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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.

The Southern Photometric Local Universe Survey (S-PLUS) is a project to map $\sim9300$ sq deg of the sky using twelve bands (seven narrow and five broadbands). Observations are performed with the T80-South telescope, a robotic telescope located at the Cerro Tololo Observatory in Chile. The survey footprint consists of several large contiguous areas, including fields at high and low galactic latitudes, and towards the Magellanic Clouds. S-PLUS uses fixed exposure times to reach point source depths of about $21$ mag in the $griz$ and $20$ mag in the $u$ and the narrow filters. This paper describes the S-PLUS Data Release 4 (DR4), which includes calibrated images and derived catalogues for over 3000 sq deg, covering the aforementioned area. The catalogues provide multi-band photometry performed with the tools \texttt{DoPHOT} and \texttt{SExtractor} -- point spread function (\PSF) and aperture photometry, respectively. In addition to the characterization, we also present the scientific potential of the data. We use statistical tools to present and compare the photometry obtained through different methods. Overall we find good agreement between the different methods, with a slight systematic offset of 0.05\,mag between our \PSF and aperture photometry. We show that the astrometry accuracy is equivalent to that obtained in previous S-PLUS data releases, even in very crowded fields where photometric extraction is challenging. The depths of main survey (MS) photometry for a minimum signal-to-noise ratio $S/N = 3$ reach from $\sim19.5$ for the bluer bands to $\sim21.5$ mag on the red. The range of magnitudes over which accurate \PSF photometry is obtained is shallower, reaching $\sim19$ to $\sim20.5$ mag depending on the filter. Based on these photometric data, we provide star-galaxy-quasar classification and photometric redshift for millions of objects., Comment: 26 pages, 17 figures, 14 tables, accepted for A&A

We present multiwavelength observations of the Swift short $\gamma$-ray burst GRB 231117A, localized to an underlying galaxy at redshift $z = 0.257$ at a small projected offset ($\sim 2~$kpc). We uncover long-lived X-ray (Chandra) and radio/millimeter (VLA, MeerKAT, and ALMA) afterglow emission, detected to $\sim 37~$days and $\sim 20~$days (rest frame), respectively. We measure a wide jet ($\sim 10.4^\circ$) and relatively high circumburst density ($\sim 0.07~{\rm cm}^{-3}$) compared to the short GRB population. Our data cannot be easily fit with a standard forward shock model, but they are generally well fit with the incorporation of a refreshed forward shock and a reverse shock at $< 1~$day. We incorporate GRB 231117A into a larger sample of 132 X-ray detected events, 71 of which were radio-observed (17 cm-band detections), for a systematic study of the distributions of redshifts, jet and afterglow properties, galactocentric offsets, and local environments of events with and without detected radio afterglows. Compared to the entire short GRB population, the majority of radio-detected GRBs are at relatively low redshifts ($z < 0.6$) and have high circumburst densities ($> 10^{-2}~{\rm cm}^{-3}$), consistent with their smaller ($< 8~$kpc) projected galactocentric offsets. We additionally find that 70% of short GRBs with opening angle measurements were radio-detected, indicating the importance of radio afterglows in jet measurements, especially in the cases of wide ($> 10^\circ$) jets where observational evidence of collimation may only be detectable at radio wavelengths. Owing to improved observing strategies and the emergence of sensitive radio facilities, the number of radio-detected short GRBs has quadrupled in the past decade., Comment: 30 pages, 11 figures, submitted to ApJ

Stellar mass, binary black hole (BBH) mergers dominates the sources of gravitational wave (GW) events so far detected the LIGO/Virgo/KAGRA (LVK) experiment. The origin of these BBHs is unknown, and no electromagnetic (EM) counterpart has been undoubtedly associated to any of such GW events. The thin discs of active galactic nuclei (AGNs) might be viable environments where BBHs can form and at the same time produce observable radiation feedback. This paper presents new physically motivated light-curve (LC) solutions for thermal flares driven by the remnant of a BBH merger within the disc of an AGN. Following previous analyses, we consider that the BBH likely creates an under-density cavity in the disc prior to its coalescence. Depending on the merger conditions, the black hole (BH) remnant can leave the cavity, interact with the unperturbed disc, and drive a transient BH wind. The wind expels disc material that expands above and below the disc plane and we consider the emission of these plasma ejections as the EM counterpart to the merger GW. We model the LC of such eruptions as mini supernovae explosions finding that stellar mass merger remnants can drive distinguishable flares in optical and UV bands, with time lags of $\sim 10-40$ ($100-400$) days after the GW event, lasting for a few days (weeks) when occurring in AGN with central engines of $\sim 10^6$ ($10^7$) M$_\odot$. The Vera C. Rubin Observatory can potentially detect the optical component of these EM counterparts up to redshifts of $z \sim 0.5 - 1$, accordingly. The present LC models are applicable to localise flaring AGNs as sources of multi-messenger emission following GW alerts., Comment: 17 pages, 10 figures

