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In linear perturbation theory, the ringdown of a gravitational wave (GW) signal is described by a linear combination of quasinormal modes (QNMs). Detecting QNMs from GW signals is a promising way to test GR, central to the developing field of black-hole spectroscopy. More robust black-hole spectroscopy tests could also consider the ringdown amplitude-phase consistency. That requires an accurate understanding of the excitation and stability of the QNM expansion coefficients. In this paper, we investigate the robustness of the extracted $m=2$ QNM coefficients obtained from a high-accuracy numerical relativity waveform. We explore a framework to assess the robustness of QNM coefficients. Within this framework, we not only consider the traditional criterion related to the constancy of a QNM's expansion coefficients over a window in time, but also emphasize the importance of consistency among fitting models. In addition, we implement an iterative greedy approach within which we fix certain QNM coefficients. We apply this approach to linear fitting, and to nonlinear fitting where the properties of the remnant black hole are treated as unknown variables. We find that the robustness of overtone coefficients is enhanced by our greedy approach, particularly for the $(2,2,2,+)$ overtone. Based on our robustness criteria applied to the $m=2$ signal modes, we find the $(2\!\sim\!4,2,0,+)$ and $(2,2,1\!\sim\!2,+)$ modes are robust, while the $(3,2,1,+)$ subdominant mode is only marginally robust. After we subtract the contributions of the $(2\!\sim\!4,2,0,+)$ and $(2\!\sim\!3,2,1,+)$ QNMs from signal mode $(4,2)$, we also find evidence for the quadratic QNM $(2,1,0,+)\times(2,1,0,+)$.

Grain boundary chemistry plays a critical role for the properties of metals and alloys, yet there is a lack of consistent datasets for alloy design and development. With the advent of artificial intelligence and machine learning in materials science, open materials models and datasets can be used to overcome such challenges. Here, we use a universal interatomic potential to compute a grain boundary segregation and embrittlement genome for the {\Sigma}5[001](210) grain boundary for FCC and BCC binary alloys. The grain boundary database calculated here serves as a design tool for the embrittlement of high-angle grain boundaries for alloys across 15 base metals system of Ag, Al, Au, Cr, Cu, Fe (both BCC and FCC), Mo, Nb, Ni, Pd, Pt, Rh, Ta, V and W with 75 solute elements for each.

In the last decade, the ringdown community has made large strides in understanding the aftermath of binary black hole mergers through the study of numerical simulations. In this note, we introduce two flavors of fitting algorithms, that have been verified against each other, for the extraction of quasinormal mode amplitudes from ringdown waveforms - $\texttt{qnmfits}$ in Python and $\texttt{KerrRingdown}$ in Mathematica., Comment: 7+2 pages, 2 figures. Software packages are available through (qnmfits) https://doi.org/10.5281/zenodo.14806974 and (KerrRingdown) https://doi.org/10.5281/zenodo.14804284

Grain boundary segregation controls properties of polycrystalline materials such as their susceptibility to intergranular cracking. It is of interest to engineer alloy chemistry to enhance grain boundary cohesion to prevent intergranular failure. While there is collectively a large first-principles dataset for grain boundary embrittlement in multiple Al-based binary alloys, the methodologies used for the first principles calculations, as well as the analyzed fracture paths, are variable amongst studies. Here, we reevaluate and compute grain boundary segregation and embrittlement from all-electron first-principles for the {\Sigma}5[001](210) Al grain boundary. We explicitly evaluate multiple fracture paths, and provide a study case of the chemical trends of the preferred fracture paths across 69 binary Al alloys. The results suggest that neglecting certain low energy fracture paths can lead to errors of estimating embrittlement potency up to the order of 1 eV per solute atom, especially for multiple d-block transition metal solutes that are of engineering interest. The database calculated here also permits a comprehensive comparison between all-electron and pseudopotential methodologies. The effects of Hubbard U density functional theory on grain boundary segregation and embrittlement in Al(Sc) are found not to be significant in terms of the relative energetic calculations of grain boundaries and free surfaces (differences are of order 0.1 eV or less).

