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Submillimeter galaxies (SMGs) constitute a key population of bright star-forming galaxies at high redshift. These galaxies challenge galaxy formation models, particularly in reproducing their observed number counts and redshift distributions. Furthermore, although SMGs contribute significantly to the cosmic star formation rate density (SFRD), their precise role remains uncertain. Upcoming surveys, such as the Ultra Deep Survey with the TolTEC camera, are expected to offer valuable insights into SMG properties and their broader impact. Robust modeling of SMGs in a cosmologically representative volume is necessary to investigate their nature in preparation for next-generation submillimeter surveys. We implement and test parametric relations derived from radiative transfer calculations across three cosmological simulations: EAGLE, IllustrisTNG, and FLAMINGO. Particular emphasis is placed on the FLAMINGO due to their large volume and robust statistical sampling of SMGs. Based on the model that best reproduces observations, we forecast submillimeter fluxes within the simulations, analyze the properties of SMGs, and evaluate their evolution over cosmic time. Our results show that the FLAMINGO reproduces the observed redshift distribution and source number counts of SMGs without requiring a top-heavy initial mass function. On the other hand, the EAGLE and IllustrisTNG show a deficit of bright SMGs. We find that SMGs with S850 > 1 mJy contribute up to 27% of the SFRD at z=2.6 in the FLAMINGO, consistent with recent observations. Flux density functions reveal a rise in SMG abundance from z = 6 to 2.5, followed by a sharp decline in the number of brighter SMGs from z = 2.5 to 0. Leveraging the SMG population in FLAMINGO, we forecast that the TolTEC UDS will detect 80,000 sources over 0.8 deg^2 at 1.1 mm (at the 4{\sigma} detection limit), capturing about 50% of the cosmic SFRD at z=2.5., Comment: 19 pages, 13 figures, submitted to Astronomy & Astrophysics Journal, abstract is reduced to meet Arxiv character limit

Young people are increasingly exposed to adverse effects of data-driven profiling, recommending, and manipulation on social media platforms, most of them without adequate understanding of the mechanisms that drive these platforms. In the context of computing education, educating learners about mechanisms and data practices of social media may improve young learners' data agency, digital literacy, and understanding how their digital services work. A four-hour technology -- supported intervention was designed and implemented in 12 schools involving 209 5th and 8th grade learners. Two new classroom apps were developed to support the classroom activities. Using Likert-scale questions borrowed from a data agency questionnaire and open-ended questions that mapped learners' data-driven reasoning on social media phenomena, this article shows significant improvement between pre- and post-tests in learners' data agency and data-driven explanations of social media mechanisms. Results present an example of improving young learners' understanding of social media mechanisms.

Brown dwarfs lack nuclear fusion and cool with time; the coldest known have an effective temperature below 500 K, and are known as Y dwarfs. We present a James Webb Space Telescope (JWST) photometric dataset of Y dwarfs: twenty-three were imaged in wide-field mode, 20 using NIRCam with the F150W and F480M filters, and 3 using NIRISS with the F480M filter. We present an F480M vs. F150W $-$ F480M color-magnitude diagram for our sample, and other brown dwarfs with F150W and F480M colors synthesized from JWST spectra by Beiler et al. (2024). For one target, WISEA J083011.95$+$283716.0, its detection in the near-infrared confirms it as one of the reddest Y dwarfs known, with F150W $-$ F480M $= 9.62$ mag. We provide its updated parallax and proper motion. One of the Beiler et al. Y dwarfs, CWISEP J104756.81+545741.6, is unusually blue, consistent with strong CO absorption seen in its spectrum which the F480M filter is particularly sensitive to. The strong CO and the kinematics of the object suggest it may be very low-mass and young. We update the resolved photometry for the close binary system WISE J033605.05$-$014350.4 AB, and find that the secondary is almost as cold as WISE 085510.83$-$071442.5, with $T_{\rm eff} \lesssim 300$ K, however the F150W $-$ F480M color is significantly bluer, possibly suggesting the presence of water clouds. Astrometry is measured at the JWST epoch for the sample which is consistent with parallax and proper motion values reported by Kirkpatrick et al. (2021) and Marocco et al. (in prep)., Comment: Accepted by AJ Jan 23 2025

This paper, submitted to the special track on resources for teaching AI in K-12, presents an explainable AI (XAI) education tool designed for K-12 classrooms, particularly for students in grades 4-9. The tool was designed for interventions on the fundamental processes behind social media platforms, focusing on four AI- and data-driven core concepts: data collection, user profiling, engagement metrics, and recommendation algorithms. An Instagram-like interface and a monitoring tool for explaining the data-driven processes make these complex ideas accessible and engaging for young learners. The tool provides hands-on experiments and real-time visualizations, illustrating how user actions influence both their personal experience on the platform and the experience of others. This approach seeks to enhance learners' data agency, AI literacy, and sensitivity to AI ethics. The paper includes a case example from 12 two-hour test sessions involving 209 children, using learning analytics to demonstrate how they navigated their social media feeds and the browsing patterns that emerged., Comment: 9 pages, 8 figures, accepted to AAAI 25 conference

We investigate various properties of extremal dyonic static black holes in Einstein-Maxwell-Dilaton-Axion theory. We obtain a simple first-order ordinary differential equation for the black hole mass in terms of its electric and magnetic charges, which we can solve explicitly for certain special values of the scalar couplings. For one such case we also construct new dyonic black hole solutions, making use of the presence of an enhanced SL(2,R) symmetry. Finally, we investigate the structure of long range forces and binding energies between non-equivalent extremal black holes. For certain special cases, we can identify regions of parameter space where the force is always attractive or repulsive. Unlike in the case without an axion, the force and binding energies between distinct black holes are not always correlated with each other. Our work is motivated in part by the question of whether long range forces between non-identical states can potentially encode information about UV constraints on low-energy physics., Comment: 29 pages, no figures

The North Dakota Department of Public Instruction (NDDPI) uses a three-pillar approach to school renewal efforts: build relationships, cultivate opportunity, and inspire growth. A key product of this approach is the North Dakota School Renewal Guide, which includes practitioner-friendly school improvement strategies grounded in evidence-based improvement practices for schools identified as Targeted Support and Improvement (TSI) or Comprehensive Support and Improvement (CSI). The guide, now in its third edition, is updated annually based on feedback from the NDDPI, the North Dakota Regional Education Association (REA), and the TSI and CSI schools and districts using the guide. This report tells the story of how the NDDPI project team created and tested a calibration activity to increase inter-rater reliability ratings among REA staff, NDDPI representatives, and schools. By using procedures for consistent and accurate ratings, REA liaisons can provide schools with tailored coaching to support their improvement journeys, help schools track progress on goals and initiatives, and help schools collect the evidence required for them to exit TSI and CSI status. The report is intended as a resource for school leaders working to implement short-cycle school improvement goals.