A quadruply lensed source, J125856.3-031944, has been discovered using the DELVE survey and WISE W1 - W2 colors. Followup direct imaging carried out with the MPIA 2.2 m and the Baade 6.5 m telescopes is analyzed, as is spectroscopy from the 2.5 m Nordic Optical Telescope. The lensed image configuration is kite-like, with the faintest image 2 magnitudes fainter than the other three. Redward of 6000 AA that image is badly blended with the lensing galaxy, which is elongated along the symmetry axis of the kite. Magellan direct imaging carried out in Sloan g permits better deblending. As the lensed image configuration is nearly circular, simple models give individual magnifications of at least 25 for the 3 brighter images. The source's narrow emission lines and low intrinsic luminosity qualify it as a type 2 AGN., Comment: 8 pages, 5 figures, submitted to AJ, comments welcome

Kilonovae represent a category of astrophysical transients, identifiable as the electromagnetic observable counterparts associated with the coalescence events of binary systems comprising neutron stars and neutron star-black hole pairs. They act as probes for heavy-element nucleosynthesis in astrophysical environments. These studies rely on inference of the physical parameters (e.g., ejecta mass, velocity, composition) that describe kilonovae based on electromagnetic observations. This is a complex inverse problem typically addressed with sampling-based methods such as Markov-chain Monte Carlo (MCMC) or nested sampling algorithms. However, repeated inferences can be computationally expensive due to the sequential nature of these methods. This poses a significant challenge to ensuring the reliability and statistical validity of the posterior approximations and, thus, the inferred kilonova parameters themselves. We present a novel approach: Simulation-Based Inference (SBI) using simulations produced by KilonovaNet. Our method employs an ensemble of Amortized Neural Posterior Estimation (ANPE) with an embedding network to directly predict posterior distributions from simulated spectral energy distributions (SEDs). We take advantage of the quasi-instantaneous inference time of ANPE to demonstrate the reliability of our posterior approximations using diagnostics tools, including coverage diagnostic and posterior predictive checks. We further test our model with real observations from AT2017gfo, the only kilonova with multi-messenger data, demonstrating agreement with previous likelihood-based methods while reducing inference time down to a few seconds. The inference results produced by ANPE appear to be conservative and reliable, paving the way for testable and more efficient kilonova parameter inference.

When a galaxy falls into a cluster, its outermost parts are the most affected by the environment. In this paper, we are interested in studying the influence of a dense environment on different galaxy's components to better understand how this affects the evolution of galaxies. We use, as laboratory for this study, the Hydra cluster which is close to virialization; yet it still shows evidence of substructures. We present a multi-wavelength bulge-disc decomposition performed simultaneously in 12 bands from S-PLUS data for 52 galaxies brighter than m$_{r}$= 16. We model the galaxies with a Sersic profile for the bulge and an exponential profile for the disc. We find that the smaller, more compact, and bulge-dominated galaxies tend to exhibit a redder colour at a fixed stellar mass. This suggests that the same mechanisms (ram-pressure stripping and tidal stripping) that are causing the compaction in these galaxies are also causing them to stop forming stars. The bulge size is unrelated to the galaxy's stellar mass, while the disc size increases with greater stellar mass, indicating the dominant role of the disc in the overall galaxy mass-size relation found. Furthermore, our analysis of the environment unveils that quenched galaxies are prevalent in regions likely associated with substructures. However, these areas also harbour a minority of star-forming galaxies, primarily resulting from galaxy interactions. Lastly, we find that ~37 percent of the galaxies exhibit bulges that are bluer than their discs, indicative of an outside-in quenching process in this type of dense environments., Comment: 17 pages, 19 figures. Submitted to the MNRAS

