Optical Variability Research Articles

Improved Object-Based Mapping of Aboveground Biomass Using Geographic Stratification with GEDI Data and Multi-Sensor Imagery

Aboveground biomass (AGB) mapping using spaceborne LiDAR data and multi-sensor images is essential for efficient carbon monitoring and climate change mitigation actions in heterogeneous forests. The optimal predictors of remote sensing-based AGB vary greatly with geographic stratification, such as topography and forest type, while the way in which geographic stratification influences the contributions of predictor variables in object-based AGB mapping is insufficiently studied. To address the improvement of mapping forest AGB by geographic stratification in heterogeneous forests, satellite multisensory data from global ecosystem dynamics investigation (GEDI) and series of advanced land observing satellite (ALOS) and Sentinel were integrated. Multi-sensor predictors for the AGB modeling of different types of forests were selected using a correlation analysis of variables calculated from topographically stratified objects. Random forests models were built with GEDI-based AGB and geographically stratified predictors to acquire wall-to-wall biomass values. It was illustrated that the mapped biomass had a similar distribution and was approximate to the sampled forest AGB. Through an accuracy comparison using independent validation samples, it was determined that the geographic stratification approach improved the accuracy by 34.79% compared to the unstratified process. Stratification of forest type further increased the mapped AGB accuracy compared to that of topography. Topographical stratification greatly influenced the predictors’ contributions to AGB mapping in mixed broadleaf–conifer and broad-leaved forests, but only slightly impacted coniferous forests. Optical variables were predominant for deciduous forests, while for evergreen forests, SAR indices outweighed the other predictors. As a pioneering estimation of forest AGB with geographic stratification using satellite multisensory data, this study offers optimal predictors and an advanced method for obtaining carbon maps in heterogeneous regional landscapes.

Theoretical Impact of Intraocular Lens Design Variations on the Accuracy of IOL Power Calculations.

To ascertain the theoretical impact of optical design variations of the intraocular lens (IOL) on the accuracy of IOL power formulas based on a single lens constant using a thick lens eye model. This impact was also simulated before and after optimization. We modeled 70 thick-lens pseudophakic eyes implanted with IOLs of symmetrical optical design and power comprised between 0.50 D and 35 D in 0.5-step increments. Modifications of the shape factor resulting in variations in the anterior and posterior radii of an IOL were made, keeping the central thickness and paraxial powers static. Geometry data from three IOL models were also used. Corresponding postoperative spherical equivalent (SE) were computed for different IOL powers and assimilated to a prediction error of the formula due to the sole change in optical design alone. Formula accuracy was studied before and after zeroization on a uniform and non-uniform realistic IOL power distribution. The impact of the incremental change in optic design variability depended on the IOL power. Design modifications theoretically induce an increase in the standard deviation (SD), Mean Absolute Error (MAE), and Root Mean Square (RMS) of the error. The values of these parameters reduce dramatically after zeroization. While the variations in optical design can affect refractive outcomes, especially in short eyes, the zeroization of the mean error theoretically reduces the impact of the IOL's design and power on the accuracy of IOL power calculation.

Urbanization influenced the interactions between dissolved organic matter and bacterial communities in rivers

Increased urbanization and anthropogenic activities can alter dissolved organic matter (DOM) and complicate its interaction with bacteria in rivers' ecosystems, however, there is limited information about how bacterial communities respond to DOM components in rivers with different urbanization levels. Here, we combined spectroscopy-based DOM analysis and 16S rRNA gene amplicon sequencing to investigate the associations of bacterial taxa and DOM properties as well as the impacts of DOM on bacterial niche breadth in North River (NR) and West River (WR) of Jiulong River watershed, southern China, which had low and high urbanization levels, respectively. Spectroscopy analysis showed that hydrophilic DOM was predominant in both rivers whereas chromophoric DOM was higher in WR. Network analysis indicated that only seven bacterial genera (i.e., hg clade, chthoniobacter, Geobacter, Acidibacter, Alphal Cluster, Fluviicola, and Lacunisphaera) showed strong associations with DOM optical variables in both rivers, whereas more than 85% of DOM-bacterial genera associations were different between rivers. These results suggest that the relationship between DOM and bacterial communities had different responses in rivers with different urbanization levels. The partial least square path model indicated that the total standardized effect of physico-chemicals on bacterial niche breadth was higher in NR (0.62) than in WR (0.35), whereas humic substances showed an opposite pattern (NR: −0.42; WR: 1.67). The distinct effects of physico-chemicals and DOM on bacterial niche breadths between rivers could be due to the different effects of urbanization and human activities on the environmental conditions of riverine ecosystems. Our findings revealed a huge dissimilarity in the bacteria-DOM co-occurrence networks between rivers with different urbanization levels and provide a novel insight that urbanization may enhance DOM's importance to bacterial niche breadths.