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the structure in response to both a user or environmental input. Active metamaterials are currently of wide interest to the physics community and encompass a range of sub-domains in applied physics (e.g. photonic, microwave, acoustic, mechanical, etc.). They possess the potential to provide solutions that are more suitable to specific applications, or which allow novel properties to be produced which cannot be achieved with passive metamaterials, such as time-varying or gain enhancement effects. They have the potential to help solve some of the important current and future problems faced by the advancement of modern society, such as achieving net-zero, sustainability, healthcare and equality goals. Despite their huge potential, the added complexity of their design and operation, compared to passive metamaterials creates challenges to the advancement of the field, particularly beyond theoretical and lab-based experiments. This roadmap brings together experts in all types of active metamaterials and across a wide range of areas of applied physics. The objective is to provide an overview of the current state of the art and the associated current/future challenges, with the hope that the required advances identified create a roadmap for the future advancement and application of this field.

This research represents a pioneering application of automated pose estimation from drone data to study elephant behavior in the wild, utilizing video footage captured from Samburu National Reserve, Kenya. The study evaluates two pose estimation workflows: DeepLabCut, known for its application in laboratory settings and emerging wildlife fieldwork, and YOLO-NAS-Pose, a newly released pose estimation model not previously applied to wildlife behavioral studies. These models are trained to analyze elephant herd behavior, focusing on low-resolution ($\sim$50 pixels) subjects to detect key points such as the head, spine, and ears of multiple elephants within a frame. Both workflows demonstrated acceptable quality of pose estimation on the test set, facilitating the automated detection of basic behaviors crucial for studying elephant herd dynamics. For the metrics selected for pose estimation evaluation on the test set -- root mean square error (RMSE), percentage of correct keypoints (PCK), and object keypoint similarity (OKS) -- the YOLO-NAS-Pose workflow outperformed DeepLabCut. Additionally, YOLO-NAS-Pose exceeded DeepLabCut in object detection evaluation. This approach introduces a novel method for wildlife behavioral research, including the burgeoning field of wildlife drone monitoring, with significant implications for wildlife conservation., Comment: Accepted to CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling Workshop in conjunction with Computer Vision and Pattern Recognition 2024

Objective: A proof-of-concept study aimed at designing and implementing VIEWER, a versatile toolkit for visual analytics of clinical data, and systematically evaluating its effectiveness across various clinical applications while gathering feedback for iterative improvements. Materials and Methods: VIEWER is an open-source and extensible toolkit that employs natural language processing and interactive visualisation techniques to facilitate the rapid design, development, and deployment of clinical information retrieval, analysis, and visualisation at the point of care. Through an iterative and collaborative participatory design approach, VIEWER was designed and implemented in one of the UK's largest NHS mental health Trusts, where its clinical utility and effectiveness were assessed using both quantitative and qualitative methods. Results: VIEWER provides interactive, problem-focused, and comprehensive views of longitudinal patient data (n=409,870) from a combination of structured clinical data and unstructured clinical notes. Despite a relatively short adoption period and users' initial unfamiliarity, VIEWER significantly improved performance and task completion speed compared to the standard clinical information system. More than 1,000 users and partners in the hospital tested and used VIEWER, reporting high satisfaction and expressed strong interest in incorporating VIEWER into their daily practice. Conclusion: VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery, including population health management and patient monitoring. The deployment of VIEWER highlights the benefits of collaborative refinement in optimizing health informatics solutions for enhanced patient care.

Background: Many open-source skin cancer image datasets are the result of clinical trials conducted in countries with lighter skin tones. Due to this tone imbalance, machine learning models derived from these datasets can perform well at detecting skin cancer for lighter skin tones. Any tone bias in these models could introduce fairness concerns and reduce public trust in the artificial intelligence health field. Methods: We examine a subset of images from the International Skin Imaging Collaboration (ISIC) archive that provide tone information. The subset has a significant tone imbalance. These imbalances could explain a model's tone bias. To address this, we train models using the imbalanced dataset and a balanced dataset to compare against. The datasets are used to train a deep convolutional neural network model to classify the images as malignant or benign. We then evaluate the models' disparate impact, based on selection rate, relative to dark or light skin tone. Results: Using the imbalanced dataset, we found that the model is significantly better at detecting malignant images in lighter tone resulting in a disparate impact of 0.577. Using the balanced dataset, we found that the model is also significantly better at detecting malignant images in lighter versus darker tones with a disparate impact of 0.684. Using the imbalanced or balanced dataset to train the model still results in a disparate impact well below the standard threshold of 0.80 which suggests the model is biased with respect to skin tone. Conclusion: The results show that typical skin cancer machine learning models can be tone biased. These results provide evidence that diagnosis or tone imbalance is not the cause of the bias. Other techniques will be necessary to identify and address the bias in these models, an area of future investigation.

Increasingly precise space-based photometry uncovers higher-order effects in transits, eclipses and phase curves which can be used to characterize exoplanets in novel ways. The subtle signature induced by a rotationally deformed exoplanet is determined by the planet's oblateness and rotational obliquity, which provide a wealth of information about a planet's formation, internal structure, and dynamical history. However, these quantities are often strongly degenerate and require sophisticated methods to convincingly constrain. We develop a new semi-analytic model for an ellipsoidal object occulting a spherical body with arbitrary surface maps expressed in terms of spherical harmonics. We implement this model in an open-source Jax-based Python package eclipsoid, allowing just-in-time compilation and automatic differentiation. We then estimate the precision obtainable with JWST observations of the long period planet population and demonstrate the best current candidates for studies of oblateness and obliquity. We test our method on the JWST NIRSpec transit of the inflated warm Neptune WASP-107 b and place an upper bound on its projected oblateness $f<0.23$, which corresponds to a rotation period of $P_{\mathrm{rot}}>13$h if the planet is not inclined to our line of sight. Further studies of long-period exoplanets will necessitate discarding the assumption of planets as spherical bodies. Eclipsoid provides a general framework allowing rotational deformation to be modelled in transits, occultations, phase curves, transmission spectra and more., Comment: 14 pages, 8 figures. Submitted to ApJ. Code available open-source at https://github.com/shishirdholakia/eclipsoid/tree/main

We present a study of asteroseismically derived surface gravities, masses, and radii of a sample of red giant stars both with and without confirmed planetary companions using TESS photometric light curves. These red giants were drawn from radial velocity surveys, and their reported properties in the literature rely on more traditional methods using spectroscopy and isochrone fitting. Our asteroseismically derived surface gravities achieved a precision of $\sim$0.01 dex; however, they were on average $\sim$0.1~dex smaller than the literature. The systematic larger gravities of the literature could plausibly present as a systematic overestimation of stellar masses, which would in turn lead to overestimated planetary masses of the companions. We find that the fractional discrepancies between our asteroseismically-determined parameters and those previously found are typically larger for stellar radii ($\sim$10% discrepancy) than for stellar masses ($<5$% discrepancy). However, no evidence of a systematic difference between methods was found for either fundamental parameter. Two stars, HD~100065 and HD~18742, showed significant disagreement with the literature in both mass and radii. We explore the impacts on updated stellar properties on inferred planetary properties and caution that red giant radii may be more poorly constrained than uncertainties suggest., Comment: 17 pages, 9 figures, 6 tables

