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Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning. This study introduces an approach to estimate the performance of deep learning-based object detection models for quantifying defects in transmission electron microscopy (TEM) images, focusing on detecting irradiation-induced cavities in TEM images of metal alloys. We developed a random forest regression model that predicts the object detection F1 score, a statistical metric used to evaluate the ability to accurately locate and classify objects of interest. The random forest model uses features extracted from the predictions of the object detection model whose uncertainty is being quantified, enabling fast prediction on new, unlabeled images. The mean absolute error (MAE) for predicting F1 of the trained model on test data is 0.09, and the $R^2$ score is 0.77, indicating there is a significant correlation between the random forest regression model predicted and true defect detection F1 scores. The approach is shown to be robust across three distinct TEM image datasets with varying imaging and material domains. Our approach enables users to estimate the reliability of a defect detection and segmentation model predictions and assess the applicability of the model to their specific datasets, providing valuable information about possible domain shifts and whether the model needs to be fine-tuned or trained on additional data to be maximally effective for the desired use case., Comment: 14 pages, 9 figures, 3 tables

Superconducting qubits equipped with quantum non-demolishing readout and active feedback can be used as information engines to probe and manipulate microscopic degrees of freedom, whether intentionally designed or naturally occurring in their environment. In the case of spin systems, the required magnetic field bias presents a challenge for superconductors and Josephson junctions. Here we demonstrate a granular aluminum nanojunction fluxonium qubit (gralmonium) with spectrum and coherence resilient to fields beyond one Tesla. Sweeping the field reveals a paramagnetic spin-1/2 ensemble, which is the dominant gralmonium loss mechanism when the electron spin resonance matches the qubit. We also observe a suppression of fast flux noise in magnetic field, suggesting the freezing of surface spins. Using an active state stabilization sequence, the qubit hyperpolarizes long-lived two-level systems (TLSs) in its environment, previously speculated to be spins. Surprisingly, the coupling to these TLSs is unaffected by magnetic fields, leaving the question of their origin open. The robust operation of gralmoniums in Tesla fields offers new opportunities to explore unresolved questions in spin environment dynamics and facilitates hybrid architectures linking superconducting qubits with spin systems.

The anticipated observation of the gravitational microlensing of gravitational waves (GWs) promises to shed light on a host of astrophysical and cosmological questions. However, extracting the parameters of the lens from the modulated GWs requires accurate modeling of the lensing amplification factor, accounting for wave-optics effects. Analytic solutions to the lens equation have not been found to date, except for a handful of simplistic lens models. While numerical solutions to this equation have been developed, the time and computational resources required to evaluate the amplification factor numerically make large-scale parameter estimation of the lens (and source) parameters prohibitive. On the other hand, surrogate modeling of GWs has proven to be a powerful tool to accurately, and rapidly, produce GW templates at arbitrary points in parameter space, interpolating from a finite set of available waveforms at discrete parameter values. In this work, we demonstrate that surrogate modeling can also effectively be applied to the evaluation of the time-domain microlensing amplification factor $\widetilde{F}(t)$, for arbitrary lens configurations. We show this by constructing $\widetilde{F}(t)$ for two lens models, viz. point-mass lens, and singular isothermal sphere, which notably includes logarithmic divergence behaviour. We find both surrogates reproduce the original lens models accurately, with mismatches $\lesssim 5 \times 10^{-4}$ across a range of plausible microlensed binary black hole sources observed by the Einstein Telescope. This surrogate is between 5 and $10^3$ times faster than the underlying lensing models, and can be evaluated in about 100 ms. The accuracy and efficiency attained by our surrogate models will enable practical parameter estimation analyses of microlensed GWs., Comment: 15 pages, 8 figures: Results unchanged, updated with a few clarifications

Precision optical filters are key components for current and future photonic technologies. Here, we demonstrate a low loss spectral filter consisting of an ultrasteep bandpass feature with a maximum gradient of (90.6$\pm$0.7) dB/GHz, centred within a notch filter with (128$\pm$6) dB of suppression. The filter consists of a fiber Bragg grating with multiple $\pi$-phase discontinuities inscribed into a single mode photosensitive fiber. The measured performance closely matches the simulated spectrum calculated from the design parameters indicating a high degree of confidence in the repeatability and manufacture of such devices. These filters show great promise for applications reliant on high-frequency resolution noise suppression, such as quantum networking, and highlight the opportunities for the versatility, efficiency, and extreme suppression offered by high-performance fiber Bragg grating devices., Comment: 7 pages, 4 figures