We analyse the performance of Belief Propagation Guided Decimation, a physics-inspired message passing algorithm, on the random $k$-XORSAT problem. Specifically, we derive an explicit threshold up to which the algorithm succeeds with a strictly positive probability $\Omega(1)$ that we compute explicitly, but beyond which the algorithm with high probability fails to find a satisfying assignment. In addition, we analyse a thought experiment called the decimation process for which we identify a (non-) reconstruction and a condensation phase transition. The main results of the present work confirm physics predictions from [RTS: J. Stat. Mech. 2009] that link the phase transitions of the decimation process with the performance of the algorithm, and improve over partial results from a recent article [Yung: Proc. ICALP 2024].

The increasing frequency and severity of wildfires pose significant risks to communities, infrastructure, and the environment, especially in Wildland-Urban Interface (WUI) areas. Effective disaster management requires understanding how the public perceives and responds to wildfire threats in real-time. This study uses social media data to assess public responses and explores how these responses are linked to city-level community characteristics. Specifically, we leveraged a transformer-based topic modeling technique called BERTopic to identify wildfire response-related topics and then utilized the Susceptible-Infectious-Recovered (SIR) model to compute two key metrics associated with wildfire responses - awareness and resilience indicators. Additionally, we used GIS-based spatial analysis to map wildfire locations along with four groups of city-level factors (racial/ethnic, socioeconomic, demographic, and wildfire-specific). Our findings reveal significant geographic and socio-spatial differences in public responses. Southern California cities with larger Hispanic populations demonstrate higher wildfire awareness and resilience. In contrast, urbanized regions in Central and Northern California exhibit lower awareness levels. Furthermore, resilience is negatively correlated with unemployment rates, particularly in southern regions where higher unemployment aligns with reduced resilience. These findings highlight the need for targeted and equitable wildfire management strategies to improve the adaptive capacity of WUI communities.

We introduce a new parallelized approach to photothermal microscopy that utilizes mid-infrared dual-comb lasers, enabling simultaneous measurements at hundreds of wavelengths. This technology allows for high-speed, label-free chemical identification with super-resolution infrared imaging, overcoming the limitations of slow, single-wavelength-laser methods.

The discovery of superconductivity in La$_3$Ni$_2$O$_7$ under pressure has motivated the investigation of a parent spin density wave (SDW) state which could provide the underlying pairing interaction. Here, we employ resonant soft x-ray scattering and polarimetry on thin films of bilayer La$_3$Ni$_2$O$_7$ to determine that the magnetic structure of the SDW forms unidirectional diagonal spin stripes with moments lying within the NiO$_2$ plane and perpendicular to $\mathbf{Q}_{SDW}$, but without the strong charge disproportionation typically associated with other nickelates. These stripes form anisotropic domains with shorter correlation lengths perpendicular versus parallel to $\mathbf{Q}_{SDW}$, revealing nanoscale rotational and translational symmetry breaking analogous to the cuprate and Fe-based superconductors, with Bloch-like antiferromagnetic domain walls separating orthogonal domains., Comment: 30 pages including supplementary, 4 figures + 9 supplementary figures

Mid-infrared microscopy is an important tool for biological analyses, allowing a direct probe of molecular bonds in their low energy landscape. In addition to the label-free extraction of spectroscopic information, the application of broadband sources can provide a third dimension of chemical specificity. However, to enable widespread deployment, mid-infrared microscopy platforms need to be compact and robust while offering high speed, broad bandwidth and high signal-to-noise ratio (SNR). In this study, we experimentally showcase the integration of a broadband, high-repetition-rate dual-comb spectrometer (DCS) in the mid-infrared range with a scanning microscope. We employ a set of 1-GHz mid-infrared frequency combs, demonstrating their capability for high-speed and broadband hyperspectral imaging of polymers and ovarian tissue. The system covers 1000 $\mathrm{cm^{-1}}$ at $\mathrm{\nu_c=2941 \; cm^{-1}}$ with 12.86 kHz spectra acquisition rate and 5 $\mathrm{\mu m}$ spatial resolution. Taken together, our experiments and analysis elucidate the trade-off between bandwidth and speed in DCS as it relates to microscopy. This provides a roadmap for the future advancement and application of high-repetition-rate DCS hyperspectral imaging.

