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One of the products of the National Solar Observatory's Integrated Synoptic Program (NISP) is the farside seismic map which shows the magnetic activity on the unobserved side of the Sun. The production of these rudimentary maps began in 2006, and they have since proven to be a valuable tool in tracking solar activity which cannot be directly observed from the earth's surface. The continuous tracking of solar active regions allows space weather forecasters to monitor critical solar events which may have larger economic and societal impacts here on Earth. In an effort to improve these maps, several steps are underway through the Windows on the Universe project (WoU) funded by the NSF. One of these steps is to improve the quality assurance measures for the images collected at individual sites throughout the GONG network and is used to develop the farside maps. To this end, we have designed a binary classification neural network to determine which of these site images should and should not be included in the farside pipeline that produces the end product maps. This convolutional neural network is a highly effective and computationally efficient method of significantly improving the quality of the farside maps currently produced by the NISP program., Comment: 31 Pages (including 27 figures and 6 tables)

The use of Gaussian processes (GPs) is a common approach to account for correlated noise in exoplanet time series, particularly for transmission and emission spectroscopy. This analysis has typically been performed for each wavelength channel separately, with the retrieved uncertainties in the transmission spectrum assumed to be independent. However, the presence of noise correlated in wavelength could cause these uncertainties to be correlated, which could significantly affect the results of atmospheric retrievals. We present a method that uses a GP to model noise correlated in both wavelength and time simultaneously for the full spectroscopic dataset while avoiding the use of a 'common-mode' correction. To make this analysis computationally tractable, we introduce a new fast and flexible GP method that can analyse 2D datasets when the input points lie on a (potentially non-uniform) 2D grid - in our case a time by wavelength grid - and the kernel function has a Kronecker product structure. This simultaneously fits all light curves and enables the retrieval of the covariance matrix of the transmission spectrum. By testing on synthetic datasets, we demonstrate that our new approach can reliably recover atmospheric features contaminated by noise correlated in time and wavelength. In contrast, fitting each spectroscopic light curve separately performed poorly when wavelength-correlated noise was present. It frequently underestimated the uncertainty of the scattering slope and overestimated the uncertainty in the strength of sharp absorption peaks in transmission spectra. Two archival VLT/FORS2 transit observations of WASP-31b were re-analysed, with our method strongly constraining the presence of wavelength-correlated noise in both datasets and recovering significantly different constraints on atmospheric features such as the scattering slope and strength of sodium and potassium features., Comment: Language revised version accepted for publication in Astronomy & Astrophysics. 35 pages, 18 figures (including appendix)

Catalyst particles or complexes suspended in liquid films can trigger chemical reactions leading to inhomogeneous concentrations of reactants and products in the film. We demonstrate that the sensitivity of the liquid film's gas-liquid surface tension to these inhomogeneous concentrations strongly impacts the film stability. Using linear stability analysis, we identify novel scenarios in which the film can be either stabilized or destabilized by the reactions. Furthermore, we find so far unrevealed rupture mechanisms which are absent in the chemically inactive case. The linear stability predictions are confirmed by numerical simulations, which also demonstrate that the shape of chemically active droplets can depart from the spherical cap and that unsteady states such as traveling and standing waves might appear. Finally, we critically discuss the relevance of our predictions by showing that the range of our selected parameters is well accessible by typical experiments.

We present an innovative cluster-based method employing linear combinations of diverse cluster mean-field (cMF) states, and apply it to describe the ground state of strongly-correlated spin systems. In cluster mean-field theory, the ground state wavefunction is expressed as a factorized tensor product of optimized cluster states. While our prior work concentrated on a single cMF tiling, this study removes that constraint by combining different tilings of cMF states. Selection criteria, including translational symmetry and spatial proximity, guide this process. We present benchmark calculations for the one- and two-dimensional $J_1-J_2$ and $XXZ$ Heisenberg models. Our findings highlight two key aspects. First, the method offers a semi-quantitative description of the $0.4 \lessapprox J_2/J_1 \lessapprox 0.6$ regime of the $J_1-J_2$ model - a particularly challenging regime for existing methods. Second, our results demonstrate the capability of our method to provide qualitative descriptions for all the models and regimes considered, establishing it as a valuable reference. However, the inclusion of additional (weak) correlations is necessary for quantitative agreement, and we explore methods to incorporate these extra correlations.

