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Solving inverse problems in physics is central to understanding complex systems and advancing technologies in various fields. Iterative optimization algorithms, commonly used to solve these problems, often encounter local minima, chaos, or regions with zero gradients. This is due to their overreliance on local information and highly chaotic inverse loss landscapes governed by underlying partial differential equations (PDEs). In this work, we show that deep neural networks successfully replicate such complex loss landscapes through spatio-temporal trajectory inputs. They also offer the potential to control the underlying complexity of these chaotic loss landscapes during training through various regularization methods. We show that optimizing on network-smoothened loss landscapes leads to improved convergence in predicting optimum inverse parameters over conventional momentum-based optimizers such as BFGS on multiple challenging problems., Comment: Ongoing work

Astrometry from Gaia DR3 has produced a sample of $\sim$170,000 Keplerian orbital solutions, with many more anticipated in the next few years. These data have enormous potential to constrain the population of binary stars, giant planets, and compact objects in the Solar neighborhood. But in order to use the published orbit catalogs for statistical inference, it is necessary to understand their selection function: what is the probability that a binary with a given set of properties ends up in a catalog? We show that such a selection function for the Gaia DR3 astrometric binary catalog can be forward-modeled from the Gaia scanning law, including individual 1D astrometric measurements, the fitting of a cascade of astrometric models, and quality cuts applied in post-processing. We populate a synthetic Milky Way model with binary stars and generate a mock catalog of astrometric orbits. The mock catalog is quite similar to the DR3 astrometric binary sample, suggesting that our selection function is a sensible approximation of reality. Our fitting also produces a sample of spurious astrometric orbits similar to those found in DR3; these are mainly the result of scan angle-dependent astrometric biases in marginally resolved wide binaries. We show that Gaia's sensitivity to astrometric binaries falls off rapidly at high eccentricities, but only weakly at high inclinations. We predict that DR4 will yield $\sim 1$ million astrometric orbits, mostly for bright ($G \lesssim 15$) systems with long periods ($P_{\rm orb} \gtrsim 1000$ d). We provide code to simulate and fit realistic Gaia epoch astrometry for any data release and determine whether any hypothetical binary would receive a cataloged orbital solution., Comment: 22 pages, 17 figures, accepted to OJAp. Code at https://github.com/kareemelbadry/gaiamock

Context. The black holes discovered using Gaia, especially Gaia BH1 and BH2, have low mass companions of solar-like metallicity in wide orbits. For standard isolated binary evolution formation channels including interactions such an extreme mass ratio is unexpected; especially in orbits of hundreds to thousands of days. Aims. Here, we investigate a non-interacting formation path for isolated binaries to explain the formation of Gaia BH1 and BH2. Methods. We use single star models computed with MESA to constrain the main characteristics of possible progenitors of long-period black hole binaries like Gaia BH1 and BH2. Then, we incorporate these model grids into the binary population synthesis code POSYDON, to explore whether the formation of the observed binaries at solar metallicity is indeed possible. Results. We find that winds of massive stars ($\gtrsim 80\,M_\odot$), especially during the Wolf-Rayet phase, tend to cause a plateau in the initial stellar mass to final black hole mass relation (at about $13\,M_\odot$ in our default wind prescription). However, stellar winds at earlier evolutionary phases are also important at high metallicity, as they prevent the most massive stars from expanding ($<100\,R_\odot$) and filling their Roche lobe. Consequently, the strength of the applied winds affects the range of the final black hole masses in non-interacting binaries, making it possible to form systems similar to Gaia BH1 and BH2. Conclusions. We deduce that wide binaries with a black hole and a low mass companion can form at high metallicity without binary interactions. There could be hundreds of such systems in the Milky Way. The mass of the black hole in binaries evolved through the non-interacting channel can potentially provide insights into the wind strength during the progenitors evolution., Comment: 8+4 pages, 6+6 figures, resubmitted to A&A

We investigate the concatenation of Markov processes. Our primary concern is to utilize processes constructed in this manner for Monte Carlo integration. To enable this using conventional methods, it is essential to demonstrate the Markov property and invariance with respect to a given target distribution. We provide mild sufficient conditions for this. Our main result is the identification of the generator of the concatenation of Markov processes. This result provides the theoretical foundation for Monte Carlo methods based on this construction.

