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Carbon stars (with atmospheric C/O$>1$) range widely in temperature and luminosity, from low mass dwarfs to asymptotic giant branch stars (AGB). The main sequence dwarf carbon (dC) stars have inherited carbon-rich material from an AGB companion, which has since transitioned to a white dwarf. The dC stars are far more common than C giants, but no reliable estimates of dC space density have been published to date. We present results from an all-sky survey for carbon stars using the low-resolution XP spectra from Gaia DR3. We developed and measured a set of spectral indices contrasting C$_{\rm 2}$ and CN molecular band strengths in carbon stars against common absorption features found in normal (C/O$<1$) stars such as CaI, TiO and Balmer lines. We combined these indices with the XP spectral coefficients as input to supervised machine-learning algorithms trained on a vetted sample of known C stars from LAMOST. We describe the selection of the carbon candidate sample, and provide a catalog of 43,574 candidates dominated by cool C giants in the Magellanic Clouds and at low galactic latitude in the Milky Way. We report the confirmation of candidate C stars using intermediate ($R\sim 1800$) resolution optical spectroscopy from the Fred Lawrence Whipple Observatory, and provide estimates of sample purity and completeness. From a carefully-vetted sample of over 600 dCs, we measure their local space density to be $\rho_0\,=\,1.96^{+0.14}_{-0.12}\times10^{-6}\,\text{pc}^{-3}$ (about one dC in every local disk volume of radius 50\,pc), with a relatively large disk scale height of $H_z\,=\,856^{+49}_{-43}\,$pc., Comment: Accepted to ApJ, 25 pages, 9 figures, 10 tables

We show how one can use the skew braces constructed using abelian maps to generate families of skew bracoids as defined by Martin-Lyons and Truman. Under certain circumstances, these bracoids give right non-degenerate solutions to the Yang-Baxter equation., Comment: 13 pages

The level of tumour-infiltrating lymphocytes (TILs) is a prognostic factor for patients with (triple-negative) breast cancer (BC). Computational TIL assessment (CTA) has the potential to assist pathologists in this labour-intensive task, but current CTA models rely heavily on many detailed annotations. We propose and validate a fundamentally simpler deep learning based CTA that can be trained in only ten minutes on hundredfold fewer pathologist annotations. We collected whole slide images (WSIs) with TILs scores and clinical data of 2,340 patients with BC from six cohorts including three randomised clinical trials. Morphological features were extracted from whole slide images (WSIs) using a pathology foundation model. Our label-efficient Computational stromal TIL assessment model (ECTIL) directly regresses the TILs score from these features. ECTIL trained on only a few hundred samples (ECTIL-TCGA) showed concordance with the pathologist over five heterogeneous external cohorts (r=0.54-0.74, AUROC=0.80-0.94). Training on all slides of five cohorts (ECTIL-combined) improved results on a held-out test set (r=0.69, AUROC=0.85). Multivariable Cox regression analyses indicated that every 10% increase of ECTIL scores was associated with improved overall survival independent of clinicopathological variables (HR 0.86, p<0.01), similar to the pathologist score (HR 0.87, p<0.001). We demonstrate that ECTIL is highly concordant with an expert pathologist and obtains a similar hazard ratio. ECTIL has a fundamentally simpler design than existing methods and can be trained on orders of magnitude fewer annotations. Such a CTA may be used to pre-screen patients for, e.g., immunotherapy clinical trial inclusion, or as a tool to assist clinicians in the diagnostic work-up of patients with BC. Our model is available under an open source licence (https://github.com/nki-ai/ectil)., Comment: Under review. 54 pages including supplementary materials, 2 main tables, 3 main figures, 14 supplementary figures, 4 supplementary tables

Inspired by previous studies and pioneers of the field, we present new results on an extensive EPR-Bell experiment using photons generated by parametric down conversion, where one of the photons is deliberately phase-shifted. Our experiments show some surprising results for particular angles of this phase shift., Comment: 20 pages, 12 figures

