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We present an observational multiwavelength campaign during 2018–19 for PBC J2333.9–2343, a giant radio galaxy with a bright central core associated to a blazar nucleus, whose structure could be due to a significant jet reorientation. We report flux increases by a factor of two or more on timescales shorter than a month, resembling flaring events. The cross correlation between the NIR and optical bands shows quasi-simultaneous variations arising from the jet. The optical variability properties of PBC J2333.9–2343 are more comparable to a sample of blazars than to non-blazar AGN. The SED of the nucleus shows two peaks, with a derived jet angle of 3 degrees, also typical of a blazar. Therefore, we confirm the presence of a blazar-like core in the center of this galaxy.

A significant challenge in the study of transient astrophysical phenomena is the identification of bogus events, among which human-made satellites and debris in Earth orbit remain major contaminants. Existing pipelines effectively identify satellite trails but can miss more complex signatures, such as collections of satellite glints. In the Rubin Observatory era, the scale of operations will increase tenfold compared to its precursor, the Zwicky Transient Facility (ZTF), requiring improvements in classification purity, data compression for informative alerts, and pipeline speed. We explore the use of the 2D Fast Fourier Transform (FFT) on difference images as a tool to enhance machine learning models for satellite detection. Using the ALeRCE single-stamp classifier as a baseline, we adapt its architecture to incorporate a cutout of the FFT of the difference image alongside the standard ZTF image triplet (science, reference, and difference stamps). We evaluate several stamp sizes and resolutions, focusing on regimes where data compression is critical due to alert size limits and real-time constraints. Incorporating the FFT significantly improves satellite classification, especially in the smallest field-of-view model (16 arcsec), where accuracy increases from 72.02.9% to 87.81.3%. This demonstrates the FFT's value in compressing and capturing extended satellite features. However, the FFT alone does not match the full-context performance of the 63 arcsec (95.91.3%) or multiscale (90.60.8%) models, highlighting the complementary role of spatial context. We show how FFTs can be leveraged to cull satellite and debris signatures from alert streams in current and future time-domain surveys.

This paper introduces a novel method for creating spectral time series, which can be used for generating synthetic light curves for photometric classification but also for applications like K-corrections and bolometric corrections. This approach is particularly valuable in the era of large astronomical surveys, where it can significantly enhance the analysis and understanding of an increasing number of SNe, even in the absence of extensive spectroscopic data. methods: By employing interpolations based on optimal transport theory, starting from a spectroscopic sequence, we derive weighted average spectra with high cadence. The weights incorporate an uncertainty factor for penalizing interpolations between spectra that show significant epoch differences and lead to a poor match between the synthetic and observed photometry. results: Our analysis reveals that even with phase difference of up to 40 days between pairs of spectra, optical transport can generate interpolated spectral time series that closely resemble the original ones. Synthetic photometry extracted from these spectral time series aligns well with observed photometry. The best results are achieved in the V band, with relative residuals of less than 10% for 87% and 84% of the data for type Ia and II, respectively. For the B, g, R and r bands, the relative residuals are between 65% and 87% within the previously mentioned 10% threshold for both classes. The worse results correspond to the i and I bands where, in the case, of SN~Ia the values drop to 53% and 42%, respectively. conclusions: We introduce a new method for constructing spectral time series for individual SNe starting from a sparse spectroscopic sequence, and demonstrate its capability to produce reliable light curves that can be used for photometric classification.

With a growing number of facilities able to monitor the entire sky and produce light curves with a cadence of days, in recent years there has been an increased rate of detection of sources whose variability deviates from standard behavior, revealing a variety of exotic nuclear transients. The aim of the present study is to disentangle the nature of the transient AT 2021hdr, whose optical light curve used to be consistent with a classic Seyfert 1 nucleus, which was also confirmed by its optical spectrum and high-energy properties. From late 2021, AT 2021hdr started to present sudden brightening episodes in the form of oscillating peaks in the Zwicky Transient Facility (ZTF) alert stream, and the same shape is observed in X-rays and UV from Swift data. The oscillations occur every about 60-90 days with amplitudes of around 0.2 mag in the g and r bands. Very Long Baseline Array (VLBA) observations show no radio emission at milliarcseconds scale. It is argued that these findings are inconsistent with a standard tidal disruption event (TDE), a binary supermassive black hole (BSMBH), or a changing-look active galactic nucleus (AGN); neither does this object resemble previous observed AGN flares, and disk or jet instabilities are an unlikely scenario. Here, we propose that the behavior of AT 2021hdr might be due to the tidal disruption of a gas cloud by a BSMBH. In this scenario, we estimate that the putative binary has a separation of about 0.83 mpc and would merge in about 70000 years. This galaxy is located at 9 kpc from a companion galaxy, and in this work we report this merger for the first time. The oscillations are not related to the companion galaxy.

The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light-curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. ATAT consists of two Transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a Balanced Hierarchical Random Forest (BHRF) trained on human-engineered features derived from light curves and metadata. When trained on light curves and metadata, ATAT achieves a macro F1-score of 82.9 +- 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1-score of 79.4 +- 0.1. The use of Transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.