Radio detections of stellar systems provide a window onto stellar magnetic activity and the space weather conditions of extrasolar planets, information that is difficult to attain at other wavelengths. There have been recent advances observing auroral emissions from radio-bright low-mass stars and exoplanets largely due to the maturation of low-frequency radio instruments and the plethora of wide-field radio surveys. To guide us in placing these recent results in context, we introduce the foremost local analogues for the field: Solar bursts and the aurorae found on Jupiter. We detail how radio bursts associated with stellar flares are foundational to the study of stellar coronae, and time-resolved radio dynamic spectra offers one of the best prospects of detecting and characterising coronal mass ejections from other stars. We highlight the prospects of directly detecting coherent radio emission from exoplanetary magnetospheres, and early tentative results. We bridge this discussion to the field of brown dwarf radio emission, in which their larger and stronger magnetospheres are amenable to detailed study with current instruments. Bright, coherent radio emission is also predicted from magnetic interactions between stars and close-in planets. We discuss the underlying physics of these interactions and implications of recent provisional detections for exoplanet characterisation. We conclude with an overview of outstanding questions in theory of stellar, star-planet interaction, and exoplanet radio emission, and the prospects of future facilities in answering them., Comment: Accepted to Nature Astronomy. The manuscript is designed to be a primer for new doctoral students and scholars to the field of radio stars and exoplanets. 36 pages, 3 figures

We study a magnitude-limited sample of 36 Broad-lined Type Ic Supernovae (SNe Ic-BL) from the Zwicky Transient Facility Bright Transient Survey (detected between March 2018 and August 2021), which is the largest systematic study of SNe Ic-BL done in literature thus far. We present the light curves (LCs) for each of the SNe, and analyze the shape of the LCs to derive empirical parameters, along with the explosion epochs for every event. The sample has an average absolute peak magnitude in the r band of $M_r^{max}$ = -18.51 $\pm$ 0.15 mag. Using spectra obtained around peak light, we compute expansion velocities from the Fe II 5169 Angstrom line for each event with high enough signal-to-noise ratio spectra, and find an average value of $v_{ph}$ = 16,100 $\pm$ 1,100 km $s^{-1}$. We also compute bolometric LCs, study the blackbody temperature and radii evolution over time, and derive the explosion properties of the SNe. The explosion properties of the sample have average values of $M_{Ni}$ = $0.37_{-0.06}^{+0.08}$ solar masses, $M_{ej}$ = $2.45_{-0.41}^{+0.47}$ solar masses, and $E_K$= $4.02_{-1.00}^{+1.37} \times 10^{51}$ erg. Thirteen events have radio observations from the Very Large Array, with 8 detections and 5 non-detections. We find that the populations that have radio detections and radio non-detections are indistinct from one another with respect to their optically-inferred explosion properties, and there are no statistically significant correlations present between the events' radio luminosities and optically-inferred explosion properties. This provides evidence that the explosion properties derived from optical data alone cannot give inferences about the radio properties of SNe Ic-BL, and likely their relativistic jet formation mechanisms., Comment: 52 pages, 34 Figures, 8 Tables; Accepted to ApJ, Revised Title from Proofs

Predicting the dynamics of turbulent fluid flows has long been a central goal of science and engineering. Yet, even with modern computing technology, accurate simulation of all but the simplest turbulent flow-fields remains impossible: the fields are too chaotic and multi-scaled to directly store them in memory and perform time-evolution. An alternative is to treat turbulence $\textit{probabilistically}$, viewing flow properties as random variables distributed according to joint probability density functions (PDFs). Turbulence PDFs are neither chaotic nor multi-scale, but are still challenging to simulate due to their high dimensionality. Here we show how to overcome the dimensionality problem by parameterising turbulence PDFs into an extremely compressed format known as a "tensor network" (TN). The TN paradigm enables simulations on single CPU cores that would otherwise be impractical even with supercomputers: for a $5+1$ dimensional PDF of a chemically reactive turbulent flow, we achieve reductions in memory and computational costs by factors of $\mathcal{O}(10^6)$ and $\mathcal{O}(10^3)$, respectively, compared to standard finite difference algorithms. A future path is opened towards something heretofore regarded as infeasible: directly simulating high-dimensional PDFs of both turbulent flows and other chaotic systems that are useful to describe probabilistically., Comment: Post peer-review version accepted for publication; link to data & code added

Data annotation, the practice of assigning descriptive labels to raw data, is pivotal in optimizing the performance of machine learning models. However, it is a resource-intensive process susceptible to biases introduced by annotators. The emergence of sophisticated Large Language Models (LLMs) presents a unique opportunity to modernize and streamline this complex procedure. While existing research extensively evaluates the efficacy of LLMs, as annotators, this paper delves into the biases present in LLMs when annotating hate speech data. Our research contributes to understanding biases in four key categories: gender, race, religion, and disability with four LLMs: GPT-3.5, GPT-4o, Llama-3.1 and Gemma-2. Specifically targeting highly vulnerable groups within these categories, we analyze annotator biases. Furthermore, we conduct a comprehensive examination of potential factors contributing to these biases by scrutinizing the annotated data. We introduce our custom hate speech detection dataset, HateBiasNet, to conduct this research. Additionally, we perform the same experiments on the ETHOS (Mollas et al. 2022) dataset also for comparative analysis. This paper serves as a crucial resource, guiding researchers and practitioners in harnessing the potential of LLMs for data annotation, thereby fostering advancements in this critical field., Comment: Accepted at NeurIPS Safe Generative AI Workshop, 2024

The sensitivity limits of space telescopes are imposed by uncalibrated errors in the point spread function, photon-noise, background light, and detector sensitivity. These are typically calibrated with specialized wavefront sensor hardware and with flat fields obtained on the ground or with calibration sources, but these leave vulnerabilities to residual time-varying or non-common path aberrations and variations in the detector conditions. It is therefore desirable to infer these from science data alone, facing the prohibitively high dimensional problems of phase retrieval and pixel-level calibration. We introduce a new Python package for physical optics simulation, dLux, which uses the machine learning framework JAX to achieve GPU acceleration and automatic differentiation (autodiff), and apply this to simulating astronomical imaging. In this first of a series of papers, we show that gradient descent enabled by autodiff can be used to simultaneously perform phase retrieval and calibration of detector sensitivity, scaling efficiently to inferring millions of parameters. This new framework enables high dimensional optimization and inference in data analysis and hardware design in astronomy and beyond, which we explore in subsequent papers in this series., Comment: 13 pages, 6 figures

The design of astronomical hardware operating at the diffraction limit requires optimization of physical optical simulations of the instrument with respect to desired figures of merit, such as throughput or astrometric accuracy. These systems can be high dimensional, with highly nonlinear relationships between outputs and the adjustable parameters of the hardware. In this series of papers we present and apply dLux, an open-source end-to-end differentiable optical modelling framework. Automatic differentiation enables not just efficient high-dimensional optimization of astronomical hardware designs, but also Bayesian experimental design directly targeting the precision of experimental outcomes. Automatic second derivatives enable the exact and numerically stable calculation of parameter covariance forecasts, and higher derivatives of these enable direct optimization of these forecasts. We validate this method against analytic theory and illustrate its utility in evaluating the astrometric precision of a parametrized telescope model, and the design of a diffractive pupil to achieve optimal astrometric performance for exoplanet searches. The source code and tutorial software are open source and publicly available, targeting researchers who may wish to harness dLux for their own optical simulation problems., Comment: 12 pages, 6 figures

