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The future of time-domain optical astronomy relies on the development of techniques and software capable of handling a rising amount of data and gradually complementing, or replacing if necessary, real observations. Next-generation surveys, like the Large Synoptic Survey Telescope, will open the door to the new era of optical astrophysics, creating, at the same time, a deficiency in spectroscopic data necessary to confirm the nature of each event and to fully recover the parametric space. In this framework, we developed Core collApse Supernovae parameTers estimatOR (CASTOR), a novel software for data analysis. CASTOR combines Gaussian process and other machine-learning techniques to build time-series templates of synthetic spectra and to estimate parameters of core-collapse supernovae (CCSNe) for which only multiband photometry is available. Techniques to build templates are fully data driven and non-parametric through empirical and robust models, and rely on the direct comparison with a training set of 111 CCSNe from the literature. Furthermore, CASTOR employees the real photometric data and the reconstructed synthetic spectra of an event to estimate parameters that belong to the supernova ejecta, to the stellar progenitor and to the event itself, in a rapid and user-friendly framework. In this work, we provide a demonstration of how CASTOR works, studying available data from SN 2015ap and comparing our results with those available in literature. [ABSTRACT FROM AUTHOR]

The article discusses the 70th anniversary celebrations of CERN, a European research center for particle physics. The center has made significant scientific discoveries and technological innovations over the years. The anniversary program includes various events and activities to highlight these achievements. The article also mentions the installation of the FAIR accelerator machine and the development of compact power plants for green hydrogen production. Additionally, the article discusses three different research projects in the fields of energy storage systems, quantum photonics, and astronomical measurements. These projects are conducted by various research institutions and funded by different organizations. The European Commission and the Swiss State Secretariat for Education, Research, and Innovation are funding a project called RF 2.0, which aims to reduce the energy consumption of particle accelerators. The project takes a comprehensive approach to reducing energy consumption and aims to operate accelerators with renewable energy and minimal environmental impact. The research findings will also benefit other energy-intensive sectors such as healthcare and industry. [Extracted from the article]

Accurate classification of celestial objects is essential for advancing our understanding of the universe. MargNet is a recently developed deep learning-based classifier applied to the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) dataset to segregate stars, quasars, and compact galaxies using photometric data. MargNet utilizes a stacked architecture, combining a Convolutional Neural Network (CNN) for image modelling and an Artificial Neural Network (ANN) for modelling photometric parameters. Notably, MargNet focuses exclusively on compact galaxies and outperforms other methods in classifying compact galaxies from stars and quasars, even at fainter magnitudes. In this study, we propose enhancing MargNet's performance by incorporating attention mechanisms and Vision Transformer (ViT)-based models for processing image data. The attention mechanism allows the model to focus on relevant features and capture intricate patterns within images, effectively distinguishing between different classes of celestial objects. Additionally, we leverage ViTs, a transformer-based deep learning architecture renowned for exceptional performance in image classification tasks. We enhance the model's understanding of complex astronomical images by utilizing ViT's ability to capture global dependencies and contextual information. Our approach uses a curated dataset comprising 240,000 compact and 150,000 faint objects. The models learn classification directly from the data, minimizing human intervention. Furthermore, we explore ViT as a hybrid architecture that uses photometric features and images together as input to predict astronomical objects. Our results demonstrate that the proposed attention mechanism augmented CNN in MargNet marginally outperforms the traditional MargNet and the proposed ViT-based MargNet models. Additionally, the ViT-based hybrid model emerges as the most lightweight and easy-to-train model with classification accuracy similar to that of the best-performing attention-enhanced MargNet. This advancement in deep learning will contribute to greater success in identifying objects in upcoming surveys like the Vera C. Rubin Large Synoptic Survey Telescope. [ABSTRACT FROM AUTHOR]

Weak gravitational lensing induces flux-dependent fluctuations in the observed galaxy number density distribution. This cosmic magnification (magnification bias) effect in principle enables lensing reconstruction alternative to cosmic shear and cosmic microwave background lensing. However, the intrinsic galaxy clustering, which otherwise overwhelms the signal, has hindered its application. Through a scaling relation found by principal component analysis of the galaxy clustering in multiband photometry space, we design a minimum variance linear estimator to suppress the intrinsic galaxy clustering and to reconstruct the lensing convergence map. In combination of the CosmoDC2 galaxy mock and the CosmicGrowth simulation, we test this proposal for an LSST (Large Synoptic Survey Telescope)-like galaxy survey with ugrizY photometry bands. (1) The scaling relation holds excellently at multipole ℓ < 103, and remains reasonably well to ℓ ∼ 3000. (2) The linear estimator efficiently suppresses the galaxy intrinsic clustering, by a factor of ∼102. (3) For galaxies in the photo- z range 0.8 < z κ < 1.2, the reconstructed convergence map is cosmic variance limited per ℓ mode at ℓ < 102, and shot noise limited at |$\ell \gtrsim 200$|⁠. (4) Its cross-correlation with cosmic shear of galaxies can achieve |$\mathrm{ S/N}\gtrsim 200$|⁠. When the source redshift of cosmic shear galaxies z γ < z κ, the systematic error is negligible at all investigated scales (ℓ < 3000). When z γ ≥ z κ, the systematic error caused by the residual intrinsic galaxy clustering becomes non-negligible. We discuss possible mitigation of the residual intrinsic galaxy clustering required for accurate measurement at ℓ > 103. This work further demonstrates the potential of lensing measurement through cosmic magnification to enhance the weak lensing cosmology. [ABSTRACT FROM AUTHOR]

