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The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

Graph neural networks (GNNs) provide state-of-the-art results in a wide variety of tasks which typically involve predicting features at the vertices of a graph. They are built from layers of graph convolutions which serve as a powerful inductive bias for describing the flow of information among the vertices. Often, more than one data modality is available. This work considers a setting in which several graphs have the same vertex set and a common vertex-level learning task. This generalizes standard GNN models to GNNs with several graph operators that do not commute. We may call this model graph-tuple neural networks (GtNN). In this work, we develop the mathematical theory to address the stability and transferability of GtNNs using properties of non-commuting non-expansive operators. We develop a limit theory of graphon-tuple neural networks and use it to prove a universal transferability theorem that guarantees that all graph-tuple neural networks are transferable on convergent graph-tuple sequences. In particular, there is no non-transferable energy under the convergence we consider here. Our theoretical results extend well-known transferability theorems for GNNs to the case of several simultaneous graphs (GtNNs) and provide a strict improvement on what is currently known even in the GNN case. We illustrate our theoretical results with simple experiments on synthetic and real-world data. To this end, we derive a training procedure that provably enforces the stability of the resulting model., Comment: NeurIPS 2024

In the asymptotic limit of a large diffusivity ratio, certain two-component reaction-diffusion (RD) systems can admit localized spike solutions on a 1-D finite domain in a far-from-equilibrium nonlinear regime. It is known that two distinct bifurcation mechanisms can occur which generate spike patterns of increased spatial complexity as the domain half-length L slowly increases; so-called spike nucleation and spike self-replication. Self-replication is found to occur via the passage beyond a saddle-node bifurcation point that can be predicted through linearization around the inner spike profile. In contrast, spike nucleation occurs through slow passage beyond the saddle-node of a nonlinear boundary-value problem defined in the outer region away from the core of a spike. Here, by treating L as a static parameter, precise conditions are established within the semi-strong interaction asymptotic regime to determine which occurs, conditions that are confirmed by numerical simulation and continuation. For the Schnakenberg and Brusselator RD models, phase diagrams in parameter space are derived that predict whether spike self-replication or spike nucleation will occur first as L is increased, or whether no such instability will occur. For the Gierer-Meinhardt model with a non-trivial activator background, spike nucleation is shown to be the only possible spike-generating mechanism. From time-dependent PDE numerical results on an exponentially slowly growing domain, it is shown that the analytical thresholds derived from the asymptotic theory accurately predict critical values of L where either spike self-replication or spike-nucleation will occur. The global bifurcation mechanism for transitions to patterns of increased spatial complexity is further elucidated by superimposing time-dependent PDE simulation results on the numerically computed solution branches of spike equilibria., Comment: 45 pages

Quasars are bright active galactic nuclei powered by the accretion of matter around supermassive black holes at the center of galaxies. Their stochastic brightness variability depends on the physical properties of the accretion disk and black hole. The upcoming Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to observe tens of millions of quasars, so there is a need for efficient techniques like machine learning that can handle the large volume of data. Quasar variability is believed to be driven by an X-ray corona, which is reprocessed by the accretion disk and emitted as UV/optical variability. We are the first to introduce an auto-differentiable simulation of the accretion disk and reprocessing. We use the simulation as a direct component of our neural network to jointly model the driving variability and reprocessing to fit simulated LSST 10-year quasar light curves. The driving variability is reconstructed using a latent stochastic differential equation, a physically motivated, generative deep learning method that can model continuous-time stochastic dynamics. By embedding these physical processes into our network, we achieve a model that is more robust and interpretable. We also use transformers to scale our model to tens of millions of parameters. We demonstrate how our model outperforms a Gaussian process regression baseline and can infer accretion disk parameters and time delays between wavebands, even for out-of-distribution driving signals. Our approach provides a powerful and scalable framework that can be adapted to solve other inverse problems in multivariate time series with irregular sampling., Comment: 31 pages, 19 figures

Characterizing the host galaxies of astrophysical transients is important to many areas of astrophysics, including constraining the progenitor systems of core-collapse supernovae, correcting Type Ia supernova distances, and probabilistically classifying transients without photometric or spectroscopic data. Given the increasing transient discovery rate in the coming years, there is substantial utility in providing public, transparent, reproducible, and automatic characterization for large samples of transient host galaxies. Here we present Blast, a web application that ingests live streams of transient alerts, matches transients to their host galaxies, and performs photometry on coincident archival imaging data of the host galaxy. The photometry is then used to infer both global host-galaxy properties and galaxy properties within 2 kpc of the transient location by using the Prospector Bayesian inference framework, with an acceleration in evaluation speed achieved via simulation-based inference. Blast provides host-galaxy properties to users via a web browser or an application program interface. The software can be extended to support alternative photometric or SED-fitting algorithms, and can be scaled via an asynchronous worker queue across multiple compute nodes to handle the processing of large volumes of transient alerts for upcoming transient surveys. Blast has been ingesting newly discovered transients from the Transient Name Server since mid-2024, and has currently measured SED parameters for more than 6000 transients. The service is publicly available at https://blast.scimma.org/., Comment: submitted to PASP

Luminous interacting supernovae are a class of stellar explosions whose progenitors underwent vigorous mass loss in the years prior to core-collapse. While the mechanism by which this material is ejected is still debated, obtaining the full density profile of the circumstellar medium (CSM) could reveal more about this process. Here, we present an extensive multi-wavelength study of PS1-11aop, a luminous and slowly declining Type IIn SN discovered by the PanSTARRS Medium Deep Survey. PS1-11aop had a peak r-band magnitude of $-$20.5\,mag, a total radiated energy $>$ 8$\times$10$^{50}$\,erg, and it exploded near the center of a star-forming galaxy with super-solar metallicity. We obtained multiple detections at the location of PS1-11aop in the radio and X-ray bands between 4 and 10\,years post-explosion, and if due to the SN, it is one of the most luminous radio supernovae identified to date. Taken together, the multiwavelength properties of PS1-11aop are consistent with a CSM density profile with multiple zones. The early optical emission is consistent with the supernova blastwave interacting with a dense and confined CSM shell which contains multiple solar masses of material that was likely ejected in the final $<$10-100 years prior to the explosion,($\sim$0.05$-$1.0 M$_{\odot}$yr$^{-1}$ at radii of $\lesssim$10$^{16}$\,cm). The radio observations, on the other hand, are consistent with a sparser environment ($\lesssim$2$\times 10^{-3}$ M$_{\odot}$yr$^{-1}$ at radii of $\sim$0.5-1$\times$10$^{17}$\,cm) -- thus probing the history of the progenitor star prior to its final mass loss episode., Comment: 37 pages, 17 figures