We examined the anomalies in the light curves of the lensing events MOA-2022-BLG-033, KMT-2023-BLG-0119, and KMT-2023-BLG-1896. We conducted detailed modeling of the light curves to uncover the nature of the anomalies. This modeling revealed that all signals originated from planetary companions to the primary lens. The planet-to-host mass ratios are very low: $q\sim 7.5\times 10^{-5}$ for MOA-2022-BLG-033, $q\sim 3.6\times 10^{-4}$ for KMT-2023-BLG-0119, and $q\sim 6.9\times 10^{-5}$ for KMT-2023-BLG-1896. The anomalies occurred as the source passed through the negative deviation region behind the central caustic along the planet-host axis. The solutions are subject to a common inner-outer degeneracy, resulting in variations in estimating the projected planet-host separation. For KMT-2023-BLG-1896, although the planetary scenario provides the best explanation of the anomaly, the binary companion scenario is marginally possible. We estimate the physical parameters of the planetary systems through Bayesian analyses based on the lensing observables. The analysis identifies MOA-2022-BLG-033L as a planetary system with an ice giant, approximately 12 times the mass of Earth, orbiting an early M dwarf star. The companion of KMT-2023-BLG-1896L is also an ice giant, with a mass around 16 Earth masses, orbiting a mid-K-type main-sequence star. The companion of KMT-2023-BLG-0119L, which has a mass about the mass of Saturn, orbits a mid-K-type dwarf star. The lens for MOA-2022-BLG-033 is highly likely to be located in the disk, whereas for the other events, the probabilities of the lens being in the disk or the bulge are roughly comparable., Comment: 9 pages, 10 figures, 6 tables

We discuss the proximal game and semi-proximality in $\Psi$-spaces of almost disjoint families over an infinite countable set and $\Psi$-spaces of ladder systems on $\omega_1$. We show that a semi-proximal almost disjoint families must be nowhere MAD, anti-Luzin and characterize semi-proximality for a class of ${\mathbb R}$-embeddable almost disjoint families. We show that a $\Psi$-spaces defined from a uniformizable ladder system is semi-proximal and a $\Psi$-space defined on a $\clubsuit^*$ sequence is not semi-proximal. Thus the existence of non-semi-proximal $\Psi$-space over a ladder system is independent of ZFC., Comment: 18 pages

Satellite to ground quantum communication typically operates at night to reduce background signals, however it remains susceptible to noise from light pollution of the night sky. In this study we compare several methodologies for determining whether a Quantum Ground Station (QGS) site is viable for exchanging quantum signals with the upcoming Quantum Encryption and Science Satellite (QEYSSat) mission. We conducted ground site characterization studies at three locations in Canada: Waterloo, Ontario, Calgary, Alberta, and Priddis, Alberta. Using different methods we estimate the background counts expected to leak into the satellite-ground quantum channel, and determined whether the noise levels could prevent a quantum key transfer. We also investigate how satellite data recorded from the Visible Infrared Imaging Radiometer Suite (VIIRS) can help estimate conditions of a particular site, and find reasonable agreement with the locally recorded data. Our results indicate that the Waterloo, Calgary, and Priddis QGS sites should allow both quantum uplinks and downlinks with QEYSSat, despite their proximity to urban centres. Furthermore, our approach allows the use of satellite borne instrument data (VIIRS) to remotely and efficiently determine the potential of a ground site., Comment: Submitted to EPJ: Prospects for Space Quantum Research, 20 pages, 12 figures

We examine lengthy radio light curves of the flat spectrum radio galaxy 3C 454.3 for possible quasi-periodic oscillations (QPOs). The data used in this work were collected at five radio frequencies, 4.8, 8.0, 14.5, 22.0, and 37.0 GHz between 1979--2013 as observed at the University of Michigan Radio Astronomical Observatory, Crimean Astrophysical Observatory, and Aalto University Mets{\"a}hovi Radio Observatory. We employ generalized Lomb-Scargle periodogram and weighted wavelet transform analyses to search for periodicities in these light curves. We confirm a QPO period of $\sim$ 2000 day to be at least 4$\sigma$ significant using both methods at all five radio frequencies between 1979 and 2007, after which a strong flare changed the character of the light curve. We also find a $\sim$~600 day period which is at least 4$\sigma$ significant, but only in the 22.0 and 37.0 GHz light curves. We briefly discuss physical mechanisms capable of producing such variations., Comment: 19 pages, 11 figures, Published in the Astrophysical Journal

