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Abstract Machine learning methods are well established in the classification of quasars (QSOs). However, the advent of light-curve observations adds a great amount of complexity to the problem. Our goal is to use the Zwicky Transient Facility (ZTF) to create a catalog of QSOs. We process the ZTF DR20 light curves with a transformer artificial neural network and combine different surveys with extreme gradient boosting. Based on ZTFg-band and Wide-field Infrared Survey Explorer (WISE) observations, we find 4,849,574 objects classified as QSOs with confidence higher than 90% (QZO). We robustly classify objects fainter than the 5σsignal-to-noise ratio (SNR) limit atg= 20.8 by requiringg < n_obs/80 + 20.375. For 33% of QZO objects, with available WISE data, we publish redshifts with estimated error Δz/(1 + z) = 0.14. We find that ZTF classification is superior to the Pan-STARRS static bands, and on par with WISE and Gaia measurements, but the light curves provide the most important features for QSO classification in the ZTF data set. Using ZTFg-band data with at least 100 observational epochs per light curve, we obtain a 97% F1 score for QSOs. We find that with 3 day median cadence, a survey time span of at least 900 days is required to achieve a 90% QSO F1 score. However, one can obtain the same score with a survey time span of 1800 days and the median cadence prolonged to 12 days. 

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

ABSTRACT We present a search for luminous long-duration ambiguous nuclear transients (ANTs) similar to the unprecedented discovery of the extreme ambiguous event AT2021lwx with a $$\gt 150$$ d rise time and luminosity $$10^{45.7}$$ erg s$$^{-1}$$. We use the Lasair transient broker to search Zwicky Transient Facility (ZTF) data for transients lasting more than one year and exhibiting smooth declines. Our search returns 59 events, 7 of which we classify as ANTs assumed to be driven by accretion onto supermassive black holes. We propose the remaining 52 are stochastic variability from regular supermassive black hole accretion rather than distinct transients. We supplement the seven ANTs with three nuclear transients in ZTF that fail the light curve selection but have clear single flares and spectra that do not resemble typical active galactic nucleus. All of these 11 ANTs have a mid-infrared flare from an assumed dust echo, implying the ubiquity of dust around the black holes giving rise to ANTs. No events are more luminous than AT2021lwx, but one (ZTF19aamrjar) has twice the duration and a higher integrated energy release. On the other extreme, ZTF20abodaps reaches a luminosity close to AT2021lwx with a rise time $$\lt 20$$ d and that fades smoothly in $$\gt 600$$ d. We define a portion of rise-time versus flare amplitude space that selects ANTs with $$\sim 50$$ per cent purity against variable AGNs. We calculate a volumetric rate of $$\gtrsim 3\times 10^{-11}$$ Mpc$$^{-1}$$ yr$$^{-1}$$, consistent with the events being caused by tidal disruptions of intermediate and high-mass stars. 

Abstract Optical surveys have become increasingly adept at identifying candidate tidal disruption events (TDEs) in large numbers, but classifying these generally requires extensive spectroscopic resources. Here we presenttdescore, a simple binary photometric classifier that is trained using a systematic census of ∼3000 nuclear transients from the Zwicky Transient Facility (ZTF). The sample is highly imbalanced, with TDEs representing ∼2% of the total.tdescoreis nonetheless able to reject non-TDEs with 99.6% accuracy, yielding a sample of probable TDEs with recall of 77.5% for a precision of 80.2%.tdescoreis thus substantially better than any available TDE photometric classifier scheme in the literature, with performance not far from spectroscopy as a method for classifying ZTF nuclear transients, despite relying solely on ZTF data and multiwavelength catalog cross matching. In a novel extension, we use “Shapley additive explanations” to provide a human-readable justification for each individualtdescoreclassification, enabling users to understand and form opinions about the underlying classifier reasoning.tdescorecan serve as a model for photometric identification of TDEs with time-domain surveys, such as the upcoming Rubin observatory. 