The detection of various molecular species, including complex organic molecules relevant to biochemical and geochemical processes, in astronomical settings such as the interstellar medium or the outer Solar System has led to the increased need for a better understanding of the chemistry occurring in these cold regions of space. In this context, the chemistry of ices prepared and processed at cryogenic temperatures has proven to be of particular interest due to the fact that many interstellar molecules are believed to originate within the icy mantles adsorbed on nano- and micro-scale dust particles. The chemistry leading to the formation of such molecules may be initiated by ionising radiation in the form of galactic cosmic rays or stellar winds, and thus there has been an increased interest in commissioning experimental set-ups capable of simulating and better characterising this solid-phase radiation astrochemistry. In this article, we describe a new facility called AQUILA (Atomki-Queen's University Ice Laboratory for Astrochemistry) which has been purposefully designed to study the chemical evolution of ices analogous to those that may be found in the dense interstellar medium or the outer Solar System as a result of their exposure to keV ion beams. The results of some ion irradiation studies of CH3OH ice at 20 K are discussed to exemplify the experimental capabilities of the AQUILA as well as to highlight its complementary nature to another laboratory astrochemistry set-up at our institute., Comment: Published in Review of Scientific Instruments

While `jailbreaks' have been central to research on the safety and reliability of LLMs (large language models), the underlying mechanisms behind these attacks are not well understood. Some prior works have used linear methods to analyze jailbreak prompts or model refusal. Here, however, we compare linear and nonlinear methods to study the features in prompts that contribute to successful jailbreaks. We do this by probing for jailbreak success based only on the portions of the latent representations corresponding to prompt tokens. First, we introduce a dataset of 10,800 jailbreak attempts from 35 attack methods. We then show that different jailbreaking methods work via different nonlinear features in prompts. Specifically, we find that while probes can distinguish between successful and unsuccessful jailbreaking prompts with a high degree of accuracy, they often transfer poorly to held-out attack methods. We also show that nonlinear probes can be used to mechanistically jailbreak the LLM by guiding the design of adversarial latent perturbations. These mechanistic jailbreaks are able to jailbreak Gemma-7B-IT more reliably than 34 of the 35 techniques that it was trained on. Ultimately, our results suggest that jailbreaks cannot be thoroughly understood in terms of universal or linear prompt features alone.

As two-dimensional (2D) materials continue to gain prominence, understanding their symmetries is critical for unlocking their full potential. In this work, we present comprehensive scanning tables that tabulate the rod group symmetries of all crystallographic lines in all 80 layer groups, which describe the symmetries of 2D materials. These tables are analogous to the scanning tables for space groups found in Volume E of the International Tables for Crystallography, but are specifically tailored for layer groups and their applications to 2D materials. This resource will aid in the analysis of line defects, such as domain walls and grain boundaries, which play a crucial role in determining the properties and functionality of 2D materials., Comment: 10 pages, 7 figures, 2 tables. Supplementary information: 37 pages, 80 tables

Based on the analysis of a large number of high-fidelity nonlinear gyrokinetic simulations, we propose a novel strategy to improve confinement in tokamak plasmas by combining up-down asymmetric flux surface shaping with the Low Momentum Diffusivity (LMD) regime. We show that the intrinsic momentum flux driven by up-down asymmetry creates strong flow shear in the LMD regime that can significantly reduce energy transport, increasing the critical gradient by up to $25\%$. In contrast to traditional methods for generating flow shear, such as neutral beam injection, this approach requires no external momentum source and is expected to scale well to large fusion devices. The experimental applicability of this strategy in spherical tokamaks is addressed via simulations by considering actual equilibria from MAST and a preliminary equilibrium from SMART., Comment: 7 pages, 5 figures

