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This study has successfully developed a high-efficiency neutron detector array with an exceptionally low background to measure the cross-section of the 13C(α, n)16O reaction at the China Jinping Underground Laboratory (CJPL). Comprising 24 3He proportional counters embedded in a polyethylene moderator, and shielded with 7% borated polyethylene layer, the neutron background at CJPL was as low as 4.5 counts/h, whereby 1.94 counts/h was attributed to the internal α radioactivity. Remarkably, the angular distribution of the 13C(α, n)16O reaction was proven to be a primary variable affecting the detection efficiency. The detection efficiency of the array for neutrons in the range of 0.1MeV to 4.5 MeV was determined using the 51V(p, n)51Cr reaction carried out with the 3 MV tandem accelerator at Sichuan University and Monte Carlo simulations. Future studies can be planned to focus on further improvement of the efficiency accuracy by measuring the angular distribution of 13C(α, n)16O reaction.

Gamow window is the range of energies which defines the optimal energy for reactions at a given temperature in stars. The nuclear cross-section of a nucleus is used to describe the probability that a nuclear reaction will occur. The 13C(α, n)16O reaction is the main neutron source for the slow neutron capture process (s-process) in asymptotic giant branch (AGB) stars, in which the 13C(α, n)16O reaction occurs at the Gamow window spanning from 150 to 230 keV. Hence, it is necessary to precisely measure the cross-section of 13C(α, n)16O reaction in this energy range. A low-background and high detection efficiency neutron detector is the essential equipment to carry out such measurements. This study developed a low-background neutron detector array that exhibited high detection efficiency to address the demands. With such development, advanced studies, including direct cross-section measurements of the key neutron source reactions in stars, can be conducted in the near future.

Low-background neutron detectors play a crucial role in facilitating research related to nuclear astrophysics, neutrino physics, and dark matter. By improving the efficiency and upgrading the technological capability of low background neutron detectors, this study indirectly contributes to the enhancement of scientific research. Additionally, fields involving material science and nuclear reactor technology would also benefit from the perfection of neutron detector technology. Taking into consideration the potential application and expansion of these findings, such innovative attempt aligns well with UNSDG9: Industry, Innovation & Infrastructure.

By probing the Universe on atomic scales and smaller, we can reveal the entirety of the Standard Model, and with it, the quantum Universe.

Fermionic atoms adhere to the Pauli exclusion principle, preventing more than one from simultaneously being in the same quantum state. As a result, they are perfect for modeling systems like molecules, superconductors, and quark-gluon plasmas where fermionic statistics are critical.

Using fermionic atoms, scientists from Austria and the USA have designed a new quantum computer to simulate complex physical systems. The processor uses programmable neutral atom arrays and has hardware-efficient fermionic gates for modeling fermionic models.

The group, under the direction of Peter Zoller, showed how the new quantum processor can simulate fermionic models from quantum chemistry and particle physics with great accuracy.