Central BH Mass of Tidal Disruption Event Candidate SDSS J0159 through Long-term Optical Variabilities

In this manuscript, central BH mass is determined in the tidal disruption event (TDE) candidate SDSS J0159, through the 9 yr long variabilities, in order to check whether the virial BH mass is consistent with the mass estimated by other independent methods. First, the host galaxy spectroscopic features are described by 350 simple stellar templates, to confirm the total stellar mass of about 7 × 1010 M ⊙ in SDSS J0159, indicating the virial BH mass is about two magnitudes larger than the BH mass estimated by the total stellar mass. Second, based on an efficient method and fitting procedure, through the theoretical TDE model applied to describe the Sloan Digital Sky Survey ugriz-band light curves of SDSS J0159, the central BH mass can be determined as , well consistent with the Mσ relation expected BH mass and the total stellar mass expected BH mass. Third, the theoretical TDE model with the parameter of central BH mass limited to be higher than 108 M ⊙ cannot lead to reasonable descriptions of the light curves of SDSS J0159, indicating central BH mass higher than 108 M ⊙ is not preferred in SDSS J0159. Therefore, the TDE model determined central BH mass of SDSS J0159 is about two magnitudes lower than the virial BH mass, to support central broad-line regions, including accreting debris contributions from central TDE, and provide interesting clues to reconfirm that outliers in the space of virial BH mass versus stellar velocity dispersion should be better candidates of TDE.

Intra-night optical flux and polarization variability of BL Lacertae during its 2020–2021 high state

ABSTRACT In this work, we report the presence of rapid intra-night optical variations in both flux and polarization of the blazar BL Lacertae during its unprecedented 2020–2021 high state of brightness. The object showed significant flux variability and some colour changes, but no firmly detectable time delays between the optical bands. The linear polarization was also highly variable in both polarization degree and angle (electric vector polarization angle). The object was observed from several observatories throughout the world, covering a total of almost 300 h during 66 nights. Based on our results, we suggest that the changing Doppler factor of an ensemble of independent emitting regions, travelling along a curved jet that at some point happens to be closely aligned with the line of sight, can successfully reproduce our observations during this outburst. This is one of the most extensive variability studies of the optical polarization of a blazar on intra-night time-scales.

Nunataryuk field campaigns: understanding the origin and fate of terrestrial organic matter in the coastal waters of the Mackenzie Delta region

Abstract. Climate warming and related drivers of soil thermal change in the Arctic are expected to modify the distribution and dynamics of carbon contained in perennially frozen grounds. Thawing of permafrost in the Mackenzie River watershed of northwestern Canada, coupled with increases in river discharge and coastal erosion, triggers the release of terrestrial organic matter (OMt) from the largest Arctic drainage basin in North America into the Arctic Ocean. While this process is ongoing and its rate is accelerating, the fate of the newly mobilized organic matter as it transits from the watershed through the delta and into the marine system remains poorly understood. In the framework of the European Horizon 2020 Nunataryuk programme, and as part of the Work Package 4 (WP4) Coastal Waters theme, four field expeditions were conducted in the Mackenzie Delta region and southern Beaufort Sea from April to September 2019. The temporal sampling design allowed the survey of ambient conditions in the coastal waters under full ice cover prior to the spring freshet, during ice breakup in summer, and anterior to the freeze-up period in fall. To capture the fluvial–marine transition zone, and with distinct challenges related to shallow waters and changing seasonal and meteorological conditions, the field sampling was conducted in close partnership with members of the communities of Aklavik, Inuvik and Tuktoyaktuk, using several platforms, namely helicopters, snowmobiles, and small boats. Water column profiles of physical and optical variables were measured in situ, while surface water, groundwater, and sediment samples were collected and preserved for the determination of the composition and sources of OMt, including particulate and dissolved organic carbon (POC and DOC), and colored dissolved organic matter (CDOM), as well as a suite of physical, chemical, and biological variables. Here we present an overview of the standardized datasets, including hydrographic profiles, remote sensing reflectance, temperature and salinity, particle absorption, nutrients, dissolved organic carbon, particulate organic carbon, particulate organic nitrogen, CDOM absorption, fluorescent dissolved organic matter intensity, suspended particulate matter, total particulate carbon, total particulate nitrogen, stable water isotopes, radon in water, bacterial abundance, and a string of phytoplankton pigments including total chlorophyll. Datasets and related metadata can be found in Juhls et al. (2021) (https://doi.org/10.1594/PANGAEA.937587).