Abstract Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. 

Abstract About 3%–10% of Type I active galactic nuclei (AGNs) have double-peaked broad Balmer lines in their optical spectra originating from the motion of gas in their accretion disk. Double-peaked profiles arise not only in AGNs, but occasionally appear during optical flares from tidal disruption events and changing-state AGNs. In this paper, we identify 250 double-peaked emitters (DPEs) among a parent sample of optically variable broad-line AGNs in the Zwicky Transient Facility (ZTF) survey, corresponding to a DPE fraction of 19%. We model spectra of the broad Hαemission-line regions and provide a catalog of the fitted accretion disk properties for the 250 DPEs. Analysis of power spectra derived from the 5 yr ZTF light curves finds that DPE light curves have similar amplitudes and power-law indices to other broad-line AGNs. Follow-up spectroscopy of 12 DPEs reveals that ∼50% display significant changes in the relative strengths of their red and blue peaks over long 10–20 yr timescales, indicating that broad-line profile changes arising from spiral arm or hotspot rotation are common among optically variable DPEs. Analysis of the accretion disk parameters derived from spectroscopic modeling provides evidence that DPEs are not in a special accretion state, but are simply normal broad-line AGNs viewed under the right conditions for the accretion disk to be easily visible. We include inspiraling supermassive black hole binary candidate SDSSJ1430+2303 in our analysis, and discuss how its photometric and spectroscopic variability is consistent with the disk-emitting AGN population in the ZTF survey. 

Abstract “Changing-look” active galactic nuclei (CL-AGNs) challenge our basic ideas about the physics of accretion flows and circumnuclear gas around supermassive black holes. Using first-year Sloan Digital Sky Survey V (SDSS-V) repeated spectroscopy of nearly 29,000 previously known active galactic nuclei (AGNs), combined with dedicated follow-up spectroscopy, and publicly available optical light curves, we have identified 116 CL-AGNs where (at least) one broad emission line has essentially (dis-)appeared, as well as 88 other extremely variable systems. Our CL-AGN sample, with 107 newly identified cases, is the largest reported to date, and includes ∼0.4% of the AGNs reobserved in first-year SDSS-V operations. Among our CL-AGNs, 67% exhibit dimming while 33% exhibit brightening. Our sample probes extreme AGN spectral variability on months to decades timescales, including some cases of recurring transitions on surprisingly short timescales (≲2 months in the rest frame). We find that CL events are preferentially found in lower-Eddington-ratio (f_Edd) systems: Our CL-AGNs have af_Edddistribution that significantly differs from that of a carefully constructed, redshift- and luminosity-matched control sample (Anderson–Darling test yieldingp_AD≈ 6 × 10⁻⁵; medianf_Edd≈ 0.025 versus 0.043). This preference for lowf_Eddstrengthens previous findings of higher CL-AGN incidence at lowerf_Edd, found in smaller samples. Finally, we show that the broad Mgiiemission line in our CL-AGN sample tends to vary significantly less than the broad Hβemission line. Our large CL-AGN sample demonstrates the advantages and challenges in using multi-epoch spectroscopy from large surveys to study extreme AGN variability and physics. 

