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Starting in 2025, the Legacy Survey of Space and Time (LSST) performed at the Vera C. Rubin Observatory will provide continuous monitoring of the entire southern sky in six bands for ten years. Tens of millions of AGN will be observed, allowing for robust statistical studies of the properties of AGN and of their supermassive black hole engines up to high redshift.
Because of the forthcoming start of the Survey, the 2024 Annual Meeting of the Rubin-LSST AGN Science Collaboration will have a strategic importance for the planning of the scientific activities and to promote collaborations among its members.
We intend to schedule a series of presentations connected to AGN science with Rubin-LSST, as well as more technical talks about the status of the Project and of the AGN Science Collaboration and its subgroups. Ample space will be given to discussions.
To provide outreach to the AGN community, the talks will be video recorded.
This meeting is part of the activities of the LSST AGN Science Collaboration - see https://agn.science.lsst.org/ for more information.
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will enable studies of the growing supermassive black holes in active galactic nuclei (AGNs) on a truly massive scale. The LSST AGN Science Collaboration (SC), currently composed of 190 members spanning the globe (and rapidly growing), aims to lead many of these investigations and is preparing for AGN science with the petabyte deluge of LSST data that will begin in mid-late 2025. I will first briefly summarize the history, membership, and organization of the AGN SC. I will then highlight its recent and ongoing activities, including contributing to survey cadence optimization, gathering and analyzing preparatory data sets, forecasting science results with simulations, serving on key working groups and committees, and performing outreach to the scientific community and general public. I will finally discuss future plans and describe how interested members of the worldwide astronomical community can become involved.
Realistic photometric catalogs including AGN, galaxies and stars are
essential tools in assessing the performance and systematics of a large-scale
survey such as LSST. Up to now the DP0 database is massively used as training
sample to forecast future LSST observations, but does not include a realistic
population of AGN.
I will present AGILE (AGN In the LSST Era), a large-scale empirical LSST end-to-end simulation pipeline we have developed as one of the INAF Italian in-kind contributions. The goal is to provide a mass-complete, large-scale realistic LSST photometric catalog including galaxies, stars and AGN up to high redshift (z ~ 5), low stellar mass (logM/Msun ~9) and down to a magnitude limit of r < 30, ~1.5mag deeper than what is expected from COSMOS DDF 10 years co-added images.
The pipeline consists of the creation of the underlying truth catalog, the image simulation and the photometric extraction. In detail, the truth catalog has been created by starting with a population of galaxies equipped with realistic distribution in redshifts, stellar masses, star-formation rates, spectral energy distributions and morphologies. The galaxies are then populated with X-ray AGN according to the most up-to-date measurements of the AGN distribution functions and to each AGN we assigned optical AGN SEDs and lightcurves based on observationally motivated models. The truth catalog generated in this way is then fed to the image simulation pipeline taking into account the observational conditions as well as the cadence of the LSST survey, and the generated single-epoch images are analyzed using the official LSST Science Pipelines in order to create realistic photometric catalogs of AGN, galaxies and stars.
The simulated images and the extracted photometric catalogs, together with the documentations, will be released to the LSST community as part of the INAF in-kind contribution.
In this talk, I will present a review of the different machine learning (ML) techniques used to select and classify active galactic nuclei (AGNs) in large photometric surveys, such as the Zwicky Transient Facility (ZTF), Pan-STARRS, La Silla QUEST survey (LSQ), and the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST). I will first introduce the most popular ML selection techniques currently used, followed by a discussion on the use of AGN variability and ML to identify different sub-classes of AGNs that the more traditional selection techniques could miss. I will examine the advantages and disadvantages of using optical alert streams to identify new AGN candidates, in contrast with other data products from the same surveys, taking as an example the experience of the ALeRCE broker. ALeRCE has been using ML techniques to separate transients and persistently variable objects, including three classes of AGNs, taking advantage of their distinct variable behaviour with respect to other classes of objects. I will focus on the currently available ZTF alert stream and their data releases and will discuss what we should expect from the LSST.
The upcoming Rubin/LSST survey will uncover millions of previously unknown AGN. However, due to the massive amount of data collected each night, it is essential to develop reliable tools that can efficiently identify quasars among billions of stars and inactive galaxies.
I will discuss the results of different machine-learning based selection algorithms, including probabilistic random forests, gradient boosting, convolutional neural networks (CNN) and recursive neural Networks (RNN) applied to photometry, images and light curves.
We employed both real (e.g. AGN data challenge sample and multi-wavelength samples assembled starting from X-ray surveys) and simulated datasets (i.e. the mock catalog which we are deriving as part of the Italian in-kind contribution) .
I will discuss how the number of features available affects the accuracy of the results and how including data from other surveys will help improve both the purity and the completeness of the selection.
I will specifically focus on the selection of luminous and high-redshift (z > 2.5) QSOs. Given the low space density of these sources, it is critical to have a selection rate as complete as possible, to better constrain the high-z luminosity function and, in the case of the even rarer z>6 QSOs, to estimate their contribution to reionization.
We have started an exploratory project aimed at assessing the nature of high redshift radio galaxies (HzRGs) candidates for the next upcoming of Rubin-LSST survey. Powerful radio-loud AGNs represent the most extreme manifestation of nuclear activity and play a pivotal role in galaxy evolution. The epoch at z~4 is essential for studying the processes that connect supermassive black holes with their hosts. However, our knowledge is extremely limited as only a handful of HzRGs at z∼4 are currently known. We then started a comprehensive search of HzRG candidates by combining existing large area radio and deep optical surveys. We selected ’g-dropout’ sources that are expected to be at z∼3.5-4.5. The preliminary results obtained from spectroscopic observations indicate that a large fraction of the sources selected with the ’g-dropout’ method are indeed HzRGs. These study will refine the selection criteria to apply the drop-out technique to the LSST data.