Due to its peculiar and highly variable nature, the blazar 3C 454.3 has been extensively monitored by the WEBT team. Here, we present for the first time these long-term optical flux and color variability results using data acquired in B, V, R, and I bands over a time span of $\sim$ 2 decades. We include data from WEBT collaborators and public archives such as SMARTS, Steward Observatory, and ZTF. The data are binned and segmented to study the source over this long term when more regular sampling was available. During our study, the long-term spectral variability reveals a redder when brighter (RWB) trend, which, however, stabilizes at a particular brightness cutoff $\sim$ 14.5 mag in the I-band, after which it saturates and evolves into a complex state. This trend indicates increasing jet emission dominance over accretion disk emission until jet emission completely dominates. Plots of the spectral index variation (following $F_{\nu} \propto \nu^{-\alpha}$) reveal a bimodal distribution using a one-day binning. These correlate with two extreme phases of 3C 454.3, an outburst or high flux state and quiescent or low flux state, which are respectively jet and accretion disk dominated. We have also conducted intra-day variability studies of nine light curves and found that six of them are variable. Discrete Correlation Function (DCF) analysis between different optical waveband pairs peak at zero lags, indicating co-spatial emission in different optical bands., Comment: 18 pages, 6 figures, 5 tables

Despite remarkable progress in knowledge transfer across visual and textual domains, extending these achievements to indoor localization, particularly for learning transferable representations among Received Signal Strength (RSS) fingerprint datasets, remains a challenge. This is due to inherent discrepancies among these RSS datasets, largely including variations in building structure, the input number and disposition of WiFi anchors. Accordingly, specialized networks, which were deprived of the ability to discern transferable representations, readily incorporate environment-sensitive clues into the learning process, hence limiting their potential when applied to specific RSS datasets. In this work, we propose a plug-and-play (PnP) framework of knowledge transfer, facilitating the exploitation of transferable representations for specialized networks directly on target RSS datasets through two main phases. Initially, we design an Expert Training phase, which features multiple surrogate generative teachers, all serving as a global adapter that homogenizes the input disparities among independent source RSS datasets while preserving their unique characteristics. In a subsequent Expert Distilling phase, we continue introducing a triplet of underlying constraints that requires minimizing the differences in essential knowledge between the specialized network and surrogate teachers through refining its representation learning on the target dataset. This process implicitly fosters a representational alignment in such a way that is less sensitive to specific environmental dynamics. Extensive experiments conducted on three benchmark WiFi RSS fingerprint datasets underscore the effectiveness of the framework that significantly exerts the full potential of specialized networks in localization., Comment: Accepted in the 1st round at WACV 2025 (Algorithm Track)

We propose iMoT, an innovative Transformer-based inertial odometry method that retrieves cross-modal information from motion and rotation modalities for accurate positional estimation. Unlike prior work, during the encoding of the motion context, we introduce Progressive Series Decoupler at the beginning of each encoder layer to stand out critical motion events inherent in acceleration and angular velocity signals. To better aggregate cross-modal interactions, we present Adaptive Positional Encoding, which dynamically modifies positional embeddings for temporal discrepancies between different modalities. During decoding, we introduce a small set of learnable query motion particles as priors to model motion uncertainties within velocity segments. Each query motion particle is intended to draw cross-modal features dedicated to a specific motion mode, all taken together allowing the model to refine its understanding of motion dynamics effectively. Lastly, we design a dynamic scoring mechanism to stabilize iMoT's optimization by considering all aligned motion particles at the final decoding step, ensuring robust and accurate velocity segment estimation. Extensive evaluations on various inertial datasets demonstrate that iMoT significantly outperforms state-of-the-art methods in delivering superior robustness and accuracy in trajectory reconstruction., Comment: Accepted as technical research paper in 39th AAAI Conference on Artificial Intelligence, 2025 (AAAI 2025)

The Euclidean Steiner Tree Problem (EST) seeks a minimum-cost tree interconnecting a given set of terminal points in the Euclidean plane, allowing the use of additional intersection points. In this paper, we consider two variants that include an additional straight line $\gamma$ with zero cost, which must be incorporated into the tree. In the Euclidean Steiner fixed Line Problem (ESfL), this line is given as input and can be treated as a terminal. In contrast, the Euclidean Steiner Line Problem (ESL) requires determining the optimal location of $\gamma$. Despite recent advances, including heuristics and a 1.214-approximation algorithm for both problems, a formal proof of NP-hardness has remained open. In this work, we close this gap by proving that both the ESL and ESfL are NP-hard. Additionally, we prove that both problems admit a polynomial-time approximation scheme (PTAS), by demonstrating that approximation algorithms for the EST can be adapted to the ESL and ESfL with appropriate modifications. Specifically, we show ESfL$\leq_{\text{PTAS}}$EST and ESL$\leq_{\text{PTAS}}$EST, i.e., provide a PTAS reduction to the EST.