Kilonovae are a rare class of astrophysical transients powered by the radioactive decay of nuclei heavier than iron, synthesized in the merger of two compact objects. Over the first few days, the kilonova evolution is dominated by a large number of radioactive isotopes contributing to the heating rate. On timescales of weeks to months, its behavior is predicted to differ depending on the ejecta composition and merger remnant. However, late-time observations of known kilonovae are either missing or limited. Here we report observations of a luminous red transient with a quasi-thermal spectrum, following an unusual gamma-ray burst of long duration. We classify this thermal emission as a kilonova and track its evolution up to two months after the burst. At these late times, the recession of the photospheric radius and the rapidly-decaying bolometric luminosity ($L_{\rm bol}\propto t^{-2.7\pm 0.4}$) support the recombination of lanthanide-rich ejecta as they cool., Comment: 47 pages, 14 figures, 9 tables; submitted; a minor typo fixed

The detection of gravitational waves (GW) with an electromagnetic counterpart enabled the first Hubble Constant $H_0$ measurement through the standard siren method. Current constraints suggest that $\sim 20-30\%$ of LIGO/Virgo/KAGRA (LVK) Binary Black Hole (BBH) mergers might occur in Active Galactic Nuclei (AGN) disks. The claim for a possible association of several BBH mergers with flaring AGNs suggests that cosmological analyses using BBH and AGNs might be promising. We explore standard siren analyses through a method that takes into account the presence of background flaring AGNs, without requiring a unique host galaxy identification, and apply it to realistic GW simulations. Depending on the fraction of LVK BBHs that induce flares, we expect to constrain $H_0$ at the $\sim 6-7\%$ ($\sim 4-5\%$) precision with $\sim 2-3$ years or $\sim 160-240$ events ($\sim 1$ year or $500$ events) of LVK at design (A+) sensitivity, assuming that systematic BBH follow-up searches are performed. %Assuming a more restrictive $\Omega_{\rm m}$ prior and that at least $20\%$ of BBHs produces detectable flares, we may reach a $3\%$ ($2\%$) precision in $H_0$ after 2 (1) year of LVK at design (A+) sensitivity. We also show that in a scenario where only $\sim 1\%$ of the BBHs induce detectable flares it is possible to achieve an $H_0$ precision from $7.5\%$ to $15\%$ with $\sim 240$ events. In addition, a $\sim 5-27\%$ precision is achievable with complete AGN catalogs and 1 year of LVK run, without the need of any flare identification., Comment: 15 pages, 7 figures, 1 table, accepted by PRD

We present photometric and spectroscopic observations of the extraordinary gamma-ray burst (GRB) 221009A in search of an associated supernova. Some past GRBs have shown bumps in the optical light curve that coincide with the emergence of supernova spectral features, but we do not detect any significant light curve features in GRB~221009A, nor do we detect any clear sign of supernova spectral features. Using two well-studied GRB-associated supernovae (SN~2013dx, $M_{r,max} = -19.54$; SN~2016jca, $M_{r,max} = -19.04$) at a similar redshift as GRB~221009A ($z=0.151$), we modeled how the emergence of a supernova would affect the light curve. If we assume the GRB afterglow to decay at the same rate as the X-ray data, the combination of afterglow and a supernova component is fainter than the observed GRB brightness. For the case where we assume the best-fit power law to the optical data as the GRB afterglow component, a supernova contribution should have created a clear bump in the light curve, assuming only extinction from the Milky Way. If we assume a higher extinction of $E(B-V)$=$1.74$ mag (as has been suggested elsewhere), the supernova contribution would have been hard to detect, with a limit on the associated supernova of $M_{r,max} \approx-$19.54. We do not observe any clear supernova features in our spectra, which were taken around the time of expected maximum light. The lack of a bright supernova associated with GRB~221009A may indicate that the energy from the explosion is mostly concentrated in the jet, leaving a lower energy budget available for the supernova., Comment: 19 pages, 8 figures, 4 tables, accepted for publication in ApJL

An accurate velocity model is essential to make a good seismic image. Conventional methods to perform Velocity Model Building (VMB) tasks rely on inverse methods, which, despite being widely used, are ill-posed problems that require intense and specialized human supervision. Convolutional Neural Networks (CNN) have been extensively investigated as an alternative to solve the VMB task. Two main approaches were investigated in the literature: supervised training and Physics-Informed Neural Networks (PINN). Supervised training presents some generalization issues since structures, and velocity ranges must be similar in training and test set. Some works integrated Full-waveform Inversion (FWI) with CNN, defining the problem of VMB in the PINN framework. In this case, the CNN stabilizes the inversion, acting like a regularizer and avoiding local minima-related problems and, in some cases, sparing an initial velocity model. Our approach combines supervised and physics-informed neural networks by using transfer learning to start the inversion. The pre-trained CNN is obtained using a supervised approach based on training with a reduced and simple data set to capture the main velocity trend at the initial FWI iterations. We show that transfer learning reduces the uncertainties of the process, accelerates model convergence, and improves the final scores of the iterative process., Comment: Paper present at machine Learning and the Physical Sciences workshop, NeurIPS 2022