ABSTRACT Stars embedded in active galactic nucleus (AGN) discs or captured by them may scatter onto the supermassive black hole (SMBH), leading to a tidal disruption event (TDE). Using the moving-mesh hydrodynamics simulations with arepo, we investigate the dependence of debris properties in in-plane TDEs in AGN discs on the disc density and the orientation of stellar orbits relative to the disc gas (pro- and retro-grade). Key findings are: (1) Debris experiences continuous perturbations from the disc gas, which can result in significant and continuous changes in debris energy and angular momentum compared to ‘naked’ TDEs. (2) Above a critical density of a disc around an SMBH with mass M• [ρcrit ∼ 10−8 g cm−3 (M•/106 M⊙)−2.5] for retrograde stars, both bound and unbound debris is fully mixed into the disc. The density threshold for no bound debris return, inhibiting the accretion component of TDEs, is $$\rho _{\rm crit,bound} \sim 10^{-9}{\rm g~cm^{-3}}(M_{\bullet }/10^{6}\, {\rm M}_{\odot })^{-2.5}$$. (3) Observationally, AGN-TDEs transition from resembling naked TDEs in the limit of ρdisc ≲ 10−2ρcrit,bound to fully muffled TDEs with associated inner disc state changes at ρdisc ≳ ρcrit,bound, with a superposition of AGN + TDE in between. Stellar or remnant passages themselves can significantly perturb the inner disc. This can lead to an immediate X-ray signature and optically detectable inner disc state changes, potentially contributing to the changing-look AGN phenomenon. (4) Debris mixing can enrich the average disc metallicity over time if the star’s metallicity exceeds that of the disc gas. We point out that signatures of AGN-TDEs may be found in large AGN surveys. 

ABSTRACT Covering $$\sim 5600\, \deg ^2$$ to rms sensitivities of ∼70−100 $$\mu$$Jy beam−1, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency (∼150 MHz) radio catalogue to date, making it an excellent tool for large-area radio cosmology studies. In this work, we use LoTSS-DR2 sources to investigate the angular two-point correlation function of galaxies within the survey. We discuss systematics in the data and an improved methodology for generating random catalogues, compared to that used for LoTSS-DR1, before presenting the angular clustering for ∼900 000 sources ≥1.5 mJy and a peak signal-to-noise ≥ 7.5 across ∼80 per cent of the observed area. Using the clustering, we infer the bias assuming two evolutionary models. When fitting angular scales of $$0.5 \le \theta \lt 5{^\circ }$$, using a linear bias model, we find LoTSS-DR2 sources are biased tracers of the underlying matter, with a bias of $$b_{\rm C}= 2.14^{+0.22}_{-0.20}$$ (assuming constant bias) and $$b_{\rm E}(z=0)= 1.79^{+0.15}_{-0.14}$$ (for an evolving model, inversely proportional to the growth factor), corresponding to $$b_{\rm E}= 2.81^{+0.24}_{-0.22}$$ at the median redshift of our sample, assuming the LoTSS Deep Fields redshift distribution is representative of our data. This reduces to $$b_{\rm C}= 2.02^{+0.17}_{-0.16}$$ and $$b_{\rm E}(z=0)= 1.67^{+0.12}_{-0.12}$$ when allowing preferential redshift distributions from the Deep Fields to model our data. Whilst the clustering amplitude is slightly lower than LoTSS-DR1 (≥2 mJy), our study benefits from larger samples and improved redshift estimates. 

ABSTRACT We present second epoch optical spectra for 30 changing-look (CL) candidates found by searching for Type-1 optical variability in a sample of active galactic nuclei (AGNs) spectroscopically classified as Type 2. We use a random-forest-based light-curve classifier and spectroscopic follow-up, confirming 50 per cent of candidates as turning-on CLs. In order to improve this selection method and to better understand the nature of the not-confirmed CL candidates, we perform a multiwavelength variability analysis including optical, mid-infrared (MIR), and X-ray data, and compare the results from the confirmed and not-confirmed CLs identified in this work. We find that most of the not-confirmed CLs are consistent with weak Type 1s dominated by host-galaxy contributions, showing weaker optical and MIR variability. On the contrary, the confirmed CLs present stronger optical fluctuations and experience a long (from five to ten years) increase in their MIR fluxes and the colour W1–W2 over time. In the 0.2–2.3 keV band, at least four out of 11 CLs with available SRG/eROSITA detections have increased their flux in comparison with archival upper limits. These common features allow us to select the most promising CLs from our list of candidates, leading to nine sources with similar multiwavelength photometric properties to our CL sample. The use of machine learning algorithms with optical and MIR light curves will be very useful to identify CLs in future large-scale surveys.