The difficulty of anonymizing text data hinders the development and deployment of NLP in high-stakes domains that involve private data, such as healthcare and social services. Poorly anonymized sensitive data cannot be easily shared with annotators or external researchers, nor can it be used to train public models. In this work, we explore the feasibility of using synthetic data generated from differentially private language models in place of real data to facilitate the development of NLP in these domains without compromising privacy. In contrast to prior work, we generate synthetic data for real high-stakes domains, and we propose and conduct use-inspired evaluations to assess data quality. Our results show that prior simplistic evaluations have failed to highlight utility, privacy, and fairness issues in the synthetic data. Overall, our work underscores the need for further improvements to synthetic data generation for it to be a viable way to enable privacy-preserving data sharing., Comment: Accepted to EMNLP 2024 (Findings)

To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method -- an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people's portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale., Comment: 15 pages, 3 figures. To appear at EMNLP'24

As Large Language Models (LLMs) become increasingly integrated into our daily lives, the potential harms from deceptive behavior underlie the need for faithfully interpreting their decision-making. While traditional probing methods have shown some effectiveness, they remain best for narrowly scoped tasks while more comprehensive explanations are still necessary. To this end, we investigate meta-models-an architecture using a "meta-model" that takes activations from an "input-model" and answers natural language questions about the input-model's behaviors. We evaluate the meta-model's ability to generalize by training them on selected task types and assessing their out-of-distribution performance in deceptive scenarios. Our findings show that meta-models generalize well to out-of-distribution tasks and point towards opportunities for future research in this area. Our code is available at https://github.com/acostarelli/meta-models-public ., Comment: 11 pages, 2 figures

To an $\mathbb{R}$-tree in the boundary of Outer space, we associate two simplices: the simplex of projective length measures, and the simplex of projective dual currents. For both kinds of simplices, we estimate the dimension of maximal simplices for arational $\mathbb{R}$-trees in the boundary of Outer space., Comment: 42 pages, 9 figures. Comments are welcome!

Plasma turbulence on disparate spatial and temporal scales plays a key role in defining the level of confinement achievable in tokamaks, with the development of reduced numerical models for cross-scale turbulence effects informed by experimental measurements an essential step. MAST-U is a well-equipped facility having instruments to measure ion and electron scale turbulence at the plasma edge. However, measurement of core electron scale turbulence is challenging, especially in H mode. Using a novel synthetic diagnostic approach, we present simulated measurement specifications of a proposed mm-wave based collective scattering instrument optimised for measuring both normal and binormal electron scale turbulence in the core and edge of MAST-U. A powerful modelling framework has been developed that combines beam-tracing techniques with gyrokinetic simulations to predict the sensitivity, localisation and spectral range of measurement. For the reconstructed MAST 022769 shot, a maximum measurable normalised bi-normal wavenumber of $k_{\perp} \rho_{e} \sim 0.6$ was predicted in the core and $k_{\perp} \rho_{e} \sim 0.79$ near the pedestal, with localisation lengths $L_{FWHM}$ ranging from $\sim$ 0.4 m in the core at $k_{\perp} \rho_{e} \sim 0.1$ to ~0.08m at $k_{\perp} \rho_{e} \sim 0.45$. Synthetic diagnostic analysis for the 022769 shot using CGYRO gyrokinetic simulation spectra reveal that ETG turbulence wavenumbers of peak spectral intensity comfortably fall within the measurable range of the instrument from the core to the pedestal. The proposed diagnostic opens up opportunities to study new regimes of turbulence and confinement in association with upcoming non-inductive, microwave based current drive experiments on MAST-U and can provide insight into cross-scale turbulence effects, while having suitability to operate during burning plasma scenarios on future reactors such as STEP., Comment: 22 pages, 19 figures, journal submission

Symmetry control is essential for realizing unconventional properties, such as ferroelectricity, nonlinear optical responses, and complex topological order, thus it holds promise for the design of emerging quantum and photonic systems. Nevertheless, fast and reversible control of symmetry in materials remains a challenge, especially for nanoscale systems. Here, we unveil reversible symmetry changes in colloidal lead chalcogenide quantum dots on picosecond timescales. Using a combination of ultrafast electron diffraction and total X-ray scattering, in conjunction with atomic-scale structural modeling and first-principles calculations, we reveal that symmetry-broken lead sulfide quantum dots restore to a centrosymmetric phase upon photoexcitation. The symmetry restoration is driven by photoexcited electronic carriers, which suppress lead off-centering for about 100 ps. Furthermore, the change in symmetry is closely correlated with the electronic properties as shown by transient optical measurements. Overall, this study elucidates reversible symmetry changes in colloidal quantum dots, and more broadly defines a new methodology to optically control symmetry in nanoscale systems on ultrafast timescales., Comment: 19 pages, 5 figures