Observations and models indicate that the fraction of active galaxies
in the local Universe is about 10% . As most large galaxies host a
supermassive black hole (SMBH), this can be interpreted as a duty
cycle, where 10% of galaxies are active at any given time. Estimating
this activation rate is important to constrain central black hole
feeding mechanisms in galaxy evolution models. Black hole ignition
events, which involve a galaxy transitioning from a quiescent or
star-forming state to a an AGN are, however, exceptionally challenging
to detect. For our work, we took advantage of the very large public
photometric monitorings that are currently ongoing (ZTF) together with
machine-learning algorithms for selecting interesting objects. Black
hole ignition event candidates were selected form a parent sample of
spectrally classified non-active galaxies (> 2.300.000 objects), that
currently show optical flux variability indicative of a type I AGN,
according to the ALeRCE light curve classifier. In this talk I will
present spectral results for the most convincing case of new AGN
activity, for a galaxy with a previous star-forming optical
classification, where the confirmation spectrum shows the appearance
of prominent, broad Balmer lines without significant changes in the
narrow line flux ratios. MIR colors have also evolved from typical non
active galaxy colors to AGN-like colors and current X-ray detections
are consitent with typical AGN emission. This work is presented in
Arévalo et al. 2024(A&A letters 683, L8). In this talk I will present
the selection strategy, statistics, and predictions for similar studies
with the Vera Rubin LSST.
The Vera C. Rubin Observatory is poised to generate an unparalleled dataset in terms of depth and sky coverage through its Legacy Survey of Space and Time (LSST). Understanding the selection criteria and biases for active galactic nuclei (AGNs) within such a dataset is crucial as the Rubin Observatory commences sciences operations. In this study, we investigate conventional broadband optical AGN selection techniques and assess their relevance to LSST data. We adopt a bolometric quasar luminosity function (QLF) and employ a template-based approach to simulate the spectral energy distributions (SEDs) of AGNs and their host galaxies across various redshifts to evaluate our capability to detect and recover objects with diverse intrinsic properties. Our modular approach facilitates the exploration of different QLFs, SEDs, and selection criteria, as well as rendering it well-suited for future time domain analyses.
The interaction of galaxies play an important role in fueling the SMBHs and accelerating their growth. In this presentation, I will first introduce the techniques that we have used to identify mergers in HSC images, namely, the morphological asymmetry parameter. With over 2400 type 1 quasars between 0.2<z<0.8, we found that only the brightest quasars (log L_bol>44.5 erg/s) have an excess of merger ratio compared to inactive galaxies at the same stellar mass and redshift. My presentation will also cover a special phase of quasar mergers when both of the SMBHs are activated simultaneously, thus form a dual quasar. We have been running a five-year program searching for dual quasars in HSC footprint, and multi-wavelength follow-ups to study the properties of the confirmed pairs. Both projects will be significantly extended in the LSST era, I will discuss about the scientific questions we expect to learn from these quasar mergers.
Supermassive black hole binaries (SMBHB) form naturally in galaxy mergers. At sub-parsec separation, the most promising method to detect binaries is to find AGN with periodic variability and ~250 candidates have been identified in time-domain surveys over the last few years. However, confirming the nature of these candidates is very challenging due to the stochastic quasar variability. I will summarize the status of the searches and the opportunities for new detections with the Rubin Observatory. I will also describe potential synergies with gravitational wave detections from Pulsar Timing Arrays.
Periodic signatures in time-domain observations of quasars have been used to search for binary supermassive black holes (SMBHs). These searches, across existing time-domain surveys, have produced several hundred candidates. The general stochastic variability of quasars, however, can masquerade as a false-positive periodic signal, especially when monitoring cadence and duration are limited. In this work, we predict the detectability of binary SMBHs in the upcoming Rubin Observatory Legacy Survey of Space and Time (LSST). We apply computationally inexpensive sinusoidal curve fits to millions of simulated LSST Deep Drilling Field light curves of both single, isolated quasars and binary quasars. Period and phase of simulated binary signals can generally be disentangled from quasar variability. Binary amplitude is overestimated and poorly recovered for two-thirds of potential binaries due to quasar accretion variability. Quasars with strong intrinsic variability can obscure a binary signal too much for recovery. We also find that the most luminous quasars mimic current binary candidate light curves and their properties: false-positive rates are 60% for these quasars. The reliable recovery of binary period and phase for a wide range of input binary LSST light curves is promising for multi-messenger characterization of binary supermassive black holes. However, pure electromagnetic detections of binaries using photometric periodicity with amplitude greater than 0.1 magnitude will result in samples that are overwhelmed by false positives. This work represents an important and computationally inexpensive way forward for understanding the true and false-positive rates for binary candidates identified by Rubin.