This is a short description of our solver OSCM submitted by our team MPPEG to the PACE 2024 challenge both for the exact track and the parameterized track, available at https://github.com/pauljngr/PACE2024 and https://doi.org/10.5281/zenodo.11546972.

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency., Comment: 9 pages, 9 figures

We present measurements of the microlensing optical depth and event rate toward the Galactic bulge using the dataset from the 2006--2014 MOA-II survey, which covers 22 bulge fields spanning ~42 deg^2 between -5 deg < l < 10 deg and -7 deg < b < -1 deg. In the central region with |l|<5 deg, we estimate an optical depth of {\tau} = [1.75+-0.04]*10^-6exp[(0.34+-0.02)(3 deg-|b|)] and an event rate of {\Gamma} = [16.08+-0.28]*10^-6exp[(0.44+-0.02)(3 deg-|b|)] star^-1 year^-1 using a sample consisting of 3525 microlensing events, with Einstein radius crossing times of tE < 760 days and source star magnitude of IsWe confirm our results are consistent with the latest measurements from OGLE-IV 8 year dataset (Mr\'oz et al. 2019). We find our result is inconsistent with a prediction based on Galactic models, especially in the central region with |b|<3 deg. These results can be used to improve the Galactic bulge model, and more central regions can be further elucidated by future microlensing experiments, such as The PRime-focus Infrared Microlensing Experiment (PRIME) and Nancy Grace Roman Space Telescope.

The details of the dynamo process that is responsible for driving the solar magnetic activity cycle are still not fully understood. In particular, whilst differential rotation provides a plausible mechanism for the regeneration of the toroidal (azimuthal) component of the large-scale magnetic field, there is ongoing debate regarding the process that is responsible for regenerating the Sun's large-scale poloidal field. Our aim is to demonstrate that magnetic buoyancy, in the presence of rotation, is capable of producing the necessary regenerative effect. Building upon our previous work, we carry out numerical simulations of a local Cartesian model of the tachocline, consisting of a rotating, fully compressible, electrically conducting fluid with a forced shear flow. An initially weak, vertical magnetic field is sheared into a strong, horizontal magnetic layer that becomes subject to magnetic buoyancy instability. By increasing the Prandtl number we lessen the back reaction of the Lorentz force onto the shear flow, maintaining stronger shear and a more intense magnetic layer. This in turn leads to a more vigorous instability and a much stronger mean electromotive force, which has the potential to significantly influence the evolution of the mean magnetic field. These results are only weakly dependent upon the inclination of the rotation vector, i.e. the latitude of the local Cartesian model. Although further work is needed to confirm this, these results suggest that magnetic buoyancy in the tachocline is a viable poloidal field regeneration mechanism for the solar dynamo., Comment: Accepted for publication in MNRAS, 13 pages, 11 figures, 1 table

SDO-FM is a foundation model using data from NASA's Solar Dynamics Observatory (SDO) spacecraft; integrating three separate instruments to encapsulate the Sun's complex physical interactions into a multi-modal embedding space. This model can be used to streamline scientific investigations involving SDO by making the enormous datasets more computationally accessible for heliophysics research and enable investigations that require instrument fusion. We discuss four key components: an ingestion pipeline to create machine learning ready datasets, the model architecture and training approach, resultant embeddings and fine-tunable models, and finally downstream fine-tuned applications. A key component of this effort has been to include subject matter specialists at each stage of development; reviewing the scientific value and providing guidance for model architecture, dataset, and training paradigm decisions. This paper marks release of our pretrained models and embedding datasets, available to the community on Hugging Face and sdofm.org.

The spatial arrangement of components within an mRNA encapsulating nanoparticle has consequences for its thermal stability, which is a key parameter for therapeutic utility. The mesostructure of mRNA nanoparticles formed with cationic polymers have several distinct putative structures: here, we develop a field theoretic simulation model to compute the phase diagram for amphiphilic block copolymers that balance coacervation and hydrophobicity as driving forces for assembly. We predict several distinct morphologies for the mesostructure of these nanoparticles, depending on salt conditions and hydrophobicity. We compare our predictions with cryogenic-electron microscopy images of mRNA encapsulated by charge altering releasable transporters. In addition, we provide a GPU-accelerated, open-source codebase for general purpose field theoretic simulations, which we anticipate will be a useful tool for the community.