Magnetoelectric materials displaying strong magnetically induced polarization have attracted considerable interest due to their potential applications in spintronics and various fast electrically controlled magnetic devices. CaMn$_7$O$_{12}$ (CMO) stands out for its giant spin-induced ferroelectric polarization. However, the origin of the induced electric polarization in CMO remains highly controversial and continues to be a subject of ongoing debate. In this paper, through room temperature X-ray powder diffraction (XRPD), temperature-dependent magnetic susceptibility, and thermally stimulated depolarizing current (TSDC) measurements, we provide experimental evidence for a route to tune the magnetically induced polarization by modifying the exchange-striction in CMO via Sr-doping. Our findings demonstrate that the large and broad current peaks observed near the first magnetic phase transition ($T_N1 \sim 90$ K) indicate polarization contributions from both thermally stimulated depolarization current (TSDC) and intrinsic magnetically induced electric polarization. We suggest that this reduction in induced electric polarization in CMO originates from the increase in the Mn$^{3+}$ -- O -- Mn$^{4+}$ bond angle due to Sr$^{2+}$ doping, weakening the exchange-striction interaction. Meanwhile, the Dzyaloshinskii-Moriya (DM) effect determines the direction of the induced electric polarization. Our result sheds light on understanding the intriguing giant-induced polarization in CMO and similar compounds with complex magnetic structures.

For over 25 years, the origin of long-duration gamma-ray bursts (lGRBs) has been linked to the collapse of rotating massive stars. However, we have yet to pinpoint the stellar progenitor powering these transients. Moreover, the dominant engine powering the explosions remains open to debate. Observations of both lGRBs, supernovae associated with these GRBs, such as broad-line (BL) stripped-envelope (type Ic) supernovae (hereafter, Ic-BL) supernovae (SNe) and perhaps superluminous SNe, fast blue optical transients, and fast x-ray transients, may provide clues to both engines and progenitors. In this paper, we conduct a detailed study of the tight-binary formation scenario for lGRBs, comparing this scenario to other leading progenitor models. Combining this progenitor scenario with different lGRB engines, we can compare to existing data and make predictions for future observational tests. We find that the combination of the tight-binary progenitor scenario with the black hole accretion disk (BHAD) engine can explain lGRBs, low-luminosity GRBs, ultra-long GRBs, and Ic-BL. We discuss the various progenitor properties required for these different subclasses and note such systems would be future gravitational wave merger sources. We show that the current literature on other progenitor-engine scenarios cannot explain all of these transient classes with a single origin, motivating additional work. We find that the tight-binary progenitor with a magnetar engine is excluded by existing observations. The observations can be used to constrain the properties of stellar evolution, the nature of the GRB and the associated SN engines in lGRBs and Ic-BL. We discuss the future observations needed to constrain our understanding of these rare, but powerful, explosions., Comment: 20 pages, 10 figures, submitted to ApJ

Learning a latent dynamics model provides a task-agnostic representation of an agent's understanding of its environment. Leveraging this knowledge for model-based reinforcement learning holds the potential to improve sample efficiency over model-free methods by learning inside imagined rollouts. Furthermore, because the latent space serves as input to behavior models, the informative representations learned by the world model facilitate efficient learning of desired skills. Most existing methods rely on holistic representations of the environment's state. In contrast, humans reason about objects and their interactions, forecasting how actions will affect specific parts of their surroundings. Inspired by this, we propose Slot-Attention for Object-centric Latent Dynamics (SOLD), a novel algorithm that learns object-centric dynamics models in an unsupervised manner from pixel inputs. We demonstrate that the structured latent space not only improves model interpretability but also provides a valuable input space for behavior models to reason over. Our results show that SOLD outperforms DreamerV3, a state-of-the-art model-based RL algorithm, across a range of benchmark robotic environments that evaluate for both relational reasoning and low-level manipulation capabilities. Videos are available at https://slot-latent-dynamics.github.io/.

Due to the difficulty of replicating the real conditions during training, supervised algorithms for spacecraft pose estimation experience a drop in performance when trained on synthetic data and applied to real operational data. To address this issue, we propose a test-time adaptation approach that leverages the temporal redundancy between images acquired during close proximity operations. Our approach involves extracting features from sequential spacecraft images, estimating their poses, and then using this information to synthesise a reconstructed view. We establish a self-supervised learning objective by comparing the synthesised view with the actual one. During training, we supervise both pose estimation and image synthesis, while at test-time, we optimise the self-supervised objective. Additionally, we introduce a regularisation loss to prevent solutions that are not consistent with the keypoint structure of the spacecraft. Our code is available at: https://github.com/JotaBravo/spacecraft-tta., Comment: Preprint

Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implementation of RAR in the real world raises important practical questions, often neglected. Motivated by an innovative phase-II stratified RAR trial, this paper addresses two challenges: (1) How to ensure that RAR allocations are both desirable and faithful to target probabilities, even in small samples? and (2) What adaptations to trigger after interim analyses in the presence of missing data? We propose a Mapping strategy that discretises the randomisation probabilities into a vector of allocation ratios, resulting in improved frequentist errors. Under the implementation of Mapping, we analyse the impact of missing data on operating characteristics by examining selected scenarios. Finally, we discuss additional concerns including: pooling data across trial strata, analysing the level of blinding in the trial, and reporting safety results.