We report discovery and characterization of a main-sequence G star orbiting a dark object with mass $1.90\pm 0.04 M_{\odot}$. The system was discovered via Gaia astrometry and has an orbital period of 731 days. We obtained multi-epoch RV follow-up over a period of 639 days, allowing us to refine the Gaia orbital solution and precisely constrain the masses of both components. The luminous star is a $\gtrsim 12$ Gyr-old, low-metallicity halo star near the main-sequence turnoff ($T_{\rm eff}\approx 6000$ K; $\log(g/\left[{\rm cm\,s^{-2}}\right])\approx 4.0$; $\rm [Fe/H]\approx-1.25$; $M\approx0.79 M_{\odot}$) with a highly enhanced lithium abundance. The RV mass function sets a minimum companion mass for an edge-on orbit of $M_2 > 1.67 M_{\odot}$, well above the Chandrasekhar limit. The Gaia inclination constraint, $i=68.7\pm 1.4$ deg, then implies a companion mass of $M_2=1.90\pm0.04 M_{\odot}$. The companion is most likely a massive neutron star: the only viable alternative is two massive white dwarfs in a close binary, but this scenario is disfavored on evolutionary grounds. The system's low eccentricity ($e=0.122\pm 0.002$) disfavors dynamical formation channels and implies that the neutron star likely formed with little mass loss ($\lesssim1\,M_{\odot}$) and with a weak natal kick ($v_{\rm kick}\lesssim 20\,\rm km\,s^{-1}$). The current orbit is too small to have accommodated the neutron star progenitor as a red supergiant or super-AGB star. The simplest formation scenario -- isolated binary evolution -- requires the system to have survived unstable mass transfer and common envelope evolution with a donor-to-accretor mass ratio $>10$. The system, which we call Gaia NS1, is likely a progenitor of symbiotic X-ray binaries and long-period millisecond pulsars. Its discovery challenges binary evolution models and bodes well for Gaia's census of compact objects in wide binaries., Comment: 19 pages, 12 figures, accepted to OJAp

Double-core-hole (DCH) states in isolated water and heavy water molecules, resulting from the sequential absorption of two x-ray photons, have been investigated. A comparison of the subsequent Auger emission spectra from the two isotopes provides direct evidence of ultrafast nuclear motion during the 1.5 fs lifetime of these DCH states. Our numerical results align well with the experimental data, providing for various DCH states an in-depth study of the dynamics responsible of the observed isotope effect.

We present photometric and spectroscopic data sets for SN 2020pvb, a Type IIn-P supernova (SN) similar to SNe 1994W, 2005cl, 2009kn and 2011ht, with a precursor outburst detected (PS1 w-band ~ -13.8 mag) around four months before the B-band maximum light. SN 2020pvb presents a relatively bright light curve peaking at M_B = -17.95 +- 0.30 mag and a plateau lasting at least 40 days before it went in solar conjunction. After this, the object is no longer visible at phases > 150 days above -12.5 mag in the B-band, suggesting that the SN 2020pvb ejecta interacts with a dense spatially confined circumstellar envelope. SN 2020pvb shows in its spectra strong Balmer lines and a forest of FeII lines with narrow P Cygni profiles. Using archival images from the Hubble Space Telescope, we constrain the progenitor of SN 2020pvb to have a luminosity of log(L/L_sun) <= 5.4, ruling out any single star progenitor over 50 M_sun. All in all, SN 2020pvb is a Type IIn-P whose progenitor star had an outburst ~ 0.5 yr before the final explosion, the material lost during this outburst is probably playing a role in shaping the physical properties of the supernova., Comment: 14 pages + 10 appendix pages, 12 figures + 2 appendix figures, 8 appendix tables. Accepted for publication in A&A