Since the turn of the century, astronomers have been exploiting the rich information afforded by combining stellar kinematic maps and imaging in an attempt to recover the intrinsic, three-dimensional (3D) shape of a galaxy. A common intrinsic shape recovery method relies on an expected monotonic relationship between the intrinsic misalignment of the kinematic and morphological axes and the triaxiality parameter. Recent studies have, however, cast doubt about underlying assumptions relating shape and intrinsic kinematic misalignment. In this work, we aim to recover the 3D shape of individual galaxies using their projected stellar kinematic and flux distributions using a supervised machine learning approach with mixture density network (MDN). Using a mock dataset of the EAGLE hydrodynamical cosmological simulation, we train the MDN model for a carefully selected set of common kinematic and photometric parameters. Compared to previous methods, we demonstrate potential improvements achieved with the MDN model to retrieve the 3D galaxy shape along with the uncertainties, especially for prolate and triaxial systems. We make specific recommendations for recovering galaxy intrinsic shapes relevant for current and future integral field spectroscopic galaxy surveys., Comment: Accepted for publication in PASA. 18 pages, 12 figures, 2 tables

Accurate diagnostics of the combustor region of ramjet engines can improve engine design and create benchmarks for computational fluid dynamics models. Previous works demonstrate that dual frequency comb spectroscopy can provide low uncertainty diagnostics of multiple flow parameters in the non-combusting regions of ramjets. However, the high temperatures present in the combustor present a challenge for broadband spectroscopic absorption models that are used to interpret measurements in these regions. Here, we utilize a new water vapor absorption database created for high temperature water-air mixtures to fit spectra measured in a ground-test ramjet engine with a broadband near-infrared dual comb absorption spectrometer. We extract 2D profiles of pressure, temperature, water mole fraction, and velocity using this new database. We demonstrate that the new database provides the lowest fit residuals compared to other water vapor absorption databases. We compare computational fluid dynamics simulations of the combustor with the measured data to demonstrate that the simulations overpredict heat release and water vapor production., Comment: This work has been cleared by the Air Force under case number AFRL-2023-4205

Laser absorption Doppler velocimeters use a crossed-beam configuration to cancel error due to laser frequency drift and absorption model uncertainty. This configuration complicates the spatial interpretation of the measurement since the two beams sample different volumes of gas. Here, we achieve single-beam velocimetry with a portable dual comb spectrometer (DCS) with high frequency accuracy and stability enabled by GPS-referencing, and a new high-temperature water vapor absorption database. We measure the inlet flow in a supersonic ramjet engine and demonstrate single-beam measurements that are on average within 19 m/s of concurrent crossed-beam measurements. We estimate that the DCS and the new database contribute 1.6 and 13 m/s to this difference respectively., Comment: This work has been cleared by the Air Force under case number AFRL-2023-4805