In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algorithmic design uprightness, and performance verifiability of new technical achievements. A clear example stems from the scarce replicability of works dealing with metaheuristics used for optimization, which is often infeasible due to ambiguity and lack of detail in the presentation of the methods to be reproduced. Additionally, in many cases, there is a questionable statistical significance of their reported results. This work aims at providing the audience with a proposal of good practices which should be embraced when conducting studies about metaheuristics methods used for optimization in order to provide scientific rigor, value and transparency. To this end, we introduce a step by step methodology covering every research phase that should be followed when addressing this scientific field. Specifically, frequently overlooked yet crucial aspects and useful recommendations will be discussed in regards to the formulation of the problem, solution encoding, implementation of search operators, evaluation metrics, design of experiments, and considerations for real-world performance, among others. Finally, we will outline important considerations, challenges, and research directions for the success of newly developed optimization metaheuristics in their deployment and operation over real-world application environments.

Theories of localised pattern formation are important to understand a broad range of natural patterns, but are less well-understood than more established mechanisms of domain-filling pattern formation. Here, we extend recent work on pattern localisation via slow spatial heterogeneity in reaction-diffusion systems to the Swift-Hohenberg equation. We use a WKB asymptotic approach to show that, in the limit of a large domain and slowly varying heterogeneity, conditions for Turing-type linear instability localise in a simple way, with the spatial variable playing the role of a parameter. For nonlinearities locally corresponding to supercritical bifurcations in the spatially homogeneous system, this analysis asymptotically predicts regions where patterned states are confined, which we confirm numerically. We resolve the inner region of this asymptotic approach, finding excellent agreement with the tails of these confined pattern regions. In the locally subcritical case, however, this theory is insufficient to fully predict such confined regions, and so we propose an approach based on numerical continuation of a local homogeneous analog system. Pattern localisation in the heterogeneous system can then be determined based on the Maxwell point of this system, with the spatial variable parameterizing this point. We compare this theory of localisation via spatial heterogeneity to localised patterns arising from homoclinic snaking, and suggest a way to distinguish between different localisation mechanisms in natural systems based on how these structures decay to the background state (i.e. how their tails decay). We also explore cases where both of these local theories of pattern formation fail to capture the interaction between spatial heterogeneity and underlying pattern-forming mechanisms, suggesting that more work needs to be done to fully disentangle exogenous and intrinsic heterogeneity., Comment: 36 pages, 17 figures

Type IIn supernovae (SNeIIn) are a highly heterogeneous subclass of core-collapse supernovae, spectroscopically characterized by signatures of interaction with a dense circumstellar medium (CSM). Here we systematically model the light curves of 142 archival SNeIIn using MOSFiT (the Modular Open Source Fitter for Transients). We find that the observed and inferred properties of SNIIn are diverse, but there are some trends. The typical SN CSM is dense ($\sim$10$^{-12}$gcm$^{-3}$) with highly diverse CSM geometry, with a median CSM mass of $\sim$1M$_\odot$. The ejecta are typically massive ($\gtrsim10$M$_\odot$), suggesting massive progenitor systems. We find positive correlations between the CSM mass and the rise and fall times of SNeIIn. Furthermore there are positive correlations between the rise time and fall times and the $r$-band luminosity. We estimate the mass-loss rates of our sample (where spectroscopy is available) and find a high median mass-loss rate of $\sim$10$^{-2}$M$_\odot$yr$^{-1}$, with a range between 10$^{-4}$--1M$_\odot$yr$^{-1}$. These mass-loss rates are most similar to the mass loss from great eruptions of luminous blue variables, consistent with the direct progenitor detections in the literature. We also discuss the role that binary interactions may play, concluding that at least some of our SNeIIn may be from massive binary systems. Finally, we estimate a detection rate of 1.6$\times$10$^5$yr$^{-1}$ in the upcoming Legacy Survey of Space and Time at the Vera C. Rubin Observatory., Comment: 55 pages, 19 figures, 6 tables

In the current NISQ-era, one of the major challenges faced by researchers and practitioners lies in figuring out how to combine quantum and classical computing in the most efficient and innovative way. In this paper, we present a mechanism coined as Quantum Initialization for Warehouse Optimization Problem that resorts to D-Wave's Quantum Annealer. The module has been specifically designed to be embedded into already existing classical software dedicated to the optimization of a real-world industrial problem. We preliminary tested the implemented mechanism through a two-phase experiment against the classical version of the software., Comment: 2 pages, 2 figures. Paper presented at the 5th IEEE International Conference on Quantum Computing and Engineering (IEEE QCE 2024)

General conditions are established under which reaction-cross-diffusion systems can undergo spatiotemporal pattern-forming instabilities. Recent work has focused on designing systems theoretically and experimentally to exhibit patterns with specific features, but the case of non-diagonal diffusion matrices has yet to be analysed. Here, a framework is presented for the design of general $n$-component reaction-cross-diffusion systems that exhibit Turing and wave instabilities of a given wavelength. For a fixed set of reaction kinetics, it is shown how to choose diffusion matrices that produce each instability; conversely, for a given diffusion tensor, how to choose linearised kinetics. The theory is applied to several examples including a hyperbolic reaction-diffusion system, two different 3-component models, and a spatio-temporal version of the Ross-Macdonald model for the spread of malaria., Comment: 49 pages, 14 figures

Weakly nonlinear amplitude equations are derived for the onset of spatially extended patterns on a general class of $n$-component bulk-surface reaction-diffusion systems in a ball, under the assumption of linear kinetics in the bulk. Linear analysis shows conditions under which various pattern modes can become unstable to either generalised pitchfork or transcritical bifurcations depending on the parity of the spatial wavenumber. Weakly nonlinear analysis is used to derive general expressions for the multi-component amplitude equations of different patterned states. These reduced-order systems are found to agree with prior normal forms for pattern formation bifurcations with $O(3)$ symmetry and provide information on the stability of bifurcating patterns of different symmetry types. The analysis is complemented with numerical results using a dedicated finite-element method. The theory is illustrated in two examples; a bulk-surface version of the Brusselator, and a four-component cell-polarity model.