We report the discovery of a transient source in the ZTF public alert stream, whose location coincides with the nucleus of a $z<0.1$ Seyfert galaxy. After the initial alert, the source has increased its brightness by up to half a magnitude five times, roughly every hundred days. While the periodic behaviour suggests a super-massive black hole binary, archival data shows that before the alert the source had a roughly constant, lower luminosity. A tidal disruption event, even one in a binary, does not fit the observations either, as the peak luminosities do not follow the usual decay. We therefore propose that the source behaviour can be due to the tidal disruption of a gas cloud by a SMBH binary. This process was modelled numerically by Goicovic, Cuadra et al (2016), who found that the binary accretes the gas in several successive peaks (two per orbit), with the relative peak intensities depending on the orbital configuration. Given a previous estimate of $\sim3\times10^7 M_\odot$ for the SMBH masses, we can calculate that the putative binary has a separation of $\sim 1$ mpc and would merge in $\sim 10^4$ years, making it an interesting target to study the hierarchical assembly of SMBHs in the local Universe.
Binaries containing a compact object orbiting a supermassive black hole are thought to be the precursors of gravitational wave events, but their electromagnetic identification has been extremely challenging. ASASSN-20qc is an optical astrophysical flare that originated from the nucleus of a seemingly quiescent galaxy at a redshift of 0.056. An extensive multi-wavelength follow-up campaign using optical, UV, X-ray, and radio telescopes revealed the presence of a central SMBH with mass of about 10^7 solar masses and the presence of a newly-formed accretion disk, likely caused by a tidal disruption of a star. Initially, the behavior seemed somehow normal, but we found something rather peculiar. The X-ray data showed a very curious behavior, never seen before in an AGN, changing-look AGN or TDE. We report the detection variable X-ray absorption repeating every 8.3 days indicating quasi-periodic outflows (QPOuts). Using general relativistic magnetohydrodynamic simulations we show that these QPOuts are explained with an intermediate-mass black hole secondary on a mildly eccentric orbit at a mean distance of about 100 gravitational radii from the primary SMBH. Powerful outflows, with an observed velocity of 30% of the speed of light and capable to exert significant feedback on the host galaxy, are naturally enhanced when the secondary crosses the primary inner accretion disk. This result suggests a scenario in which possibly multiple compact objects (such as black holes and stars) may be zooming through a gaseous disk, in comparison to the classical assumed picture of a simple SMBH accretion flow. This can be an example of the many possible synergies we may get soon combining Rubin-LSST and other space- and ground-based facilities for repeating transients in galactic nuclei, and it opens the possibility to explore the electromagnetic precursors/counterparts of intermediate/extreme-mass ratio inspirals (I/EMRI) expected to be observed with LISA through gravitational waves.
A model is proposed for finding the parameters of close binary systems of supermassive black holes based only on observational data in the radio range. The methodology for determining the physical characteristics of the binary systems of supermassive black holes includes conducting harmonic and wavelet analyses, determining the masses of the satellites and their orbital characteristics. It is shown that 3C 273, 3C 454.3, OJ 287, AO 0235+164 and S 0528+134 can be a very massive and close binary system, containing companions with similar masses [1-3].
The main physical characteristics of binary supermassive black holes (SMBHs) located in the central regions of the system are obtained. These data were used to find the masses of the SMBH companions, the parameters of their orbits, the energy reserve of the system, and the lifetime of the object before the SMBHs merger.
The level of gravitational waves on the Earth's surface was determined and the possibility of their detection by International Pulsar Timing Array (IPTA) gravitational wave detectors was considered. Blazar S 0528+134 is the most powerful emitter in the universe, including the range of gravitational waves.
This research was funded by the RSF, grant number 23-22-10032.
1. Volvach A.E., Volvach L.N., Larionov M.G. Most massive double black hole 3C 454.3 and powerful gravitational wave radiation. Astron. Astrophys. 2021, 648, A27.
2. Volvach A.E., Volvach L.N., Larionov M.G. A close binary supermassive black hole model for the galaxy 3C 273. Galaxies 2023, 11, 96.
3. Volvach A.E., Volvach L.N., Larionov M.G. Electromagnetic and gravitational radiation of blazar OJ 287. iScience. 2024. Volume 27, Issue 4, 109427.
Variability is a signature of AGN emission at all wavelengths and has
therefore been adopted, in the past decades, as one of the main AGN
discovery approaches, together with complementary selection
techniques.
My talk will focus on optical variability and will review its crucial
role in the context of the research we expect to carry out exploiting
the data mine from the Rubin-LSST.
We study the variability structure function of AGN from optical light curves of two samples, using ~500 Southern Seyfert 1 to 1.8 nuclei at z<0.1 from the 6dF Galaxy Survey, and the ~5,000 brightest QSO in the sky at z=[0.5;2.5]. In both samples, we find that a fixed amplitude is reached on a timescale related to the expected orbital or thermal timescale of the accretion disc. At low black-hole mass, this involves the expected scaling due to faster orbital motions with increasing mass, but at higher masses, among the most luminous QSOs, the mass dependence seems to disappear. This is corroborated by numerical calculations of accretion disc emission approximating a GR regime, where the timescales turn over from decreasing with higher BH mass to increasing as the growth of the ISCO outpaces the orbital speed increase at fixed radius. We discuss remaining uncertainties of timescales from BH spin, disc inclination and dust extinction. Finally, we present our observations of damping timescales around low-mass black holes.
I will present the status of the Timedomes project, proposed as an in-kind contribution to the LSST collaboration. The program is targeting 4 sq.deg. of four of the LSST DDFs in two bands with 8 or more epochs each semester, in order to cover the time span from now until the beginning of LSST operations. These data, complemented by all archival VST observations dating back to 2011, will allow to build a legacy dataset of images and light curves to extend the DDF monitoring effort up to 20 years, to study the AGN power spectrum, identify AGNs by means of variability and test the unified model predictions.