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid , and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research. [ABSTRACT FROM AUTHOR]

The National Science Foundation's (NSF) decision to rename the Large Synoptic Survey Telescope (LSST) to the Vera C. Rubin Observatory in 2020 made it the first U.S. observatory to be named after a female astronomer. While this move fosters a heightened awareness of women's struggles for advancement in the sciences, it also indicates a need for additional historical studies of women in these fields. Our research seeks to create a new narrative for the historical experiences of women in astronomy and astrophysics through the poetry of second-wave feminist Adrienne Rich, author of Diving into the Wreck and Of Woman Born. Rich's 1968 poem "Planetarium" celebrates the career of Caroline Herschel, while also hinting at the more recent accomplishments of women astronomers like Dame Jocelyn Bell Burnell. Literary historiography is a theoretical methodology for understanding historical subjects like Herschel, through their representations in literary works. Despite the intimate format, Rich's retelling does not focus on Herschel's personal relationships but on her professional pursuits. Rich uses astronomy as a metaphor for her and other women scientists' experiences as they struggle to tune out the noise of obligations, expectations, and invisible labour, and strive to locate the signal representing individual motivation and discovery. This paper examines the professional methodologies of women in astronomy from the lens of feminist literary historiography to support science educators and policymakers in adopting gender inclusive practices. Using Rich's poem "Planetarium," alongside the scientific processes involved in astronomical discovery, this work explores the ways in which Rich's poem reimagines astronomers like Caroline Herschel, Jocelyn Bell Burnell, and Vera Rubin, and how the piece conjures from their lives a new narrative for women in pursuit of their own vocations. The exploration of the intertwining nature of history and culture allows for an analysis of how the professional methodologies of women in astronomy relate more profoundly than differing historical periods would suggest and provides additional insight into the unique challenges currently facing women of colour in these same scientific areas. This paper seeks to contribute a new perspective in space and science communication discussions and help mitigate the dissemination of gender exclusivity in research and education. • Uses astronomy as a metaphor for women scientists' experiences. • Examines professional methodologies of women in astronomy. • Applies feminist historiography to the lives of women in astronomy. • Attempts to mitigate the dissemination of gender exclusivity in research and education. [ABSTRACT FROM AUTHOR]

Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined types. As this challenge focuses on developing a predictive model that is robust to classifying unseen data, those previous attempts similarly encounter the lack of discriminate features, since distribution of training and actual test datasets are largely different. As a result, well-known classification algorithms prove to be sub-optimal, while more complicated feature extraction techniques may help to slightly boost the predictive performance. Given such a burden, this research is set to explore an unsupervised alternative to the difficult quest, where common classifiers fail to reach the 50% accuracy mark. A clustering technique is exploited to transform the space of training data, from which a more accurate classifier can be built. In addition to a single clustering framework that provides a comparable accuracy to the front runners of supervised learning, a multiple-clustering alternative is also introduced with improved performance. In fact, it is able to yield a higher accuracy rate of 58.32% from 51.36% that is obtained using a simple clustering. For this difficult problem, it is rather good considering for those achieved by well-known models like support vector machine (SVM) with 51.80% and Naïve Bayes (NB) with only 2.92%. [ABSTRACT FROM AUTHOR]

We compare the peak optical luminosity with the orbital period for a sample of 22 stellar-mass black hole candidates with good measurements of both quantities. We find that the peak absolute magnitude for the outbursts follows a linear relation with M V ,  peak  = 3.48(± 0.85) − 3.89(± 0.91) log Porb , which corresponds to a |$L_V \propto P_{orb}^{1.56\pm 0.36}$| power-law relation. Excluding V4641 Sgr which is a strong outlier and not likely to have outbursts produced by the standard disc instability model, in addition to BW Cir and V821 Ara – both of which have highly uncertain distances; the new correlation for the 19 sources is found to be M V ,  peak  = 3.01 (± 0.93) − 3.21 (± 1.04) log Porb , which corresponds to |$L_V \propto P_{orb}^{1.28 \pm 0.42}$|⁠. This is an analogous relationship to the 'Warner relation' between orbital period and peak luminosity found for cataclysmic variables. We discuss the implications of these results for finding black hole X-ray binaries in other galaxies and in our own Galaxy with the Large Synoptic Survey Telescope and other future large time domain surveys. [ABSTRACT FROM AUTHOR]

The sample of time-delay gravitational lenses appropriate for studying the geometry of the Universe continues to grow as dedicated campaigns, such as the Dark Energy Survey, the (Very Large Telescope) VLT Survey Telescope (VST) ATLAS survey, and the Large Synoptic Survey Telescope, complete their census of high-redshift sources. This catalogue now includes hundreds of strong lensing systems, at least 31 of which have reasonably accurate time-delay measurements. In this paper, we use them to compare the predictions of two competing Friedmann–Lemaître–Robertson–Walker models: flat Lambda cold dark matter (ΛCDM), characterized by two adjustable parameters (H 0 and Ωm), and the R h = ct universe (with H 0 as the single free variable). Over the past decade, the latter has accounted for the data better than the standard model, most recently the emergence of well-formed galaxies discovered by JWST at cosmic dawn. Here, we show that the current sample of time-delay lenses favours R h = ct with a likelihood of ∼ |$84{{\ \rm per\ cent}}$| versus ∼ |$16{{\ \rm per\ cent}}$| for the standard model. This level of accuracy will greatly improve as the ongoing surveys uncover many thousands of additional lens systems over the next several years. [ABSTRACT FROM AUTHOR]