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

We analyze JWST MIRI/MRS observations of the infrared PAH bands in the nuclear and circumnuclear regions of local AGN from the GATOS Survey. In this work, we examine the PAH properties in the circumnuclear regions of AGN and AGN-outflows, and compare them to those in star-forming regions and the innermost regions of AGN. This study employs 4.9-28.1 micron sub-arcsecond angular resolution data to investigate the properties of PAH in three nearby sources (DL~30-40 Mpc). Our findings align with previous JWST studies, showing that the central regions of AGN show a larger fraction of neutral PAH molecules (i.e. elevated 11.3/6.2 and 11.3/7.7 PAH ratios) compared to star-forming galaxies. We find that the AGN might affect not only the PAH population in the innermost region but also in the extended regions up to ~kpc scales. By comparing our observations to PAH diagnostic diagrams, we find that, in general, regions located in the projected direction of the AGN-outflow occupy similar positions on the PAH diagnostic diagrams as those of the innermost regions of AGN. Star-forming regions that are not affected by the AGN in these galaxies share the same part of the diagram as Star-forming galaxies. We examine the potential of the PAH-H2 diagram to disentangle AGN versus star-forming activity. Our results suggest that in Sy-like AGN, illumination and feedback from the AGN might affect the PAH population at nuclear and kpc scales, in particular, the ionization state of the PAH grains. However, PAH sizes are rather similar. The carriers of the ionized PAH bands (6.2 and 7.7 micron) are less resilience than those of neutral PAH bands (11.3 micron), which might be particularly important for strongly AGN-host coupled systems. Therefore, caution must be applied when using PAH bands as star-formation rate indicators in these systems even at kpc scales, with the ionized ones being more affected by the AGN., Comment: Accepted for publication in A&A. 21 pages, 13 Figures

We present multi-wavelength data of SN2020acct, a double-peaked stripped-envelope supernova (SN) in NGC2981 at ~150 Mpc. The two peaks are temporally distinct, with maxima separated by 58 rest-frame days, and a factor of 20 reduction in flux between. The first is luminous (M$_{r}$ = -18.00 $\pm$ 0.02 mag), blue (g - r = 0.27 $\pm$ 0.03 mag), and displays spectroscopic signatures of interaction with hydrogen-free circumstellar material. The second peak is fainter (M$_{r}$ = -17.29 $\pm$ 0.03 mag), and spectroscopically similar to an evolved stripped-envelope SNe, with strong blended forbidden [Ca II] and [O II] features. No other known double-peak SN exhibits a light curve similar to that of SN 2020acct. We find the likelihood of two individual SNe occurring in the same star-forming region within that time to be highly improbable, while an implausibly fine-tuned configuration would be required to produce two SNe from a single binary system. We find that the peculiar properties of SN2020acct match models of pulsational pair instability (PPI), in which the initial peak is produced by collisions of shells of ejected material, shortly followed by a terminal explosion. Pulsations from a star with a 72 M$_{\odot}$ helium core provide an excellent match to the double-peaked light curve. The local galactic environment has a metallicity of 0.4 Z$_{\odot}$, a level where massive single stars are not expected retain enough mass to encounter the PPI. However, late binary mergers or a low-metallicity pocket may allow the required core mass. We measure the rate of SN 2020acct-like events to be $<3.3\times10^{-8}$ Mpc$^{-3}$ yr$^{-1}$ at z = 0.07, or <0.1% of the total core-collapse SN rate., Comment: 31 pages, 14 figures. Submitted to ApJL, comments welcome

We present a comprehensive analysis of the photometric and spectroscopic evolution of SN~2021foa, unique among the class of transitional supernovae for repeatedly changing its spectroscopic appearance from hydrogen-to-helium-to-hydrogen-dominated (IIn-to-Ibn-to-IIn) within 50 days past peak brightness. The spectra exhibit multiple narrow ($\approx$ 300--600~km~s$^{-1}$) absorption lines of hydrogen, helium, calcium and iron together with broad helium emission lines with a full-width-at-half-maximum (FWHM) of $\sim 6000$~km~s$^{-1}$. For a steady, wind-mass loss regime, light curve modeling results in an ejecta mass of $\sim 8$ M$_{\odot}$ and CSM mass below 1 M$_{\odot}$, and an ejecta velocity consistent with the FWHM of the broad helium lines. We obtain a mass-loss rate of $\approx 2$ M$_{\odot} {\rm yr}^{-1}$. This mass-loss rate is three orders of magnitude larger than derived for normal Type II SNe. We estimate that the bulk of the CSM of SN~2021foa must have been expelled within half a year, about 15 years ago. Our analysis suggests that SN~2021foa had a helium rich ejecta which swept up a dense shell of hydrogen rich CSM shortly after explosion. At about 60 days past peak brightness, the photosphere recedes through the dense ejecta-CSM region, occulting much of the red-shifted emission of the hydrogen and helium lines, which results in observed blue-shift ($\sim -3000$~km~s$^{-1}$). Strong mass loss activity prior to explosion, such as those seen in SN~2009ip-like objects and SN~2021foa as precursor emission, are the likely origin of a complex, multiple-shell CSM close to the progenitor star., Comment: Revised. Accepted in ApJ

A common setting in astronomy is the availability of a small number of high-quality observations, and larger amounts of either lower-quality observations or synthetic data from simplified models. Time-domain astrophysics is a canonical example of this imbalance, with the number of supernovae observed photometrically outpacing the number observed spectroscopically by multiple orders of magnitude. At the same time, no data-driven models exist to understand these photometric and spectroscopic observables in a common context. Contrastive learning objectives, which have grown in popularity for aligning distinct data modalities in a shared embedding space, provide a potential solution to extract information from these modalities. We present Maven, the first foundation model for supernova science. To construct Maven, we first pre-train our model to align photometry and spectroscopy from 0.5M synthetic supernovae using a constrastive objective. We then fine-tune the model on 4,702 observed supernovae from the Zwicky Transient Facility. Maven reaches state-of-the-art performance on both classification and redshift estimation, despite the embeddings not being explicitly optimized for these tasks. Through ablation studies, we show that pre-training with synthetic data improves overall performance. In the upcoming era of the Vera C. Rubin Observatory, Maven serves as a Rosetta Stone for leveraging large, unlabeled and multimodal time-domain datasets., Comment: code: https://github.com/ThomasHelfer/multimodal-supernovae data: https://huggingface.co/datasets/thelfer/multimodal_supernovae