Our understanding of AGN variability is continually increasing with data from ongoing surveys, but the Rubin Observatory will open a new discovery window in parameter space.
LSST will be able to identify extreme variability events in AGN, which could be driven by rapid changes in the SMBH accretion flow, changes in obscuration, jet activity, microlensing, or possibly even more exotic physics. This multiplicity of physical mechanisms means that changes in the LSST photometric time series will not always correlate with changes in the spectroscopic type classification. Timely spectroscopic and/or multiwavelength follow-up will be required to classify such phenomena, and subsequent monitoring will be crucially important to provide the observational foundation for physical interpretation. However, such objects will first need to be selected from the LSST data stream - all AGN are variable at some level and care will be needed to identify “extreme” variability events from the background defined by the rest of the AGN population.
We conduct an analysis of over 60,000 dwarf ($7\lesssim \log{M_*/M_\odot} \lesssim10$) galaxies in search of photometric variability indicative of active galactic nuclei (AGN). Using data from the Young Supernova Experiment and the PanSTARRS telescopes, we construct light curves for each of the galaxies in up to five bands where available. We fit each light curve to a damped random walk, whose fit significances are used to select for AGN. We also fit for a dampening timescale. From these candidates, we apply additional selection criteria until we reach a satisfactory confidence in the results. Finally, we analyze the spectra of these dwarf AGN candidates to measure various emission lines and estimate black hole mass.
Modeling of quasar light curves is essential for analyzing the physical processes and structure of the source. The QNPy is a modeling tool based on a conditional neural process, developed specifically for modeling a large number of quasar light curves. This process can be time-consuming and its accuracy, depending on the data set, could vary.
The goal of this research was to determine the effect of prepossessing the quasar light curve data set by clustering with the Self-Organizing Map (SOM) algorithm.
The SOM algorithm was selected for this task since it can capture non-linear and non-stationary variability in quasar light curves by clustering similar light curves based on their intrinsic properties, such as variability amplitude, and time scales of variability. Additionally, SOM works fast, which is essential for preprocessing purposes.
The SOM method was trained and tested with 139 quasar light curves detected by the SWIFT Burst Alert Telescope (9-Month BAT Survey). The results have shown that the classification/clustering of quasar light curves as a means of preprocessing could significantly improve modeling via the QNPy. Future work could be focused on improving the classification process to achieve more optimal results.
Within the framework of LSST SER-SAG-S1 team’s Quasar Neural Process Python package for modeling quasar light curves (QNPy), we integrate Self-Organizing Maps (SOMs) and upgrade the pipeline with Attentive Latent Neural Processes, to catch more nuanced variability. We present the pilot results of our analysis of both models and features sampled from latent layers of neural process on LSST AGN Data Challenge, GAIA, ZTF, and Swift quasar light curves.
Quasar variability is thought to be a stochastic process and often modeled as the solution of stochastic differential equations (SDEs) such as the damped random walk. 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. We discuss modeling quasar variability using latent SDEs, a type of physically motivated, generative deep learning model that we modify to simultaneously reconstruct noisy time series with irregular sampling and perform parameter inference. We show how latent SDEs model LSST-like quasar light curves better than a Gaussian process regression baseline and can predict variability and accretion disk parameters such as the black hole mass, inclination angle, and temperature slope. We further improve this work through the construction of auto-differentiable simulations of the accretion disk to explicitly link the accretion disk parameters to the light curve reconstruction in a physics-informed network. We then use a recurrent inference machine technique to iteratively improve the light curve reconstruction and parameter inference.
A key feature of active galactic nuclei (AGN) is their variability across all wavelengths. Typically, AGN vary by a few tenthsof a magnitude or more over periods lasting from hours to years. By contrast, extreme variability of AGN – large luminosity changes that are a significant departure from the baseline variability – are known as AGN flares. These events are rare and their timescales poorly constrained, with most of the literature focusing on individual events. It has been suggested that extreme AGN variability including flares can provide insights into the accretion processes in the disk. With surveys such as the Legacy Survey of Space and Time (LSST) promising millions of transient detections per night in the coming decade, there is a need for fast and efficient classification of AGN flares. The problem with the systematic detection of AGN flares is the requirement to detect them against a stochastically variable baseline; the ability to define a signal as a significant departure from the ever-present variability is a statistical challenge. Recently, Gaussian Processes (GPs) have revolutionised the analysis of time-series data in many areas of astronomical research. They have, however, seen limited uptake within the field of transient detection and classification. Here we investigate the efficacy of Gaussian Processes to detect AGN flares in both simulated and real optical light curves. We show that GP analysis can successfully detect AGN flares with a false-positive rate of less than seven per cent, and we present examples of AGN light curves that show extreme variability.
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) is scheduled to begin in less than a year. Hundreds of millions of accreting massive black holes in active galactic nuclei (AGNs) will be monitored by LSST for a period of ten years. Scalable and flexible modeling of LSST AGN light curves is essential to AGN classification, AGN accretion flow characterization, as well as the estimation of AGN fundamental properties (e.g., L/LEdd). We present a recent software development to enhance the scalability and flexibility of current AGN light curve modeling approaches using Gaussian process regression. We also showcase examples demonstrating how this new software can facilitate various LSST AGN science cases, such as red-noise variability characterization, in a scalable manner. The presented work constitutes part of the Canadian in-kind contribution to the LSST project.