Spatially-resolved images of debris disks are necessary to determine disk morphological properties and the scattering phase function (SPF) which quantifies the brightness of scattered light as a function of phase angle. Current high-contrast imaging instruments have successfully resolved several dozens of debris disks around other stars, but few studies have investigated trends in the scattered-light, resolved population of debris disks in a uniform and consistent manner. We have combined Karhunen-Loeve Image Projection (KLIP) with radiative-transfer disk forward modeling in order to obtain the highest quality image reductions and constrain disk morphological properties of eight debris disks imaged by the Gemini Planet Imager at H-band with a consistent and uniformly-applied approach. In describing the scattering properties of our models, we assume a common SPF informed from solar system dust scattering measurements and apply it to all systems. We identify a diverse range of dust density properties among the sample, including critical radius, radial width, and vertical width. We also identify radially narrow and vertically extended disks that may have resulted from substellar companion perturbations, along with a tentative positive trend in disk eccentricity with relative disk width. We also find that using a common SPF can achieve reasonable model fits for disks that are axisymmetric and asymmetric when fitting models to each side of the disk independently, suggesting that scattering behavior from debris disks may be similar to Solar System dust., Comment: 23+5 pages, 12+6 figures, 15 pages of Online Supplemental Material included; Accepted for publication in MNRAS

Concurrency is an important aspect of Petri nets to describe and simulate the behavior of complex systems. Knowing which places and transitions could be executed in parallel helps to understand nets and enables analysis techniques and the computation of other properties, such as causality, exclusivity, etc.. All techniques based on concurrency detection depend on the efficiency of this detection methodology. Kovalyov and Esparza have developed algorithms that compute all concurrent places in $O\big((P+T)TP^2\big)$ for live and bounded nets (where $P$ and $T$ are the numbers of places and transitions) and in $O\big(P(P+T)^2\big)$ for live and bounded free-choice nets. Although these algorithms have a reasonably good computational complexity, large numbers of concurrent pairs of nodes may still lead to long computation times. This paper complements the palette of concurrency detection algorithms with the Concurrent Paths (CP) algorithm for sound free-choice workflow nets. The algorithm allows parallelization and has a worst-case computational complexity of $O(P^2 + T^2)$ for acyclic nets and of $O(P^3 + PT^2)$ for cyclic nets. Although the computational complexity of cyclic nets has not improved, the evaluation shows the benefits of CP, especially, if the net contains many nodes in concurrency relation., Comment: 19 pages, 14 figures, 5 algorithms

Knowledge of the formation of neutron stars (NSs) in supernova (SN) explosions is of fundamental importance in wide areas of contemporary astrophysics: X-ray binaries, magnetars, radio pulsars, and, not least, double NS systems which merge and become gravitational wave sources. A recent study by Richardson et al. reported that the NS in the Be-star/X-ray binary SGR 0755-2933 (CPD -29 2176) descended from an ultra-stripped SN. Using the same observational data as Richardson et al., however, we find that the majority of progenitor solutions for SGR 0755-2933 are of normal Type Ib/c SNe, which allows for up to several solar masses of material to be ejected in the SN event. To correctly probe the SN explosion physics and inferring pre-SN conditions in a binary system, a full kinematic analysis based on post-SN data is always needed., Comment: Nature, accepted for publication (10 pages, 5 figures, incl. Extended Data), Matters Arising, Authors' version