Deep learning detection of types of water-bodies using optical variables and ensembling

Water features are one of the most crucial environmental elements for strengthening climate-change adaptation. Remote sensing (RS) technologies driven by artificial intelligence (AI) have emerged as one of the most sought-after approaches for automating water information extraction and indeed. In this paper, a stacked ensemble model approach is proposed on AquaSat dataset (more than 500,000 images collection via satellite and Google Earth Engine). A one-way Analysis of variance (ANOVA) test and the Kruskal Wallis test are conducted for various optical-based variables at 99% significance level to understand how these vary for different water bodies. An oversampling is done on the training data using Synthetic Minority Oversampling Technique (SMOTE) to solve the problem of class imbalance while the model is tested on an imbalanced data, replicating the real-life situation. To enhance state-of-the-art, the pros of standalone machine learning classifiers and neural networks have been utilized. The stacked model obtained 100% accuracy on the testing data when using the decision tree classifier as the meta model. This study has been cross validated five-fold and will help researchers working in in-situ water bodies detection with the use of stacked model classification.

Investigating mixing patterns of suspended sediment in a river confluence using high-resolution hyperspectral imagery

In a river confluence, the fluvial process is complex because of the merging of different flows from rivers with distinct morphologies. However, the mixing of suspended sediment in river confluences has been inadequately investigated owing to the low resolution of conventional techniques for measuring suspended sediment. In this study, we investigated the mixing of suspended sediment at the confluence of the Hwang and Nakdong Rivers in South Korea by analyzing the spectral characteristics of suspended sediment from high-resolution hyperspectral data. We retrieved the suspended sediment concentration (SSC) from high-resolution hyperspectral imagery using the conversion technique, which is a cluster-based machine learning regression with optical variability. Hyperspectral clustering was first applied to classify the water regions of the main river (Nakdong) and its tributary (Hwang). The clustering result at a near-field after the confluence point was strongly related to the mixing degree of flow and sediments from two different rivers. Through segmentation, regressors with hyperspectral clusters enabled accurate SSC measurement. In particular, they precisely retrieved the concentrations adjacent to the mixing layer, even when the relationship between the spectral data and suspended sediment concentrations of the main river and tributary was substantially different. Using a detailed SSC map, we compared two cases of mixing patterns by calculating the variation in sediment concentration along the downstream river confluence. We found that the mixing pattern was different because of the flow velocity and a large sandbar near the stagnant area even though river discharge was similar for both cases. In the slow mixing case, the sandbar caused a velocity-deficit with a wake that reduced the variance of SSC due to the irregular flow near the mixing layer. In this case, dual counter-rotating secondary flow cells also limited mixing across the post-confluence reach. However, when the wake and dual counter-rotating secondary flows weakened due to the erosion of the sandbar and lower flow velocity, the transverse mixing coefficient increased by approximately 40 % compared to the slow mixing case. This study demonstrated that the hyperspectral approach can be used to investigate the mixing of suspended sediment in greater detail in complex river systems, as it provides high-resolution information that conventional measurement methods cannot explore.

Linking Dissolved Organic Matter to CO2 and CH4 Concentrations in Canadian and Chilean Peatland Pools