Giant flares are the brightest and rarest outbursts from magnetars, with isotropic energies of 1044–1046 erg. GRB 200415A is suggested as a magnetar giant flare from NGC 253. Fermi Large Area Telescope detected the GeV afterglow emission from the flare, which is the first time that the GeV emission is detected from a giant flare. In this paper, we study the multiwavelength afterglow radiation of electrons accelerated by the forward and reverse shocks, produced via interactions between the ejecta and the circumburst medium in the afterglow phase of GRB 200415A. We found that in the GeV band, the forward shock emission is usually higher than the reverse shock emission, and can explain observations well, while in the optical and infrared bands, contributions from the forward and reverse shocks can be comparable for reasonable parameter sets, and the brightness of the forward-shock emission can reach 20 mag (AB). We predict that future telescopes such as Wide Field Survey Telescope, Chinese Space Station Telescope, the Large Synoptic Survey Telescope, and James Webb Space Telescope can detect the optical and infrared afterglow emission from giant flares similar to GRB 200415A. In addition, we consider two cases where the ejecta are dominated by protons or electron–positron pairs. We find that the reverse shock emission is comparable in these two cases for a hard electron spectrum, while for a soft electron spectrum, the reverse shock emission is much weaker in the pair-dominating case.. [ABSTRACT FROM AUTHOR]

Next-generation surveys will provide photometric and spectroscopic data of millions to billions of galaxies with unprecedented precision. This offers a unique chance to improve our understanding of the galaxy evolution and the unresolved nature of dark matter (DM). At galaxy scales, the density distribution of DM is strongly affected by feedback processes, which are difficult to fully account for in classical techniques to derive galaxy masses. We explore the capability of supervised machine learning (ML) algorithms to predict the DM content of galaxies from 'luminous' observational-like parameters, using the TNG100 simulation. In particular, we use photometric (magnitudes in different bands), structural (the stellar half-mass radius and three different baryonic masses), and kinematic (1D velocity dispersion and the maximum rotation velocity) parameters to predict the total DM mass, DM half-mass radius, and DM mass inside one and two stellar half-mass radii. We adopt the coefficient of determination, R 2, as a metric to evaluate the accuracy of these predictions. We find that using all observational quantities together (photometry, structural, and kinematics), we reach high accuracy for all DM quantities (up to R 2 ∼ 0.98). This first test shows that ML tools are promising to predict the DM in real galaxies. The next steps will be to implement the observational realism of the training sets, by closely selecting samples that accurately reproduce the typical observed 'luminous' scaling relations. The so-trained pipelines will be suitable for real galaxy data collected from Rubin/Large Synoptic Survey Telescope (LSST), Euclid, Chinese Survey Space Telescope (CSST), 4-metre Multi-Object Spectrograph Telescope (4MOST), Dark Energy Spectroscopic Instrument (DESI), to derive e.g. the properties of their central DM fractions. [ABSTRACT FROM AUTHOR]

As we enter the era of large-scale imaging surveys with the upcoming telescopes such as the Large Synoptic Survey Telescope (LSST) and the Square Kilometre Array (SKA), it is envisaged that the number of known strong gravitational lensing systems will increase dramatically. However, these events are still very rare and require the efficient processing of millions of images. In order to tackle this image processing problem, we present machine learning techniques and apply them to the gravitational lens finding challenge. The convolutional neural networks (CNNs) presented here have been reimplemented within a new, modular, and extendable framework, Lens EXtrActor CaTania University of Malta (LEXACTUM). We report an area under the curve (AUC) of 0.9343 and 0.9870, and an execution time of 0.0061 and 0.0594 s per image, for the Space and Ground data sets, respectively, showing that the results obtained by CNNs are very competitive with conventional methods (such as visual inspection and arc finders) for detecting gravitational lenses. [ABSTRACT FROM AUTHOR]

Focusing on both small separations and baryonic acoustic oscillation scales, the cosmic evolution of the clustering properties of peak, void, wall, and filament-type critical points is measured using two-point correlation functions in ΛCDM dark matter simulations as a function of their relative rarity. A qualitative comparison to the corresponding theory for Gaussian random fields allows us to understand the following observed features: (i) the appearance of an exclusion zone at small separation, whose size depends both on rarity and signature (i.e. the number of negative eigenvalues) of the critical points involved; (ii) the amplification of the baryonic acoustic oscillation bump with rarity and its reversal for cross-correlations involving negatively biased critical points; (iii) the orientation-dependent small-separation divergence of the cross-correlations of peaks and filaments (respectively voids and walls) that reflects the relative loci of such points in the filament's (respectively wall's) eigenframe. The (cross-) correlations involving the most non-linear critical points (peaks, voids) display significant variation with redshift, while those involving less non-linear critical points seem mostly insensitive to redshift evolution, which should prove advantageous to model. The ratios of distances to the maxima of the peak-to-wall and peak-to-void over that of the peak-to-filament cross-correlation are |${\sim} \sqrt{2}$| and |${\sim} \sqrt{3}$|⁠ , respectively, which could be interpreted as the cosmic crystal being on average close to a cubic lattice. The insensitivity to redshift evolution suggests that the absolute and relative clustering of critical points could become a topologically robust alternative to standard clustering techniques when analysing upcoming surveys such as Euclid or Large Synoptic Survey Telescope (LSST). [ABSTRACT FROM AUTHOR]