In many scientific disciplines, the features of interest cannot be observed directly, so must instead be inferred from observed behaviour. Latent variable analyses are increasingly employed to systematise these inferences, and Principal Components Analysis (PCA) is perhaps the simplest and most popular of these methods. Here, we examine how the assumptions that we are prepared to entertain, about the latent variable system, mediate the likelihood that PCA-derived components will capture the true sources of variance underlying data. As expected, we find that this likelihood is excellent in the best case, and robust to empirically reasonable levels of measurement noise, but best-case performance is also: (a) not robust to violations of the method's more prominent assumptions, of linearity and orthogonality; and also (b) requires that other subtler assumptions be made, such as that the latent variables should have varying importance, and that weights relating latent variables to observed data have zero mean. Neither variance explained, nor replication in independent samples, could reliably predict which (if any) PCA-derived components will capture true sources of variance in data. We conclude by describing a procedure to fit these inferences more directly to empirical data, and use it to find that components derived via PCA from two different empirical neuropsychological datasets, are less likely to have meaningful referents in the brain than we hoped., Comment: 26 pages, 3 figures, 3 tables

We measure the trajectories of macroscopic magnetic particles pulled against gravity between twisted alternating magnetic square patterns in a superposed homogeneous magnetic field normal to both patterns. The two patterns are built from a set of magentic cubes having a distribution of magnetization. The magnetic potential between the patterns is a sum of three contributions: two being periodic on two lattices with different magnitude and orientation, and the third random contribution arising from the distribution of magnetization of the cubes. As one varies the twist angle between the two patterns each time the twist angle coincides with a magic twist angle one of the two periodic lattices becomes a sublattice of the other lattice. Simulations of particles moving through patterns with a precise cube magnetization produce pronounced mobility peaks near magic twist angles that are associated with flat channels. Weak random fluctuations of the cube magnetization in the experiment and the simulations cause enhanced random disorder of the potential and reduce the mobility by scattering particles into the interior of the twisted Wigner Seitz cells. The mobility undergoes an Anderson transition from magic to generic behavior as the magnetization disorder increases beyond half of a percent of the cube magnetization.

The ability to perceive and reason about individual objects and their interactions is a goal to be achieved for building intelligent artificial systems. State-of-the-art approaches use a feedforward encoder to extract object embeddings and a latent graph neural network to model the interaction between these object embeddings. However, the feedforward encoder can not extract {\it object-centric} representations, nor can it disentangle multiple objects with similar appearance. To solve these issues, we introduce {\it Slot Structured World Models} (SSWM), a class of world models that combines an {\it object-centric} encoder (based on Slot Attention) with a latent graph-based dynamics model. We evaluate our method in the Spriteworld benchmark with simple rules of physical interaction, where Slot Structured World Models consistently outperform baselines on a range of (multi-step) prediction tasks with action-conditional object interactions. All code to reproduce paper experiments is available from \url{https://github.com/JonathanCollu/Slot-Structured-World-Models}.

Reaction time studies with computers investigate how and how quickly participants respond to changing sensory input. They promise simple and precise measurement of time and inputs and offer interesting insights into human behavior. However, several previous studies have discovered imprecisions in timing appearing as delays, depending on the browser, software and programming used for conducting such studies. Since the accuaracy of the collected data is widely discussed, we aim to provide new results on the effect of unintended delays on participants' behavior. For this purpose, a new reaction time study was conducted. Computer delays were added to the experiment to investigate their effects on participants' performance and repulsion. Minimal changes in participants' behavior did occur and should be furtherly investigated, as the power of this study was rather low and might not have uncovered all underlying effects. The following report details our study design and results and offers several suggestions for improvements in further studies., Comment: 16 pages, 5 figures, 4 tables

The objective of the Cyclotron Radiation Emission Spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time-frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization - may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment - a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium $\beta^-$-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction., Comment: submitted to Machine Learning: Science and Technology

Surface charges play a fundamental role in physics and chemistry, particularly in shaping the catalytic properties of nanomaterials. Tracking nanoscale surface charge dynamics remains challenging due to the involved length and time scales. Here, we demonstrate real-time access to the nanoscale charge dynamics on dielectric nanoparticles employing reaction nanoscopy. We present a four-dimensional visualization of the non-linear charge dynamics on strong-field irradiated single SiO$_2$ nanoparticles with femtosecond-nanometer resolution and reveal how surface charges affect surface molecular bonding with quantum dynamical simulations. We performed semi-classical simulations to uncover the roles of diffusion and charge loss in the surface charge redistribution process. Understanding nanoscale surface charge dynamics and its influence on chemical bonding on a single nanoparticle level unlocks an increased ability to address global needs in renewable energy and advanced healthcare., Comment: 26 pages with (4+6(SI)) figures