We formulate theoretical modeling approaches and develop practical computational simulation methods for investigating the non-equilibrium statistical mechanics of fluid interfaces with passive and active immersed particles. Our approaches capture phenomena taking into account thermal and dissipative energy exchanges, hydrodynamic coupling, and correlated spontaneous fluctuations. Our methods allow for modeling non-uniform time-varying temperature fields, fluid momentum fields, and their impacts on particle drift-diffusion dynamics. We show how practical stochastic numerical methods can be developed for these systems by performing analysis to factor operators analytically to obtain efficient algorithms for generating the fluctuating fields. We demonstrate our methods in a few simulation studies showing how they can be used to capture particle heating of the interface and the roles of thermal gradients on hydrodynamic fluctuations. The methods developed provide modeling and simulation approaches that can be used for further investigations of non-equilibrium phenomena in active soft materials, complex fluids, and biophysical systems., Comment: 21 pages, 5 figures

We use first-principles scattering calculations based upon wave-function matching and implemented with a tight-binding MTO basis to evaluate the orbital Hall conductivity $\sigma_{\rm oH}$ for Ti, V, Cr, Cu and Pt metals with temperature-induced lattice disorder. Only interatomic fluxes of orbital angular momentum are included in these estimates; intraatomic fluxes which do not contribute to the transfer of angular momentum are explicitly excluded. The resistivity and orbital Hall angle are both found to be linear in temperature so $\sigma_{\rm oH}$ is at most weakly temperature dependent. The value of $\sigma_{\rm oH}$ we obtain for bulk Cr is $ \approx 2 \times 10^3 (\hbar/e) \, (\Omega \, {\rm cm})^{-1}$ which is substantially lower than previously obtained theoretical results but agrees well with experiment. In units of $10^3 (\hbar/e) (\Omega \, {\rm cm})^{-1}$, the values obtained for Ti, V and Pt are $5$, $6$ and $7$, respectively.

An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand the relationship between functional fluctuation and human cognition/behavior using a data-driven approach. To that end, tremendous efforts have been made in machine learning to predict cognitive states from evolving volumetric images of blood-oxygen-level-dependent (BOLD) signals. Due to the complex nature of brain function, however, the evaluation on learning performance and discoveries are not often consistent across current state-of-the-arts (SOTA). By capitalizing on large-scale existing neuroimaging data (34,887 data samples from six public databases), we seek to establish a well-founded empirical guideline for designing deep models for functional neuroimages by linking the methodology underpinning with knowledge from the neuroscience domain. Specifically, we put the spotlight on (1) What is the current SOTA performance in cognitive task recognition and disease diagnosis using fMRI? (2) What are the limitations of current deep models? and (3) What is the general guideline for selecting the suitable machine learning backbone for new neuroimaging applications? We have conducted a comprehensive evaluation and statistical analysis, in various settings, to answer the above outstanding questions.

After the main sequence phase, stars more massive than 2.5 M$_\odot$ rapidly evolve through the Hertzsprung gap as yellow giants and supergiants (YSG), before settling into the red giant branch. Identifying YSG in nearby galaxies is crucial for pinpointing progenitors of luminous red novae (LRNe) - astrophysical transients attributed to stellar mergers. In the era of extensive transient surveys like the Vera Rubin Observatory's LSST, this approach offers a new way to predict and select common envelope transients. This study investigates potential progenitors and precursors of LRNe by analysing Hubble Space Telescope (HST) photometry of stellar populations in galaxies within 20 Mpc to identify YSG candidates. Additionally, we use ZTF and MeerLICHT/BlackGEM to identify possible precursors, preparing for future observations by the LSST. We compiled a sample of 369 galaxies with HST exposures in the F475W, F555W, F606W, and F814W filters. We identified YSG candidates using MESA stellar evolution tracks and statistical analysis of color-magnitude diagrams (CMDs). Our sample includes 154,494 YSG candidates with masses between 3 and 20 $M_\odot$ and is affected by various contaminants, such as foreground stars and extinguished main-sequence stars. After excluding foreground stars using Gaia proper motions, contamination is estimated at 1\% from foreground stars and 20\% from extinction affecting main-sequence stars. Combining our YSG candidates with time-domain catalogs yielded several interesting candidates. Notably, we identified 12 LRN precursor candidates for which followup is encouraged. We highlight the importance of monitoring future transients that match YSG candidates to avoid missing potential LRNe and other rare transients. LSST will be a game changer in the search for LRN progenitors and precursors, discovering over 300,000 new YSG and 100 precursors within 20 Mpc., Comment: Revised version submitted to A\&A. 17 pages, 21 figures