Conformal Autoencoders are a neural network architecture that imposes orthogonality conditions between the gradients of latent variables towards achieving disentangled representations of data. In this letter we show that orthogonality relations within the latent layer of the network can be leveraged to infer the intrinsic dimensionality of nonlinear manifold data sets (locally characterized by the dimension of their tangent space), while simultaneously computing encoding and decoding (embedding) maps. We outline the relevant theory relying on differential geometry, and describe the corresponding gradient-descent optimization algorithm. The method is applied to standard data sets and we highlight its applicability, advantages, and shortcomings. In addition, we demonstrate that the same computational technology can be used to build coordinate invariance to local group actions when defined only on a (reduced) submanifold of the embedding space.

We present JWST/MIRI observations of the Type I superluminous supernova (SLSN) 2017gci taken over 2000 rest-frame days after the supernova (SN) exploded, which represent the latest phase images taken of any known SLSN. We find that archival \WISE detections of SN\,2017gci taken 70 to 200 days after explosion are most likely explained by an IR dust echo from a $\sim 3 \times 10^{-4}$ M$_\odot$ shell of pre-existing dust, as opposed to freshly-formed dust. New JWST observations reveal IR emission in the field of SN\,2017gci, which we determine is most likely dominated by the host galaxy of the SN, based on the expected flux of the galaxy and the measurable separation between said emission and the location of the SN. Based on models for IR emission of carbonate dust, we place a $3\sigma$ upper limit of $0.83$ M$_\odot$ of dust formed in SN\,2017gci, with a lowest $1\sigma$ limit of $0.44$ M$_\odot$. Infrared (IR) detections of other SLSNe have suggested that SLSNe could be among the most efficient dust producers in the universe. Our results suggest that SLSNe do not necessarily form more dust than other types of SNe, but instead might have a more accelerated dust formation process. More IR observations of a larger sample of SLSNe will be required to determine how efficient dust production is in SLSNe., Comment: 11 pages, 5 figures, submitted to ApJ

Enhanced emission in the months to years preceding explosion has been detected for several core-collapse supernovae (SNe). Though the physical mechanisms driving the emission remain hotly debated, the light curves of detected events show long-lived ($\geq$50 days), plateau-like behavior, suggesting hydrogen recombination may significantly contribute to the total energy budget. The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will provide a decade-long photometric baseline to search for this emission, both in binned pre-explosion observations after an SN is detected and in single-visit observations prior to the SN explosion. In anticipation of these searches, we simulate a range of eruptive precursor models to core-collapse SNe and forecast the discovery rates of these phenomena in LSST data. We find a detection rate of ~40-130 yr$^{-1}$ for SN IIP/IIL precursors and ~110 yr$^{-1}$ for SN IIn precursors in single-epoch photometry. Considering the first three years of observations with the effects of rolling and observing triplets included, this number grows to a total of 150-400 in binned photometry, with the highest number recovered when binning in 100-day bins for 2020tlf-like precursors and in 20-day bins for other recombination-driven models from the literature. We quantify the impact of using templates contaminated by residual light (from either long-lived or separate precursor emission) on these detection rates, and explore strategies for estimating baseline flux to mitigate these issues. Spectroscopic follow-up of the eruptions preceding core-collapse SNe and detected with LSST will offer important clues to the underlying drivers of terminal-stage mass loss in massive stars., Comment: 20 pages, 12 figures. Comments welcome

We present a method to prepare non-classical states of the electromagnetic field in a microwave resonator. It is based on a controlled gate that applies a squeezing operation on a SQUID-terminated resonator conditioned on the state of a dispersively coupled qubit. This controlled-squeeze gate, when combined with Gaussian operations on the resonator, is universal. We explore the use of this tool to map an arbitrary qubit state into a superposition of squeezed states. In particular, we target a bosonic code with well-defined superparity which makes photon losses detectable by nondemolition parity measurements. We analyze the possibility of implementing this using state-of-the-art circuit QED tools and conclude that it is within reach of current technologies., Comment: 5 pages, 2 figures and supplemental material

We present and analyze the extensive optical broadband photometry of the Type II SN 2023ixf up to one year after explosion. We find that, when compared to two pre-existing model grids, the pseudo-bolometric light curve is consistent with drastically different combinations of progenitor and explosion properties. This may be an effect of known degeneracies in Type IIP light-curve models. We independently compute a large grid of ${\tt MESA+STELLA}$ single-star progenitor and light-curve models with various zero-age main-sequence masses, mass-loss efficiencies, and convective efficiencies. Using the observed progenitor variability as an additional constraint, we select stellar models consistent with the pulsation period and explode them according to previously established scaling laws to match plateau properties. Our hydrodynamic modeling indicates that SN 2023ixf is most consistent with a moderate-energy ($E_{\rm exp}\approx7\times10^{50}$ erg) explosion of an initially high-mass red supergiant progenitor ($\gtrsim 17\ M_{\odot}$) that lost a significant amount of mass in its prior evolution, leaving a low-mass hydrogen envelope ($\lesssim 3\ M_{\odot}$) at the time of explosion, with a radius $\gtrsim 950\ R_{\odot}$ and a synthesized $^{56}$Ni mass of $0.07\ M_{\odot}$. We posit that previous mass transfer in a binary system may have stripped the envelope of SN 2023ixf's progenitor. The analysis method with pulsation period presented in this work offers a way to break degeneracies in light-curve modeling in the future, particularly with the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time, when a record of progenitor variability will be more common., Comment: 18 pages, 7 figures, submitted to ApJ. Comments welcome