The ionization potential (IP) of radium monofluoride (RaF) was measured to be 4.969(2)[10] eV, revealing a relativistic enhancement in the series of alkaline earth monofluorides. The results are in agreement with a relativistic coupled-cluster prediction of 4.969[7] eV, incorporating up to quantum electrodynamics corrections. Using the same computational methodology, an improved calculation for the dissociation energy ($D_{0}$) of 5.54[5] eV is presented. This confirms that radium monofluoride joins the small group of diatomic molecules for which $D_{0}>\mathrm{IP}$, paving the way for precision control and interrogation of its Rydberg states.

Strong $E\times B$ plasma flow shear is beneficial for reducing turbulent transport. However, traditional methods of driving flow shear do not scale well to large devices such as future fusion power plants. In this paper, we use a large number of nonlinear gyrokinetic simulations to study a novel approach to increase flow shear: decreasing the momentum diffusivity to make the plasma ``easier to push''. We first use an idealized circular geometry and find that one can obtain low momentum diffusivity at tight aspect ratio, low safety factor, high magnetic shear and low temperature gradient. This is the so-called Low Momentum Diffusivity (LMD) regime. To drive intrinsic momentum flux, we then tilt the flux surface, making it up-down asymmetric. In the LMD regime, this intrinsic momentum flux drives strong flow shear that can significantly reduce the heat flux and increase the critical temperature gradient. We also consider the actual experimental geometry of the MAST tokamak to illustrate that this strategy can be practical and create experimentally significant flow shear. Lastly, a preliminary prediction for the SMART tokamak is made., Comment: 43 pages, 23 figures

Highly precise and robust waveform models are required as improvements in detector sensitivity enable us to test general relativity with more precision than ever before. In this work, we introduce a spin-aligned surrogate ringdown model. This ringdown surrogate, NRSur3dq8_RD, is built with numerical waveforms produced using Cauchy-characteristic evolution. In addition, these waveforms are in the superrest frame of the remnant black hole allowing us to do a correct analysis of the ringdown spectrum. The novel prediction of our surrogate model is complex-valued quasinormal mode (QNM) amplitudes, with median relative errors of $10^{-2}-10^{-3}$ over the parameter space. Like previous remnant surrogates, we also predict the remnant black hole's mass and spin. The QNM mode amplitude errors translate into median errors on ringdown waveforms of $10^{-4}$. The high accuracy and QNM mode content provided by our surrogate will enable high-precision ringdown analyses such as tests of general relativity. Our ringdown model is publicly available through the python package surfinBH., Comment: 11+2 pages, 13 figures, 1 table. This new model is publicly available through surfinBH https://pypi.org/project/surfinBH/

gwNRHME is a recently developed framework that seamlessly converts a multi-modal (i.e with several spherical harmonic modes) quasi-circular waveform into multi-modal eccentric waveform if the quadrupolar eccentric waveform is known. Here, we employ the gwNRHME framework to combine a multi-modal quasi-circular waveform model NRHybSur3dq8 and quadrupolar non-spinning eccentric waveform model EccentricIMR to construct multi-modal non-spinning eccentric model NRHybSur3dq8-gwNRHME. Using a total of 35 eccentric numerical relativity (NR) simulations obtained from the SXS and RIT catalogs, we demonstrate that NRHybSur3dq8-gwNRHME model predictions agree well with NR (with typical relative $L_2$ errors of ~0.01 for the dominant quadrupolar mode) for mass ratios $ 1 \leq q \leq 4$ and eccentricities up to ~0.2 measured about 10 cycles before the merger. To demonstrate the modularity of the gwNRHME framework, we further combine EccentricIMR with BHPTNRSur1dq1e4 model and develop a non-spinning eccentric models named BHPTNRSur1dq1e4-gwNRHME. Finally, we develop a different variant of these models by replacing EccentricIMR with EccentricTD. Both the gwNRHME framework and associated models are available through the gwModels package., Comment: 14 pages, 11 figures