Storyline drawings are a popular visualization of interactions of a set of characters over time, e.g., to show participants of scenes in a book or movie. Characters are represented as $x$-monotone curves that converge vertically for interactions and diverge otherwise. Combinatorially, the task of computing storyline drawings reduces to finding a sequence of permutations of the character curves for the different time points, with the primary objective being crossing minimization of the induced character trajectories. In this paper, we revisit exact integer linear programming (ILP) approaches for this NP-hard problem. By enriching previous formulations with additional problem-specific insights and new heuristics, we obtain exact solutions for an extended new benchmark set of larger and more complex instances than had been used before. Our experiments show that our enriched formulations lead to better performing algorithms when compared to state-of-the-art modelling techniques. In particular, our best algorithms are on average 2.6-3.2 times faster than the state-of-the-art and succeed in solving complex instances that could not be solved before within the given time limit. Further, we show in an ablation study that our enrichment components contribute considerably to the performance of the new ILP formulation., Comment: 28 pages, 7 figures

Understanding space weather is vital for the protection of our terrestrial and space infrastructure. In order to predict space weather accurately, large amounts of data are required, particularly in the extreme ultraviolet (EUV) spectrum. An exquisite source of information for such data is provided by the Solar Dynamic Observatory (SDO), which has been gathering solar measurements for the past 13 years. However, after a malfunction in 2014 affecting the onboard Multiple EUV Grating Spectrograph A (MEGS-A) instrument, the scientific output in terms of EUV measurements has been significantly degraded. Building upon existing research, we propose to utilize deep learning for the virtualization of the defective instrument. Our architecture features a linear component and a convolutional neural network (CNN) -- with EfficientNet as a backbone. The architecture utilizes as input grayscale images of the Sun at multiple frequencies -- provided by the Atmospheric Imaging Assembly (AIA) -- as well as solar magnetograms produced by the Helioseismic and Magnetic Imager (HMI). Our findings highlight how AIA data are all that is needed for accurate predictions of solar irradiance. Additionally, our model constitutes an improvement with respect to the state-of-the-art in the field, further promoting the idea of deep learning as a viable option for the virtualization of scientific instruments., Comment: Submission to Machine Learning and the Physical Sciences Workshop, NeurIPS 2023

"Changing-look quasars" (CLQs), discovered less than a decade ago, show dramatic, rapid changes in optical/UV continuum and broad line emission. The majority of CLQs have been found dimming as "turn-off" CLQs because most selection methods start from samples of spectroscopically-confirmed quasars. We present here a sample of 82 spectroscopically confirmed "turn-on" CLQs, 70 of which are newly identified. The turn-on CLQs are selected from spectroscopically classified galaxies with subsequent significant and dramatic variability in both the optical and mid-infrared bands, indicating a mechanism of changing accretion rate of the supermassive black holes rather than variable obscuration. Based on their bright state Eddington ratios, turn-on CLQs are associated with lower accretion rates compared to turn-off CLQs or typical SDSS quasars with similar redshift and magnitude distributions, even though turn-on CLQs have lower black hole masses. Most turn-on CLQs reside in host galaxies that follow local relations between the central black hole mass and host galaxy properties, such as stellar mass and velocity dispersion. However, their host galaxies have higher mass than normal inactive galaxies, with star formation rates more similar to hosts of Type 2 AGN than to the overall galaxy population., Comment: Submitted to ApJ; the spectral fitting code QGfit will be available on GitHub; 36 pages, 20 figures

We present X-ray to radio frequency observations of the bright long gamma-ray burst GRB 210702A. Our ALMA 97.5 GHz observations show a significant rebrightening by a factor of ~2 beginning at 8.2 days post-burst and rising to peak brightness at 18.1 days before declining again. This is the first such rebrightening seen in a millimeter afterglow light curve. A standard forward shock model in a stellar wind circumburst medium can explain most of our X-ray, optical and millimeter observations prior to the rebrightening, but significantly over-predicts the self-absorbed radio emission, and cannot explain the millimeter rebrightening. We investigate possible explanations for the millimeter rebrightening and find that energy injection or a reverse shock from a late-time shell collision are plausible causes. Similar to other bursts, our radio data may require alternative scenarios such as a thermal electron population or a structured jet to explain the data. Our observations demonstrate that millimeter light curves can exhibit some of the rich features more commonly seen in optical and X-ray afterglow light curves, motivating further millimeter wavelength studies of GRB afterglows., Comment: 22 pages, 10 figures, accepted for publication in ApJ

Resistive tearing instabilities are common in fluids that are highly electrically conductive and carry strong currents. We determine the effect of stable stratification on the tearing instability under the Boussinesq approximation. Our results generalise previous work that considered only specific parameter regimes, and we show that the length scale of the fastest growing mode depends non-monotonically on the stratification strength. We confirm our analytical results by solving the linearised equations numerically, and we discuss whether the instability could operate in the solar tachocline.