What happens when – instead of recording a single particle track or energy deposit in your detector – you see a complex collection of many particles, with many tracks, that leaves a large amount of energy in your calorimeters? Then congratulations: you’ve recorded a “jet”! Jets are the complicated experimental signatures left behind by showers of strongly-interacting quarks and gluons. By studying the internal energy flow of a jet – also known as the “jet substructure” – physicists can learn about the kind of particle that created it. For instance, several hypothesised new particles could decay into heavy Standard Model particles at extremely high (or “boosted”) energies. These particles could then decay into multiple quarks, leaving behind “boosted”, multi-pronged jets in the ATLAS experiment. Physicists use “taggers” to distinguish these jets from background jets created by single quarks and gluons. The type of quarks produced in the jet can also give extra information about the original particle. For example, Higgs bosons and top quarks often decay to b-quarks – seen in ATLAS as “b-jets” – which can be distinguished from other kinds of jets using the long lifetime of the B-hadron. The complexity of jets naturally lends itself to Artificial Intelligence (AI) algorithms, which are able to efficiently distil large amounts of information into accurate decisions. AI algorithms have been a regular part of ATLAS data analysis for several years, with ATLAS physicists continuously pushing these tools to new limits. This week, ATLAS physicists presented four exciting new results about jet tagging using AI algorithms at the BOOST 2023 conference held at Lawrence Berkeley National Lab (USA). Figure 1: The graphs showing the full declustering shower development and the primary Lund jet plane in red are shown in (left) for a jet originating from a W-boson and in (right) for a jet originating from a light-quark. (Image: ATLAS Collaboration/CERN) Artificial intelligence is revolutionising how ATLAS researchers identify – or ‘tag’ – what types of particles create jets in the experiment. Two results showcased new ATLAS taggers used for identifying jets coming from a boosted W-boson decay as opposed to background jets originating from light quarks and gluons. Typically, AI algorithms are trained on “high-level” jet substructure information recorded by the ATLAS inner detector and calorimeters – such as the jet mass, energy correlation ratios and jet splitting scales. These new studies instead use “low-level” information from these same detectors – such as the direct kinematic properties of a jet’s constituents or the novel two-dimensional parameterisation of radiation within a jet (known as the “Lund Jet plane”), built from the jet’s constituents and using graphs based on the particle-shower development (see Figure 1). These new taggers made it possible to separate the shape of signal and background far more effectively than any high-level taggers could do alone (see Figure 2). In particular, the Lund Jet plane-based tagger outperforms the other methods, by using the same input to the AI networks but in a different format inspired by the physics of the jet shower development. A similar evolution was followed for the development of a new boosted Higgs tagger, which identifies jets originating from boosted Higgs bosons decaying hadronically to two b-quarks or c-quarks. It also uses low-level information – in this case, tracks reconstructed from the inner detector associated with the single jet containing the Higgs boson decays. This new tagger is the most performant tagger to date, and represents a factor of 1.6 to 2.5 improvement, at a 50% boosted Higgs signal efficiency, over the previous version of the tagger, which used high-level information from the jet and b/c-quark decays as input for a neural network (see Figure 3). Figure 2: Signal efficiency as a function of the background rejection for the different W-boson taggers: one is based on the Lund jet plane, while the others use unordered sets of particles or graphs with additional structure. (Image: ATLAS Collaboration/CERN) Figure 3: Top and multijet rejections as a function of the H→bb signal efficiency. Performance of the new boosted Higgs tagger is compared to the previous taggers using high-level information from the jet b-quark decays. (Image: ATLAS Collaboration/CERN) Finally, ATLAS researchers presented two new taggers that aim to differentiate between jets originating from quarks and those originating from gluons. One tagger looked at the charged-particle constituent multiplicity of the jets being tagged, while the other combined several jet kinematic and jet substructure variables using a Boosted Decision Tree. Physicists compared the performance of these quark/gluon taggers; Figure 4 shows the rejection of gluon jets as a function of quark selection efficiency in simulation. Several studies of Standard-Model processes – including vector boson fusion – and new physics searches with quark-rich signals could greatly benefit from these taggers. However, in order for them to be used in analyses, additional corrections on the signal efficiency and background rejection need to be applied to bring the performance of the taggers in data and simulation to be the same. Researchers measured both the efficiency and rejection rates in Run-2 data for these taggers, and found good agreement between the measured data and predictions; therefore, only small corrections are needed. The excellent performance of these new jet taggers does not come without questions. Crucially, how can researchers interpret what the machine-learning models learned? And why do more complex architectures show a stronger dependence on the modelling of simulated physics processes used for the training, as shown in the two W-tagging studies? Challenges aside, these taggers set an outstanding baseline for analysing LHC Run-3 data. Given the current strides being made in machine learning, its continued application to particle physics will hopefully increase the understanding of jets and revolutionise the ATLAS physics programme in the years to come. Figure 4: Signal efficiency as a function of the background rejection for different quark taggers. The use of machine learning (BDT) results in an improved performance. (Image: ATLAS Collaboration/CERN) Learn more Tagging boosted W bosons with the Lund jet plane in ATLAS (ATL-PHYS-PUB-2023–017) Constituent-based W-boson tagging with the ATLAS detector (ATL-PHYS-PUB-2023–020) Transformer Neural Networks for Identifying Boosted Higgs Bosons decaying into bb and cc in ATLAS (ATL-PHYS-PUB-2023–021) Performance and calibration of quark/gluon-jet taggers using 140 fb−1 of proton–proton collisions at 13 TeV with the ATLAS detector (JETM-2020–02) Comparison of ML algorithms for boosted W boson tagging (JETM-2023–003) Summary of new ATLAS results from BOOST 2023, ATLAS News, 31 July 2023.