Industries learn productivity improvements from their suppliers. The observed empirical importance of these interactions, often omitted by input-output models, mandates larger attention. This article embeds interdependent total factor productivity (TFP) growth into a general non-parametric input-output model. TFP growth is assumed to be Cobb-Douglas in TFP-stocks of adjacent sectors, where elasticities are the input-output coefficients. Studying how the steady state of the system reacts to changes in research effort bears insight for policy and the input-output literature. First, industries higher in the supply chain see a greater multiplication of their productivity gains. Second, the presence of `laggard' industries can bottleneck the the rest of the economy. By deriving these insights formally, we review a canonical method for aggregating TFP -- Hulten's Theorem -- and show the potential importance of backward linkages., Comment: 25 pages, 1 figure

Large impacts onto young rocky planets may transform their compositions, creating highly reducing conditions at their surfaces and reintroducing highly siderophile metals to their mantles. Key to these processes is the availability of an impactor's chemically reduced core material (metallic iron). It is, therefore, important to constrain how much of an impactor's core remains accessible to a planet's mantle/surface, how much is sequestered to its core, and how much escapes. Here, we present 3D simulations of such impact scenarios using the shock physics code iSALE to determine the fate of impactor iron. iSALE's inclusion of material strength is vital in capturing the behavior of both solid and fluid components of the planet and thus characterizing iron sequestration to the core. We find that the mass fractions of impactor core material that accretes to the planet core ($f_{core}$) or escapes ($f_{esc}$) can be readily parameterized as a function of a modified specific impact energy, with $f_{core} > f_{esc}$ for a wide set of impacts. These results differ from previous works that do not incorporate material strength. Our work shows that large impacts can place substantial reducing impactor core material in the mantles of young rocky planets. Impact-generated reducing atmospheres may thus be common for such worlds. However, through escape and sequestration to a planet's core, large fractions of an impactor's core can be geochemically hidden from a planet's mantle. Consequently, geochemical estimates of late bombardments of planets based on mantle siderophile element abundances may be underestimates., Comment: Submitted July 25, 2023; Revised October 26, 2023; PSJ

We describe a testbed to characterize the optical response of compact superconducting on-chip spectrometers in development for the Experiment for Cryogenic Large-Aperture Intensity Mapping (EXCLAIM) mission. EXCLAIM is a balloonborne far-infrared experiment to probe the CO and CII emission lines in galaxies from redshift 3.5 to the present. The spectrometer, called u-Spec, comprises a diffraction grating on a silicon chip coupled to kinetic inductance detectors (KIDs) read out via a single microwave feedline. We use a prototype spectrometer for EXCLAIM to demonstrate our ability to characterize the spectrometers spectral response using a photomixer source. We utilize an on-chip reference detector to normalize relative to spectral structure from the off-chip optics and a silicon etalon to calibrate the absolute frequency.

We developed a statistical theory of zero-count-detector (ZCD), which is defined as a zero-class Poisson under conditions outlined in the paper. ZCD is often encountered in the studies of rare events in physics, health physics, and many other fields where counting of events occurs. We found no acceptable solution to ZCD in classical statistics and affirmed the need for the Bayesian statistics. Several uniform and reference priors were studied and we derived Bayesian posteriors, point estimates, and upper limits. It was showed that the maximum-entropy prior, containing the most information, resulted in the smallest bias and the lowest risk, making it the most admissible and acceptable among the priors studied. We also investigated application of zero-inflated Poisson and Negative-binomial distributions to ZCD. It was showed using Bayesian marginalization that, under limited information, these distributions reduce to the Poisson distribution., Comment: 28 pages, including tables and figures