Physicists with the LIGO/Virgo/KAGRA collaboration have detected the gravitational wave signal (dubbed GW231123) of the most massive merger between two black holes yet observed, resulting in a new black hole that is 225 times more massive than our Sun. The results were presented at the Edoardo Amaldi Conference on Gravitational Waves in Glasgow, Scotland.
The LIGO/Virgo/KAGRA collaboration searches the universe for gravitational waves produced by the mergers of black holes and neutron stars. LIGO detects gravitational waves via laser interferometry, using high-powered lasers to measure tiny changes in the distance between two objects positioned kilometers apart. LIGO has detectors in Hanford, Washington, and in Livingston, Louisiana. A third detector in Italy, Advanced Virgo, came online in 2016. In Japan, KAGRA is the first gravitational-wave detector in Asia and the first to be built underground. Construction began on LIGO-India in 2021, and physicists expect it will turn on sometime after 2025.
To date, the collaboration has detected dozens of merger events since its first Nobel Prize-winning discovery. Early detected mergers involved either two black holes or two neutron stars. In 2021, LIGO/Virgo/KAGRA confirmed the detection of two separate "mixed" mergers between black holes and neutron stars.
© Caltech-LIGO
Type Ia supernovae are critical tools in astronomy, since they all appear to explode with the same intensity, allowing us to use their brightness as a measure of distance. The distance measures they've given us have been critical to tracking the expansion of the Universe, which led to the recognition that there's some sort of dark energy hastening the Universe's expansion. Yet there are ongoing arguments over exactly how these events are triggered.
There's widespread agreement that type Ia supernovae are the explosions of white dwarf stars. Normally, these stars are composed primarily of moderately heavy elements like carbon and oxygen, and lack the mass to trigger additional fusion. But if some additional material is added, the white dwarf can reach a critical mass and reignite a runaway fusion reaction, blowing the star apart. But the source of the additional mass has been somewhat controversial.
But there's an additional hypothesis that doesn't require as much mass: a relatively small explosion on a white dwarf's surface can compress the interior enough to restart fusion in stars that haven't yet reached a critical mass. Now, observations of the remains of a supernova provide some evidence of the existence of these so-called "double detonation" supernovae.
© ESO/P. Das et al. Background stars (Hubble): K. Noll et al.
On Thursday, Microsoft's Azure Quantum group announced that it has settled on a plan for getting error correction on quantum computers. While the company pursues its own hardware efforts, the Azure team is a platform provider that currently gives access to several distinct types of hardware qubits. So it has chosen a scheme that is suitable for several different quantum computing technologies (notably excluding its own). The company estimates that the system it has settled on can take hardware qubits with an error rate of about 1 in 1,000 and use them to build logical qubits where errors are instead 1 in 1 million.
While it's describing the scheme in terms of mathematical proofs and simulations, it hasn't shown that it works using actual hardware yet. But one of its partners, Atom Computing, is accompanying the announcement with a description of how its machine is capable of performing all the operations that will be needed.
There are similarities and differences between what the company is talking about today and IBM's recent update of its roadmap, which described another path to error-resistant quantum computing. In IBM's case, it makes both the software stack that will perform the error correction and the hardware needed to implement it. It uses chip-based hardware, with the connections among qubits mediated by wiring that's laid out when the chip is fabricated. Since error correction schemes require a very specific layout of connections among qubits, once IBM decides on a quantum error correction scheme, it can design chips with the wiring needed to implement that scheme.
© Atom Computing
Galaxies are far more than the sum of their stars. Long before stars even formed, dark matter clumped up and drew regular matter together with its gravity, providing the invisible scaffolding upon which stars and galaxies eventually grew.
Today, nearly all galaxies are still embedded in giant “halos” of dark matter that extend far beyond their visible borders and hold them together, anchoring stars that move so quickly they would otherwise break out of their galaxy’s gravitational grip and spend their lives adrift in intergalactic space.
The way dark matter and stars interact influences how galaxies change over time. But until recently, scientists had mainly only examined one side of that relationship, exploring the way dark matter pulls on normal matter.
© ESO/S. Brunier
Everyone in quantum computing agrees that error correction will be the key to doing a broad range of useful calculations. But early every company in the field seems to have a different vision of how best to get there. Almost all of their plans share a key feature: some variation on logical qubits built by linking together multiple hardware qubits.
A key exception is Nord Quantique, which aims to dramatically cut the amount of hardware needed to support an error-corrected quantum computer. It does this by putting enough quantum states into a single piece of hardware, allowing each of those pieces to hold an error-corrected qubit. Last week, the company shared results showing that it could make hardware that used photons at two different frequencies to successfully identify every case where a logical qubit lost its state.
That still doesn't provide complete error correction, and they didn't use the logical qubit to perform operations. But it's an important validation of the company's approach.