AbstractPeatland open‐water pools can be net carbon (C) emitters within heterogeneous peatland ecosystems that are generally net C sinks. However, the intra‐ and inter‐regional patterns and drivers of CO2 and CH4 production, as well as their link with dissolved organic matter (DOM) quality and quantity, remain poorly understood. We analyzed a range of optical characteristics and chemical variables controlling DOM and CO2 and CH4 concentrations in peatland pools across two regions with contrasting geographical properties (i.e., climate, topography, morphometry, and vegetation cover) of eastern Canada and Chilean Patagonia. We found inter‐regional patterns in CO2, CH4 and DOM concentrations and composition that were coherent with patterns in mean annual temperature and precipitation, and vegetation cover. Cross‐regional patterns of CO2 and CH4 were driven by morphometry, vegetation cover, and protein‐like DOM composition, a proxy of high biological activity, whereas temporal variations of CO2 and CH4 concentrations were further influenced by seasonal changes in humic‐like DOM composition, dissolved organic carbon and nutrients (i.e., total phosphorus and total nitrogen) concentrations, as well as pH and oxygen levels. Our results suggest that geophysical constraints associated with local peat and pool characteristics as well as climate patterns are major drivers of DOM and greenhouse gases concentrations and the links between them in broadly distributed peatland pools.

Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions

The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating soil salinity. Although the long-standing laboratory method of measuring salinity composition is accurate for determining soil salinity parameters, its use is frequently constrained by the high expense and difficulty of long-term in situ measurement. Soil salinity in the northern Nile Delta of Egypt severely affects agriculture sustainability and food security in Egypt. Understanding the spatial distribution of soil salinity is a critical factor for agricultural development and management in drylands. This research aims to improve soil salinity prediction by using a combined data collection method consisting of Sentinel-1 C radar data and Sentinel-2 optical data acquired simultaneously via integrated radar and optical sensor variables. The modelling approach focuses on feature selection strategies and regression learning. Feature selection approaches that include the filter, wrapper, and embedded methods were used with 47 selected variables depending on a genetic algorithm to scrutinize whether regions of the spectrum from optical indices and SAR texture choose the optimum combinations of selected variables. The sub-setting variables resulting from each feature selection method were used to train the regression learners’ random forest (RF), linear regression (LR), backpropagation neural network (BPNN), and support vector regression (SVR). Combining the BPNN feature selection method with the RF regression learner better predicted soil salinity (RME 0.000246; sub-setting variables = 18). Integrating different remote sensing data and machine learning provides an opportunity to develop a robust prediction approach to predict soil salinity in drylands. This research evaluated the performances of various machine learning models, overcame the limitations of conventional techniques, and optimized the variable input combinations. This research can assist farmers in soil-salinization-affected areas in better managing planting procedures and enhancing the sustainability of their lands.

Multiband optical variability of a newly discovered 12 blazars sample from 2013–2019

ABSTRACT Here we present the first optical photometric monitoring results of a sample of 12 newly discovered blazars from the ICRF – Gaia CRF astrometric link. The observations were performed from April 2013 to August 2019 using eight telescopes located in Europe. For a robust test for the brightness and colour variability, we use Abbé criterion and F-test. Moreover, linear fittings are performed to investigate the relation in the colour-magnitude variations of the blazars. Variability was confirmed in the case of 10 sources; two sources, 1429+249 and 1556+335 seem to be possibly variable. Three sources (1034+574, 1722+119, and 1741+597) have displayed large amplitude brightness change of more than one magnitude. We found that the seven sources displayed bluer-when-brighter variations, and one source showed redder-when-brighter variations. We briefly explain the various AGN emission models which can explain our results.

Correction to: Optical variability, rotation period, and inclination angle of the M9.5 dwarf BRI 0021−0214

Correction to: Optical variability, rotation period, and inclination angle of the M9.5 dwarf BRI 0021−0214

Multiwavelength monitoring of the nucleus in PBC J2333.9−2343: the giant radio galaxy with a blazar-like core

ABSTRACT 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 suggests a dramatic change of the jet direction. We present observations obtained between 2018 September and 2019 January (cadence larger than three days) with Effeslberg, SMARTS-1.3m, ZTF, ATLAS, Swift, and Fermi-LAT, and between 2019 April and 2019 July (daily cadence) with SMARTS-1.3 m and ATLAS. Large (>2 ×) flux increases are observed on time-scales shorter than a month, which are interpreted as flaring events. The cross correlation between the SMARTS-1.3 m 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 show 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 deg is also typical of a blazar. Therefore, we confirm the presence of a blazar-like core in the centre of this giant radio galaxy, likely a Flat Spectrum Radio Quasar with peculiar properties.