Introduction: This article introduces DisTrack, a methodology and a tool developed for tracking and analyzing misinformation within Online Social Networks (OSNs). DisTrack is designed to combat the spread of misinformation through a combination of Natural Language Processing (NLP) Social Network Analysis (SNA) and graph visualization. The primary goal is to detect misinformation, track its propagation, identify its sources, and assess the influence of various actors within the network. Methods: DisTrack's architecture incorporates a variety of methodologies including keyword search, semantic similarity assessments, and graph generation techniques. These methods collectively facilitate the monitoring of misinformation, the categorization of content based on alignment with known false claims, and the visualization of dissemination cascades through detailed graphs. The tool is tailored to capture and analyze the dynamic nature of misinformation spread in digital environments. Results: The effectiveness of DisTrack is demonstrated through three case studies focused on different themes: discredit/hate speech, anti-vaccine misinformation, and false narratives about the Russia-Ukraine conflict. These studies show DisTrack's capabilities in distinguishing posts that propagate falsehoods from those that counteract them, and tracing the evolution of misinformation from its inception. Conclusions: The research confirms that DisTrack is a valuable tool in the field of misinformation analysis. It effectively distinguishes between different types of misinformation and traces their development over time. By providing a comprehensive approach to understanding and combating misinformation in digital spaces, DisTrack proves to be an essential asset for researchers and practitioners working to mitigate the impact of false information in online social environments.

This paper explores artificial intelligence (AI) methods for the approximate compiling of unitaries, focusing on the use of fixed two-qubit gates and arbitrary single-qubit rotations typical in superconducting hardware. Our approach involves three main stages: identifying an initial template that approximates the target unitary, predicting initial parameters for this template, and refining these parameters to maximize the fidelity of the circuit. We propose AI-driven approaches for the first two stages, with a deep learning model that suggests initial templates and an autoencoder-like model that suggests parameter values, which are refined through gradient descent to achieve the desired fidelity. We demonstrate the method on 2 and 3-qubit unitaries, showcasing promising improvements over exhaustive search and random parameter initialization. The results highlight the potential of AI to enhance the transpiling process, supporting more efficient quantum computations on current and future quantum hardware.

The Hartmann test is a method used to measure the wavefront error in a focal optical system, wherein a mask with a pattern of small holes is placed at the system's aperture stop. By taking an image at a defocused plane, the differences between the ideal and real positions of the reimaged holes (called the transverse ray aberrations) can be measured, which can then be used to estimate the wavefront error. However, the Hartmann test is usually used with an on-axis field. In this paper, we present a wavefront sensing method which generalizes the classical Hartmann test for off-axis field angles and arbitrary reference wavefronts. Our method involves taking images at two defocused planes, and then using the real reimaged hole positions on both planes to estimate the trajectories of rays from the system's exit pupil, at which the reference wavefront is situated. We then propagate the rays forward from the reference wavefront to one of the two defocused planes, in order to find the ideal reimaged hole positions, from which we can compute transverse ray aberrations. We derive and solve a pair of nonlinear partial differential equations relating transverse ray aberrations to wavefront error, using Zernike decomposition and nonlinear least squares. Our method has been verified on simulated data from the 7-lens f/2.25 red camera system of the GMT-Consortium Large Earth Finder (G-CLEF), a high resolution optical echelle spectrograph which will be a first light instrument for the Giant Magellan Telescope (GMT)., Comment: 18 pages, 23 figures, SPIE Astronomical Telescopes + Instrumentation 2024

We present observations of the type-2 Seyfert NGC7172 obtained with the medium-resolution spectrometer (MRS) of the Mid-Infrared Instrument (MIRI) on board of the JWST. This galaxy hosts one of the lowest ionised gas mass outflow rates (Mout~0.005 M/yr) in a sample of six AGN with similar bolometric luminosities (log Lbol~44erg/s) within the Galactic Activity, Torus and Outflow Survey (GATOS). We aim to understand the properties of the ionised gas outflow, mainly using the emission lines from the neon transitions, that cover a broad range of ionisation potentials (IP) from ~20 eV to ~130 eV. We applied parametric and non-parametric methods to characterise the line emission and kinematics. The low excitation lines (IP<25eV, e.g.[NeII]) trace the rotating disc emission. The high excitation lines (IP>90eV, e.g.[NeV]), which are likely photoionised exclusively by the AGN, are expanding in the direction nearly perpendicular to the disc of the galaxy, with maximum projected velocities of ~350-500 km/s. In particular, [NeV] and [NeVI] lines reveal a biconical ionised gas outflow emerging N-S from the nuclear region, extending at least ~2.5"N and 3.8"S (projected distance of ~450 and 680 pc). Most of the emission arising in the northern part of the cone was not previously detected due to obscuration. Given the almost face-on orientation of the outflow and the almost edge-on orientation of the galaxy, NGC7172 may be a case of weak coupling. Nevertheless, we found evidence for positive feedback in two distinct outflowing clumps at projected distances of 3.1" and 4.3" (i.e. ~560 and 780 pc) SW from the AGN. We estimated a star formation rate in these regions using the [NeII] and [NeIII] luminosities of 0.08 M/yr, that is ~10% of that found in the circumnuclear ring. The star formation activity might have been triggered by the interaction between the ionised gas outflow and the ISM of the galaxy., Comment: 20 pages, 6 figures. Accepted for publication in A&A. Abstract adapted for the arxiv version