© Nord Quantique
It's been textbook knowledge for over a century that our Milky Way galaxy is doomed to collide with another large spiral galaxy, Andromeda, in the next 5 billion years and merge into one even bigger galaxy. But a fresh analysis published in the journal Nature Astronomy is casting that longstanding narrative in a more uncertain light. The authors conclude that the likelihood of this collision and merger is closer to the odds of a coin flip, with a roughly 50 percent probability that the two galaxies will avoid such an event during the next 10 billion years.
Both the Milky Way and the Andromeda galaxies (M31) are part of what's known as the Local Group (LG), which also hosts other smaller galaxies (some not yet discovered) as well as dark matter (per the prevailing standard cosmological model). Both already have remnants of past mergers and interactions with other galaxies, according to the authors.
"Predicting future mergers requires knowledge about the present coordinates, velocities, and masses of the systems partaking in the interaction," the authors wrote. That involves not just the gravitational force between them but also dynamical friction. It's the latter that dominates when galaxies are headed toward a merger, since it causes galactic orbits to decay.
© NASA/Joseph DePasquale (STScI)
It's a regrettable reality that there is never time to cover all the interesting scientific stories we come across each month. In the past, we've featured year-end roundups of cool science stories we (almost) missed. This year, we're experimenting with a monthly collection. May's list includes a nifty experiment to make a predicted effect of special relativity visible; a ping-pong playing robot that can return hits with 88 percent accuracy; and the discovery of the rare genetic mutation that makes orange cats orange, among other highlights.
Credit:
TU Wien
Perhaps the most well-known feature of Albert Einstein's special theory of relativity is time dilation and length contraction. In 1959, two physicists predicted another feature of relativistic motion: An object moving near the speed of light should also appear to be rotated. It has not been possible to demonstrate this experimentally, however—until now. Physicists at the Vienna University of Technology figured out how to reproduce this rotational effect in the lab using laser pulses and precision cameras, according to a paper published in the journal Communications Physics.
© David Nguyen, Kendrick Cancio and Sangbae Kim
More than 45 million people in the US are fans of bowling, with national competitions awarding millions of dollars. Bowlers usually rely on instinct and experience, earned through lots and lots of practice, to boost their strike percentage. A team of physicists has come up with a mathematical model to better predict ball trajectories, outlined in a new paper published in the journal AIP Advances. The resulting equations take into account such factors as the composition and resulting pattern of the oil used on bowling lanes, as well as the inevitable asymmetries of bowling balls and player variability.
The authors already had a strong interest in bowling. Three are regular bowlers and quite skilled at the sport; a fourth, Curtis Hooper of Longborough University in the UK, is a coach for Team England at the European Youth Championships. Hooper has been studying the physics of bowling for several years, including an analysis of the 2017 Weber Cup, as well as papers devising mathematical models for the application of lane conditioners and oil patterns in bowling.
The calculations involved in such research are very complicated because there are so many variables that can affect a ball's trajectory after being thrown. Case in point: the thin layer of oil that is applied to bowling lanes, which Hooper found can vary widely in volume and shape among different venues, plus the lack of uniformity in applying the layer, which creates an uneven friction surface.
© Universal Pictures
Concerns about AI's energy use have a lot of people looking into ways to cut down on its power requirements. Many of these focus on hardware and software approaches that are pretty straightforward extensions of existing technologies. But a few technologies are much farther out there. One that's definitely in the latter category? Quantum computing.
In some ways, quantum hardware is a better match for some of the math that underlies AI than more traditional hardware. While the current quantum hardware is a bit too error-prone for the more elaborate AI models currently in use, researchers are starting to put the pieces in place to run AI models when the hardware is ready. This week, a couple of commercial interests are releasing a draft of a paper describing how to get classical image data into a quantum processor (actually, two different processors) and perform a basic AI image classification.
All of which gives us a great opportunity to discuss why quantum AI may be more than just hype.
© Jason Marz/Getty Images
Coffee is one of the most popular beverages in the world, counting many scientists among its fans. Naturally those scientists are sometimes drawn to study their beloved beverage from various angles with an eye toward achieving the perfect cup.
While espresso has received the lion's share of such attention, physicists at the University of Pennsylvania have investigated the physics behind brewing so-called "pour-over" coffee, in which hot water is poured over coffee grounds in a filter within a funnel-shaped cone and allowed to percolate and drip into a cup below. The trick is to pour the water from as high as possible without letting the jet of water break up upon impact with the grounds, according to their new paper published in the journal Physics of Fluids.
In 2020, we reported on a mathematical model for brewing the perfect cup of espresso with minimal waste. Many variables can affect the quality of a steaming cup of espresso, including so-called "channeling" during the brewing process, in which the water doesn't seep uniformly through the grounds but branches off in various preferential paths instead. This significantly reduces the extraction yield (EY)—the fraction of coffee that dissolves into the final beverage—and thus the quality of the final brew. That, in turn, depends on controlling water flow and pressure as the liquid percolates through the coffee grounds.
© Ernest Park
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