To understand the formation and evolution of massive cosmic structures, studying them at high redshift, in the epoch when they formed the majority of their mass is essential. The One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey is undertaking the widest-area narrowband program to date, to use Ly$\alpha$-emitting galaxies (LAEs) to trace the large-scale structure (LSS) of the Universe on the scale of 10 - 100 cMpc at three cosmic epochs. In this work, we present results at $z$ = 3.1 based on early ODIN data in the COSMOS field. We identify and characterize protoclusters and cosmic filaments using multiple methods and discuss their strengths and weaknesses. We then compare our observations against the IllustrisTNG suite of cosmological hydrodynamical simulations. The two are in excellent agreement, with a similar number and angular size of structures identified above a specified density threshold. We are able to recover the simulated protoclusters with $\log$(M$_{z=0}$/$M_\odot$) $\gtrsim$ 14.4 in $\sim$ 60% of the cases. With these objects we show that the descendant masses of the protoclusters in our sample can be estimated purely based on our 2D measurements, finding a median $z$ = 0 mass of $\sim10^{14.5}$M$_\odot$. The lack of information on the radial extent of each protocluster introduces a $\sim$0.4 dex uncertainty in its descendant mass. Finally, we show that the recovery of the cosmic web in the vicinity of protoclusters is both efficient and accurate. The similarity of our observations and the simulations imply that our structure selection is likewise robust and efficient, demonstrating that LAEs are reliable tracers of the LSS., Comment: 29 pages, 20 figures; Accepted for publication in ApJ

In this paper we describe the set of ``New Worlds Simulations'', three very large cosmology simulations, Qo'noS, Vulcan, and Ferenginar, that were carried out on the Summit supercomputer with the Hardware/Hybrid Cosmology Code, HACC. The gravity-only simulations follow the evolution of structure in the Universe by each employing 12,288^3 particles in (3 Gpc/h)^3 volumes, leading to a mass resolution of m_p~10^9 Msun/h. The simulations cover three different cosmologies, one LambdaCDM model, consistent with measurements from Planck, one simulation with massive neutrinos, and one simulation with a varying dark energy equation of state. All simulations have the same phases to allow a detailed comparison of the results and the investigation of the impact of different cosmological parameters. We present measurements of some basic statistics, such as matter power spectra, correlation function, halo mass function and concentration-mass relation and investigate the differences due to the varying cosmologies. Given the large volume and high resolution, these simulations provide excellent bases for creating synthetic skies. A subset of the data is made publicly available as part of this paper., Comment: 10 pages, 13 figures, simulation data portal: https://cosmology.alcf.anl.gov/transfer/newworlds

We present an analysis of eight JWST Mid-Infrared Instrument (MIRI) 5.6 micron images with $5\,\sigma$ depths of ~0.1 uJy. We detect 2854 sources within our combined area of 18.4 square arcminutes. We compute the MIRI 5.6um number counts including an analysis of the field-to-field variation. Compared to earlier published MIRI 5.6 um counts, our counts have a more pronounced knee, at roughly 2 uJy. The location and amplitude of the counts at the knee are consistent with the Cowley et al. (2018) model predictions, although these models tend to overpredict the counts below the knee. In areas of overlap, 84% of the MIRI sources have a counterpart in the COSMOS2020 catalog. These MIRI sources have redshifts that are mostly in the $z\sim0.5-2$, with a tail out to $z\sim5$. They are predominantly moderate to low stellar masses ($10^8-10^{10}$M$_{\odot}$) main sequence star-forming galaxies, suggesting that with ~2hr exposures, MIRI can reach well below $M^*$ at cosmic noon and reach higher mass systems out to $z\sim5$. Nearly 70% of the COSMOS2020 sources in areas of overlap now have a data point at 5.6um (rest-frame near-IR at cosmic noon) which allows for more accurate stellar population parameter estimates. Finally, we discover 31 MIRI-bright sources not present in COSMOS2020. A cross-match with IRAC channel 1 suggests that 10-20% of these are likely lower mass (M$_*\approx10^9$M$_{\odot}$), $z\sim1$ dusty galaxies. The rest (80--90%) are consistent with more massive but still very dusty galaxies at $z>3$., Comment: 24 pages, 15 figures, ApJ in press

Tracking in education is a process that started in the early 1900s. What started as a tool to help migrant students, evolved into a discrete form of segregation. Tracking normally negatively affects marginalized students. It often separates students by socioeconomic status and race. Black and Hispanic students normally make up a small percentage of students placed in higher-tracked math classes. When students are placed in a lower-track math class, they often continue that track until they graduate, which gives them little to no room for advancement once they are on that particular track. Tracking is developed by grades, test scores, and possibly a biased teacher's idea of perceived student potential. To remedy the problems that tracking creates, de-tracking in classes, especially math class, must occur. De-tracking can help enable equity in math classrooms. It will also help close the opportunity gap which in turn can help close the achievement gap between minority and nonminority students. There has been a lot of research on tracking but not as much on the benefits that de-tracking can have on marginalized students. My research will study the benefits of de-tracking for marginalized students. The research will also study the confidence and mental psyche of students that are placed on lower tracks versus higher tracks. Given the opportunity, all students can be successful. De-tracking can help all students grow and be victorious in all classes, especially in math class.

The study of perturbations around black hole backgrounds in general relativity and Einstein-Maxwell theory has a long history, going back to the work of Regge and Wheeler in the 1950s. As part of a broader investigation of perturbations around black holes in supergravity, we describe here our results for the perturbations around the Gibbons-Maeda static charged black holes in a class of Einstein-Maxwell-Dilaton theories. Our analysis follows the general strategy developed by Chandrasekhar and Xanthopoulos for the perturbations of the Reissner-Nordstr\"om black hole. Here, the analysis is considerably more involved, because of the presence of the dilaton field, which couples to the other polar modes. We nonetheless find that the problem is completely solvable, in the sense that one can separate variables and eventually describe all the perturbations in terms of diagonalised second-order radial equations. We are able to prove the mode stability of all the Gibbons-Maeda black hole solutions., Comment: 43 pages

We present the molecular gas content and ISM conditions of MACSJ0717 Az9, a strong gravitationally lensed $z=4.273$, $M_{*} \simeq 2\times10^9M_{\odot}$ star-forming galaxy with an unusually high ($\sim 80\%$) obscured star formation fraction. We detect CO(4-3) in two independent lensed images, as well as [N II]205$\mu$m, with ALMA. We derive a molecular gas mass of log$_{10}[M_{H_{2}} (M_{\odot})] = 9.77$ making it moderately deficient in molecular gas compared to the lower redshift gas fraction scaling relation. Leveraging photodissociation region (PDR) models, we combine our CO(4-3) measurements with existing measurements of the [C II] 158$\mu$m line and total infrared luminosity to model the PDR conditions. We find PDR conditions similar to local star-forming galaxies, with a mean hydrogen density log$_{10}$[$n_H$ $cm^{-3}$] = $4.80\pm0.39$ and a mean radiation field strength log$_{10}$[G$_0$ Habing] = $2.83\pm0.26$. Based on Band 3 continuum data, we derive an upper limit on the intrinsic dust mass of log$_{10}[M_{\rm dust} (M_{\odot})] < 7.73$, consistent with existing estimates. We use the 3D tilted-ring model fitting code 3D-Barolo to determine the kinematic properties of the CO(4-3) emitting gas. We find that it is rotationally dominated, with a $V/\sigma=4.6 \pm 1.7$, consistent with the kinematics of the [C II]. With PDR conditions remarkably similar to normal dusty star-forming galaxies at z ~ 0.2 and a stable molecular disk, our observations of Az9 suggest that the dust-obscured phase for a low-mass galaxy at z$\sim$4 is relatively long. Thus, Az9 may be representative of a more widespread population that has been missed due to insufficiently deep existing millimeter surveys., Comment: 19 pages, 8 figures