The accurate and fast estimation of velocity models is crucial in seismic imaging. Conventional methods, like Tomography and Full-Waveform Inversion (FWI), obtain appropriate velocity models; however, they require intense and specialized human supervision and consume much time and computational resources. In recent years, some works investigated deep learning(DL) algorithms to obtain the velocity model directly from shots or migrated angle panels, obtaining encouraging predictions of synthetic models. This paper proposes a new flow to increase the complexity of velocity models recovered with DL. Inspired by the conventional geophysical velocity model building methods, instead of predicting the entire model in one step, we predict the velocity model iteratively. We implement the iterative nature of the process when, for each iteration, we train the DL algorithm to determine the velocity model with a certain level of precision/resolution for the next iteration; we name this process as Deep-Tomography. Starting from an initial model that roughly approaches the true model, the Deep-Tomography is able to predict an appropriate final model, even in complete unseen data, like the Marmousi model., Comment: 27 pages, 9 figures. First manuscript version submitted to Geophysical Journal International in February 2022

Delimiting salt inclusions from migrated images is a time-consuming activity that relies on highly human-curated analysis and is subject to interpretation errors or limitations of the methods available. We propose to use migrated images produced from an inaccurate velocity model (with a reasonable approximation of sediment velocity, but without salt inclusions) to predict the correct salt inclusions shape using a Convolutional Neural Network (CNN). Our approach relies on subsurface Common Image Gathers to focus the sediments' reflections around the zero offset and to spread the energy of salt reflections over large offsets. Using synthetic data, we trained a U-Net to use common-offset subsurface images as input channels for the CNN and the correct salt-masks as network output. The network learned to predict the salt inclusions masks with high accuracy; moreover, it also performed well when applied to synthetic benchmark data sets that were not previously introduced. Our training process tuned the U-Net to successfully learn the shape of complex salt bodies from partially focused subsurface offset images., Comment: Manuscript published at Geophysics

In this paper we will list a few important goals that need to be addressed in the next decade, also taking into account the current discordances between the different cosmological probes, such as the disagreement in the value of the Hubble constant $H_0$, the $\sigma_8$--$S_8$ tension, and other less statistically significant anomalies. While these discordances can still be in part the result of systematic errors, their persistence after several years of accurate analysis strongly hints at cracks in the standard cosmological scenario and the necessity for new physics or generalisations beyond the standard model. In this paper, we focus on the $5.0\,\sigma$ tension between the {\it Planck} CMB estimate of the Hubble constant $H_0$ and the SH0ES collaboration measurements. After showing the $H_0$ evaluations made from different teams using different methods and geometric calibrations, we list a few interesting new physics models that could alleviate this tension and discuss how the next decade's experiments will be crucial. Moreover, we focus on the tension of the {\it Planck} CMB data with weak lensing measurements and redshift surveys, about the value of the matter energy density $\Omega_m$, and the amplitude or rate of the growth of structure ($\sigma_8,f\sigma_8$). We list a few interesting models proposed for alleviating this tension, and we discuss the importance of trying to fit a full array of data with a single model and not just one parameter at a time. Additionally, we present a wide range of other less discussed anomalies at a statistical significance level lower than the $H_0$--$S_8$ tensions which may also constitute hints towards new physics, and we discuss possible generic theoretical approaches that can collectively explain the non-standard nature of these signals.[Abridged], Comment: Contribution to Snowmass 2021. 224 pages, 27 figures. Accepted for publication in JHEAp