Structure determination is a key application of XFELs and 4th generation synchrotron sources, particularly using the coherent and pulsed X-ray radiation from X-ray free-electron lasers (XFEL). Scientific interest focuses on understanding the physical, biological, and chemical properties of samples at the nanometer scale. The X-rays from XFELs enable Coherent X-ray Diffraction Imaging (CXDI), where coherent X-rays irradiate a sample, and a far-field diffraction pattern is captured by an imaging detector. By the nature of the underlying physics, the resolution, at which the sample can be probed with the CXDI technique, is limited by the wavelength of the X-ray radiation and the exposure time if a detector can record the diffraction pattern to very large scattering angles. The resolution that can be achieved under real experimental conditions, depends strongly on additional parameters. The Shannon pixel size, linked to the detector resolution, the coherent dose that can be deposited in the sample without changing its structure, the image contrast, and the signal-to-noise ratio of the detected scattered radiation at high $q$, i.e. at high scattering angles $2\Theta$, have a strong influence on the resolution. The signal-to-noise ratio at high $q$ defines the "effective" maximum solid angle in a specific experiment setup up to which a detector can efficiently detect a signal and in consequence determines the achievable resolution. The image contrast defines how well bright image features can be distinguished from dark ones. We present the preliminary results of our study on the influence of the PSF on SNR, image contrast, position resolution, and achievable sample resolution for different pixel sizes., Comment: 7 pages, 4 figures, submitted to the Journal of Physics: Conference Series (JPCS)

Classical generative diffusion models learn an isotropic Gaussian denoising process, treating all spatial regions uniformly, thus neglecting potentially valuable structural information in the data. Inspired by the long-established work on anisotropic diffusion in image processing, we present a novel edge-preserving diffusion model that is a generalization of denoising diffusion probablistic models (DDPM). In particular, we introduce an edge-aware noise scheduler that varies between edge-preserving and isotropic Gaussian noise. We show that our model's generative process converges faster to results that more closely match the target distribution. We demonstrate its capability to better learn the low-to-mid frequencies within the dataset, which plays a crucial role in representing shapes and structural information. Our edge-preserving diffusion process consistently outperforms state-of-the-art baselines in unconditional image generation. It is also more robust for generative tasks guided by a shape-based prior, such as stroke-to-image generation. We present qualitative and quantitative results showing consistent improvements (FID score) of up to 30% for both tasks. We provide source code and supplementary content via the public domain edge-preserving-diffusion.mpi-inf.mpg.de .

Markov chain Monte Carlo (MCMC) algorithms come to rescue when sampling from a complex, high-dimensional distribution by a conventional method is intractable. Even though MCMC is a powerful tool, it is also hard to control and tune in practice. Simultaneously achieving both local exploration of the state space and global discovery of the target distribution is a challenging task. In this work, we present a MCMC formulation that subsumes all existing MCMC samplers employed in rendering. We then present a novel framework for adjusting an arbitrary Markov chain, making it exhibit invariance with respect to a specified target distribution. To showcase the potential of the proposed framework, we focus on a first simple application in light transport simulation. As a by-product, we introduce continuous-time MCMC sampling to the computer graphics community. We show how any existing MCMC-based light transport algorithm can be embedded into our framework. We empirically and theoretically prove that this embedding is superior to running the standalone algorithm. In fact, our approach will convert any existing algorithm into a highly parallelizable variant with shorter running time, smaller error and less variance.