With current and upcoming experiments such as the Wide Field Infrared Survey Telescope, Euclid, and Large Synoptic Survey Telescope, we can observe up to billions of galaxies. While such surveys cannot obtain spectra for all observed galaxies, they produce galaxy magnitudes in color filters. This data set behaves like a high-dimensional nonlinear surface, an excellent target for machine learning. In this work, we use a lightcone of semianalytic galaxies tuned to match Cosmic Assembly Near-infrared Deep Legacy Survey (CANDELS) observations from Lu et al. to train a set of neural networks on a set of galaxy physical properties. We add realistic photometric noise and use trained neural networks to predict stellar masses and average star formation rates (SFRs) on real CANDELS galaxies, comparing our predictions to SED-fitting results. On semianalytic galaxies, we are nearly competitive with template-fitting methods, with biases of 0.01 dex for stellar mass, 0.09 dex for SFR, and 0.04 dex for metallicity. For the observed CANDELS data, our results are consistent with template fits on the same data at 0.15 dex bias in and 0.61 dex bias in the SFR. Some of the bias is driven by SED-fitting limitations, rather than limitations on the training set, and some is intrinsic to the neural network method. Further errors are likely caused by differences in noise properties between the semianalytic catalogs and data. Our results show that galaxy physical properties can in principle be measured with neural networks at a competitive degree of accuracy and precision to template-fitting methods. [ABSTRACT FROM AUTHOR]

eROSITA , launched on 2019 July 13, will be completing the first all-sky survey in the soft and medium X-ray band in nearly three decades. This 4-yr survey, finishing in late 2023, will present a rich legacy for the entire astrophysics community and complement upcoming multiwavelength surveys (with, e.g. the Large Synoptic Survey Telescope and the Dark Energy Survey). Besides the major scientific aim to study active galactic nuclei (AGNs) and galaxy clusters, eROSITA will contribute significantly to X-ray studies of normal (i.e. not AGN) galaxies. Starting from multiwavelength catalogues, we measure star formation rates and stellar masses for 60 212 galaxies constrained to distances of 50–200 Mpc. We chose this distance range to focus on the relatively unexplored volume outside the local Universe, where galaxies will be largely spatially unresolved and probe a range of X-ray luminosities that overlap with the low luminosity and/or highly obscured AGN population. We use the most recent X-ray scaling relations as well as the on-orbit eROSITA instrument performance to predict the X-ray emission from XRBs and diffuse hot gas and to perform both an analytic prediction and an end-to-end simulation using the mission simulation software, sixte. We consider potential contributions from hidden AGN and comment on the impact of normal galaxies on the measurement of the faint end of the AGN luminosity function. We predict that the eROSITA 4-yr survey, will detect ≳15 000 galaxies (3σ significance) at 50–200 Mpc, which is ∼100 × more normal galaxies than detected in any X-ray survey to date. [ABSTRACT FROM AUTHOR]

There is a persistent H0-tension, now at more than ≳4σ level, between the local distance ladder value and the Planck cosmic microwave background measurement, in the context of flat Lambda-cold-dark-matter (ΛCDM) model. We reconstruct H(z) in a cosmological-model-independent way using three low-redshift distance probes including the latest data from baryon acoustic oscillation, supernova Ia (SN Ia) and gravitational lensing time-delay (GLTD) observations. We adopt general parametric models of H(z) and assume a Gaussian sound horizon at drag epoch, , from Planck measurement. The reconstructed Hubble constant H0,rec using Pantheon SN Ia and Baryon Acoustic Oscillations (BAO) data are consistent with the Planck flat ΛCDM value. When including the GLTD data, H0,rec increases mildly, yet remains discrepant with the local measurement at ∼2.2σ level. With our reconstructions being blind to the dark sectors at low redshift, we reaffirm the earlier claims that the Hubble tension is not likely to be solved by modifying the energy budget of the low-redshift universe. We further forecast the constraining ability of future realistic mock BAO data from Dark Energy Spectroscopic Instrument and GLTD data from Large Synoptic Survey Telescope, combining which, we anticipate that the uncertainty of H0,rec would be improved by ∼27%, reaching uncertainty level. [ABSTRACT FROM AUTHOR]