The most active phases of star formation and black hole accretion are strongly affected by dust extinction, making far-infrared (far-IR) observations the best way to disentangle and study the co-evolution of galaxies and super massive black holes. The plethora of fine structure lines and emission features from dust, ionised and neutral atomic and warm molecular gas in the rest-frame mid- and far-IR provide unmatched diagnostic power to determine the properties of gas and dust, measure gas-phase metallicities and map cold galactic outflows in even the most obscured galaxies. By combining multi-band photometric surveys with low and high-resolution far-IR spectroscopy, the PRobe far-Infrared Mission for Astrophysics (PRIMA), a concept for a far-IR, 1.8m-diameter, cryogenically cooled observatory, will revolutionise the field of galaxy evolution by taking advantage of this IR toolkit to find and study dusty galaxies across galactic time. In this work, we make use of the phenomenological simulation SPRITZ and the Santa Cruz semi-analytical model to describe how a moderately deep multi-band PRIMA photometric survey can easily reach beyond previous IR missions to detect and study galaxies down to $10^{11}\,L_{\odot}$ beyond cosmic noon and at least up to z=4, even in the absence of gravitational lensing. By decomposing the spectral energy distribution (SED) of these photometrically selected galaxies, we show that PRIMA can be used to accurately measure the relative AGN power, the mass fraction contributed by polycyclic aromatic hydrocarbon (PAH) and the total IR luminosity. At the same time, spectroscopic follow up with PRIMA will allow to trace both the star formation and black hole accretion rates (SFR, BHAR), the gas phase metallicities and the mass outflow rates of cold gas in hundreds to thousands of individual galaxies to z=2., Comment: 16 pages, 17 figures, accepted for publication in A&A

This paper presents the application, testing and first results of a new adaptive Bayesian inference analysis which utilises conventional and ultrafast spectroscopic measurements made in the divertor chamber to investigate the divertor physics during detachment. Validation of this software is performed prior and during analyses of results, demonstrated by compelling reproductions of ideal test cases and synthetic spectroscopic measurements. Application on real diagnostic data shows strong agreement with results from previous analysis methods. We identify unprecedented success in significant advances in time and computational efficiencies. We demonstrate a $\lesssim$1000$\times$ reduction in analysis time for spectroscopic measurements from simulated and real Super-X configurations, with the analysis technique presented in this report completing in <3 minutes. Analysis of synthetic and real diagnostic measurements identifies detachment physics in agreement with previous literature.

We observed the planet-hosting system PDS 70 with the James Webb Interferometer, JWST's Aperture Masking Interferometric (AMI) mode within NIRISS. Observing with the F480M filter centered at 4.8 $\mu$m, we simultaneously fit geometrical models to the outer disk and the two known planetary companions. We re-detect the protoplanets PDS 70 b and c at an SNR of 14.7 and 7.0, respectively. Our photometry of both PDS 70 b and c provides tentative evidence of mid-IR circumplanetary disk emission through fitting SED models to these new measurements and those found in the literature. We also newly detect emission within the disk gap at an SNR of $\sim$4, at a position angle of $220^{+10}_{-15}$ degrees, and an unconstrained separation within $\sim$200 mas. Follow-up observations will be needed to determine the nature of this emission. We place a 5$\sigma$ upper limit of 208 $\pm$ 10 $\mu$Jy on the flux of the candidate PDS 70 d at 4.8 $\mu$m, which indicates that if the previously observed emission at shorter wavelengths is due to a planet, this putative planet has a different atmospheric composition than PDS 70 b or c. Finally, we place upper limits on emission from any additional planets in the disk gap. We find an azimuthally averaged 5$\sigma$ contrast upper limit $>$7 magnitudes at separations greater than 110 mas. These are the deepest limits to date within $\sim$250 mas at 4.8 $\mu$m and the first space-based interferometric observations of this system., Comment: Accepted for publication in AJ

We present the Sydney Radio Star Catalogue, a new catalogue of stars detected at megahertz to gigahertz radio frequencies. It consists of 839 unique stars with 3,405 radio detections, more than doubling the previously known number of radio stars. We have included stars from large area searches for radio stars found using circular polarisation searches, cross-matching, variability searches, and proper motion searches as well as presenting hundreds of newly detected stars from our search of Australian SKA Pathfinder observations. The focus of this first version of the catalogue is on objects detected in surveys using SKA precursor instruments; however we will expand this scope in future versions. The 839 objects in the Sydney Radio Star Catalogue are distributed across the whole sky and range from ultracool dwarfs to Wolf-Rayet stars. We find that the radio luminosities of cool dwarfs are lower than the radio luminosities of more evolved sub-giant and giant stars. We use X-ray detections of 530 radio stars by the eROSITA soft X-ray instrument onboard the SRG spacecraft to show that almost all of the radio stars in the catalogue are over-luminous in the radio, indicating that the majority of stars at these radio frequencies are coherent radio emitters. The Sydney Radio Star Catalogue can be found in Vizier or at https://radiostars.org., Comment: 21 pages, 8 Figures, 7 Tables, Submitted to PASA

The PRobe far-Infrared Mission for Astrophysics (PRIMA) concept aims to perform mapping with spectral coverage and sensitivities inaccessible to previous FIR space telescopes. PRIMA's imaging instrument, PRIMAger, provides unique hyperspectral imaging simultaneously covering 25-235 $\mu$m. We synthesise images representing a deep, 1500 hr deg$^{-2}$ PRIMAger survey, with realistic instrumental and confusion noise. We demonstrate that we can construct catalogues of galaxies with a high purity ($>95$ per cent) at a source density of 42k deg$^{-2}$ using PRIMAger data alone. Using the XID+ deblending tool we show that we measure fluxes with an accuracy better than 20 per cent to flux levels of 0.16, 0.80, 9.7 and 15 mJy at 47.4, 79.7, 172, 235 $\mu$m respectively. These are a factor of $\sim$2 and $\sim$3 fainter than the classical confusion limits for 72-96 $\mu$m and 126-235 $\mu$m, respectively. At $1.5 \leq z \leq 2$, we detect and accurately measure fluxes in 8-10 of the 10 channels covering 47-235 $\mu$m for sources with $2 \leq$ log(SFR) $\leq 2.5$, a 0.5 dex improvement on what might be expected from the classical confusion limit. Recognising that PRIMager will operate in a context where high quality data will be available at other wavelengths, we investigate the benefits of introducing additional prior information. We show that by introducing even weak prior flux information when employing a higher source density catalogue (more than one source per beam) we can obtain accurate fluxes an order of magnitude below the classical confusion limit for 96-235 $\mu$m., Comment: 14 pages, 11 figures