The integration of machine learning (ML) models enhances the efficiency, affordability, and reliability of feature detection in microscopy, yet their development and applicability are hindered by the dependency on scarce and often flawed manually labeled datasets and a lack of domain awareness. We addressed these challenges by creating a physics-based synthetic image and data generator, resulting in a machine learning model that achieves comparable precision (0.86), recall (0.63), F1 scores (0.71), and engineering property predictions (R2=0.82) to a model trained on human-labeled data. We enhanced both models by using feature prediction confidence scores to derive an image-wide confidence metric, enabling simple thresholding to eliminate ambiguous and out-of-domain images resulting in performance boosts of 5-30% with a filtering-out rate of 25%. Our study demonstrates that synthetic data can eliminate human reliance in ML and provides a means for domain awareness in cases where many feature detections per image are needed.

The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We present Pathfinder, a machine learning framework designed to enable literature review and knowledge discovery in astronomy, focusing on semantic searching with natural language instead of syntactic searches with keywords. Utilizing state-of-the-art large language models (LLMs) and a corpus of 350,000 peer-reviewed papers from the Astrophysics Data System (ADS), Pathfinder offers an innovative approach to scientific inquiry and literature exploration. Our framework couples advanced retrieval techniques with LLM-based synthesis to search astronomical literature by semantic context as a complement to currently existing methods that use keywords or citation graphs. It addresses complexities of jargon, named entities, and temporal aspects through time-based and citation-based weighting schemes. We demonstrate the tool's versatility through case studies, showcasing its application in various research scenarios. The system's performance is evaluated using custom benchmarks, including single-paper and multi-paper tasks. Beyond literature review, Pathfinder offers unique capabilities for reformatting answers in ways that are accessible to various audiences (e.g. in a different language or as simplified text), visualizing research landscapes, and tracking the impact of observatories and methodologies. This tool represents a significant advancement in applying AI to astronomical research, aiding researchers at all career stages in navigating modern astronomy literature., Comment: 25 pages, 9 figures, submitted to AAS jorunals. Comments are welcome, and the tools mentioned are available online at https://pfdr.app

Precipitates are main microstructural features to provide high temperature creep strength and radiation resistance in structural materials for fusion energy systems. However, the mechanisms of precipitate stability under irradiation in candidate structural materials for fusion first-wall and blanket components are poorly understood. In particular, the dual effects of helium transmutation and irradiation-induced damage on precipitate evolution have not been systematically studied in candidate materials, the leading of which are Fe-9Cr reduced activation ferritic/martensitic (RAFM) alloys. To fill this knowledge gap, a fundamental understanding of the single and combined interactions of helium (0-25 appm He/dpa), temperature (300-600C), and atomic displacements (15-100 dpa) on the behavior of MX (M=metal, X=C and/or N) precipitates in an advanced Fe-9Cr RAFM alloy were studied through the use of dual ion irradiation experiments. It was found that helium suppressed the diffusion-mediated mechanisms of precipitate stability (i.e. radiation-enhanced growth) at elevated temperatures and intermediate damage levels but had no effect on precipitate dissolution in the high dose conditions (>50 dpa). A precipitate stability model was used to rationalize the impacts of helium on ballistic dissolution and radiation-enhanced diffusion which are key contributors to overall precipitate stability. This is the second paper in a series of three to provide a systematic evaluation of MX precipitate behavior in RAFM steels under various fusion-relevant ion irradiation conditions., Comment: 39 pages, includes supplemental information, 9 figures, 4 tables. arXiv admin note: text overlap with arXiv:2407.10002