High-redshift blazars are valuable tools to study the early universe. So far, only a handful of γ-ray blazars have been found at redshifts above 3. Gamma-ray signals are detected in the direction of PMN J2219–2719 (z = 3.63) and PMN J2321–0827 (z = 3.16) by analyzing the 10 yr Fermi-LAT Pass 8 data. PMN J2219–2719 is not distinguished from the background in the global analysis. During the 5 month epoch, the TS value is 47.8 and the flux is more than 10 times that of the 10 yr averaged flux. In addition, the angular distance between the γ-ray position and the radio position of PMN J2219–2719 is only 0.°04. Moreover, the long timescale γ-ray and infrared light curves are very similar, which supports the association between the γ-ray source and PMN J2219–2719. The global analysis of PMN J2321–0827 suggest a new γ-ray source; during the flare phase, the TS value is 61.4 and the γ-ray flux increased significantly. The association probability suggests that PMN J2321–0827 may be the counterpart of the new γ-ray source. In the future, the number of high-redshift γ-ray sources will increase by combining Fermi-LAT and the upcoming Large Synoptic Survey Telescope. [ABSTRACT FROM AUTHOR]

We report new high-quality galaxy-scale strong lens candidates found in the Kilo-Degree Survey data release 4 using machine learning. We have developed a new convolutional neural network (CNN) classifier to search for gravitational arcs, following the prescription by Petrillo et al. and using only r-band images. We have applied the CNN to two "predictive samples": a luminous red galaxy (LRG) and a "bright galaxy" (BG) sample (r < 21). We have found 286 new high-probability candidates, 133 from the LRG sample and 153 from the BG sample. We have ranked these candidates based on a value that combines the CNN likelihood of being a lens and the human score resulting from visual inspection (P-value), and here we present the highest 82 ranked candidates with P-values ≥0.5. All of these high-quality candidates have obvious arc or pointlike features around the central red defector. Moreover, we define the best 26 objects, all with P-values ≥0.7, as a "golden sample" of candidates. This sample is expected to contain very few false positives; thus, it is suitable for follow-up observations. The new lens candidates come partially from the more extended footprint adopted here with respect to the previous analyses and partially from a larger predictive sample (also including the BG sample). These results show that machine-learning tools are very promising for finding strong lenses in large surveys and more candidates can be found by enlarging the predictive samples beyond the standard assumption of LRGs. In the future, we plan to apply our CNN to the data from next-generation surveys such as the Large Synoptic Survey Telescope, Euclid, and the Chinese Space Station Optical Survey. [ABSTRACT FROM AUTHOR]

It is unclear how galaxies and their central supermassive black holes coevolve across cosmic time, especially for the nonlocal universe (z ≳ 0.5). The High-z Universe probed via Lensing by QSOs project proposes to utilize quasi-stellar object (QSO) host galaxies acting as gravitational lenses (QSO lenses) to investigate this topic. This paper focuses on the feasibility of this project, that is, whether sufficiently large numbers of QSO lenses are expected to be found in various concurrent and future imaging surveys. We find that ∼440 QSO lenses will reside in the Hyper Suprime-Cam Wide survey (HSC/Wide), which is expected to be the most prolific concurrent survey, with this number being boosted by one to two orders of magnitude (to ∼10,000) with upcoming surveys such as that conducted with the Large Synoptic Survey Telescope (LSST). We discuss several methods of how to study the redshift evolution of the relation, which is a standout illustration of the coevolution. In addition, we demonstrate how the intimacy of lensed images to the bright deflector QSO for most systems will affect the detectability of QSO lenses. We estimate that only ∼82 and 900 will be detectable for HSC/Wide and LSST, respectively; the decrease is significant yet still yields an acceptable sample for the main objective. This decrease will be less of a problem for space-based imaging surveys, for their small point-spread function FWHMs will allow detections of lensed images lying relatively close to the deflector QSO, and thus unveil the less massive yet more numerous QSO hosts. [ABSTRACT FROM AUTHOR]

As part of the effort to meet the needs of the Large Synoptic Survey Telescope Dark Energy Science Collaboration (LSST DESC) for accurate, realistically complex mock galaxy catalogues, we have developed galsampler , an open-source python package that assists in generating large volumes of synthetic cosmological data. The key idea behind galsampler is to recast hydrodynamical simulations and semi-analytic models as physically motivated galaxy libraries. galsampler populates a new, larger volume halo catalogue with galaxies drawn from the baseline library; by using weighted sampling guided by empirical modelling techniques, galsampler inherits statistical accuracy from the empirical model and physically motivated complexity from the baseline library. We have recently used galsampler to produce the cosmoDC2 extragalactic catalogue made for the LSST DESC Data Challenge 2. Using cosmoDC2 as a guiding example, we outline how galsampler can continue to support ongoing and near-future galaxy surveys such as the Dark Energy Survey, the Dark Energy Spectroscopic Instrument, WFIRST , and Euclid. [ABSTRACT FROM AUTHOR]

We present ZTF18abvkwla (the "Koala"), a fast blue optical transient discovered in the Zwicky Transient Facility (ZTF) One-Day Cadence (1DC) Survey. ZTF18abvkwla has a number of features in common with the groundbreaking transient AT 2018cow: blue colors at peak (mag), a short rise time from half-max of under two days, a decay time to half-max of only three days, a high optical luminosity (mag), a hot (≳40,000 K) featureless spectrum at peak light, and a luminous radio counterpart. At late times (), the radio luminosity of ZTF18abvkwla (at 10 , observer-frame) is most similar to that of long-duration gamma-ray bursts (GRBs). The host galaxy is a dwarf starburst galaxy (,) that is moderately metal-enriched (), similar to the hosts of GRBs and superluminous supernovae. As in AT2018cow, the radio and optical emission in ZTF18abvkwla likely arise from two separate components: the radio from fast-moving ejecta () and the optical from shock-interaction with confined dense material (<0.07 M⊙ in). Compiling transients in the literature with and mag, we find that a significant number are engine-powered, and suggest that the high peak optical luminosity is directly related to the presence of this engine. From 18 months of the 1DC survey, we find that transients in this rise-luminosity phase space are at least two to three orders of magnitude less common than CC SNe. Finally, we discuss strategies for identifying such events with future facilities like the Large Synoptic Survey Telescope, as well as prospects for detecting accompanying X-ray and radio emission. [ABSTRACT FROM AUTHOR]