In this work, we studied photon generation due to the Dynamical Casimir Effect (DCE) in a one dimensional (1+1) double superconducting cavity. The cavity consists of two perfectly conducting mirrors and a dielectric membrane of infinitesimal depth that effectively couples two cavities. The total length of the double cavity $L$, the difference in length between the two cavities $\Delta L$, and the electric susceptibility $\chi$ and conductivity $v$ of the dielectric membrane are tunable parameters. All four parameters are treated as independent and are allowed to be tuned at the same time, even with different frequencies. We analyzed the cavity's energy spectra under different conditions, finding a transition between two distinct regimes that is accurately described by $k_c=\sqrt{v/\chi}$. In particular, a lowest energy mode is forbidden in one of the regimes while it is allowed in the other. We compared analytical approximations obtained through the Multiple Scale Analysis method with exact numeric solutions, obtaining the typical results when $\chi$ is not being tuned. However, when the susceptibility $\chi$ is tuned, different behaviours (such as oscillations in the number of photons of a cavity prepared in a vacuum state) might arise if the frequencies and amplitudes of all parameters are adequate. These oscillations can be considered as adiabatic shortcuts where all generated photons are eventually destroyed. Finally, we present an equivalent quantum circuit that would allow to experimentally simulate the DCE under the studied conditions., Comment: 22 pages, 20 figures

The jets of radio AGN provide one of the most important forms of AGN feedback, yet considerable uncertainties remain about how they are triggered. Since the molecular gas reservoirs of the host galaxies can supply key information about the dominant triggering mechanism(s), here we present Atacama Large Millimeter/sub-millimeter Array (ALMA) CO(1-0) observations of a complete sample of 29 powerful radio AGN ($P_{1.4GHz} > 10^{25}$ W Hz$^{-1}$ and $0.05 < z < 0.3$) with an angular resolution of about 2 - 3 arcsec (corresponding to 2 - 8 kpc). We detect molecular gas with masses in the range $10^{8.9} < M_{H_2} < 10^{10.2}$ M$_{\odot}$ in the early-type host galaxies of 10 targets, while for the other 19 sources we derive upper limits. The detection rate of objects with such large molecular masses -- $34\pm9$% -- is higher than in the general population of non-active early-type galaxies (ETG: $<$10%). The kinematics of the molecular gas are dominated in most cases by rotating disk-like structures, with diameters up to 25 kpc. Compared with the results for samples of quiescent ETG in the literature, we find a larger fraction of more massive, more extended and less settled molecular gas structures. In most of the CO-detected sources, the results are consistent with triggering of the AGN as the gas settles following a merger or close encounter with a gas-rich companion. However, in a minority of objects at the centres of rich clusters of galaxies, the accretion of gas cooling from the hot X-ray halos is a plausible alternative to galaxy interactions as a triggering mechanisms., Comment: 25 pages, 10 figures, accepted for publication in MNRAS

We present the most comprehensive catalog to date of Type I Superluminous Supernovae (SLSNe), a class of stripped envelope supernovae (SNe) characterized by exceptionally high luminosities. We have compiled a sample of 262 SLSNe reported through 2022 December 31. We verified the spectroscopic classification of each SLSN and collated an exhaustive data set of UV, optical and IR photometry from both publicly available data and our own FLEET observational follow-up program, totaling over 30,000 photometric detections. Using these data we derive observational parameters such as the peak absolute magnitudes, rise and decline timescales, as well as bolometric luminosities, temperature and photospheric radius evolution for all SLSNe. Additionally, we model all light curves using a hybrid model that includes contributions from both a magnetar central engine and the radioactive decay of $^{56}$Ni. We explore correlations among various physical and observational parameters, and recover the previously found relation between ejecta mass and magnetar spin, as well as the overall progenitor pre-explosion mass distribution with a peak at $\approx 6.5$ M$_\odot$. We find no significant redshift dependence for any parameter, and no evidence for distinct sub-types of SLSNe. We find that $< 3$\% of SLSNe are best fit with a significant contribution from radioactive decay $\gtrsim 50$\%, representing a set of relatively dim and slowly declining SNe. We provide several analytical tools designed to simulate typical SLSN light curves across a broad range of wavelengths and phases, enabling accurate K-corrections, bolometric scaling calculations, and inclusion of SLSNe in survey simulations or future comparison works. The complete catalog, including all of the photometry, models, and derived parameters, is made available as an open-source resource on GitHub., Comment: 59 pages, 22 Figures, Submitted to MNRAS

Giant (>100 kpc) nebulae associated with active galaxies provide rich information about the circumgalactic medium (CGM) around galaxies, its link with the interstellar medium (ISM) of the hosts and the mechanisms involved in their evolution. We have studied the giant nebula associated with the Teacup (z=0.085) quasar based on VLT MUSE integral field spectroscopy to investigate whether the well known giant (~10 kpc) active galactic nucleus (AGN) induced outflow has an impact on the distribution of heavy elements in and outside the host galaxy. For this, we have mapped the O/H and N/O abundances in two spatial dimensions across the giant nebula and within the galaxy by means of comparing emission line ratios with photoionisation model predictions. We have found that the widely studied AGN driven outflow responsible for the 10 kpc ionised bubble is enhancing the gas metal abundance up to 10 kpc from the AGN. O/H is solar or slightly higher in the bubble edges, in comparison with the subsolar abundances across the rest of the nebula (median O/H~0.63 O/H_sun)). The main conclusion is that AGN feedback can produce metal enrichment at large extranuclear distances in galaxies (>~10 kpc)., Comment: Accepted for publicacion in A&A. 8 pages, 4 figures

Response-adaptive (RA) designs of clinical trials allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. RA designs face greater regulatory scrutiny due to potential type I error inflation, which limits their uptake in practice. Existing approaches to type I error control either only work for specific designs, have a risk of Monte Carlo/approximation error, are conservative, or computationally intractable. We develop a general and computationally tractable approach for exact analysis in two-arm RA designs with binary outcomes. We use the approach to construct exact tests applicable to designs that use either randomized or deterministic RA procedures, allowing for complexities such as delayed outcomes, early stopping or allocation of participants in blocks. Our efficient forward recursion implementation allows for testing of two-arm trials with 1,000 participants on a standard computer. Through an illustrative computational study of trials using randomized dynamic programming we show that, contrary to what is known for equal allocation, a conditional exact test has, almost uniformly, higher power than the unconditional test. Two real-world trials with the above-mentioned complexities are re-analyzed to demonstrate the value of our approach in controlling type I error and/or improving the statistical power., Comment: 35 pages, 6 figures, 11 tables

