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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