Deep Modeling of Quasar Variability

Tachibana, Y.; Graham, M. J.; Kawai, Nobuyuki; Djorgovski, S. G.; Drake, A. J.; Mahabal, A.; Stern, Daniel

doi:10.3847/1538-4357/abb9a9

Cited by 31 publications

(58 citation statements)

References 64 publications

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“…It could be due to the intrinsic shape of the variability PSD, e.g., resulting from the break in the driving variability PSD and/or damping processes in the accretion disk (e.g., Sun et al 2020). An alternative explanation, as pointed out by, e.g., Tachibana et al (2020), is due to an averaging effect. Even if the flux of the accretion disk varies coherently, emission from different parts of the disk or from more spatially-extended regions [46.18, 47.04].…”

Section: Validity Of the Drw Prescriptionmentioning

confidence: 96%

“…The observed variable flux is then the convolution of the intrinsic variability pattern with the transfer function describing the time delays from different locations. Tachibana et al (2020) showed that, with a likely transfer function form (a semi-circle with a characteristic timescale of a month), the short-time variability power will be reduced due to averaging, producing a PSD slope close to −4 beyond this characteristic frequency.…”

Section: Validity Of the Drw Prescriptionmentioning

confidence: 99%

“…In general, such transfer functions would reduce the high-frequency power, leading to a steeper high-frequency end slope in the PSD. In both the intrinsic PSD scenario and the "smearing" scenario, it is possible that the characteristic timescale of this second high-frequency-end break, which reflects some characteristic size of the emission region, depends on the physical properties of the quasar, such as the black hole mass (Sun et al 2020;Tachibana et al 2020), in a similar way as the long-term damping timescale 𝜏 DRW .…”

Section: Validity Of the Drw Prescriptionmentioning

confidence: 99%

“…The optical photometric (continuum) variability of quasars encodes critical information about physical processes within the accretion disk of a rapidly accreting supermassive black hole (SMBH) that primarily emits in the rest-frame UV through optical. There has been significant progress in the past few decades in quantifying the observed optical variability of quasars with increasing sample sizes and light curve quality (e.g., Giveon et al 1999;Hawkins 2002;Vanden Berk et al 2004;de Vries et al 2005;Sesar et al 2006;Bauer et al 2009;MacLeod et al 2010MacLeod et al , 2012Sun et al 2014;Morganson et al 2014;Kasliwal et al 2015;Chen & Wang 2015;Simm et al 2016;Caplar et al 2017;Smith et al 2018; Sánchez-Sáez ★ E-mail: stone28@illinois.edu (ZS) Li et al 2018;De Cicco et al 2019;Luo et al 2020;Tachibana et al 2020;Laurenti et al 2020;Xin et al 2020;Suberlak et al 2021). However, the nature of optical variability of quasars is still poorly understood (e.g., Ulrich et al 1997;Padovani et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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Optical Variability of Quasars with 20-Year Photometric Light Curves

Stone,

Shen,

Burke

et al. 2022

Preprint

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We study the optical 𝑔𝑟𝑖 photometric variability of a sample of 190 quasars within the SDSS Stripe 82 region that have long-term photometric coverage during ∼ 1998 − 2020 with SDSS, PanSTARRS-1, the Dark Energy Survey, and dedicated follow-up monitoring with Blanco 4m/DECam. With on average ∼ 200 nightly epochs per quasar per filter band, we improve the parameter constraints from a Damped Random Walk (DRW) model fit to the light curves over previous studies with 10-15 yr baselines and 100 epochs. We find that the average damping timescale 𝜏 DRW continues to rise with increased baseline, reaching a median value of ∼ 750 days (𝑔 band) in the rest-frame of these quasars using the 20-yr light curves. Some quasars may have gradual, long-term trends in their light curves, suggesting that either the DRW fit requires very long baselines to converge, or that the underlying variability is more complex than a single DRW process for these quasars. Using a subset of quasars with better-constrained 𝜏 DRW (less than 20% of the baseline), we confirm a weak wavelength dependence of 𝜏 DRW ∝ 𝜆 0.51±0.20 . We further quantify optical variability of these quasars over days to decades timescales using structure function (SF) and power spectrum density (PSD) analyses. The SF and PSD measurements qualitatively confirm the measured (hundreds of days) damping timescales from the DRW fits. However, the ensemble PSD is steeper than that of a DRW on timescales less than ∼ a month for these luminous quasars, and this second break point correlates with the longer DRW damping timescale.