Because of their limited angular resolution, far-infrared telescopes are usually affected by confusion phenomenon. Since several galaxies can be located in the same instrumental beam, only the brightest objects emerge from the fluctuations caused by fainter sources. The probe far-infrared mission for astrophysics imager (PRIMAger) will observe the mid- and far-infrared (25-235 $\mu$m) sky both in intensity and polarization. We aim to provide predictions of the confusion level and its consequences for future surveys. We produced simulated PRIMAger maps affected only by the confusion noise using the simulated infrared extragalactic sky (SIDES) semi-empirical simulation. We then estimated the confusion limit in these maps and extracted the sources using a basic blind extractor. By comparing the input galaxy catalog and the extracted source catalog, we derived various performance metrics as completeness, purity, and the accuracy of various measurements. In intensity, we predict that the confusion limit increases rapidly with increasing wavelength. The confusion limit in polarization is more than 100x lower. The measured flux density is dominated by the brightest galaxy in the beam, but other objects also contribute at longer wavelength (~30% at 235 $\mu$m). We also show that galaxy clustering has a mild impact on confusion in intensity (up to 25%), while it is negligible in polarization. In intensity, a basic blind extraction will be sufficient to detect galaxies at the knee of the luminosity function up to z~3 and 10$^{11}$ M$_\odot$ main-sequence galaxies up to z~5. In polarization for a conservative sensitivity, we expect ~8 000 detections up to z=2.5 opening a totally new window on the high-z dust polarization. Finally, we show that intensity surveys at short wavelength and polarization surveys at long wavelength tend to reach confusion at similar depth. There is thus a strong synergy., Comment: 24 pages, 18 figures, 6 tables, Accepted by A&A

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

The widespread presence of hateful languages on social media has resulted in adverse effects on societal well-being. As a result, addressing this issue with high priority has become very important. Hate speech or offensive languages exist in both explicit and implicit forms, with the latter being more challenging to detect. Current research in this domain encounters several challenges. Firstly, the existing datasets primarily rely on the collection of texts containing explicit offensive keywords, making it challenging to capture implicitly offensive contents that are devoid of these keywords. Secondly, common methodologies tend to focus solely on textual analysis, neglecting the valuable insights that community information can provide. In this research paper, we introduce a novel dataset OffensiveLang, a community based implicit offensive language dataset generated by ChatGPT 3.5 containing data for 38 different target groups. Despite limitations in generating offensive texts using ChatGPT due to ethical constraints, we present a prompt-based approach that effectively generates implicit offensive languages. To ensure data quality, we evaluate the dataset with human. Additionally, we employ a prompt-based zero-shot method with ChatGPT and compare the detection results between human annotation and ChatGPT annotation. We utilize existing state-of-the-art models to see how effective they are in detecting such languages. The dataset is available here: https://github.com/AmitDasRup123/OffensiveLang

Due to various sources of uncertainty, emergent behavior, and ongoing changes, the reliability of many socio-technical systems depends on an iterative and collaborative process in which organizations (1) analyze and learn from system failures, and then (2) co-evolve both the technical and human parts of their systems based on what they learn. Many organizations have defined processes for learning from failure, often involving postmortem analyses conducted after any system failures that are judged to be sufficiently severe. Despite established processes and tool support, our preliminary research, and professional experience, suggest that it is not straightforward to take what was learned from a failure and successfully improve the reliability of the socio-technical system. To better understand this collaborative process and the associated challenges, we are conducting a study of how teams learn from failure. We are gathering incident reports from multiple organizations and conducting interviews with engineers and managers with relevant experience. Our analytic interest is in what is learned by teams as they reflect on failures, the learning processes involved, and how they use what is learned. Our data collection and analysis are not yet complete, but we have so far analyzed 13 incident reports and seven interviews. In this short paper we (1) present our preliminary findings, and (2) outline our broader research plans.

The distributional simplicity bias (DSB) posits that neural networks learn low-order moments of the data distribution first, before moving on to higher-order correlations. In this work, we present compelling new evidence for the DSB by showing that networks automatically learn to perform well on maximum-entropy distributions whose low-order statistics match those of the training set early in training, then lose this ability later. We also extend the DSB to discrete domains by proving an equivalence between token $n$-gram frequencies and the moments of embedding vectors, and by finding empirical evidence for the bias in LLMs. Finally we use optimal transport methods to surgically edit the low-order statistics of one class to match those of another, and show that early-training networks treat the edited samples as if they were drawn from the target class. Code is available at https://github.com/EleutherAI/features-across-time.

This paper introduces an educational tool for classroom use, based on explainable AI (XAI), designed to demystify key social media mechanisms - tracking, profiling, and content recommendation - for novice learners. The tool provides a familiar, interactive interface that resonates with learners' experiences with popular social media platforms, while also offering the means to "peek under the hood" and exposing basic mechanisms of datafication. Learners gain first-hand experience of how even the slightest actions, such as pausing to view content, are captured and recorded in their digital footprint, and further distilled into a personal profile. The tool uses real-time visualizations and verbal explanations to create a sense of immediacy: each time the user acts, the resulting changes in their engagement history and their profile are displayed in a visually engaging and understandable manner. This paper discusses the potential of XAI and educational technology in transforming data and digital literacy education and in fostering the growth of children's privacy and security mindsets., Comment: 5 pages, 5 figures, submitted to ICALT 2024

Directly observing exoplanets with coronagraphs is impeded by the presence of speckles from aberrations in the optical path, which can be mitigated in hardware with wavefront control as well as in post-processing. This work explores using an instrument model in post-processing to separate astrophysical signals from residual aberrations in coronagraphic data. The effect of wavefront error (WFE) on the coronagraphic intensity consists of a linear contribution and a quadratic contribution. When either of the terms is much larger than the other, the instrument response can be approximated by a transfer matrix mapping WFE to detector plane intensity. From this transfer matrix, a useful projection onto instrumental modes that removes the dominant error modes can be derived. We apply this projection to synthetically generated Roman Space Telescope hybrid Lyot coronagraph (HLC) data to extract "robust observables," which can be used instead of raw data for applications such as detection testing. The projection improves planet flux ratio detection limits by about 28% in the linear regime and by over a factor of 2 in the quadratic regime, illustrating that robust observables can increase sensitivity to astrophysical signals and improve the scientific yield from coronagraphic data. While this approach does not require additional information such as observations of reference stars or modulations of a deformable mirror, it can and should be combined with these other techniques, acting as a model-informed prior in an overall post-processing strategy., Comment: 19 pages, 10 figures

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding. The Gemini family consists of Ultra, Pro, and Nano sizes, suitable for applications ranging from complex reasoning tasks to on-device memory-constrained use-cases. Evaluation on a broad range of benchmarks shows that our most-capable Gemini Ultra model advances the state of the art in 30 of 32 of these benchmarks - notably being the first model to achieve human-expert performance on the well-studied exam benchmark MMLU, and improving the state of the art in every one of the 20 multimodal benchmarks we examined. We believe that the new capabilities of the Gemini family in cross-modal reasoning and language understanding will enable a wide variety of use cases. We discuss our approach toward post-training and deploying Gemini models responsibly to users through services including Gemini, Gemini Advanced, Google AI Studio, and Cloud Vertex AI.