Active galactic nuclei (AGN) are among the most interesting objects in the universe and are tools to probe both cosmology and general relativity. They are powered by the accretion of matter onto supermassive black holes and thus emit stochastic signals across the electromagnetic spectrum. With the first light of wide-field surveys such as LSST, variability will naturally be a focus of AGN studies. We present AMOEBA, a new AGN modeling code designed to join together the many AGN components semi-analytically and generically. It is built modularly such that components may be added or removed, and intrinsic variability is followed through each component to build a self-consistent model. Furthermore, we present some features found in strongly lensed AGN caused by microlensing. The microlensing time delay is found to be significant when compared to the time lags assumed from baseline models.
Quasars optical variability gives us clues to understand the accretion disc around supermassive black holes, which are responsible for the emission in this band and also for at least part of its variability. We can expect variability properties to correlate with the main physical properties of the accreting black hole, i.e. its mass and accretion rate. Specifically, we aim at establishing the dependence of variability properties, such as characteristic timescales ($T_b$) and amplitude of the variability on black hole mass and accretion rate, controlling for the restframe wavelength of emission. We selected the g-band light curves for 4770 objects from the Zwicky Transient Facility archive that fall into a narrow redshift bin, but cover a wide range of accretion rates in Eddington units(REdd) and black hole masses (M). With these, we found a clear dependence of $T_b$ on REdd, on top of the known dependence of $T_b$ on mass. In our fits, $T_b \propto M^{0.65} REdd^{0.35}$, scaling $T_b$ to the orbital timescale of the ISCO, $T_{ISCO}$, results approximately in $T_b/T_{ISCO}\propto$ (REdd/M)$^{0.35}$. In the standard thin disk model, (REdd/M) $\propto T_{max}^4$, where $T_{max}$ is the maximum disk temperature, so that $T_b/T_{ISCO}$ appears to scale approximately with the maximum temperature of the disc. The observed values of $T_b$ are about 10 times the orbital time at the light-weighted average radius of the disc region emitting in the (observer frame) g-band. The different scaling of the break frequency with M and REdd shows that the shape of the variability power spectrum cannot be solely a function of the quasar luminosity, even for a single rest-frame wavelength. This work is presented in Arévalo et al. 2024 (A&A 68, 133). In this talk I will extrapolate these results to predict the level of variability expected for AGN of different masses, accretion rates and redshifts in the deeper light curves of the Vera Rubin LSST.
Variation of AGN activity is usually stochastic and does not exceed a few tens of percent. However, a small fraction of AGNs show much more dramatic and coherent changes in their spectral and photometric activity over a 100-1000 days time scale and manifest themselves as “Changing-Look” AGN (hereafter CL AGN). I will start this talk with a history of the CL AGN study (“yesterday”). Then I will present a brief overview of recent observational and theoretical efforts to understand the nature of CL AGNs and introduce the current observational campaigns to discover CL AGNs (“today”). Lastly, I will discuss the prospect of CL AGN study in the era of Rubin LSST (“tomorrow”).
AGN are known to show flux variability over all observable timescales and across the entire electromagnetic spectrum. Over the past decade, a growing number of sources have been observed to show dramatic flux and spectral changes, both in the X-rays and in the optical/UV. Such events, commonly described as “changing-look AGN”, can be divided into two well-defined classes. Changing-obscuration objects show strong variability of the line-of-sight column density, mostly associated with clouds or outflows eclipsing the central engine of the AGN. Changing-state AGN are instead objects in which the optical/UV continuum emission and broad emission lines appear or disappear, and are typically triggered by strong changes in the accretion rate of the supermassive black hole. In my talk I will review our current understanding of Changing-state AGN, and then focus on future developments with LSST, particularly in combination with new-generation optical spectroscopic surveys (e.g. 4MOST).
Photometric reverberation mapping serves as a vital complementary technique to spectroscopy reverberation mapping (RM) for probing the Active Galactic Nuclei (AGN). Even that photometric RM could not provide detailed information as spectroscopy RM, the obvious advantage of photometric RM is to efficiently measure accretion disk and Broad Line Region (BLR) sizes and host-subtracted luminosities for large AGN samples. The imminent Vera C. Rubin Legacy Survey of Space and Time (LSST), equipped with six broad-band filters, promises to revolutionize our understanding by analyzing the properties of hundreds of millions of variable AGN. For example, the redshifted Halpha line, transitioning into the LSST i and z bands at redshifts around 0.16 and 0.38, respectively, may be strong enough to contribute about >30% to these LSST broad bands.
In anticipation of this groundbreaking survey, we aim to foster an interactive dialogue with the LSST AGN Science Collaboration and In-kind teams to collaboratively explore:
Rubin will soon dramatically increase the monitoring of active galactic nuclei (AGN), potentially unlocking the ability to perform disk reverberation mapping (RM) over a wide range of black hole masses, luminosities, and redshifts. I will present a numerical and observational approach to studying the structure and internal physics of quasar accretion disks using two types of disk RM. The first is the traditional continuum RM, which measures lags in the variability of quasar light curves from high to low frequency wave bands on the light-crossing time scale due to the reprocessing of light in different temperature regions of the disk. Multi-frequency radiation magnetohydrodynamic codes make it possible to directly simulate the reprocessing of light by a quasar disk. I will present multi-waveband light curves from new radiation Athena++ simulations and discuss how they can be compared to observations to better understand reprocessing. I will also discuss a second type of lag, the “long negative lag.” The long negative lag occurs when fluctuations in the outer UV/optical region of the disk are accreted inward on the longer inflow timescale leading variability from these fluctuations in high frequency bands to lag the corresponding variability at low frequency. Because the inflow rate, unlike the speed of light, also depends on disk properties, long lags can provide additional information about disk structure. Rubin’s high cadence and long baseline make it ideal for detecting more long negative lags. I will present the latest in my search for long negative lag candidates in current long baseline AGN light curves.