We present BHPTNRSur2dq1e3, a reduced order surrogate model of gravitational waves emitted from binary black hole (BBH) systems in the comparable to large mass ratio regime with aligned spin ($\chi_1$) on the heavier mass ($m_1$). We trained this model on waveform data generated from point particle black hole perturbation theory (ppBHPT) with mass ratios varying from $3 \leq q \leq 1000$ and spins from $-0.8 \leq \chi_1 \leq 0.8$. The waveforms are $13,500 \ m_1$ long and include all spin-weighted spherical harmonic modes up to $\ell = 4$ except the $(4,1)$ and $m = 0$ modes. We find that for binaries with $\chi_1 \lesssim -0.5$, retrograde quasi-normal modes are significantly excited, thereby complicating the modeling process. To overcome this issue, we introduce a domain decomposition approach to model the inspiral and merger-ringdown portion of the signal separately. The resulting model can faithfully reproduce ppBHPT waveforms with a median time-domain mismatch error of $8 \times 10^{-5}$. We then calibrate our model with numerical relativity (NR) data in the comparable mass regime $(3 \leq q \leq 10)$. By comparing with spin-aligned BBH NR simulations at $q = 15$, we find that the dominant quadrupolar (subdominant) modes agree to better than $\approx 10^{-3} \ (\approx 10^{-2})$ when using a time-domain mismatch error, where the largest source of calibration error comes from the transition-to-plunge and ringdown approximations of perturbation theory. Mismatch errors are below $\approx 10^{-2}$ for systems with mass ratios between $6 \leq q \leq 15$ and typically get smaller at larger mass ratio. Our two models - both the ppBHPT waveform model and the NR-calibrated ppBHPT model - will be publicly available through gwsurrogate and the Black Hole Perturbation Toolkit packages., Comment: 20 pages, 17 figures

Reduced activation ferritic/martensitic (RAFM) steels are the leading candidate structural materials for first-wall and blanket components in fusion reactors. This work is the first in a series to provide a systematic roadmap of MX precipitate stability in RAFM steels under various ion irradiation conditions. Here, the MX-TiC precipitate behavior in an advanced Fe-9Cr RAFM steel is assessed under self-ion irradiation to damage levels ranging from 1 to 100 displacements per atom (dpa) at temperatures ranging from 300-600{\deg}C to isolate the effects of temperature and damage level on precipitate stability. The pre-existing MX-TiC precipitates are shown to exhibit temperature-dominated responses, including coarsening above 400{\deg}C at damage levels of 15 dpa, while damage levels studied at 50 dpa and higher showed dissolution across all temperature ranges studied. The effects of ballistic dissolution and diffusion on precipitate behavior are outlined as a function of precipitate characteristics (number density, size, and 25 volume fraction) and irradiation parameters with the use of the recoil resolution model of precipitate stability. This work provides critical insights into MX-TiC stability to high dose in-order to further optimize advanced steels with improved radiation resistance., Comment: 32 pages, 19 pages supplemental

Thick metamorphic buffers are perceived to be indispensable for the heteroepitaxial integration of III-V semiconductors on silicon substrates with large thermal expansion and lattice mismatches. However, III-nitride buffers in conventional GaN-on-Si high electron mobility transistor (HEMT) heterostructures impose a substantial thermal resistance, throttling heat extraction, which reduces device efficiency and lifetime. Herein, bypassing the buffer, we demonstrate the direct growth of GaN after the AlN nucleation layer on silicon by metal-organic vapor phase epitaxy (MOVPE). By varying reactor pressure, we modulate the growth stress in the submicron epilayers and realise threading dislocation densities similar to that in thick buffered structures. We achieve a GaN-to-substrate thermal resistance of (11(+/-)4) ((m^2)K(GW^-1)), an order of magnitude reduction over conventional designs on silicon and one of the lowest on any non-native substrate. AlGaN/AlN/GaN heterojunctions on this platform show a characteristic 2D electron gas (2DEG), the room-temperature Hall-effect mobility of which, at over 2000 (cm^2/(V-s)), rivals the best-reported values. The low-temperature magnetoresistance of this 2DEG shows clear Shubnikov-de-Haas oscillations, a quantum lifetime > 0.180 ps, and tell-tale signatures of spin-splitting. These results may establish a new paradigm for nitride HEMTs, potentially accelerating applications from energy-efficient transistors to fundamental investigations on electron dynamics in this 2D wide-bandgap system.

