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Artificial Intelligence (AI) has the potential to significantly advance scientific discovery by finding solutions beyond human capabilities. However, these super-human solutions are often unintuitive and require considerable effort to uncover underlying principles, if possible at all. Here, we show how a code-generating language model trained on synthetic data can not only find solutions to specific problems but can create meta-solutions, which solve an entire class of problems in one shot and simultaneously offer insight into the underlying design principles. Specifically, for the design of new quantum physics experiments, our sequence-to-sequence transformer architecture generates interpretable Python code that describes experimental blueprints for a whole class of quantum systems. We discover general and previously unknown design rules for infinitely large classes of quantum states. The ability to automatically generate generalized patterns in readable computer code is a crucial step toward machines that help discover new scientific understanding -- one of the central aims of physics., Comment: 10+3 pages, 5 figures

As hydrogen is touted as a key player in the decarbonization of modern society, it is critical to enable quantitative H analysis at high spatial resolution, if possible at the atomic scale. Indeed, H has a known deleterious impact on the mechanical properties (strength, ductility, toughness) of most materials that can hinder their use as part of the infrastructure of a hydrogen-based economy. Enabling H mapping, including local hydrogen concentration analyses at specific microstructural features, is essential for understanding the multiple ways that H affect the properties of materials, including for instance embrittlement mechanisms and their synergies, but also spatial mapping and quantification of hydrogen isotopes is essential to accurately predict tritium inventory of future fusion power plants, ensuring their safe and efficient operation for example. Atom probe tomography (APT) has the intrinsic capabilities for detecting hydrogen (H), and deuterium (D), and in principle the capacity for performing quantitative mapping of H within a material's microstructure. Yet the accuracy and precision of H analysis by APT remain affected by the influence of residual hydrogen from the ultra-high vacuum chamber that can obscure the signal of H from within the material, along with a complex field evaporation behavior. The present article reports the essence of discussions at a focused workshop held at the Max-Planck Institute for Sustainable Materials in April 2024. The workshop was organized to pave the way to establishing best practices in reporting APT data for the analysis of H. We first summarize the key aspects of the intricacies of H analysis by APT and propose a path for better reporting of the relevant data to support interpretation of APT-based H analysis in materials.

Online advertising represents a main instrument for publishers to fund content on the World Wide Web. Unfortunately, a significant number of online advertisements often accommodates potentially malicious content, such as cryptojacking hidden in web banners - even on reputable websites. In order to protect Internet users from such online threats, the thorough detection of ad-malware campaigns plays a crucial role for a safe Web. Today, common Internet services like VirusTotal can label suspicious content based on feedback from contributors and from the entire Web community. However, it is open to which extent ad-malware is actually taken into account and whether the results of these services are consistent. In this pre-study, we evaluate who defines ad-malware on the Internet. In a first step, we crawl a vast set of websites and fetch all HTTP requests (particularly to online advertisements) within these websites. Then we query these requests both against popular filtered DNS providers and VirusTotal. The idea is to validate, how much content is labeled as a potential threat. The results show that up to 0.47% of the domains found during crawling are labeled as suspicious by DNS providers and up to 8.8% by VirusTotal. Moreover, only about 0.7% to 3.2% of these domains are categorized as ad-malware. The overall responses from the used Internet services paint a divergent picture: All considered services have different understandings to the definition of suspicious content. Thus, we outline potential research efforts to the automated detection of ad-malware. We further bring up the open question of a common definition of ad-malware to the Web community., Comment: 4 pages, 4 figures

The investigation of the fluctuations in interacting quantum systems at finite temperatures showcases the ongoing challenges in understanding complex quantum systems. Recently, atom number fluctuations in weakly interacting Bose-Einstein condensates were observed, motivating an investigation of the thermal component of partially condensed Bose gases. Here, we present a combined analysis of both components, revealing the presence of fluctuations in the thermal component. This analysis includes a comprehensive statistical evaluation of uncertainties in the preparation and parameter estimation of partially condensed Bose gases. Using Monte Carlo simulations of optical density profiles, we estimate the noise contributions to the atom number and temperature estimation of the condensed and thermal cloud, which is generally applicable in the field of ultracold atoms. Furthermore, we investigate the specific noise contributions in the analysis of atom number fluctuations and show that preparation noise in the total atom number leads to an important technical noise contribution. Subtracting all known noise contributions from the variance of the atom number in the BEC and thermal component allows us to improve the estimate of the fundamental peak fluctuations.