Researchers from Austria and the U.S. have designed a new type of quantum computer that uses fermionic atoms to simulate complex physical systems. The processor uses programmable neutral atom arrays and is capable of simulating fermionic models in a hardware-efficient manner using fermionic gates.

The team led by Peter Zoller demonstrated how the new quantum processor can efficiently simulate fermionic models from quantum chemistry and particle physics. The paper is published in the journal Proceedings of the National Academy of Sciences.

Fermionic atoms are atoms that obey the Pauli exclusion principle, which means that no two of them can occupy the same quantum state simultaneously. This makes them ideal for simulating systems where fermionic statistics play a crucial role, such as molecules, superconductors and quark-gluon plasmas.

Since the start of the quantum race, Microsoft has placed its bets on the elusive but potentially game-changing topological qubit. Now the company claims its Hail Mary has paid off, saying it could build a working processor in less than a decade.

Today’s leading quantum computing companies have predominantly focused on qubits—the quantum equivalent of bits—made out of superconducting electronics, trapped ions, or photons. These devices have achieved impressive milestones in recent years, but are hampered by errors that mean a quantum computer able to outperform classical ones still appears some way off.

Microsoft, on the other hand, has long championed topological quantum computing. Rather than encoding information in the states of individual particles, this approach encodes information in the overarching structure of the system. In theory, that should make the devices considerably more tolerant of background noise from the environment and therefore more or less error-proof.

A molecule common to Earth and usually associated with life has been detected in the depths of space by scientists.

Carbonic acid (HOCOOH), which you may know as the chemical that makes your soda fizzy, was discovered lurking near the center of our galaxy in a galactic center molecular cloud named G+0.693–0.027, a study published in The Astrophysical Journal revealed.

This marks the third time that carboxylic acids—this class of chemicals, often thought to be some of the building blocks of life —have been detected in space, after acetic acid and formic, and the first time that an interstellar molecule has been found to contain three or more oxygen atoms.

Through a series of innovative experiments, he and his team found that a sheet of glass crystal just a few atoms thick could trap and carry light. Not only that, but it was surprisingly efficient and could travel relatively long distances—up to a centimeter, which is very far in the world of light-based computing.

The research, published Aug. 10 in Science, demonstrates what are essentially 2D photonic circuits, and could open paths to new technology.

2D optical waveguides could point way to new technology.

Continue reading “University of Chicago scientists invent smallest known way to guide light” »

The researchers had to look at light mechanically to begin seeing similarities in properties usually seen in quantum states.

In 1,673, Christiaan Huygens wrote a book on pendulums and how they work. A mechanical theorem mentioned in the book was used 350 years later by researchers at the Stevens Institute of Technology to explain the complex behaviors of light, a university statement said.

Although known to us for eons, humanity has found it difficult to explain the very nature of light. For centuries scientists have been divided on whether to call it a wave or a particle and when there seemed to be some agreement on what light could actually be, quantum physics threw a new curveball by suggesting that it existed as both at once.