In this paper, we describe a new method to select globular cluster (GC) candidates, including galaxy subtraction with unsharp masking, template fitting techniques and the inclusion of Gaia's proper motions. We report the use of the 12-band photometric system of S-PLUS to determine radial velocities and stellar populations of GCs around nearby galaxies. Specifically, we assess the effectiveness of identifying GCs around nearby and massive galaxies (D $< 20$ Mpc and $\sigma > 200$ km/s) in a multi-band survey such as S-PLUS by using spectroscopically confirmed GCs and literature GC candidate lists around the bright central galaxy in the Fornax cluster, NGC 1399 (D = 19 Mpc), and the isolated lenticular galaxy NGC 3115 (D = 9.4 Mpc). Despite the shallow survey depth, that limits the present work to $r < 21.3$ mag, we measure reliable photometry and perform robust SED fitting for a sample of 115 GCs around NGC 1399 and 42 GCs around NGC 3115, recovering radial velocities, ages, and metallicities for the GC populations., Comment: Accepted for publication in MNRAS. 11 pages, 5 figures, 2 tables

We present a new constraint on the Hubble constant $H_0$ using a sample of well-localized gravitational wave (GW) events detected during the first three LIGO/Virgo observing runs as dark standard sirens. In the case of dark standard sirens, a unique host galaxy is not identified, and the redshift information comes from the distribution of potential host galaxies. From the third LIGO/Virgo observing run detections, we add the asymmetric-mass binary black hole GW190412, the high-confidence GW candidates S191204r, S200129m, and S200311bg to the sample of dark standard sirens analyzed. Our sample contains the top $20\%$ (based on localization) GW events and candidates to date with significant coverage by the Dark Energy Spectroscopic Instrument (DESI) Legacy Survey. We combine the $H_0$ posterior for eight dark siren events, finding $H_0 = 79.8^{+19.1}_{-12.8}~{\rm km~s^{-1}~Mpc^{-1}}$ ($68\%$ Highest Density Interval) for a prior in $H_0$ uniform between $[20,140]~{\rm km~s^{-1}~Mpc^{-1}}$. This result shows that a combination of 8 well-localized dark sirens combined with an appropriate galaxy catalog is able to provide an $H_0$ constraint that is competitive ($\sim 20\%$ versus $18\%$ precision) with a single bright standard siren analysis (i.e. assuming the electromagnetic counterpart) using GW170817. When combining the posterior with that from GW170817, we obtain $H_0 = 72.77^{+11.0}_{-7.55}~{\rm km~s^{-1}~Mpc^{-1}}$. This result is broadly consistent with recent $H_0$ estimates from both the Cosmic Microwave Background and Supernovae., Comment: 14 pages, 5 figures

This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included., Comment: 30 pages (incl. 12 in appendix), 12 figures. Version accepted for publication in the Open Journal of Astrophysics

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact detection algorithms, this process simultaneously returns several classes of false positives: false detections from poor quality image subtractions, false detections from low signal-to-noise images, and detections of pre-existing variable sources. Currently, human visual inspection to remove the false positives is a central part of multimessenger follow-up observations, but when next generation gravitational wave and neutrino detectors come online and increase the rate of multimessenger events, the visual inspection process will be prohibitively expensive. We approach this problem with two convolutional neural networks operating on the difference imaging outputs. The first network focuses on removing false detections and demonstrates an accuracy of 92 percent on our dataset. The second network focuses on sorting all real detections by the probability of being a transient source within a host galaxy and distinguishes between various classes of images that previously required additional human inspection. We find the number of images requiring human inspection will decrease by a factor of 1.5 using our approach alone and a factor of 3.6 using our approach in combination with existing algorithms, facilitating rapid multimessenger counterpart identification by the astronomical community., Comment: Published in ApJ

We present optical follow-up imaging obtained with the Katzman Automatic Imaging Telescope, Las Cumbres Observatory Global Telescope Network, Nickel Telescope, Swope Telescope, and Thacher Telescope of the LIGO/Virgo gravitational wave (GW) signal from the neutron star-black hole (NSBH) merger GW190814. We searched the GW190814 localization region (19 deg$^{2}$ for the 90th percentile best localization), covering a total of 51 deg$^{2}$ and 94.6% of the two-dimensional localization region. Analyzing the properties of 189 transients that we consider as candidate counterparts to the NSBH merger, including their localizations, discovery times from merger, optical spectra, likely host-galaxy redshifts, and photometric evolution, we conclude that none of these objects are likely to be associated with GW190814. Based on this finding, we consider the likely optical properties of an electromagnetic counterpart to GW190814, including possible kilonovae and short gamma-ray burst afterglows. Using the joint limits from our follow-up imaging, we conclude that a counterpart with an $r$-band decline rate of 0.68 mag day$^{-1}$, similar to the kilonova AT 2017gfo, could peak at an absolute magnitude of at most $-17.8$ mag (50% confidence). Our data are not constraining for ''red'' kilonovae and rule out ''blue'' kilonovae with $M>0.5 M_{\odot}$ (30% confidence). We strongly rule out all known types of short gamma-ray burst afterglows with viewing angles $<$17$^{\circ}$ assuming an initial jet opening angle of $\sim$$5.2^{\circ}$ and explosion energies and circumburst densities similar to afterglows explored in the literature. Finally, we explore the possibility that GW190814 merged in the disk of an active galactic nucleus, of which we find four in the localization region, but we do not find any candidate counterparts among these sources., Comment: 86 pages, 9 figures