Stellar radio emission can measure a star's magnetic field strength and structure, plasma density and dynamics, and the stellar wind pressure impinging on exoplanet atmospheres. However, properly interpreting the radio data often requires temporal baselines that cover the rotation of the stars, orbits of their planets and any longer-term stellar activity cycles. Here we present our monitoring campaign on the young, active M dwarf AU Microscopii with the Australia Telescope Compact Array between 1.1 and 3.1 GHz. With over 250 hours of observations, these data represent the longest radio monitoring campaign on a single main-sequence star to date. We find that AU Mic produces a wide variety of radio emission, for which we introduce a phenomenological classification scheme predicated on the polarisation fraction and time-frequency structure of the emission. The radio emission detected on AU Mic can be broadly categorised into five distinct types of bursts, and broadband quiescent emission. The radio bursts are highly circularly polarised and periodic with the rotation period of the star, implying that the emission is beamed. It is therefore most likely produced by the electron cyclotron maser instability. We present a model to show that the observed emission can be explained with auroral rings on the magnetic poles. The total intensity of the broadband emission is stochastic, but we show that its circular polarisation fraction is also periodic with the rotation of the star. We present a qualitative model to describe the periodicity in the polarisation fraction of the broadband emission using gyromagnetic emission, and infer a magnetic obliquity of at least 20 degrees from the observed variation in polarisation fraction. Finally, we show that the radio emission might be evolving on long timescales, hinting at a potential stellar magnetic activity cycle., Comment: Accepted for publication in A&A, typos corrected

We analyse the STU sectors of the four-dimensional maximal gauged supergravities with gauge groups ${\rm SO(8)}$, ${\rm SO(6)}\ltimes\mathbb{R}^{12}$ and $[{\rm SO(6)}\times{\rm SO(2)}]\ltimes\mathbb{R}^{12}$, and construct new domain-wall black-hole solutions in $D=4$. The consistent Kaluza-Klein embedding of these theories is obtained using the techniques of Exceptional Field Theory combined with the 4$d$ tensor hierarchies, and their respective uplifts into $D=11$ and type IIB supergravities are connected through singular limits that relate the different gaugings., Comment: 38 pages. References added. Version to appear in JHEP

This experimental study presents an in-depth investigation of the performance of the MAST-U Super-X divertor during NBI-heated operation (up to 2.5 MW) focussing on volumetric ion sources and sinks as well as power losses during detachment. The particle balance and power loss analysis revealed the crucial role of Molecular Activated Recombination and Dissociation (MAR and MAD) ion sinks in divertor particle and power balance, which remain pronounced in the change from ohmic to higher power (NBI heated) L-mode conditions. The importance of MAR and MAD remains with double the absorbed NBI heating. MAD results in significant power dissipation (up to $\sim 20 \%$ of $P_{SOL}$), mostly in the cold ($T_e < 5$ eV) detached region. Theoretical and experimental evidence is found for the potential contribution of $D^-$ to MAR and MAD, which warrants further study. These results suggest that MAR and MAD can be relevant in higher power conditions than the ohmic conditions studied previously. Post-processing reactor-scale simulations shows that MAR and MAD can play a significant role in divertor physics and synthetic diagnostic signals of reactor-scale devices, which are currently underestimated in exhaust simulations. This raises implications for the accuracy of reactor-scale divertor simulations of particularly tightly baffled (alternative) divertor configurations.

Most of the recent work in psychedelic neuroscience has been done using non-invasive neuroimaging, with data recorded from the brains of adult volunteers under the influence of a variety of drugs. While this data provides holistic insights into the effects of psychedelics on whole-brain dynamics, the effects of psychedelics on the meso-scale dynamics of cortical circuits remains much less explored. Here, we report the effects of the serotonergic psychedelic N,N-diproptyltryptamine (DPT) on information-processing dynamics in a sample of in vitro organotypic cultures made from rat cortical tissue. Three hours of spontaneous activity were recorded: an hour of pre-drug control, and hour of exposure to 10$\mu$M DPT solution, and a final hour of washout, once again under control conditions. We found that DPT reversibly alters information dynamics in multiple ways: first, the DPT condition was associated with higher entropy of spontaneous firing activity and reduced the amount of time information was stored in individual neurons. Second, DPT also reduced the reversibility of neural activity, increasing the entropy produced and suggesting a drive away from equilibrium. Third, DPT altered the structure of neuronal circuits, decreasing the overall information flow coming into each neuron, but increasing the number of weak connections, creating a dynamic that combines elements of integration and disintegration. Finally, DPT decreased the higher-order statistical synergy present in sets of three neurons. Collectively, these results paint a complex picture of how psychedelics regulate information processing in meso-scale cortical tissue. Implications for existing hypotheses of psychedelic action, such as the Entropic Brain Hypothesis, are discussed., Comment: 19 pages, 2 figures

The Kohn-Sham equations underlie many important applications such as the discovery of new catalysts. Recent machine learning work on catalyst modeling has focused on prediction of the energy, but has so far not yet demonstrated significant out-of-distribution generalization. Here we investigate another approach based on the pointwise learning of the Kohn-Sham charge-density. On a new dataset of bulk catalysts with charge densities, we show density models can generalize to new structures with combinations of elements not seen at train time, a form of combinatorial generalization. We show that over 80% of binary and ternary test cases achieve faster convergence than standard baselines in Density Functional Theory, amounting to an average reduction of 13% in the number of iterations required to reach convergence, which may be of independent interest. Our results suggest that density learning is a viable alternative, trading greater inference costs for a step towards combinatorial generalization, a key property for applications., Comment: Published at NeurIPS 2023

LES/PDF approaches can be used for simulating challenging turbulent combustion configurations with strong turbulence chemistry interactions. Transported PDF methods are computationally expensive compared to flamelet-like turbulent combustion models. The pre-partitioned adaptive chemistry (PPAC) methodology was developed to address this cost differential. PPAC entails an offline preprocessing stage, where a set of reduced models are generated starting from an initial database of representative compositions. At runtime, this set of reduced models are dynamically utilized during the reaction fractional step leading to computational savings. We have recently combined PPAC with in-situ adaptive tabulation (ISAT) to further reduce the computational cost. We have shown that the combined method reduced the average wall-clock time per time step of large-scale LES/particle PDF simulations of turbulent combustion by 39\%. A key assumption in PPAC is that the initial database used in the offline stage is representative of the compositions encountered at runtime. In our previous study this assumption was trivially satisfied as the initial database consisted of compositions extracted from the turbulent combustion simulation itself. Consequently, a key open question remains as to whether such databases can be generated without having access to the turbulent combustion simulation. Towards answering this question, in the current work, we explore whether the compositions for forming such a database can be extracted from computationally-efficient low-dimensional simulations such as 1D counterflow flames and partially stirred reactors. We show that a database generated using compositions extracted from a partially stirred reactor configuration leads to performance comparable to the optimal case, wherein the database is comprised of compositions extracted directly from the LES/PDF simulation itself.