InAs quantum wells (QWs) grown on InP substrates are interesting for their applications in devices with high spin-orbit coupling (SOC) and their potential role in creating topologically nontrivial hybrid heterostructures. The highest mobility QWs are limited by interfacial roughness scattering and alloy disorder scattering in the cladding and buffer layers. Increasing QW thickness has been shown to reduce the effect of both of these scattering mechanisms. However, for current state-of-the-art devices with As-based cladding and barrier layers, the critical thickness is limited to $\leq7$ nm. In this report, we demonstrate the use of strain compensation techniques in the InGaAs cladding layers to extend the critical thickness well beyond this limit. We induce tensile strain in the InGaAs cladding layers by reducing the In concentration from In$_{0.81}$Ga$_{0.19}$As to In$_{0.70}$Ga$_{0.30}$As and we observe changes in both the critical thickness of the well and the maximum achievable mobility. The peak electron mobility at 2 K is $1.16\times10^6$ cm$^2/$Vs, with a carrier density of $4.2\times10^{11}$ /cm$^2$. Additionally, we study the quantum lifetime and Rashba spin splitting in the highest mobility device as these parameters are critical to determine if these structures can be used in topologically nontrivial devices.

The ionisation cones of NGC5728 have a deficit of molecular gas based on millimetre observations of CO(2-1) emission. Although photoionisation from the active nucleus may lead to suppression of this transition, warm molecular gas can still be present. We report the detection of eight mid-infrared rotational H$_2$ lines throughout the central kiloparsec, including the ionisation cones, using integral field spectroscopic observations with JWST/MIRI MRS. The H$_2$ line ratios, characteristic of a power-law temperature distribution, indicate that the gas is warmest where it enters the ionisation cone through disk rotation, suggestive of shock excitation. In the nucleus, where the data can be combined with an additional seven ro-vibrational H$_2$ transitions, we find that moderate velocity (30 km s$^{-1}$) shocks in dense ($10^5$ cm$^{-3}$) gas, irradiated by an external UV field ($G_0 = 10^3$), do provide a good match to the full set. The warm molecular gas in the ionisation cone that is traced by the H$_2$ rotational lines has been heated to temperatures $>200$ K. Outside of the ionisation cone the molecular gas kinematics are undisturbed. However, within the ionisation cone, the kinematics are substantially perturbed, indicative of a radial flow, but one that is quantitatively different from the ionised lines. We argue that this outflow is in the plane of the disk, implying a short 50 pc acceleration zone up to speeds of about 400 km s$^{-1}$ followed by an extended deceleration over $\sim$700 pc where it terminates. The deceleration is due to both the radially increasing galaxy mass, and mass-loading as ambient gas in the disk is swept up., Comment: A&A accepted; 16 pages

Data acquired in space operational conditions is scarce due to the costs and complexity of space operations. This poses a challenge to learning-based visual-based navigation algorithms employed in autonomous spacecraft navigation. Existing datasets, which largely depend on computer-simulated data, have partially filled this gap. However, the image generation tools they use are proprietary, which limits the evaluation of methods to unseen scenarios. Furthermore, these datasets provide limited ground-truth data, primarily focusing on the spacecraft's translation and rotation relative to the camera. To address these limitations, we present SPIN (SPacecraft Imagery for Navigation), an open-source realistic spacecraft image generation tool for relative navigation between two spacecrafts. SPIN provides a wide variety of ground-truth data and allows researchers to employ custom 3D models of satellites, define specific camera-relative poses, and adjust various settings such as camera parameters and environmental illumination conditions. For the task of spacecraft pose estimation, we compare the results of training with a SPIN-generated dataset against existing synthetic datasets. We show a %50 average error reduction in common testbed data (that simulates realistic space conditions). Both the SPIN tool (and source code) and our enhanced version of the synthetic datasets will be publicly released upon paper acceptance on GitHub https://github.com/vpulab/SPIN.

This work characterizes equivariant polynomial functions from tuples of tensor inputs to tensor outputs. Loosely motivated by physics, we focus on equivariant functions with respect to the diagonal action of the orthogonal group on tensors. We show how to extend this characterization to other linear algebraic groups, including the Lorentz and symplectic groups. Our goal behind these characterizations is to define equivariant machine learning models. In particular, we focus on the sparse vector estimation problem. This problem has been broadly studied in the theoretical computer science literature, and explicit spectral methods, derived by techniques from sum-of-squares, can be shown to recover sparse vectors under certain assumptions. Our numerical results show that the proposed equivariant machine learning models can learn spectral methods that outperform the best theoretically known spectral methods in some regimes. The experiments also suggest that learned spectral methods can solve the problem in settings that have not yet been theoretically analyzed. This is an example of a promising direction in which theory can inform machine learning models and machine learning models could inform theory.

Autonomous systems, such as self-driving cars, rely on reliable semantic environment perception for decision making. Despite great advances in video semantic segmentation, existing approaches ignore important inductive biases and lack structured and interpretable internal representations. In this work, we propose MCDS-VSS, a structured filter model that learns in a self-supervised manner to estimate scene geometry and ego-motion of the camera, while also estimating the motion of external objects. Our model leverages these representations to improve the temporal consistency of semantic segmentation without sacrificing segmentation accuracy. MCDS-VSS follows a prediction-fusion approach in which scene geometry and camera motion are first used to compensate for ego-motion, then residual flow is used to compensate motion of dynamic objects, and finally the predicted scene features are fused with the current features to obtain a temporally consistent scene segmentation. Our model parses automotive scenes into multiple decoupled interpretable representations such as scene geometry, ego-motion, and object motion. Quantitative evaluation shows that MCDS-VSS achieves superior temporal consistency on video sequences while retaining competitive segmentation performance., Comment: Accepted for publication at BMVC 2024