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Section: Validity Of the Drw Prescriptionmentioning

confidence: 96%

Section: Validity Of the Drw Prescriptionmentioning

confidence: 99%

Section: Validity Of the Drw Prescriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optical Variability of Quasars with 20-Year Photometric Light Curves

Stone,

Shen,

Burke

et al. 2022

Preprint

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“…In addition, notable efforts have been made for the classification of other astronomical light-curves, such as variable stars and stochastic events. Deep Learning encoder and autoencoder models (encoder-decoder), based on RNNs (Naul et al 2018;Jamal & Bloom 2020;Tachibana et al 2020;Donoso-Oliva et al 2021) and TCNN models (Jamal & Bloom 2020;Zhang & Bloom 2021), have been proposed for the automatic feature extraction from light-curves. Moreover, the direct processing of image-stamp sequences has been also proposed using Recurrent Convolutional Neural Networks (RCNNs) (Carrasco-Davis et al 2019;Gómez et al 2020).…”

Section: Previous Workmentioning

confidence: 99%

Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves

Pimentel¹,

Estévez²,

Förster³

2022

Preprint

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In astronomical surveys, such as the Zwicky Transient Facility (ZTF), supernovae (SNe) are relatively uncommon objects compared to other classes of variable events. Along with this scarcity, the processing of multi-band light-curves is a challenging task due to the highly irregular cadence, long time gaps, missing-values, low number of observations, etc. These issues are particularly detrimental for the analysis of transient events with SN-like light-curves. In this work, we offer three main contributions. First, based on temporal modulation and attention mechanisms, we propose a Deep Attention model called TimeModAttn to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-values assumptions, and explicit imputation and interpolation methods. Second, we propose a model for the synthetic generation of SN multi-band light-curves based on the Supernova Parametric Model (SPM). This allows us to increase the number of samples and the diversity of the cadence. The TimeModAttn model is first pre-trained using synthetic light-curves in a semi-supervised learning scheme. Then, a fine-tuning process is performed for domain adaptation. The proposed TimeModAttn model outperformed a Random Forest classifier, increasing the balanced-F 1 score from ≈ .525 to ≈ .596. The TimeModAttn model also outperformed other Deep Learning models, based on Recurrent Neural Networks (RNNs), in two scenarios: late-classification and early-classification. Finally, we conduct interpretability experiments. High attention scores are obtained for observations earlier than and close to the SN brightness peaks, which are supported by an early and highly expressive learned temporal modulation.

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Conditional Neural Process for nonparametric modeling of active galactic nuclei light curves

et al. 2021

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The consequences of complex disturbed environments in the vicinity of a supermassive black hole are not well represented by standard statistical models of optical variability in active galactic nuclei (AGN). Thus, developing new methodologies for investigating and modeling AGN light curves is crucial. Conditional Neural Processes (CNPs) are nonlinear function models that forecast stochastic time series based on a finite amount of known data without the use of any additional parameters or prior knowledge (kernels). We provide a CNP algorithm that is specifically designed for simulating AGN light curves. It was trained using data from the All‐Sky Automated Survey for Supernovae, which included 153 AGN. We present CNP modeling performance for a subsample of five AGNs with distinctive difficult‐to‐model properties. The performance of CNP in predicting temporal flux fluctuation was assessed using a minimizing loss function, and the results demonstrated the algorithm's usefulness. Our preliminary parallelization experiments show that CNP can efficiently handle large amounts of data. These results imply that CNP can be more effective than standard tools in modeling large volumes of AGN data (as anticipated from time‐domain surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time).

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Deep Modeling of Quasar Variability

Cited by 31 publications

References 64 publications

Optical Variability of Quasars with 20-Year Photometric Light Curves

Optical Variability of Quasars with 20-Year Photometric Light Curves

Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves

Conditional Neural Process for nonparametric modeling of active galactic nuclei light curves

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