We investigate the influence of various parameters within the accretion disk model on the time lag observed in reverberation mapping. Our analysis incorporates the lamp-post model, the color corrections for blackbody spectra, and the wind model, focusing on their effects on the temperature distribution across the disk. Through the examination of specific cases, we propose potential solutions to reconcile discrepancies in observed disk size.
Continuum reverberation mapping (CRM) measures the time delay in the variability seen in different photometric bands to constrain accretion disk structure and supermassive black hole (SMBH) properties. However, CRM has only been applied to a handful of objects to date due to the stringent observing requirements and large computation time associated with model fitting. I will present a fast and flexible inference framework for CRM using simulation-based inference (SBI) with deep learning to estimate SMBH properties from simulated light curves. We first use a forward-modeling simulator to simulate light curves given the input priors (black hole mass, accretion rate, and inclination angle), then train deep neural networks to learn the mapping between the physical parameters and the simulated data. Once trained, we can estimate an amortized posterior for the physical parameters corresponding to a new simulated light curve with negligible additional computation time. SBI can be applied to different accretion disk models, which is particularly useful for comparing data to models with likelihood functions that are difficult to formulate or compute. I will present how the inference speed and accuracy compare against traditional methods, such as CCF, Javelin, and CREAM. This framework will be useful in estimating physical parameters for the thousands of AGN monitored with LSST, paving the way for new insights into the physics of AGN and the evolution of supermassive black holes.
In a recent paper (Lira et al., 2024, MNRAS, in press), we carried out Dust Reverberation Mapping (DRM) for 13 AGN from the Ultra-VISTA survey found at 0.3<z<0.8. The z~0.8 limit ensured that emission in the rest-frame ~1um could be detected in the Ks band. Our determined lags are systematically found below the radius-luminosity relationship determined for local sources. Following previous works, we introduced a relation that corrects lags by the rest-frame wavelength of the band that samples the dust emission, as shorter wavelengths arise from hotter regions of the torus (i.e., a dust K-correction). When the correction is introduced, our results are consistent with previous findings. Finally, we show how these results impact future DRM campaigns to be carried out by the Legacy Survey of Space and Time for local AGN sampling dust emission in the y-band.
Reverberation mapping (RM) approach to the Broad Line Region (BLR) started over 40 years ago and revolutionized our knowledge of this region. However, the geometry of the region is much more complex that was expected, and the physical processes are also complex so a number of key questions remain unsolved (origin of the clouds, exact dynamics and geometry, possible presence of dust). In addition, our expectations from the modelling accuracy rises with the hope to use the BLR light echo for measuring the distances to AGN needed for cosmological applications.
LSST will increase the number of sources with RM measurements by three orders of magnitude but will create at the same time considerable problems of data limitations and possible systematic issues. Careful approach to object selection will be necessary for high quality results.
Active Galactic Nuclei (AGN) are variable sources, and analyzing the time delay of the strong broad optical emission lines relative to the underlying continuum serves as a crucial tool to investigate this important trait. This method aids in measuring black hole masses and deciphering the Broad Line Region (BLR) structure responsible for these emission lines, which can be extended to test existing cosmological models. The relationship between an AGN's absolute luminosity and the measured time delay contributes to these investigations. To facilitate time delay measurements, we have developed a pipeline that simulates the efficiency of such measurements using the survey strategies for the Vera C. Rubin Observatory's Legacy Survey of Space and Time. This code incorporates simulated light curves, incorporating strong emission lines (e.g., Hbeta, MgII, CIV), iron pseudo-continuum in the optical and UV bands, and contamination from starlight. By identifying optimal bands representing the continuum and dominant lines for a given redshift, the code enables the recovery of time delay under any available LSST cadences. Simulations are conducted for the main survey and the Deep Drilling Fields (DDFs) using representative cadence strategies. The pipeline is modular, and multi-faceted, can be used to determine time delays from actual data (e.g., ZTF), and easily be extended to forthcoming surveys. I will highlight the salient features of the pipeline and discuss relevant applications and plans in anticipation of the first light of the Observatory.
The scaling relations between the mass of black holes and the properties of galaxies are becoming more specific to the different galaxy morphologies. I will present an update on the current status of these relations and explain how we can refine the virial factor(s), f, used to calculate black hole masses from AGN virial products.
We employ natural language processing algorithms with attention, repurposed to receive QSO spectra to predict unseen spectra, broad lines, and super massive black hole masses. We find that the trained algorithm is able to reproduce with high significance masked broad lines and/or continua in QSO spectra, highlighting an ability to learn from and leverage physical information imprinted amongst the entire spectrum. A key implication is that this information may help to refine physical properties such as single-epoch black hole masses. We tested the algorithm’s ability to directly predict black hole masses, with no spectral fitting or decomposition, finding reasonable success to reproduce single-epoch prescriptions. In the near future, we plan to make this model multi-modal so that QSO spectra and photometric light curves, from multiple bands, can be input for training. We can then test the predicting power when the model is solely fed light curves. We will discuss the attention mechanism, which allows us to peek inside and probe what information is being used to make the above predictions and several broad future applications that we envision.