Building on previous works to construct a homogeneous sample of brown dwarfs in binary systems, we investigate microlensing events detected by the Korea Microlensing Telescope Network (KMTNet) survey during the 2022 and 2023 seasons. Given the difficulty in distinguishing brown-dwarf events from those produced by binary lenses with nearly equal-mass components, we analyze all lensing events detected during the seasons that exhibit anomalies characteristic of binary-lens systems. Using the same criteria consistently applied in previous studies, we identify six additional brown dwarf candidates through the analysis of lensing events KMT-2022-BLG-0412, KMT-2022-BLG-2286, KMT-2023-BLG-0201, KMT-2023-BLG-0601, KMT-2023-BLG-1684, and KMT-2023-BLG-1743. An examination of the mass posteriors shows that the median mass of the lens companions ranges from 0.02 $M_\odot$ to 0.05 $M_\odot$, indicating that these companions fall within the brown-dwarf mass range. The mass of the primary lenses ranges from 0.11 $M_\odot$ to 0.68 $M_\odot$, indicating that they are low-mass stars with substantially lower masses compared to the Sun., Comment: 13 pages, 17 figures, 12 tables

Many super-Earths are on very short orbits around their host star and, therefore, more likely to be tidally locked. Because this locking can lead to a strong contrast between the dayside and nightside surface temperatures, these super-Earths could exhibit mantle convection patterns and tectonics that could differ significantly from those observed in the present-day solar system. The presence of an atmosphere, however, would allow transport of heat from the dayside towards the nightside and thereby reduce the surface temperature contrast between the two hemispheres. On rocky planets, atmospheric and geodynamic regimes are closely linked, which directly connects the question of atmospheric thickness to the potential interior dynamics of the planet. Here, we study the interior dynamics of super-Earth GJ 486b ($R=1.34$ $R_{\oplus}$, $M=3.0$ $M_{\oplus}$, T$_\mathrm{eq}\approx700$ K), which is one of the most suitable M-dwarf super-Earth candidates for retaining an atmosphere produced by degassing from the mantle and magma ocean. We investigate how the geodynamic regime of GJ 486b is influenced by different surface temperature contrasts by varying possible atmospheric circulation regimes. We also investigate how the strength of the lithosphere affects the convection pattern. We find that hemispheric tectonics, the surface expression of degree-1 convection with downwellings forming on one hemisphere and upwelling material rising on the opposite hemisphere, is a consequence of the strong lithosphere rather than surface temperature contrast. Anchored hemispheric tectonics, where downwellings und upwellings have a preferred (day/night) hemisphere, is favoured for strong temperature contrasts between the dayside and nightside and higher surface temperatures., Comment: 36 pages, 13 figures, updated with version accepted for publication in JGR: Planets

A central pillar of the UK's response to the SARS-CoV-2 pandemic was the provision of up-to-the moment nowcasts and short term projections to monitor current trends in transmission and associated healthcare burden. Here we present a detailed deconstruction of one of the 'real-time' models that was key contributor to this response, focussing on the model adaptations required over three pandemic years characterised by the imposition of lockdowns, mass vaccination campaigns and the emergence of new pandemic strains. The Bayesian model integrates an array of surveillance and other data sources including a novel approach to incorporating prevalence estimates from an unprecedented large-scale household survey. We present a full range of estimates of the epidemic history and the changing severity of the infection, quantify the impact of the vaccination programme and deconstruct contributing factors to the reproduction number. We further investigate the sensitivity of model-derived insights to the availability and timeliness of prevalence data, identifying its importance to the production of robust estimates., Comment: 35 pages, 7 figures, 3 tables