[abridged] Probing the faint end of the number counts at mm wavelengths is important to identify the origin of the extragalactic background light in this regime. Aided by strong gravitational lensing, ALMA observations towards massive galaxy clusters have opened a window to disentangle this origin, allowing to resolve sub-mJy dusty star-forming galaxies. We aim to derive number counts at 1.1 mm down to flux densities fainter than 0.1 mJy, based on ALMA observations towards five Hubble Frontier Fields (FF) galaxy clusters, following a statistical approach to correct for lensing effects. We created a source catalog that includes 29 ALMA 1.1 mm continuum detections down to a 4.5sigma significance. We derived source intrinsic flux densities using public lensing models. We folded the uncertainties in both magnifications and source redshifts into the number counts through Monte Carlo simulations. We derive cumulative number counts over two orders of magnitude down to 0.01 mJy after correction for lensing effects. Cosmic variance estimates are all exceeded by uncertainties in our median combined cumulative counts that come from both our Monte Carlo simulations and Poisson statistics. Our number counts are consistent to 1sigma with most of recent ALMA estimates and galaxy evolution models. However, below 0.1 mJy, they are lower by 0.4 dex compared to two deep ALMA studies but consistent with ASPECS-LP to 1sigma. Importantly, the flattening found for our cumulative counts extends further to 0.01 mJy. Our results bring further support in line of the flattening of the number counts reported previously by us and ASPECS-LP, which has been interpreted by a recent galaxy evolution model as a measurement of the \"knee\" of the infrared luminosity function at high redshift. Our estimates of the contribution to the EBL in the FFs suggest that we may be resolving most of the EBL at 1.1mm down to 0.01 mJy.

We present a variability, color and morphology based classifier, designed to identify transients, persistently variable, and non-variable sources, from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST ChANGES project. Still, it serves as a more general time-domain astronomy study. The model uses nine colors computed from CatWISE and PS1, a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11 g and r light curves. We trained two versions of the model, one for each ZTF band. We used a hierarchical local classifier per parent node approach, where each node was composed of a balanced random forest model. We adopted a 17-class taxonomy, including non-variable stars and galaxies, three transient classes, five classes of stochastic variables, and seven classes of periodic variables. The macro averaged precision, recall and F1-score are 0.61, 0.75, and 0.62 for the g-band model, and 0.60, 0.74, and 0.61, for the r-band model. When grouping the four AGN classes into one single class, its precision, recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both the g and r bands. We applied the model to all the sources in the ZTF/4MOST overlapping sky, avoiding ZTF fields covering the Galactic bulge, including 86,576,577 light curves in the g-band and 140,409,824 in the r-band. Only 0.73\\% of the g-band light curves and 2.62\\% of the r-band light curves were classified as stochastic, periodic, or transient with high probability (\\(P_init0.9\\)). We found that, in general, more reliable results are obtained when using the g-band model. Using the latter, we identified 384,242 AGN candidates, 287,156 of which have \\(P_init0.9\\).

PBC J2333.9-2343 is a giant radio galaxy at z = 0.047 with a bright central core associated to a blazar nucleus. If the nuclear blazar jet is a new phase of the jet activity, then the small orientation angle suggest a dramatic change of the jet direction. We present observations obtained between September 2018 and January 2019 (cadence larger than three days) with Effeslberg, SMARTS-1.3m, ZTF, ATLAS, Swift, and Fermi-LAT, and between April-July 2019 (daily cadence) with SMARTS-1.3m and ATLAS. Large (>2x) flux increases are observed on timescales shorter than a month, which are interpreted as flaring events. The cross correlation between the SMARTS-1.3m monitoring in the NIR and optical shows that these data do not show significant time lag within the measured errors. A comparison of the optical variability properties between non-blazars and blazars AGN shows that PBC J2333.9-2343 has properties more comparable to the latter. The SED of the nucleus shows two peaks, that were fitted with a one zone leptonic model. Our data and modelling shows that the high energy peak is dominated by External Compton from the dusty torus with mild contribution from Inverse Compton from the jet. The derived jet angle of 3 degrees is also typical of a blazar. Therefore, we confirm the presence of a blazar-like core in the center of this giant radio galaxy, likely a Flat Spectrum Radio Quasar with peculiar properties.

The classic classification scheme for Active Galactic Nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs (CSAGNs). The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGN right in the act of changing-state. Here we present an anomaly detection (AD) technique designed to identify AGN light curves with anomalous behaviors in massive datasets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive datasets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behaviour of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm, that is an anomaly detector for a \"one class\" kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8,809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.

[abridged] Characterizing the number counts of faint, dusty star-forming galaxies is currently a challenge even for deep, high-resolution observations in the FIR-to-mm regime. They are predicted to account for approximately half of the total extragalactic background light at those wavelengths. Searching for dusty star-forming galaxies behind massive galaxy clusters benefits from strong lensing, enhancing their measured emission while increasing spatial resolution. Derived number counts depend, however, on mass reconstruction models that properly constrain these clusters. We estimate the 1.1 mm number counts along the line of sight of three galaxy clusters, i.e. Abell 2744, MACSJ0416.1-2403 and MACSJ1149.5+2223, which are part of the ALMA Frontier Fields Survey. We perform detailed simulations to correct these counts for lensing effects. We use several publicly available lensing models for the galaxy clusters to derive the intrinsic flux densities of our sources. We perform Monte Carlo simulations of the number counts for a detailed treatment of the uncertainties in the magnifications and adopted source redshifts. We find an overall agreement among the number counts derived for the different lens models, despite their systematic variations regarding source magnifications and effective areas. Our number counts span ~2.5 dex in demagnified flux density, from several mJy down to tens of uJy. Our number counts are consistent with recent estimates from deep ALMA observations at a 3\\(\\sigma\\) level. Below \\(\\approx\\) 0.1 mJy, however, our cumulative counts are lower by \\(\\approx\\) 1 dex, suggesting a flattening in the number counts. In our deepest ALMA mosaic, we estimate number counts for intrinsic flux densities \\(\\approx\\) 4 times fainter than the rms level. This highlights the potential of probing the sub-10 uJy population in larger samples of galaxy cluster fields with deeper ALMA observations.