Blazars are the most persistently bright objects in the observable Universe characterized by extreme variability across the electromagnetic spectrum. Their multimessenger emission manifests in the launching of relativistic jets and the acceleration of extremely energetic particles all of which are still poorly understood. The coming of Vera C. Rubin's LSST promises to revolutionize our understanding of blazars by providing the necessary data to explore the multiwavelength variability of the jets on diverse timescales. I will discuss bread-and-butter as well as exotic blazar science that will be possible with the unique capabilities of the LSST.
In 1991 the launch of the Compton Gamma-Ray Observatory (CGRO) satellite renewed interest in blazars, discovering that they can be strong gamma-ray emitters. In 1994 a CCD camera was installed on the 105 cm REOSC telescope of the Torino Observatory, and we began a blazar monitoring program targeting a list of bright gamma-loud sources. We also joined the OJ-94 Project, aiming to confirm the predicted outburst of the alleged periodic blazar OJ 287. The outburst was observed and its double-peak shape followed in detail, leading to a wealth of possible interpretations (e.g. Villata, Raiteri et al. 1998).
In 2000 we took the leadership of the Whole Earth Blazar Telescope (WEBT) Collaboration, which was born in 1997 to provide low-energy observing support to the CGRO observations. From then on, we have been studying blazar multiwavelength variability mainly through the results of massive WEBT observing campaigns, involving many tens of observers, mostly in the optical, but also in the radio and near-infrared bands. The WEBT observations are often complemented by high-energy contemporaneous data to conduct a broad-band analysis of the blazar behaviour. The exceptional sampling of the WEBT light curves allows us to study blazar variability in detail. We have proposed a model according to which the long-term multiwavelength behaviour is explained in terms of variations in the Doppler beaming due to changes of the viewing angle in an inhomogeneous, curved and twisting jet (Raiteri et al. 2017, Nature).
From 2017, the Torino team is involved in the Rubin-LSST Project in the TVS and AGN Science Collaborations. Our main interest is to study blazar variability, census, and environment. We wrote the sessions on blazars of the TVS Roadmap. We also investigated the effects of various LSST observing strategies on blazar studies. This analysis was the subject of a cadence white paper in 2018 and a cadence note in 2021, and led to a paper (Raiteri et al. 2022) for the special issue of the ApJS on the LSST cadence.
High-redshift jetted (or radio-loud) QSOs offer invaluable insights into the early epochs of the Universe. Understanding the role of relativistic jets in the SMBH formation and early accretion is of significant interest. The key question is whether SMBHs hosted in jetted QSOs have undergone distinct evolutionary paths compared to the general population. Another important issue is whether relativistic jets in the primordial Universe differ from their local counterparts. If the observed evolution can be explained by the interaction of the jet plasma with CMB photons, this could be extremely useful to probe the composition, energetics and physics of relativistic jets. Furthermore, the census of radio-loud population at different viewing angles, with different beaming factors, can shed light on the obscuration levels and the geometry of the obscuring material
of the high-redshift population QSOs.
In this presentation, I will show the results achieved by our group utilizing current operational facilities (RACS, CLASS, PS1, DELVE) and discuss the exciting expectations we have from the LSST survey.
Blazars, a population of active galactic nuclei (AGN), stand out for their intense and variable emissions spanning the electromagnetic spectrum. This study introduces a novel catalog of blazar candidates derived from a synergistic combination of the Very Large Array Sky Survey (VLASS) radio data with optical data from the Panoramic Survey Telescope and Rapid Response System (PanSTARRS). Leveraging the broad sky coverage and high resolution of VLASS, alongside the depth of PanSTARRS optical observations, we first identify radio sources with optical counterparts across the sky. The compact nature and the typical colors of blazars in the optical and infrared bands (thanks to the data of the Wide-field Infrared Survey Explorer - WISE), have been a guide to pinpoint candidates to be included in our catalog. Additionally, taking advantage of the extensive temporal coverage provided by PanSTARRS, we conducted an investigation into the variability of blazar sources, unveiling insights into their properties. To ensure a comprehensive view, we validate our findings with known catalogs such as Roma-BZCAT5, which is the most extensive collection of confirmed blazars to date, encompassing 3561 sources.
We anticipate significant advancements in blazar research with upcoming surveys such as the Vera C. Rubin Legacy Survey of Space and Time (Rubin-LSST). LSST's unprecedented combination of depth, area, and cadence will provide unparalleled opportunities for studying blazars across various wavelengths and timescales. The approach used in this study promises to yield deeper knowledge on the blazar population, in anticipation of the LSST era.
Photometric redshifts play a crucial role in all areas of extragalactic astronomy. Applying standard methods to AGN has shown there are specific challenges due to the features of typical spectral energy distributions. In this talk I will present an overview of the current state of the art and talk about prospects for future development. Machine learning methods applied to both catalogue and image data promise to provide robust and reliable photometric redshifts. The continuing importance of template fitting will also be discussed and the synthesis of both methods will be encouraged. The importance of using an array of metrics for assessing performance will be discussed including both point estimate metrics and those for the full posterior. We will review some of the infrastructure being developed for the Vera C. Rubin Observatory and discuss the Redshift Assessment and Infrastructure Layers (RAIL) which will play a key role in comparing performance between many different methods and producing photometric redshifts at scale for large surveys such as LSST.