Superconducting quantum processors are one of the leading platforms for realizing scalable fault-tolerant quantum computation (FTQC). The recent demonstration of post-fabrication tuning of Josephson junctions using alternating-bias assisted annealing (ABAA) technique and a reduction in junction loss after ABAA illuminates a promising path towards precision tuning of qubit frequency while maintaining high coherence. Here, we demonstrate precision tuning of the maximum $|0\rangle\rightarrow |1\rangle$ transition frequency ($f_{01}^{\rm max}$) of tunable transmon qubits by performing ABAA at room temperature using commercially available test equipment. We characterize the impact of junction relaxation and aging on resistance spread after tuning, and demonstrate a frequency equivalent tuning precision of 7.7 MHz ($0.17\%$) based on targeted resistance tuning on hundreds of qubits, with a resistance tuning range up to $18.5\%$. Cryogenic measurements on tuned and untuned qubits show evidence of improved coherence after ABAA with no significant impact on tunability. Despite a small global offset, we show an empirical $f_{01}^{\rm max}$ tuning precision of 18.4 MHz by tuning a set of multi-qubit processors targeting their designed Hamiltonians. We experimentally characterize high-fidelity parametric resonance iSWAP gates on two ABAA-tuned 9-qubit processors with fidelity as high as $99.51\pm 0.20\%$. On the best-performing device, we measured across the device a median fidelity of $99.22\%$ and an average fidelity of $99.13\pm 0.12 \%$. Yield modeling analysis predicts high detuning-edge-yield using ABAA beyond the 1000-qubit scale. These results demonstrate the cutting-edge capability of frequency targeting using ABAA and open up a new avenue to systematically improving Hamiltonian targeting and optimization for scaling high-performance superconducting quantum processors.

We investigate the late-time tail behavior in gravitational waves from merging eccentric binary black holes (BBH) using black hole perturbation theory. For simplicity, we focus only on the dominant quadrupolar mode of the radiation. We demonstrate that such tails become more prominent as eccentricity increases. Exploring the phenomenology of the tails in both spinning and non-spinning eccentric binaries, with the spin magnitude varying from $\chi=-0.6$ to $\chi=+0.6$ and eccentricity as high as $e=0.98$, we find that these tails can be well approximated by a slowly decaying power law. We study the power law for varying systems and find that the power law exponent lies close to the theoretically expected value $-4$. Finally, using both plunge geodesic and radiation-reaction-driven orbits, we perform a series of numerical experiments to understand the origin of the tails in BBH simulations. Our results suggest that the late-time tails are strongly excited in eccentric BBH systems when the smaller black hole is in the neighborhood of the apocenter, as opposed to any structure in the strong field of the larger black hole. Our analysis framework is publicly available through the \texttt{gwtails} Python package., Comment: 16 pages, 18 figures

The development of fault-tolerant quantum computers (FTQCs) is gaining increased attention within the quantum computing community. Like their digital counterparts, FTQCs, equipped with error correction and large qubit numbers, promise to solve some of humanity's grand challenges. Estimates of the resource requirements for future FTQC systems are essential to making design choices and prioritizing R&D efforts to develop critical technologies. Here, we present a resource estimation framework and software tool that estimates the physical resources required to execute specific quantum algorithms, compiled into their graph-state form, and laid out onto a modular superconducting hardware architecture. This tool can predict the size, power consumption, and execution time of these algorithms at as they approach utility-scale according to explicit assumptions about the system's physical layout, thermal load, and modular connectivity. We use this tool to study the total resources on a proposed modular architecture and the impact of tradeoffs between and inter-module connectivity, latency and resource requirements.

Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communities like astronomy might benefit from them. However, there is currently no standard for evaluating the use of LLMs in astronomy. Therefore, we present the experimental design for an evaluation study on how astronomy researchers interact with LLMs. We deploy a Slack chatbot that can answer queries from users via Retrieval-Augmented Generation (RAG); these responses are grounded in astronomy papers from arXiv. We record and anonymize user questions and chatbot answers, user upvotes and downvotes to LLM responses, user feedback to the LLM, and retrieved documents and similarity scores with the query. Our data collection method will enable future dynamic evaluations of LLM tools for astronomy., Comment: 7 pages, 3 figures. Code available at https://github.com/jsalt2024-evaluating-llms-for-astronomy/astro-arxiv-bot

Like a criminal under investigation, Large Language Models (LLMs) might pretend to be aligned while evaluated and misbehave when they have a good opportunity. Can current interpretability methods catch these 'alignment fakers?' To answer this question, we introduce a benchmark that consists of 324 pairs of LLMs fine-tuned to select actions in role-play scenarios. One model in each pair is consistently benign (aligned). The other model misbehaves in scenarios where it is unlikely to be caught (alignment faking). The task is to identify the alignment faking model using only inputs where the two models behave identically. We test five detection strategies, one of which identifies 98% of alignment-fakers.