Mergers of neutron stars (NS) and white dwarfs (WD) may give rise to observable explosive transient events. We use 3D hydrodynamical (smoothed particle hydrodynamics – SPH) simulations and 2D hydrodynamical–thermonuclear simulations (using the flash adaptive mesh refinement code) to model the disruption of CO-WDs by NSs, which produce faint transient events. We post-process the simulations using a large nuclear network and make use of the SuperNu radiation transfer code to predict the observational signatures and detailed properties of these transients. We calculate the light curves and spectra for five models of NS–CO-WD mergers. The small yields of 56Ni (few |$\times 10^{-3}\, {\rm M_{\odot }}$|⁠) result in faint, rapidly evolving reddened transients (RRTs) with B (R) peak magnitudes of at most ∼−12 (−13) to ∼−13 (−15), much shorter and fainter than both regular and faint/peculiar Type Ia supernovae. These transients are likely to be accompanied by several months long, 1–2 mag dimmer red/infrared afterglows. We show that the spectra of RRTs share some similarities with rapidly evolving transients such as SN 2010X, although RRTs are significantly fainter, especially in the I / R bands, and show far stronger Si lines. We estimate that the upcoming Large Synoptic Survey Telescope could detect RRTs at a rate of up to ∼10–70 yr−1 through observations in the R / I bands. The qualitative agreement between the SPH and flash approaches supports the earlier hydrodynamical studies of these systems. [ABSTRACT FROM AUTHOR]

Context. Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature. Aims. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements. Methods. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model. Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade. Conclusions. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope. [ABSTRACT FROM AUTHOR]

A class of optical transients known as Luminous Red Novae (LRNe) have recently been associated with mass ejections from binary stars undergoing common-envelope evolution. We use the population synthesis code COMPAS to explore the impact of a range of assumptions about the physics of common-envelope evolution on the properties of LRNe. In particular, we investigate the influence of various models for the energetics of LRNe on the expected event rate and light curve characteristics, and compare with the existing sample. We find that the Galactic rate of LRNe is ∼0.2 yr−1, in agreement with the observed rate. In our models, the luminosity function of Galactic LRNe covers multiple decades in luminosity and is dominated by signals from stellar mergers, consistent with observational constraints from iPTF and the Galactic sample of LRNe. We discuss how observations of the brightest LRNe may provide indirect evidence for the existence of massive (>40 M⊙) red supergiants. Such LRNe could be markers along the evolutionary pathway leading to the formation of double compact objects. We make predictions for the population of LRNe observable in future transient surveys with the Large Synoptic Survey Telescope and the Zwicky Transient Facility. In all plausible circumstances, we predict a selection-limited observable population dominated by bright, long-duration events caused by common envelope ejections. We show that the Large Synoptic Survey Telescope will observe 20–750 LRNe per year, quickly constraining the luminosity function of LRNe and probing the physics of common-envelope events. [ABSTRACT FROM AUTHOR]

In the last years, automatic classification of variable stars has received substantial attention. Using machine learning techniques for this task has proven to be quite useful. Typically, machine learning classifiers used for this task require to have a fixed training set, and the training process is performed offline. Upcoming surveys such as the Large Synoptic Survey Telescope will generate new observations daily, where an automatic classification system able to create alerts online will be mandatory. A system with those characteristics must be able to update itself incrementally. Unfortunately, after training, most machine learning classifiers do not support the inclusion of new observations in light curves, they need to re-train from scratch. Naively re-training from scratch is not an option in streaming settings, mainly because of the expensive pre-processing routines required to obtain a vector representation of light curves (features) each time we include new observations. In this work, we propose a streaming probabilistic classification model; it uses a set of newly designed features that work incrementally. With this model, we can have a machine learning classifier that updates itself in real time with new observations. To test our approach, we simulate a streaming scenario with light curves from Convention, Rotation and planetary Transits (CoRoT), Orbital Gravitational Lensing Experiment (OGLE), and Massive Compact Halo Object (MACHO) catalogues. Results show that our model achieves high classification performance, staying an order of magnitude faster than traditional classification approaches. [ABSTRACT FROM AUTHOR]

The joint observation of core-collapse supernovae with gamma-ray bursts shows that jets can be launched in the aftermath of stellar core collapse, likely by a newly formed black hole that accretes matter from the star. Such gamma-ray bursts have only been observed accompanying Type Ibc supernovae, indicating a stellar progenitor that lost its hydrogen envelope before collapse. According to recent hypothesis, it is possible that jets are launched in core-collapse events even when the progenitors still retain their hydrogen envelopes; however, such jets are not able to burrow through the star and will be stalled into the interior of the progenitor star before escaping. These jets are called choked jets. High-energy neutrinos produced by such choked jets could escape the stellar envelope and could be observed. Here, we examine how multimessenger searches for high-energy neutrinos and core-collapse supernovae can detect or limit the fraction of stellar collapses that produce jets. We find that a high fraction of jet production is already limited by previous observational campaigns. We explore possibilities with future observations using Large Synoptic Survey Telescope, IceCube, and Km3NET. [ABSTRACT FROM AUTHOR]