Quasars are known for their intrinsic variability which is stochastic at several time scales. Studying these objects in very large samples helps to improve the statistics and allows systematic studies of the time scales of variability and their relation with the physical mechanisms driving these flux changes. With the upcoming large time domain photometric surveys such as the Vera C. Rubin LSST, we expect to discover at least a few millions of new AGN. The large number of sources precludes the possibility of exhaustive spectroscopic follow-up. As such, candidate confirmation and estimation of their redshifts from photometric data presents an active research problem. I will present ongoing research results of a novel technique for redshift estimation that couples cosmological time dilation with intrinsic variability for inference. We used MCMC iterations, effectively modelling the optical light curves of AGN by constraining the structure function as means to obtain their redshift priors. I will present a validation of this method, conducted using a well-sampled light curve from the Zwicky Transient Facility (ZTF) and from simulations as a proof of concept of our approach which demonstrates that the obtained redshift priors align very well with spectroscopically determined redshift values.
Though only making up 0.1% of all known quasars, high redshift (z>5) quasars are vital to understanding the formation and evolution of galaxies, the growth of supermassive black holes, and the changing ionisation state of the early Universe. The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to increase the number of known high-z quasars from ~1000 to 10,000, enabling precise quantification of their evolutionary parameters. However, conventional methods for identifying high-z quasars are prone to high false-positive rates and require resource-intensive spectroscopic follow-up; more efficient methodologies are needed for the upcoming influx of data from LSST.
We introduce a robust approach to identifying and characterising high-z quasars via optical and infrared spectral energy distribution (SED) fitting, with an emphasis on reliable photometric redshifts. Our method will combine LSST photometry with infrared (1-4 microns) photometry from VISTA surveys including VHS, VIKING, VIDEO and VEILS (http://casu.ast.cam.ac.uk/vistasp/overview) and WISE. We present results using optical photometry from DECAM Legacy Survey and Subaru HyperSuprime-Cam Subaru Strategic Program as proxies for LSST. We use the parametric quasar model from Temple et al. 2021 to characterise quasar candidates by redshift, host galaxy contribution, intrinsic reddening, and luminosity. We also present an updated empirical model for intergalactic hydrogen absorption. By comparing SED fits between quasar, foreground galaxy, and foreground galactic star models, we estimate and reduce rates of foreground contaminates. This methodology shows promise in identifying high-z quasar candidates and measuring reliable redshifts and properties of these objects, presenting an efficient avenue for studying quasars in the early Universe from the first data release of LSST.
In June 2022, the highly anticipated Gaia data release (DR3) was unveiled, marking a significant milestone with the introduction of the first-ever AGN catalog, specifically tailored to capture variability patterns. Leveraging insights from the established Gaia-CRF3 AGN catalog and employing key parameters indicative of object variability (such as Fractional Variability and Slope of the Structure Function), this new catalog, known as GLEAN, was meticulously curated through a series of rigorous selection criteria, resulting in an impressive compilation of approximately 872,000 sources.
Looking ahead to 2026, the forthcoming fourth data release of Gaia (DR4) promises an array of exciting advancements. With an extended data span of 66 months, researchers will gain access to photometric light curves, offering deeper insights into AGN variability. Furthermore, the updated AGN catalog accompanying DR4 will introduce additional parameters related to variability, enriching classification and characterization endeavors within the scientific community. Notably, DR4 opens avenues for more detailed investigations into temporal delays in lens quasars, potentially facilitating estimations of H0, albeit within the complexities of this endeavor.
Additionally, DR4 will facilitate access to low-resolution spectroscopic time series, presenting valuable opportunities for reverberation mapping studies. By observing the temporal delay between line and continuum measurements, researchers can glean insights into the structural dynamics of Broad Line Regions (BLR), particularly for the brightest objects.
Another notable enhancement in DR4 is the inclusion of astrometric time series, enabling investigations into both Radio-loud and Radio-quiet AGN. For instance, researchers may explore the detection of motion within extragalactic jets, as exemplified by studies such as Blinov et al. (2021) on Blazar 3C 279. Additionally, studies related to AGN suggest that large-scale modifications in the accretion disk and surrounding dusty torus can induce shifts in the photocentre, as evidenced by works such as Souchay et al. (2022), highlighting 41 sources with significant proper motion. While challenging, these endeavors underscore the transformative potential of Gaia data in advancing our understanding of AGN dynamics and morphology.
I will give a review of the main results from eROSITA related to AGN, with an eye to LSST and future developments.
Observations in the high-energy domain have proven to be key for the multiwavelength characterization and modeling of various transients, including gamma-ray bursts, gravitational wave and high-energy neutrino counterparts, tidal disruption events, and AGNs, including blazars. Gamma-ray facilities currently operational, both ground-based such as MAGIC, LST-1, H.E.S.S., and VERITAS, as well as space-based such as Fermi/LAT, along with those under construction like the Cherenkov Telescope Array Observatory (CTAO) and ASTRI, serve as ideal complements to the Rubin-LSST, facilitating the comprehensive study of high-energy transients within a multi-frequency and multi-messenger framework. We will delineate how these facilities can augment the LSST survey. To foster these synergies, INAF has proposed the in-kind project "Non-Directable SW contribution for the TVS SC: A Bridge from Gamma to Optical," which aims to facilitate cross-matching of high-energy events detected by X-ray and Gamma-ray facilities with LSST data and enable multiwavelength analysis.