Mapping bathymetry and shallow water benthic habitats in inland and coastal waters with Sentinel-2

Accurate determination of the water depth and benthic macroalgae composition in coastal and inland water bodies is important due to the high commercial and ecological value of these regions. Benthic habitat mapping by conventional methods provides good accuracy, but these methods are very expensive and limited by manpower and time factor, which is necessary for mapping large areas. Remote sensing methods significantly complement contact measurements and give additional information about the hard-to-reach areas. The usefulness of free Sentinel-2 data in bathymetry and habitat mapping has been demonstrated in clear oceanic waters. The aim of this study was to further test the suitability of Sentinel-2 imagery in creating maps of dominant benthic types, as well as in estimating bathymetry in optically complex marine and lake waters. Two study sites were selected to cover a representative range of optical variability - Lake Garda in northern Italy (an intermediate between clear ocean and optically very complex waters) and Viimsi peninsula on the Estonian side of the Gulf of Finland, in the Baltic Sea. The results show that Sentinel-2 imagery with 10 m spatial resolution is suitable for bathymetry and habitat mapping in optically complex inland and coastal waters. Our results show that bathymetry mapping is sufficiently accurate in waters less than 4 m deep in the case of the Baltic Sea and up to 7 m deep in Lake Garda. In such depths, the R2 was above 0.93 in all four Sentinel-2 images used in the study. Bottom type mapping accuracy was in all cases over 73%, which is considered to be good, but due to the limited number of sampling points in both test sites, further studies are needed. The Sentinel-2 data quality and no cost of the imagery for users make it very useful for mapping bathymetry and shallow water habitats over large coastal areas or high number of lakes, especially in hard to reach by in situ methods areas. Moreover, the frequent revisit time allows moving from one-off maps to monitoring of temporal changes happening in dynamic shallow inland and coastal waters.

The mass distribution of quasars in optical time-domain surveys

ABSTRACTThe determination of supermassive black hole (SMBH) masses is the key to understanding the host galaxy build-up and the SMBH mass assembly histories. The SMBH masses of non-local quasars are frequently estimated via the single-epoch virial black-hole mass estimators, which may suffer from significant biases. Here we demonstrate a new approach to infer the mass distribution of SMBHs in quasars by modelling quasar ultraviolet /optical variability. Our inferred black hole masses are systematically smaller than the virial ones by 0.3–0.6 dex; the ∼0.3 dex offsets are roughly consistent with the expected biases of the virial black-hole mass estimators. In the upcoming time-domain astronomy era, our methodology can be used to constrain the cosmic evolution of quasar mass distributions.

Evaluating historic and modern optical techniques for monitoring phytoplankton biomass in the Atlantic Ocean

Traditional measurements of the Secchi depth (zSD) and Forel-Ule colour were collected alongside modern radiometric measurements of ocean clarity and colour, and in-situ measurements of chlorophyll-a concentration (Chl-a), on four Atlantic Meridional Transect (AMT) cruises. These data were used to evaluate historic and modern optical techniques for monitoring Chl-a, and to evaluate remote-sensing algorithms. Historic and modern optical measurements were broadly consistent with current understanding, with Secchi depth inversely related to Forel-Ule colour and to beam and diffuse attenuation, positively related to the ratio of blue to green remote-sensing reflectance and euphotic depth. The relationship between Secchi depth and Forel-Ule on AMT was found to be in closer agreement to historical relationships when using data of the Forel-Ule colour of infinite depth, rather than the Forel-Ule colour of the water above the Secchi disk at half zSD. Over the range of 0.03-2.95 mg m-3, Chl-a was tightly correlated with these optical variables, with the ratio of blue to green remote-sensing reflectance explaining the highest amount of variance in Chl-a (89%), closely followed by the Secchi depth (85%) and Forel-Ule colour (71-81%, depending on the scale used). Existing algorithms that predict Chl-a from these variables were evaluated, and found to perform well, albeit with some systematic differences. Remote sensing algorithms of Secchi depth were in good agreement with in-situ data over the range of values collected (8.5 - 51.8 m, r2>0.77, unbiased root mean square differences around 4.5 m), but with a slight positive bias (2.0 - 5.4 m). Remote sensing algorithms of Forel-Ule agreed well with Forel-Ule colour data of infinite water (r2>0.68, mean differences <1). We investigated the impact of environmental conditions and found wind speed to impact the estimation of zSD, and propose a path forward to include the effect of wind in current Secchi depth theory. We discuss the benefits and challenges of collecting measurements of the Secchi depth and Forel-Ule colour and propose future directions for research. Our dataset is made publicly available to support the research community working on the topic.