A robust and extended characterization of the point spread function (PSF) is crucial to extract the photometric information produced by deep imaging surveys. Here, we present the extended PSFs of the Sloan Digital Sky Survey (SDSS), one of the most productive astronomical surveys of all time. By stacking ∼1000 images of individual stars with different brightness, we obtain the bidimensional SDSS PSFs extending over 8 arcmin in radius for all the SDSS filters (u, g, r, i, z). This new characterization of the SDSS PSFs is near a factor of 10 larger in extension than previous PSFs characterizations of the same survey. We found asymmetries in the shape of the PSFs caused by the drift scanning observing mode. The flux of the PSFs is larger along the drift scanning direction. Finally, we illustrate with an example how the PSF models can be used to remove the scattered light field produced by the brightest stars in the central region of the Coma cluster field. This particular example shows the huge importance of PSFs in the study of the low-surface brightness Universe, especially with the upcoming of ultradeep surveys, such as the Large Synoptic Survey Telescope (LSST). Following a reproducible science philosophy, we make all the PSF models and the scripts used to do the analysis of this paper publicly available (snapshot v0.4-0-gd966ad0). [ABSTRACT FROM AUTHOR]

We propose a new model-independent strategy to calibrate the distance relation in Type Ia supernova (SN) observations and to probe the intrinsic properties of SNe Ia, especially the absolute magnitude M B , basing on strong lensing observations in the upcoming Large Synoptic Survey Telescope (LSST) era. The strongly lensed quasars can provide the Time Delay Distances (TDD) and the Angular Diameter Distances (ADD) to the lens galaxies. These absolute distance measurements can model-independently anchor the SNe Ia at cosmological distances. We simulated 55 high-quality lensing systems with 5 % uncertainties for both TDD and ADD measurements basing on future observation conditions. For the time delay distances and the angular diameter distances as the calibration standards, the calibrated 1 σ uncertainties of M B are approximately 0.24 mag and 0.03 mag, respectively. Besides, we also consider an evolving distance relation, for example, caused by the cosmic opacity. In this case, the 1 σ uncertainties of M B calibrated with TDD and ADD are approximately 0.31 mag and 0.06 mag, respectively. The results show that the ADD method will be a promising tool for calibrating supernovae. [ABSTRACT FROM AUTHOR]

Supernovae (SNe) could be powerful probes of the properties of stars and galaxies at high redshifts in future surveys. Wide fields and longer exposure times are required to offset diminishing star formation rates and lower fluxes to detect useful number of events at high redshift. In principle, the Large Synoptic Survey Telescope (LSST) could discover large numbers of early SNe because of its wide fields but only at lower redshifts because of its AB mag limit of ∼24. However, gravitational lensing by galaxy clusters and massive galaxies could boost flux from ancient SNe and allow LSST to detect them at earlier times. Here, we calculate detection rates for lensed SNe at z ∼ 5–7 for LSST. We find that the LSST Wide Fast Deep survey could detect up to 120 lensed Population (Pop) I and II SNe but no lensed Pop III SNe. Deep-drilling programs in 10 deg2 fields could detect Pop I and II core-collapse SNe at AB magnitudes of 27–28 and 26, respectively. [ABSTRACT FROM AUTHOR]

The cosmic infrared background (CIB) provides a fundamental observational constraint on the star formation history of galaxies over cosmic history. We estimate the contribution to the CIB from catalogued galaxies in the COSMOS field by using a novel map fitting technique on the Herschel SPIRE maps. Prior galaxy positions are obtained using detections over a large range in wavelengths in the K s–3 GHz range. Our method simultaneously fits the galaxies, the system foreground, and the leakage of flux from galaxies located in masked areas and corrects for an 'overfitting' effect not previously accounted for in stacking methods. We explore the contribution to the CIB as a function of galaxy survey wavelength and depth. We find high contributions to the CIB with the deep r (m AB ≤ 26.5), K s (m AB ≤ 24.0), and 3.6  |$\mathrm{ \mu}$| m (m AB ≤ 25.5) catalogues. We combine these three deep catalogues and find a total CIB contributions of 10.5 ± 1.6, 6.7 ± 1.5, and 3.1 ± 0.7 nWm−2 sr−1 at 250, 350, and 500  |$\mathrm{ \mu}$| m, respectively. Our CIB estimates are consistent with recent phenomenological models, prior based SPIRE number counts and with (though more precise than) the diffuse total measured by FIRAS. Our results raise the interesting prospect that the CIB contribution at |$\lambda \le 500\, \mu$| m from known galaxies has converged. Future large-area surveys like those with the Large Synoptic Survey Telescope are therefore likely to resolve a substantial fraction of the population responsible for the CIB at 250  |$\mathrm{ \mu}$| m ≤ λ ≤ 500  |$\mathrm{ \mu}$| m. [ABSTRACT FROM AUTHOR]