The first generation of exascale systems will include a variety of machine architectures, featuring GPUs from multiple vendors. As a result, many developers are interested in adopting portable programming models to avoid maintaining multiple versions of their code. It is necessary to document experiences with such programming models to assist developers in understanding the advantages and disadvantages of different approaches. To this end, this paper evaluates the performance portability of a SYCL implementation of a large-scale cosmology application (CRK-HACC) running on GPUs from three different vendors: AMD, Intel, and NVIDIA. We detail the process of migrating the original code from CUDA to SYCL and show that specializing kernels for specific targets can greatly improve performance portability without significantly impacting programmer productivity. The SYCL version of CRK-HACC achieves a performance portability of 0.96 with a code divergence of almost 0, demonstrating that SYCL is a viable programming model for performance-portable applications., Comment: 12 pages, 13 figures, 2023 International Workshop on Performance, Portability & Productivity in HPC

The $g$ factors of $11/2^-$ isomers in semimagic $^{109}$Sn and $^{111}$Sn (isomeric lifetimes $\tau = 2.9(3)$ ns and $\tau = 14.4(7)$ ns, respectively) were measured by an extension of the Time Differential Perturbed Angular Distribution technique, which uses \LaBr detectors and the hyperfine fields of a gadolinium host to achieve precise measurements in a new regime of short-lived isomers. The results, $g(11/2^-; {^{109}\textrm{Sn}}) = -0.186(8)$ and $g(11/2^-; {^{111}\textrm{Sn}}) = -0.214(4)$, are significantly lower in magnitude than those of the $11/2^-$ isomers in the heavier isotopes and depart from the value expected for a near pure neutron $h_{11/2}$ configuration. Broken-symmetry density functional theory calculations applied to the sequence of $11/2^-$ states reproduce the magnitude and location of this deviation. The $g(11/2^-)$ values are affected by shape core polarization; the odd $0h_{11/2}$ neutron couples to $J^{\pi}=2^+,4^+,6^+...$ configurations in the weakly-deformed effective core, causing a decrease in the $g$-factor magnitudes., Comment: 8 pages, 7 figures. Accepted in Physics Letters B

We characterize the LHS 1610 system, a nearby ($d=9.7$ pc) M5 dwarf hosting a brown dwarf in a $10.6$ day, eccentric ($e \sim 0.37$) orbit. A joint fit of the available Gaia two-body solution, discovery radial velocities (RVs) from TRES, and new RVs obtained with the Habitable-zone Planet Finder, yields an orbital inclination of $117.2\pm0.9^\circ$ and a mass constraint of $50.9\pm0.9$ M$_J$. This gives LHS 1610 b the second most precise mass of brown dwarfs orbiting M stars within 25pc. We highlight a discrepancy between the Gaia two-body solution eccentricity ($e=0.52 \pm 0.03$) and that from the RVs ($e=0.3702\pm0.0003$), which requires the astrometric time-series release (Gaia DR4) for further diagnostics. With a flare rate of $0.28\pm 0.07$ flares/day from TESS photometry, and a rotation period of $84 \pm 8$ days, LHS 1610 joins other mid M stars -- including Proxima Centauri and YZ Ceti -- as nearby mid M dwarfs with flare rates on the higher end for their long rotation periods. These stars are promising candidates for searching for sub-Alfv\'enic star-companion interactions, raising the question whether LHS 1610 b could be driving the flares on its host star. However, the available TESS photometry is insufficient to confirm or rule out any orbital phase-dependence of the flares. We show that the LHS 1610 system, as a nearby mid M star with a large, short-period companion, is a promising target to look for evidence of star-companion interactions or aural emission from the brown dwarf at radio wavelengths., Comment: 24 pages, 7 figures, 3 tables. Submitted to AAS Journals on Oct 11, 2023

We present JWST/MIRI/MRS observations of an infrared luminous disk galaxy, FLS1, at z=0.54. With a lookback time of 5 Gyr, FLS1 is chronologically at the midpoint between the peak epoch of star formation and the present day. The MRS data provide maps of the atomic fine structure lines [Ar II]6.99 micron, [Ar III]8.99 micron, [Ne II]12.81 micron, and [Ne III]15.55 micron, polycyclic aromatic hydrocarbon (PAH) features at 3.3 micron, 6.2 micron, and 11.3 micron, and the warm molecular gas indicators H2S(5) and H2S(3); all these emission features are spatially resolved. We find that the PAH emission is more extended along the Northern side of the galaxy when compared to the well-studied star-formation tracer [Ne II]. The H2 rotational lines, which are shock indicators, are strongest and most extended on the Southern side of the galaxy. [Ar II] is the second brightest fine structure line detected in FLS1 and we show that it is a useful kinematic probe which can be detected with JWST out to z=3. Velocity maps of [Ar II] show a rotating disk with signs of turbulence. Our results provide an example of how spatially resolved mid-infrared spectroscopy can allow us to better understand the star formation and ISM conditions in a galaxy halfway back to the peak epoch of galaxy evolution., Comment: 10 pages, 5 figures, 1 table

G106.3$+$2.7, commonly considered a composite supernova remnant (SNR), is characterized by a boomerang-shaped pulsar wind nebula (PWN) and two distinct ("head" & "tail") regions in the radio band. A discovery of very-high-energy (VHE) gamma-ray emission ($E_\gamma > 100$ GeV) followed by the recent detection of ultra-high-energy (UHE) gamma-ray emission ($E_\gamma > 100$ TeV) from the tail region suggests that G106.3$+$2.7 is a PeVatron candidate. We present a comprehensive multi-wavelength study of the Boomerang PWN (100" around PSR J2229+6114) using archival radio and Chandra data obtained from two decades ago, a new NuSTAR X-ray observation from 2020, and upper limits on gamma-ray fluxes obtained by Fermi and VERITAS observatories. The NuSTAR observation allowed us to detect a 51.67 ms spin period from the pulsar PSR J2229+6114 and the PWN emission characterized by a power-law model with $\Gamma = 1.52\pm0.06$ up to 20 keV. Contrary to the previous radio study by Kothes et al. 2006, we prefer a much lower PWN B-field ($B\sim3$ $\mu$G) and larger distance ($d \sim 8$ kpc) based on (1) the non-varying X-ray flux over the last two decades, (2) the energy-dependent X-ray PWN size resulting from synchrotron burn-off and (3) the multi-wavelength spectral energy distribution (SED) data. Our SED model suggests that the PWN is currently re-expanding after being compressed by the SNR reverse shock $\sim 1000$ years ago. In this case, the head region should be formed by GeV--TeV electrons injected earlier by the pulsar propagating into the low density environment.

The evolution of charge carriers in photoexcited room temperature ZnO nanoparticles in solution is investigated using ultrafast ultraviolet photoluminescence spectroscopy, ultrafast Zn K-edge absorption spectroscopy and ab-initio molecular dynamics (MD) simulations. The photoluminescence is excited at 4.66 eV, well above the band edge, and shows that electron cooling in the conduction band and exciton formation occur in <500 fs, in excellent agreement with theoretical predictions. The X-ray absorption measurements, obtained upon excitation close to the band edge at 3.49 eV, are sensitive to the migration and trapping of holes. They reveal that the 2 ps transient largely reproduces the previously reported transient obtained at 100 ps time delay in synchrotron studies. In addition, the X-ray absorption signal is found to rise in ~1.4 ps, which we attribute to the diffusion of holes through the lattice prior to their trapping at singly-charged oxygen vacancies. Indeed, the MD simulations show that impulsive trapping of holes induces an ultrafast expansion of the cage of Zn atoms in <200 fs, followed by an oscillatory response at a frequency of ~100 cm-1, which corresponds to a phonon mode of the system involving the Zn sub-lattice.