Finding model parameters from data is an essential task in science and engineering, from weather and climate forecasts to plasma control. Previous works have employed neural networks to greatly accelerate finding solutions to inverse problems. Of particular interest are end-to-end models which utilize differentiable simulations in order to backpropagate feedback from the simulated process to the network weights and enable roll-out of multiple time steps. So far, it has been assumed that, while model inference is faster than classical optimization, this comes at the cost of a decrease in solution accuracy. We show that this is generally not true. In fact, neural networks trained to learn solutions to inverse problems can find better solutions than classical optimizers even on their training set. To demonstrate this, we perform both a theoretical analysis as well an extensive empirical evaluation on challenging problems involving local minima, chaos, and zero-gradient regions. Our findings suggest an alternative use for neural networks: rather than generalizing to new data for fast inference, they can also be used to find better solutions on known data., Comment: Source code to follow soon: https://ge.in.tum.de

Active soft matter frequently shows motility-induced phase separation (MIPS), where self-propelled particles condensate into clusters with an inner liquid-like structure. Such activity may also result in motility-induced crystallization (MIC) into clusters with an inner crystalline structure. We present a higher-order active Phase-Field-Crystal (PFC) model and employ it to study the interplay of passive (i.e., thermodynamic) and active (i.e., motility-induced) condensation and crystallization. Morphological phase diagrams indicate the various occurring phase coexistences and transitions, e.g., the destruction of passive clusters by density-independent activity and the creation of such clusters by a density-dependent activity. Finally, rotating crystallites are analyzed in some detail.

The neutron-rich unbound fluorine isotope $^{30}$F$_{21}$ has been observed for the first time by measuring its neutron decay at the SAMURAI spectrometer (RIBF, RIKEN) in the quasi-free proton knockout reaction of $^{31}$Ne nuclei at 235 MeV/nucleon. The mass and thus one-neutron-separation energy of $^{30}$F has been determined to be $S_n = -472\pm 58 \mathrm{(stat.)} \pm 33 \mathrm{(sys.)}$ keV from the measurement of its invariant-mass spectrum. The absence of a sharp drop in $S_n$($^{30}$F) shows that the ``magic'' $N=20$ shell gap is not restored close to $^{28}$O, which is in agreement with our shell-model calculations that predict a near degeneracy between the neutron $d$ and $fp$ orbitals, with the $1p_{3/2}$ and $1p_{1/2}$ orbitals becoming more bound than the $0f_{7/2}$ one. This degeneracy and reordering of orbitals has two potential consequences: $^{28}$O behaves like a strongly superfluid nucleus with neutron pairs scattering across shells, and both $^{29,31}$F appear to be good two-neutron halo-nucleus candidates., Comment: 9 pages, 2 figures, accepted for publication in Physical Review Letters

The task of $\textit{keyword extraction}$ is often an important initial step in unsupervised information extraction, forming the basis for tasks such as topic modeling or document classification. While recent methods have proven to be quite effective in the extraction of keywords, the identification of $\textit{class-specific}$ keywords, or only those pertaining to a predefined class, remains challenging. In this work, we propose an improved method for class-specific keyword extraction, which builds upon the popular $\textbf{KeyBERT}$ library to identify only keywords related to a class described by $\textit{seed keywords}$. We test this method using a dataset of German business registry entries, where the goal is to classify each business according to an economic sector. Our results reveal that our method greatly improves upon previous approaches, setting a new standard for $\textit{class-specific}$ keyword extraction., Comment: 7 pages, 1 figure, 1 table. Accepted to KONVENS 2024

High-intensity neutron beams, such as those available at the European Spallation Source (ESS), provide new opportunities for fundamental discoveries. Here we discuss a novel Ramsey neutron-beam experiment to search for ultralight axion dark matter through its coupling to neutron spins, which would cause the neutron spins to rotate about the velocity of the neutrons relative to the dark matter halo. We estimate that experiments at the HIBEAM beamline at the ESS can improve the sensitivity to the axion-neutron coupling compared to the current best laboratory limits by up to $2-3$ orders of magnitude over the axion mass range $10^{-22} \, \textrm{eV} - 10^{-16}$ eV., Comment: 8 pages, 3 figures