Diving into the magnetosphere of the Of?p star HD 108

ABSTRACT We analyse optical and X-ray spectroscopy of the Of?p star HD 108, known for its strong dipolar magnetic field and its optical line profile variability with a time-scale of 54 ± 3 yrs, interpreted as the stellar rotation period. Optical emission lines have now recovered from their minimum emission state reached in 2007–2008. The variations of the equivalent width of the Hα emission provide constraints on the inclination of the rotation axis (i) and the obliquity of the magnetic axis (β). The best agreement between model and observations is found for (i, β) pairs with i + β ≃ 85° and i ∈ [30°, 55°]. The Balmer emission lines display stochastic variability at the ∼5 per cent level on time-scales of a few days. TESS photometry unveils transient modulations on similar time-scales in addition to prominent red noise variations. A Chandra X-ray observation of December 2021, when the star was at a higher emission level, indicates a slight increase of the flux and a spectral hardening compared to the August 2002 XMM-Newton observation, taken near minimum emission state. Magnetohydrodynamic simulations are used to compute synthetic X-ray spectra. With our current best estimate of the $\dot{M}_{B=0}$ mass-loss rate, the simulated X-ray luminosity and spectral energy distribution agree very well with the observations. Finally, the radial velocities vary on a period of 8.5 yr with a peak-to-peak amplitude of 10–11 km s−1, suggesting orbital motion with an unseen companion of at least 4 M⊙.

Correction to: Optical variability of quasars with 20-year photometric light curves

Correction to: Optical variability of quasars with 20-year photometric light curves

Long-term optical variability of the Be/X-ray binary GRO J2058+42

Context. GRO J2058+42 is a transient hard X-ray pulsar that occasionally goes into outburst. The optical counterpart is a poorly studied OB-type companion. Aims. We investigate the long-term optical variability of the Be/X-ray binary GRO J2058+42 and the possible connection with periods of enhanced X-ray activity. Methods. We performed an optical spectroscopic and photometric analysis on data collected during about 18 yr. We also present the first optical polarimetric observations of this source. Results. The long-term optical light curves in the BVRI bands and the evolution of the Hα equivalent width display a sinusoidal pattern with maxima and minima that repeat every ∼9.5 yr. The amplitude of this variability increases as the wavelength increases from 0.3 mag in the B band to 0.7 in the I band. The Hα equivalent width varied from about −0.3 to −15 Å. We found a significant decrease in the polarization degree during the low optical state. The intrinsic polarization degree changed by ∼1% from maximum to minimum. The optical maxima occur near periods of enhanced X-ray activity and are followed by a drop in the optical emission. Unlike many other Be/X-ray binaries, GRO J2058+42 does not display V/R variability. Conclusions. The long-term optical variability agrees with the standard model of a Be/X-ray binary, where the circumstellar disk of the Be star grows and dissipates on timescales of 9−10 yr. We find that the dissipation of the disk started after a major X-ray outburst. However, the stability of the Hα line shape as a double-peak profile and the lack of asymmetries suggest the absence of a warped disk and argue against the presence of a highly distorted disk during major X-ray outbursts.

Dwarf AGNs from variability for the origins of seeds (DAVOS): optical variability of broad-line dwarf AGNs from the zwicky transient facility

ABSTRACTWe study the optical variability of a sample of candidate low-mass (dwarf and Seyfert) active galactic nuclei (AGNs) using Zwicky Transient Facility g-band light curves. Our sample is compiled from broad-line AGNs in dwarf galaxies reported in the literature with single-epoch virial black hole (BH) masses in the range MBH ∼ 104–108 M⊙. We measure the characteristic ‘damping’ time-scale of the optical variability τDRW, beyond which the power spectral density flattens, of a final sample of 79 candidate low-mass AGNs with high-quality light curves. Our results provide further confirmation of the MBH–τDRW relation from previous work within 1σ agreement, adding 78 new low-mass AGNs to the relation. The agreement suggests that the virial BH mass estimates for these AGNs are generally reasonable. We expect that the optical light curve of an accreting intermediate-mass black hole (IMBH) to vary with a rest-frame damping time-scale of ∼tens of hours, which could enable detection and direct mass estimation of accreting IMBHs in wide-field time-domain imaging surveys with sufficient cadence like with the Vera C. Rubin Observatory.