Generative Artificial Intelligence (AI) models can propose solutions to scientific problems beyond human capability. To truly make conceptual contributions, researchers need to be capable of understanding the AI-generated structures and extracting the underlying concepts and ideas. When algorithms provide little explanatory reasoning alongside the output, scientists have to reverse-engineer the fundamental insights behind proposals based solely on examples. This task can be challenging as the output is often highly complex and thus not immediately accessible to humans. In this work we show how transferring part of the analysis process into an immersive Virtual Reality (VR) environment can assist researchers in developing an understanding of AI-generated solutions. We demonstrate the usefulness of VR in finding interpretable configurations of abstract graphs, representing Quantum Optics experiments. Thereby, we can manually discover new generalizations of AI-discoveries as well as new understanding in experimental quantum optics. Furthermore, it allows us to customize the search space in an informed way - as a human-in-the-loop - to achieve significantly faster subsequent discovery iterations. As concrete examples, with this technology, we discover a new resource-efficient 3-dimensional entanglement swapping scheme, as well as a 3-dimensional 4-particle Greenberger-Horne-Zeilinger-state analyzer. Our results show the potential of VR for increasing a human researcher's ability to derive knowledge from graph-based generative AI that, which is a common abstract data representation used in diverse fields of science., Comment: 12 pages, 6 figures, comments welcome

The accurate determination of atom numbers is an ubiquitous problem in the field of ultracold atoms. For modest atom numbers, absolute calibration techniques are available, however, for large numbers and high densities, the available techniques neglect many-body scattering processes. Here, a spatial calibration technique for time-of-flight absorption images of ultracold atomic clouds is presented. The calibration is obtained from radially averaged absorption images and we provide a practical guide to the calibration process. It is shown that the calibration coefficient scales linearly with optical density and depends on the absorbed photon number for the experimental conditions explored here. This allows for the direct inclusion of a spatially dependent calibration in the image analysis. For typical ultracold atom clouds the spatial calibration technique leads to corrections in the detected atom number up to $\approx\! 12\,\%$ and temperature up to $\approx \!14\,\%$ in comparison to previous calibration techniques. The technique presented here addresses a major difficulty in absorption imaging of ultracold atomic clouds and prompts further theoretical work to understand the scattering processes in ultracold dense clouds of atoms for accurate atom number calibration.

The physics of impurities in a bosonic quantum environment is a paradigmatic and challenging many-body problem that remains to be understood in its full complexity. Here, this problem is investigated for impurities with strong repulsive interactions based on Ramsey interferometry in a quantum degenerate gas of 39K atoms. We observe an oscillatory signal that is consistent with a quantum beat between two co-existing coherent quasiparticle states: the attractive and repulsive polarons. The interferometric signal allows us to extract the polaron energies for a wide range of interaction strengths, complimenting earlier spectroscopic measurements. We furthermore identify several dynamical regimes towards the formation of the Bose polaron in good agreement with theory. Our results improve the understanding of quantum impurities interacting strongly with a bosonic environment, and demonstrate how quasiparticles as well as short-lived non-equilibrium many-body states can be probed using Ramsey interferometry.

Driven by human ingenuity and creativity, the discovery of super-resolution techniques, which circumvent the classical diffraction limit of light, represent a leap in optical microscopy. However, the vast space encompassing all possible experimental configurations suggests that some powerful concepts and techniques might have not been discovered yet, and might never be with a human-driven direct design approach. Thus, AI-based exploration techniques could provide enormous benefit, by exploring this space in a fast, unbiased way. We introduce XLuminA, an open-source computational framework developed using JAX, which offers enhanced computational speed enabled by its accelerated linear algebra compiler (XLA), just-in-time compilation, and its seamlessly integrated automatic vectorization, auto-differentiation capabilities and GPU compatibility. Remarkably, XLuminA demonstrates a speed-up of 4 orders of magnitude compared to well-established numerical optimization methods. We showcase XLuminA's potential by re-discovering three foundational experiments in advanced microscopy. Ultimately, XLuminA identified a novel experimental blueprint featuring sub-diffraction imaging capabilities. This work constitutes an important step in AI-driven scientific discovery of new concepts in optics and advanced microscopy., Comment: 29 pages, 15 figures. GitHub repo available!

Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI (XAI) technique called $inception$ or $deep$ $dreaming$, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. Once trained, we "invert" the neural network -- effectively asking how it imagines a quantum system with a specific property, and how it would continuously modify the quantum system to change a property. We find that the network can shift the initial distribution of properties of the quantum system, and we can conceptualize the learned strategies of the neural network. Interestingly, we find that, in the first layers, the neural network identifies simple properties, while in the deeper ones, it can identify complex quantum structures and even quantum entanglement. This is in reminiscence of long-understood properties known in computer vision, which we now identify in a complex natural science task. Our approach could be useful in a more interpretable way to develop new advanced AI-based scientific discovery techniques in quantum physics., Comment: Modified Figure 2. Fixed minor typos

In 1987, Vaidman, Aharanov, and Albert put forward a puzzle called the Mean King's Problem (MKP) that can be solved only by harnessing quantum entanglement. Prime-powered solutions to the problem have been shown to exist, but they have not yet been experimentally realized for any dimension beyond two. We propose a general first-of-its-kind experimental scheme for solving the MKP in prime dimensions ($D$). Our search is guided by the digital discovery framework PyTheus, which finds highly interpretable graph-based representations of quantum optical experimental setups; using it, we find specific solutions and generalize to higher dimensions through human insight. As proof of principle, we present a detailed investigation of our solution for the three-, five-, and seven-dimensional cases. We obtain maximum success probabilities of $72.8 \%$, $45.8\%$, and $34.8 \%$, respectively. We, therefore, posit that our computer-inspired scheme yields solutions that exceed the classical probability ($1/D$) twofold, demonstrating its promise for experimental implementation., Comment: Fixed unusual visual aberration in Figure 2, and moved Acknowledgements section

We study the statistical properties of a gas of interacting bosons trapped in a box potential in two and three dimensions. Our primary focus is the characteristic temperature $\tchar$, i.e. the temperature at which the fluctuations of the number of condensed atoms (or, in 2D, the number of motionless atoms) is maximal. Using the Fock State Sampling method, we show that $\tchar$ increases due to interaction. In 3D, this temperature converges to the critical temperature in the thermodynamic limit. In 2D we show the general applicability of the method by obtaining a generalized dependence of the characteristic temperature on the interaction strength. Finally, we discuss the experimental conditions necessary for the verification of our theoretical predictions., Comment: This article belongs to the special issue of Acta Physica Polonica A printed in honor of Professor Iwo Bialynicki-Birula on the occasion of his 90th birthday (Ed. Tomasz Sowinski, DOI:10.12693/APhysPolA.143.S0)

Differential dynamic microscopy (DDM) can be used to extract mean particle size from videos of suspensions. However, many suspensions have multimodal particle size distributions (PSDs), for which this is not a sufficient description. After clarifying how different particle sizes contribute to the signal in DDM, we show that standard DDM analysis can extract the mean sizes of two populations in a bimodal suspension given prior knowledge of the sample's bimodality. Further, the use of the CONTIN algorithm obviates the need for such prior knowledge. Finally, we show that by selectively analysing portions of the DDM images, we can size a trimodal suspension where the large particles would otherwise dominate the signal, again without prior knowledge of the trimodality., Comment: Main Body: 10 pages, 8 figures. Appendix: 5 pages, 6 figures. Manuscript accepted at Soft Matter with open access licence; for version of record see https://doi.org/10.1039/D3SM00593C. Relevant data available at https://doi.org/10.7488/ds/3851