We present broadband dual frequency comb laser absorption measurements of 2% H$_2$O (natural isotopic abundance of 99.7% H$_2^{16}$O) in air from 6600-7650 cm$^{-1}$ (1307-1515 nm) with a spectral point spacing of 0.0068 cm$^{-1}$. Twenty-nine datasets were collected at temperatures between 300 and 1300 K ($\pm$0.82% average uncertainty) and pressures ranging from 20 to 600 Torr ($\pm$0.25%) with an average residual absorbance noise of 8.0E-4 across the spectrum for all measurements. We fit measurements using a quadratic speed-dependent Voigt profile to determine 7088 absorption parameters for 3366 individual transitions found in HITRAN2020. These measurements build on the line strength, line center, self-broadening, and self-shift parameters determined in the Part I companion of this work. Here we measure air-broadened width (with temperature- and speed-dependence) and air pressure shift (with temperature dependence) parameters. Various trends are explored for extrapolation to weak transitions that were not covered in this work. Improvements made in this work are predominantly due to the inclusion of air pressure shift temperature dependence values. In aggregate, these updates improved RMS absorbance error by a factor of 4.2 on average, and the remaining residual is predominantly spectral noise. This updated database improves high temperature spectroscopic knowledge across the 6600 7650 cm$^{-1}$ region of H$_2$O absorption., Comment: Database files available upon request. Will be included with published manuscript following review process

We study the 2-offer semirandom 3-uniform hypergraph model on $n$ vertices. At each step, we are presented with 2 uniformly random vertices. We choose any other vertex, thus creating a hyperedge of size 3. We show a strategy that constructs a perfect matching, and another that constructs a loose Hamilton cycle, both succeeding asymptotically almost surely within $\Theta(n)$ steps. Both results extend to $s$-uniform hypergraphs. The challenges with hypergraphs, and our methods, are qualitatively different from what has been seen for semirandom graphs. Much of our analysis is done on an auxiliary graph that is a uniform $k$-out subgraph of a random bipartite graph, and this tool may be useful in other contexts., Comment: This version makes the title more explicit, revises the introduction and background, corrects some minor errors, and adds a few clarifications

Developing accurate computational models of wildfire dynamics is increasingly important due to the substantial and expanding negative impacts of wildfire events on human health, infrastructure, and the environment. Wildfire spread and emissions depend on a number of factors, including fuel type, environmental conditions (moisture, wind speed, etc.) and terrain/location. However, there currently exist only a few experimental facilities that enable testing of the interplay of these factors at length scales <1 m with carefully controlled and characterized boundary conditions and advanced diagnostics. Experiments performed at such facilities are required for informing and validating computational models. Here we present the design and characterization of a novel tilting wind tunnel (the 'WindCline') for studying wildfire dynamics. The WindCline is unique in that the entire tunnel platform is constructed to pivot around a central axis, which enables sloping of the entire system without compromising the quality of the flow properties. In addition, this facility has a configurable design for the test section and diffuser to accommodate a suite of advanced diagnostics to aid in the characterization of 1) the parameters needed to establish boundary conditions and 2) flame properties and dynamics. The WindCline thus allows for measurement and control of several critical wildfire variables and boundary conditions, especially at the small length scales important to the development of high fidelity computational simulations (10 - 100 cm). Computational modeling frameworks developed and validated under these controlled conditions can expand understanding of fundamental combustion processes, promoting greater confidence when leveraging these processes in complex combustion environments., Comment: 27 pages, 12 figures + 6 in supplemental material, submitted to Review of Scientific Instruments

Effective disaster response is critical for affected communities. Responders and decision-makers would benefit from reliable, timely measures of the issues impacting their communities during a disaster, and social media offers a potentially rich data source. Social media can reflect public concerns and demands during a disaster, offering valuable insights for decision-makers to understand evolving situations and optimize resource allocation. We used Bidirectional Encoder Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Then, we conducted a temporal-spatial analysis to examine the distribution of these topics across different regions during the 2020 western U.S. wildfire season. Our results show that Twitter users mainly focused on three topics:"health impact," "damage," and "evacuation." We used the Susceptible-Infected-Recovered (SIR) theory to explore the magnitude and velocity of topic diffusion on Twitter. The results displayed a clear relationship between topic trends and wildfire propagation patterns. The estimated parameters obtained from the SIR model in selected cities revealed that residents exhibited a high level of several concerns during the wildfire. Our study details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response and support their decision-making processes.