We present analyses of Suzaku XIS light curves and spectra of the BL Lac object OJ 287 with observations positioned primarily around proposed recurrent optical outbursts. The first two observations were performed in 2007 April 10 - 13 (epoch 1) and 2007 November 7 - 9 (epoch 2) that respectively correspond to a low and a high optical state and which, within the binary supermassive black hole model for OJ 287, precede and follow the impact flare. The last three observations, made consecutively during 2015 May 3 - 9 (epoch 3), were during the post-impact state of the 2013 disc impact and are the longest continuous X-ray observation of OJ 287 taken before the optical outburst in 2015 December. Intraday variability is found in both the soft (0.5 - 2 keV) and hard (2 - 10 keV) bands. The discrete correction function analysis of the light curves in both bands peaks at zero lag during epochs 2 and 3, indicating that the emission in both bands was cospatial and emitted from the same population of leptons. Power spectral densities of all three light curves are red noise dominated, with a rather wide range of power spectrum slopes. These X-ray spectra are overall consistent with power-laws but with significantly different spectral indices. In the 2015 observations the X-ray spectrum softens during the flare, showing an obvious soft X-ray excess that was not evident in the 2007 observations. We discuss the implications of these observations on the jet, the possible accretion disc, and the binary supermassive black hole model proposed for the nearly periodic optical flaring of OJ 287., Comment: 15 pages,9 figures,9 tables

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of predictive features for a given clinical problem. Purpose: Develop a methodology and tools to identify and explain the smallest set of predictive radiomic features. Materials and Methods: 89,714 radiomic features were extracted from five cancer datasets: low-grade glioma, meningioma, non-small cell lung cancer (NSCLC), and two renal cell carcinoma cohorts (n=2104). Features were categorized by computational complexity into morphological, intensity, texture, linear filters, and nonlinear filters. Models were trained and evaluated on each complexity level using the area under the curve (AUC). The most informative features were identified, and their importance was explained. The optimal complexity level and associated most informative features were identified using systematic statistical significance analyses and a false discovery avoidance procedure, respectively. Their predictive importance was explained using a novel tree-based method. Results: MEDimage, a new open-source tool, was developed to facilitate radiomic studies. Morphological features were optimal for MRI-based meningioma (AUC: 0.65) and low-grade glioma (AUC: 0.68). Intensity features were optimal for CECT-based renal cell carcinoma (AUC: 0.82) and CT-based NSCLC (AUC: 0.76). Texture features were optimal for MRI-based renal cell carcinoma (AUC: 0.72). Tuning the Hounsfield unit range improved results for CECT-based renal cell carcinoma (AUC: 0.86). Conclusion: Our proposed methodology and software can estimate the optimal radiomics complexity level for specific medical outcomes, potentially simplifying the use of radiomics in predictive modeling across various contexts.

We investigated the nature of the anomalies appearing in four microlensing events KMT-2020-BLG-0757, KMT-2022-BLG-0732, KMT-2022-BLG-1787, and KMT-2022-BLG-1852. The light curves of these events commonly exhibit initial bumps followed by subsequent troughs that extend across a substantial portion of the light curves. We performed thorough modeling of the anomalies to elucidate their characteristics. Despite their prolonged durations, which differ from the usual brief anomalies observed in typical planetary events, our analysis revealed that each anomaly in these events originated from a planetary companion located within the Einstein ring of the primary star. It was found that the initial bump arouse when the source star crossed one of the planetary caustics, while the subsequent trough feature occurred as the source traversed the region of minor image perturbations lying between the pair of planetary caustics. The estimated masses of the host and planet, their mass ratios, and the distance to the discovered planetary systems are $(M_{\rm host}/M_\odot, M_{\rm planet}/M_{\rm J}, q/10^{-3}, \dl/{\rm kpc}) = (0.58^{+0.33}_{-0.30}, 10.71^{+6.17}_{-5.61}, 17.61\pm 2.25,6.67^{+0.93}_{-1.30})$ for KMT-2020-BLG-0757, $(0.53^{+0.31}_{-0.31}, 1.12^{+0.65}_{-0.65}, 2.01 \pm 0.07, 6.66^{+1.19}_{-1.84})$ for KMT-2022-BLG-0732, $(0.42^{+0.32}_{-0.23}, 6.64^{+4.98}_{-3.64}, 15.07\pm 0.86, 7.55^{+0.89}_{-1.30})$ for KMT-2022-BLG-1787, and $(0.32^{+0.34}_{-0.19}, 4.98^{+5.42}_{-2.94}, 8.74\pm 0.49, 6.27^{+0.90}_{-1.15})$ for KMT-2022-BLG-1852. These parameters indicate that all the planets are giants with masses exceeding the mass of Jupiter in our solar system and the hosts are low-mass stars with masses substantially less massive than the Sun., Comment: 10 pages, 12 figures, 7 tables