French astronomer Honor\'e Flaugergues compiled astronomical observations in a series of hand-written notebooks for 1782$\unicode{x2013}$1830, which are preserved at Paris Observatory. We reviewed these manuscripts and encoded the records that contain sunspot measurements into a numerical table for further analysis. All measurements are timings and we found three types of measurements allowing the reconstruction of heliographic coordinates. In the first case, the Sun and sunspots cross vertical and horizontal wires, in the second case, one vertical and two mirror-symmetric oblique wires, and in the third case, a rhombus-shaped set of wires. Additionally, timings of two solar eclipses also provided a few sunspot coordinates. As a result, we present the time--latitude (butterfly) diagram of the reconstructed sunspot coordinates, which covers the period of the Dalton Minimum and confirms consistency with those of Derfflinger and Prantner. We identify four solar cycles in this diagram and discuss the observed peculiarities as well as the data reliability.

We report progress on the ongoing recalibration of the Wolf sunspot number (SN) and Group sunspot number (GN) following the release of version 2.0 of SN in 2015. This report constitutes both an update of the efforts reported in the 2016 Topical Issue of Solar Physics and a summary of work by the International Space Science Institute (ISSI) International Team formed in 2017 to develop optimal SN and GN re-construction methods while continuing to expand the historical sunspot number database. Significant progress has been made on the database side while more work is needed to bring the various proposed SN and (primarily) GN reconstruction methods closer to maturity, after which the new reconstructions (or combinations thereof) can be compared with (a) ``benchmark'' expectations for any normalization scheme (e.g., a general increase in observer normalization factors going back in time), and (b) independent proxy data series such as F10.7 and the daily range of variations of Earth's undisturbed magnetic field. New versions of the underlying databases for SN and GN will shortly become available for years through 2022 and we anticipate the release of next versions of these two time series in 2024., Comment: 21 figures, 4 tables. To be published in Solar Physics

We characterize the full spatiotemporal gait of populations of swimming {\it Escherichia coli} using renewal processes to analyze the measurements of intermediate scattering functions. This allows us to demonstrate quantitatively how the persistence length of an engineered strain can be controlled by a chemical inducer and to report a controlled transition from perpetual tumbling to smooth swimming. For wild-type {\it E.~coli}, we measure simultaneously the microscopic motility parameters and the large-scale effective diffusivity, hence quantitatively bridging for the first time small-scale directed swimming and macroscopic diffusion.

We introduce a numerical method to extract the parameters of run-and-tumble dynamics from experimental measurements of the intermediate scattering function. We show that proceeding in Laplace space is unpractical and employ instead renewal processes to work directly in real time. We first validate our approach against data produced using agent-based simulations. This allows us to identify the length and time scales required for an accurate measurement of the motility parameters, including tumbling frequency and swim speed. We compare different models for the run-and-tumble dynamics by accounting for speed variability at the single-cell and population level, respectively. Finally, we apply our approach to experimental data on wild-type Escherichia coli obtained using differential dynamic microscopy., Comment: 10 pages, 5 figures

Alkali dimers, $\mathrm{Ak}_2$, residing on the surface of He nanodroplets are doubly ionized due to multiphoton absorption from an intense, 50-fs laser pulse leading to fragmentation into a pair of alkali cations. Based on the measured kinetic energy distributions, $P(E_{\text{kin}})$, of the $\mathrm{Ak}^+$ fragment ions, we retrieve the distribution of internuclear distances, $P(R)$, via the $\mathrm{Ak}_2^{2+}$ potential curve. Results are obtained for $\mathrm{{Na}_2}$, $\mathrm{K}_2$, $\mathrm{Rb}_2$, and $\mathrm{Cs}_2$ in both the 1 $^1\Sigma_{g}^+$ ground state and in the lowest-lying triplet state 1 $^3\Sigma_{u}^+$, and for $\mathrm{Li}_2$ in the 1 $^3\Sigma_{u}^+$ state. For $\mathrm{Li}_2$, $\mathrm{K}_2$, and $\mathrm{Rb}_2$, the center of the measured $P(R)$'s is close to the center of the wave function, $\Psi(R)$, of the vibrational ground state in the 1 $^1\Sigma_{g}^+$ and 1 $^3\Sigma_{u}^+$ states, whereas for $\mathrm{{Na}_2}$ and $\mathrm{{Cs}_2}$ small shifts are observed. For all the $\mathrm{{Ak}_2}$, the width of the measured $P(R)$ is broader than $|\Psi(R)|^2$ by a factor of 2-4. We discuss that resonance effects in the multiphoton ionization and interaction of the $\mathrm{Ak}^+$ ion with the He droplet give rise to the observed deviations of $P(R)$ from $|\Psi(R)|^2$. Despite these deviations, we deem that timed Coulomb explosion will allow imaging of vibrational wave packets in alkali dimers on He droplets surfaces., Comment: 10 pages, 6 figures