We introduce Deep-CEE (Deep Learning for Galaxy C luster E xtraction and E valuation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for photometric catalogues. This technique is complementary to traditional methods and could also be used in combination with them to confirm existing galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localize and classify galaxy clusters from other astronomical objects in Sloan Digital Sky Survey imaging. As there is an abundance of labelled data for galaxy clusters from previous classifications in publicly available catalogues, we do not need to rely on simulated data. This means we keep our training data as realistic as possible, which is advantageous when training a deep learning algorithm. Ultimately, we will apply our model to surveys such as Large Synoptic Survey Telescope and Euclid to probe wider and deeper into unexplored regions of the Universe. This will produce large samples of both high-redshift and low-mass clusters, which can be utilized to constrain both environment-driven galaxy evolution and cosmology. [ABSTRACT FROM AUTHOR]

We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn , is publicly available at https://github.com/burke86/astro_rcnn. [ABSTRACT FROM AUTHOR]

Future cosmological galaxy surveys such as the Large Synoptic Survey Telescope (LSST) will photometrically observe very large numbers of galaxies. Without spectroscopy, the redshifts required for the analysis of these data will need to be inferred using photometric redshift techniques that are scalable to large sample sizes. The high number density of sources will also mean that around half are blended. We present a Bayesian photometric redshift method for blended sources that uses Gaussian mixture models to learn the joint flux–redshift distribution from a set of unblended training galaxies, and Bayesian model comparison to infer the number of galaxies comprising a blended source. The use of Gaussian mixture models renders both of these applications computationally efficient and therefore suitable for upcoming galaxy surveys. [ABSTRACT FROM AUTHOR]

Summary: Coating technology on the summit – Mirror Layers for the Large Synoptic Survey Telescope in Chile This article focuses on mirror coating for the Large Synoptic Survey Telescope, which is being installed in Chile. The LSST coating system consists of a coating chamber for the deposition of highly reflective optical coatings and a cleaning and stripping station for the M1M3 and M2 mirrors. With the help of the sputtering process, bare and protected silver/aluminum coating recipes can be deposited. The cleaning and stripping station consists of a rotating washing/drying boom, perimeter platforms, and an effluent handling system within the M1M3 mirror cell. The article describes the status of the assembly work on the coating system. Furthermore, it provides information about the progress of factory testing, a about the basic design features and reflective/coating requirements and the coating results. [ABSTRACT FROM AUTHOR]

The Canada–France–Hawaii Telescope (CFHT) Large Area U -band Deep Survey (CLAUDS) uses data taken with the MegaCam mosaic imager on CFHT to produce images of 18.60 deg |$^2$| with median seeing of FWHM = 0.92 arcsec and to a median depth of |$U = 27.1$| AB (5 |$\sigma$| in 2 arcsec apertures), with selected areas that total 1.36 deg |$^2$| reaching a median depth of |$U=27.7$|  AB. These are the deepest U -band images assembled to date over this large an area. These data are located in four fields also imaged to comparably faint levels in |$grizy$| and several narrowband filters as part of the Hyper Suprime–Cam (HSC) Subaru Strategic Program (HSC-SSP). These CFHT and Subaru data sets will remain unmatched in their combination of area and depth until the advent of the Large Synoptic Survey Telescope. This paper provides an overview of the scientific motivation for CLAUDS and gives details of the observing strategy, observations, data reduction, and data merging with the HSC-SSP. Three early applications of these deep data are used to illustrate the potential of the data set: deep U -band galaxy number counts, z |$\sim$| 3 Lyman break galaxy selection, and photometric redshifts improved by adding CLAUDS U to the Subaru HSC |$grizy$| photometry. [ABSTRACT FROM AUTHOR]

The upcoming Large Synoptic Survey Telescope (LSST) will detect many strongly lensed Type Ia supernovae (LSNe Ia) for time-delay cosmography. This will provide an independent and direct way for measuring the Hubble constant H0, which is necessary to address the current 4.4σ tension in H0 between the local distance ladder and the early Universe measurements. We present a detailed analysis of different observing strategies (also referred to as cadence strategy) for the LSST, and quantify their impact on time-delay measurement between multiple images of LSNe Ia. For this, we simulated observations by using mock LSNe Ia for which we produced mock-LSST light curves that account for microlensing. Furthermore, we used the free-knot splines estimator from the software PyCS to measure the time delay from the simulated observations. We find that using only LSST data for time-delay cosmography is not ideal. Instead, we advocate using LSST as a discovery machine for LSNe Ia, enabling time delay measurements from follow-up observations from other instruments in order to increase the number of systems by a factor of 2–16 depending on the observing strategy. Furthermore, we find that LSST observing strategies, which provide a good sampling frequency (the mean inter-night gap is around two days) and high cumulative season length (ten seasons with a season length of around 170 days per season), are favored. Rolling cadences subdivide the survey and focus on different parts in different years; these observing strategies trade the number of seasons for better sampling frequency. In our investigation, this leads to half the number of systems in comparison to the best observing strategy. Therefore rolling cadences are disfavored because the gain from the increased sampling frequency cannot compensate for the shortened cumulative season length. We anticipate that the sample of lensed SNe Ia from our preferred LSST cadence strategies with rapid follow-up observations would yield an independent percent-level constraint on H0. [ABSTRACT FROM AUTHOR]