This first VELOCE data release comprises 18,225 high-precision RV measurements of 258 bona fide classical Cepheids on both hemispheres collected mainly between 2010 and 2022, alongside 1161 additional observations of 164 other stars. The median per-observation RV uncertainty is 0.037 km/s, and some reach 0.002 km/s. Non-variable standard stars characterize RV zero-point stability and provide a base for future cross-calibrations. We determined zero-point differences between VELOCE and 31 literature data sets using template fitting and measured linear period changes of 146 Cepheids. Seventy six spectroscopic binary Cepheids and 14 candidates are identified using VELOCE data alone and are investigated in detail in a companion paper (VELOCE II). Several new insights into Cepheid pulsations were obtained, including: a) the most detailed description of the Hertzsprung progression by RVs; b) the identification of double-peaked bumps in the RV curve; c) clear evidence that virtually all Cepheids feature spectroscopic variability signals that lead to modulated RV variability. We identified 36 such stars, of which 4 also exhibit orbital motion. Linear radius variations depend strongly on pulsation period and a steep increase in slope of the $\Delta$R/p versus logP-relation is found near 10d, challenging the existence of a tight relation between Baade-Wesselink projection factors and pulsation periods. We investigated the accuracy of RV time series measurements, v$_\gamma$, and RV amplitudes published in Gaia's DR3 and determined an average offset of 0.65 \pm 0.11 km/s relative to VELOCE. We recommend adopting a single set of template correlation parameters for distinct classes of large-amplitude variable stars to avoid systematic offsets in v$_\gamma$ among stars belonging to the same class. Peak-to-peak amplitudes of Gaia RVs exhibit significant (16%) dispersion compared to VELOCE. [abridged], Comment: A&A in press, 46 pages, 35 figures, 20 tables. Some data only available via the CDS at publication. VELOCE DR1 data will be made public in FITS format via zenodo.org at DOI: 10.5281/zenodo.10793507 upon publication of the paper in A&A

The Gaia Collaboration has recently reported the detection of a 33 M$_\odot$ black hole in a wide binary system located in the Solar neighbourhood. Here we explore the relationship between this black hole, known as Gaia BH3, and the nearby ED-2 halo stellar stream. We study the orbital characteristics of the Gaia BH3 binary and present measurements of the chemical abundances of ED-2 member stars derived from high-resolution spectra obtained with the VLT. We find that the Galactic orbit of the Gaia BH3 system and its metallicity are entirely consistent with being part of the ED-2 stream. The characteristics of the stream, particularly its negligible spread in metallicity and in other chemical elements as well as its single stellar population, suggest that it originated from a disrupted star cluster of low mass. Its age is comparable to that of the globular cluster M92 that has been estimated to be as old as the Universe. This is the first black hole unambiguously associated with a disrupted star cluster. We infer a plausible mass range for the cluster to be relatively narrow, between $2\times 10^3M_\odot$ and $4.2\times 10^4M_\odot$. This implies that the black hole could have formed directly from the collapse of a massive very-metal-poor star, but that the alternative scenario of binary interactions inside the cluster environment also deserves to be explored., Comment: Submitted to A&A Letters; updated author list

Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes

Reddiment is a web-based dashboard that links sentiment analysis of subreddit texts with share prices. The system consists of a backend, frontend and various services. The backend, in Node.js, manages the data and communicates with crawlers that collect Reddit comments and stock market data. Sentiment is analyzed with the help of Vader and TextBlob. The frontend, based on SvelteKit, provides users with a dashboard for visualization. The distribution is carried out via Docker containers and Docker Compose. The project offers expansion options, e.g. the integration of cryptocurrency rates. Reddiment enables the analysis of sentiment and share prices from subreddit data., Comment: Ostbayerische Technische Hochschule Amberg-Weiden, CyberLytics-Lab, Technical Reports, CL-2022-06, July 2022, in German language