Entanglement is a crucial resource for quantum technologies ranging from quantum communication to quantum-enhanced measurements and computation. Finding experimental setups for these tasks is a conceptual challenge for human scientists due to the counterintuitive behavior of multiparticle interference and the enormously large combinatorial search space. Recently, new possibilities have been opened by artificial discovery where artificial intelligence proposes experimental setups for the creation and manipulation of high-dimensional multi-particle entanglement. While digitally discovered experiments go beyond what has been conceived by human experts, a crucial goal is to understand the underlying concepts which enable these new useful experimental blueprints. Here, we present Halo (Hyperedge Assembly by Linear Optics), a new form of multiphoton quantum interference with surprising properties. Halos were used by our digital discovery framework to solve previously open questions. We -- the human part of this collaboration -- were then able to conceptualize the idea behind the computer discovery and describe them in terms of effective probabilistic multi-photon emitters. We then demonstrate its usefulness as a core of new experiments for highly entangled states, communication in quantum networks, and photonic quantum gates. Our manuscript has two conclusions. First, we introduce and explain the physics of a new practically useful multi-photon interference phenomenon that can readily be realized in advanced setups such as integrated photonic circuits. Second, our manuscript demonstrates how artificial intelligence can act as a source of inspiration for the scientific discoveries of new actionable concepts in physics., Comment: 6 pages, 6 figures, comments welcome

Photons are the physical system of choice for performing experimental tests of the foundations of quantum mechanics. Furthermore, photonic quantum technology is a main player in the second quantum revolution, promising the development of better sensors, secure communications, and quantum-enhanced computation. These endeavors require generating specific quantum states or efficiently performing quantum tasks. The design of the corresponding optical experiments was historically powered by human creativity but is recently being automated with advanced computer algorithms and artificial intelligence. While several computer-designed experiments have been experimentally realized, this approach has not yet been widely adopted by the broader photonic quantum optics community. The main roadblocks consist of most systems being closed-source, inefficient, or targeted to very specific use-cases that are difficult to generalize. Here, we overcome these problems with a highly-efficient, open-source digital discovery framework PyTheus, which can employ a wide range of experimental devices from modern quantum labs to solve various tasks. This includes the discovery of highly entangled quantum states, quantum measurement schemes, quantum communication protocols, multi-particle quantum gates, as well as the optimization of continuous and discrete properties of quantum experiments or quantum states. PyTheus produces interpretable designs for complex experimental problems which human researchers can often readily conceptualize. PyTheus is an example of a powerful framework that can lead to scientific discoveries -- one of the core goals of artificial intelligence in science. We hope it will help accelerate the development of quantum optics and provide new ideas in quantum hardware and technology., Comment: 44 pages, 62 figures, comments welcome!

We analysed visual observation data of the Tau Herculids collected between 2022 May 28 and June 1. The population index for the entire sample is 2.4. For the pre-peak period we find r=2.57+-0.23, the peak period yields r=2.38+-0.06. The ZHR maximum of the 1995 ejecta from comet Schwassmann-Wachmann3 (SW3) is found at 69.450 deg solar longitude (May 31, 05h04mUT, +-5min) with a ZHR=55+-7, corresponding to a spatial number density ND of approx. 380 meteoroids larger than 10mg in a cube with 1000km edge length. An earlier maximum occurred at 69.207 deg solar longitude (centred May 30, 23h UT) with a ZHR=18+-3 and is tentatively associated with SW3-ejecta from 1892. Effects of the radiant shift due to the large zenith attraction of about 10 deg for the radiant close to the horizon are discussed.