknowledging that the problem exists.

Tracking down noise in the machine

Theory aside, the question still remains whether all of the above is hypothetical or if in real tests of common sense there is noise. The best way to prove or disprove the presence of noise is to take an existing test, remove the answers and get multiple people to independently label them, meaning answers. By measuring disagreement among humans, researchers can know just how much noise is in the test.

The details behind measuring this disagreement are complex, involving significant statistics and math. Besides, who is to say how common sense should be defined? How do you know the human judges are motivated enough to think through the question? These issues lie at the intersection of good experimental design and statistics. Robustness is key: One result, test or set of human labelers is unlikely to convince anyone. As a pragmatic matter, human labor is expensive. Perhaps for this reason, there haven't been any studies of possible noise in AI tests.

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To address this gap, my colleagues and I designed such a study and published our findings in Nature Scientific Reports, showing that even in the domain of common sense, noise is inevitable. Because the setting in which judgments are elicited can matter, we did two kinds of studies. One type of study involved paid workers from Amazon Mechanical Turk, while the other study involved a smaller-scale labeling exercise in two labs at the University of Southern California and the Rensselaer Polytechnic Institute.

You can think of the former as a more realistic online setting, mirroring how many AI tests are actually labeled before being released for and evaluation. The latter is more of an extreme, guaranteeing high quality but at much smaller scales. The question we set out to answer was how inevitable is noise, and is it just a matter of quality control?

The results were sobering. In both settings, even on commonsense questions that might have been expected to elicit high – even universal – agreement, we found a nontrivial degree of noise. The noise was high enough that we inferred that between 4% and 10% of a system's performance could be attributed to noise.

To emphasize what this means, suppose I built an AI system that achieved 85% on a test, and you built an AI system that achieved 91%. Your system would seem to be a lot better than mine. But if there is noise in the human labels that were used to score the answers, then we're not sure anymore that the 6% improvement means much. For all we know, there may be no real improvement.

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On AI leaderboards, where large language models like the one that powers ChatGPT are , performance differences between rival systems are far narrower, typically less than 1%. As we show in the paper, ordinary statistics do not really come to the rescue for disentangling the effects of noise from those of true performance improvements.

Noise audits

What is the way forward? Returning to Kahneman's book, he proposed the concept of a “noise audit” for quantifying and ultimately mitigating noise as much as possible. At the very least, AI researchers need to estimate what influence noise might be .

Auditing AI systems for bias is somewhat commonplace, so we believe that the concept of a noise audit should naturally follow. We hope that this study, as well as others like it, to their adoption.The Conversation

Mayank Kejriwal, Research Assistant Professor of Industrial & Systems Engineering, University of Southern California

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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