Molecular diagnostics are a primary tool of modern chemistry, enabling researchers to map chemical reaction pathways and rates to better design and control chemical systems. Many chemical reactions are complex and fast, and existing diagnostic approaches provide incomplete information. For example, mass spectrometry is optimized to gather snapshots of the presence of many chemical species, while conventional laser spectroscopy can quantify a single chemical species through time. Here we optimize for multiple objectives by introducing a high-speed and broadband, mid-infrared dual frequency comb absorption spectrometer. The optical bandwidth of >1000 cm-1 covers absorption fingerprints of many species with spectral resolution <0.03 cm-1 to accurately discern their absolute quantities. Key to this advance are 1 GHz pulse repetition rate frequency combs covering the 3-5 um region that enable microsecond tracking of fast chemical process dynamics. We demonstrate this system to quantify the abundances and temperatures of each species in the complete reactants-to-products breakdown of 1,3,5-trioxane, which exhibits a formaldehyde decomposition pathway that is critical to modern low temperature combustion systems. By maximizing the number of observed species and improving the accuracy of temperature and concentration measurements, this spectrometer advances understanding of chemical reaction pathways and rates and opens the door for novel developments such as combining high-speed chemistry with machine learning.

A hallmark of many unconventional superconductors is the presence of many-body interactions which give rise to broken symmetry states intertwined with superconductivity. Recent resonant soft x-ray scattering experiments report commensurate $3a_0$ charge density wave order in the infinite layer nickelates, which has important implications regarding the universal interplay between charge order and superconductivity in both the cuprates and nickelates. Here, we present x-ray scattering and spectroscopy measurements on a series of NdNiO$_{2+x}$ samples which reveal that the signatures of charge density wave order are absent in fully reduced, single-phase NdNiO$_2$. The $3a_0$ superlattice peak instead originates from a partially reduced impurity phase where excess apical oxygens form ordered rows with 3 unit cell periodicity. The absence of any observable charge density wave order in NdNiO$_2$ highlights a crucial difference between the phase diagrams of the cuprate and nickelate superconductors., Comment: Main Text: 8 pages, 4 figures. Supplemental: 12 pages, 12 figures

The fraction of ionizing photons that escape their host galaxies to ionize hydrogen in the inter-galactic medium (IGM) is a critical parameter in analyses of the reionization era. In this paper we use the Meraxes semi-analytic galaxy formation model to infer the mean ionizing photon escape fraction and its dependence on galaxy properties through joint modelling of the observed high redshift galaxy population and existing constraints on the reionization history. Using a Bayesian framework, and under the assumption that escape fraction is primarily related to halo mass, we find that the joint constraints of the UV luminosity function, CMB optical depth, and the Ly$\alpha$ forest require an escape fraction of $(18\pm5)\%$ for galaxies within haloes of $M\lesssim10^{9}$M$_\odot$ and $(5\pm2)\%$ for more massive haloes. In terms of galaxy properties, this transition in escape fraction occurs at stellar masses of $M_\star\sim10^7$M$_\odot$, nearly independent of redshift. As a function of redshift, reionization is dominated by the smaller $M_\star\lesssim10^7$M$_\odot$ galaxies with high escape fractions at $z\gtrsim6$ and by the larger $M_\star\gtrsim10^7$M$_\odot$ galaxies with lower escape fractions at $z\lesssim6$. Galaxies with star formation rates of $10^{-2.5}$M$_\odot$yr$^{-1}$ to $10^{-1.5}$M$_\odot$yr$^{-1}$ provide the dominant source of ionizing photons throughout reionization. Our results are consistent with recent direct measurements of a $\sim5\%$ escape fraction from massive galaxies at the end of reionization and support the picture of low mass galaxies being the dominant sources of ionizing photons during reionization., Comment: Updated to match accepted version for publication in MNRAS. Includes two new figures showing model predictions and related discussion