Blazars are among the most studied sources in high-energy astrophysics as they form the largest fraction of extragalactic gamma-ray sources and are considered prime candidates for being the counterparts of high-energy astrophysical neutrinos. Their reliable identification amid the many faint radio sources is a crucial step for multi-messenger counterpart associations. As the astronomical community prepares for the coming of a number of new facilities able to survey the non-thermal sky at unprecedented depths, from radio to gamma-rays, machine learning techniques for fast and reliable source identification are ever more relevant. The purpose of this work was to develop a deep learning architecture to identify blazar within a population of AGN based solely on non-contemporaneous spectral energy distribution information, collected from publicly available multi-frequency catalogues. This study uses an unprecedented amount of data, with SEDs for $\approx 14,000$ sources collected with the Open Universe VOU-Blazars tool. It uses a convolutional long-short term memory neural network purposefully built for the problem of SED classification, which we describe in detail and validate. The network was able to distinguish blazars from other types of AGNs to a satisfying degree (achieving a ROC area under curve of $0.98$), even when trained on a reduced subset of the whole sample. This initial study does not attempt to classify blazars among their different sub-classes, or quantify the likelihood of any multi-frequency or multi-messenger association, but is presented as a step towards these more practically-oriented applications., Comment: 13 pages, 17 figures, updated with accepted version

The non-homogeneity in the critical temperature $T_{c}$ of an Microwave Kinetic Inductance Detector (MKID) could be caused by non-uniformity in the deposition process of the thin superconducting film. This produces low percent yield and frequency collision in the readout of the MKIDs. Here, we show the homogeneity that offers Atomic Layer Deposition (ALD). We report an improvement of up to a factor of 50 in the fractional variation of the $T_{c}$ for TiN MKIDs fabricated with Atomic Layer Deposition in comparison with MKIDs fabricated with sputtering. We measured the critical temperature of 48 resonators. We extracted the $T_{c}$ of the MKIDs by fitting the fractional resonance frequency to the complex conductivity of their resonators. We observed uniformity on the critical temperature for MKIDs belonging to the same fabrication process, with a maximum change in the $T_{c}$ of 60 mK for MKIDs fabricated on different wafers.

Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged), Comment: 22 pages, 16 figures, 4 tables, accepted version for A&A

Recently it has been claimed that the Wheeler-DeWitt quantization of gravity is unable to avoid cosmological singularities. However, in order to make this assertion, one must specify the underlying interpretation of quantum mechanics which has been adopted. For instance, several nonsingular models were obtained in Wheeler-DeWitt quantum cosmology in the framework of the de Broglie-Bohm quantum theory. Conversely, there are specific situations where the singularity cannot be avoided in the framework of the Consistent Histories approach to quantum mechanics. In these specific situations, the matter content is described by a scalar field, and the Wheeler-DeWitt equation looks-like a Klein-Gordon equation. The aim of this work is to study the Wheeler-DeWitt quantization of cosmological models where the matter content is described by an hydrodynamical perfect fluid, where the Wheeler-DeWitt equation reduces to a genuine Schr\"odinger equation. In this case, it is shown that the conclusions of the Consistent Histories and the de Broglie-Bohm approaches coincide in the quantum cosmological models where the curvature of the spatial sections is not positive definite, namely, that the cosmological singularities are eliminated. In the case of positive spatial curvature, the family of histories is no longer consistent, and no conclusion can be given in this framework., Comment: 11 pages, 4 figures

We describe the Mediatrix filamentation method, an iterative procedure that decomposes image shapes in filaments over their intensity ridgeline along their main direction using perpendicular bisectors. From this decomposition several morphological features can be derived, such as the length along the main direction and thickness of the object and, for curved objects, estimates of its center of curvature and curvature radius. As an example, we apply this technique to arc-shaped objects: simulated gravitational arcs.