Do you want to publish a course? Click here

On possible proxies of AGN light curves cadence selection in future time domain surveys

69   0   0.0 ( 0 )
 Added by Andjelka Kovacevic
 Publication date 2021
  fields Physics
and research's language is English




Ask ChatGPT about the research

Motivated by upcoming photometric and spectroscopic surveys (Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Manuakea Spectroscopic Explorer), we design the statistical proxies to measure the cadence effects on active galactic nuclei (AGN) variability-observables (time-lags, periodicity, and structure-function (SF)). We constructed a multiple-regression model to statistically identify the cadence-formal error pattern knowing AGN time-lags and periodicity from different surveys. We defined the simple metric for the SFs properties, accounting for the observed SFs deviation relative to those obtained from the homogenously-sampled light curves. We tested the regression models on different observing strategies: the optical dataset of long light-curves of eight AGN with peculiarities and the artificial datasets based on several idealized and LSST-like cadences. The SFs metric is assessed on synthetic datasets. The regression models (for both data types) predict similar cadences for time-lags and oscillation detection, whereas for light curves with low variability ($sim 10%$), cadences for oscillation detection differ. For higher variability ($sim20%$), predicted cadences are larger than for $F_{var}sim 10%$. The predicted cadences are decreasing with redshift. SFs with dense and homogenous cadences are more likely to behave similarly. SFs with oscillatory signals are sensitive to the cadences, possibly impacting LSST-like operation strategy. The proposed proxies can help to select spectroscopic and photometric-surveys cadence strategies, and they will be tested further in larger samples of objects.



rate research

Read More

Active Galactic Nuclei (AGN) vary in their brightness across all wavelengths. Moreover, longer wavelength ultraviolet - optical continuum light curves appear to be delayed with respect to shorter wavelength light curves. A simple way to model these delays is by assuming thermal reprocessing of a variable point source (a lamp post) by a blackbody accretion disc. We introduce a new method, CREAM (textbf{C}ontinuum textbf{RE}processed textbf{A}GN textbf{M}arkov Chain Monte Carlo), that models continuum variations using this lamp post model. The disc light curves lag the lamp post emission with a time delay distribution sensitive to the disc temperature-radius profile and inclination. We test CREAMs ability to recover both inclination and product of black hole mass and accretion rate $mmdot$, and show that the code is also able to infer the shape of the driving light curve. CREAM is applied to synthetic light curves expected from 1000 second exposures of a 17th magnitude AGN with a 2m telescope in Sloan g and i bands with signal to noise of 500 - 900 depending on the filter and lunar phase. We also tests CREAM on poorer quality g and i light curves with SNR = 100. We find in the high SNR case that CREAM can recover the accretion disc inclination to within an uncertainty of 5 degrees and an $mmdot$ to within 0.04 dex.
We quantitatively assess, by means of comprehensive numerical simulations, the ability of broad-band photometric surveys to recover the broad emission line region (BLR) size in quasars under various observing conditions and for a wide range of object properties. Focusing on the general characteristics of the Large Synoptic Survey Telescope (LSST), we find that the slope of the size-luminosity relation for the BLR in quasars can be determined with unprecedented accuracy, of order a few percent, over a broad luminosity range and out to $zsim 3$. In particular, major emission lines for which the BLR size can be reliably measured with LSST include H$alpha$, MgII $lambda 2799$, CIII] $lambda 1909$, CIV $lambda 1549$, and Ly$alpha$, amounting to a total of $gtrsim 10^5$ time-delay measurements for all transitions. Combined with an estimate for the emission line velocity dispersion, upcoming photometric surveys will facilitate the estimation of black hole masses in AGN over a broad range of luminosities and redshifts, allow for refined calibrations of BLR size-luminosity-redshift relations in different transitions, as well as lead to more reliable cross-calibration with other black hole mass estimation techniques.
We examine the effects of time dilation on the temporal profiles of gamma-ray burst (GRB) pulses. By using prescriptions for the shape and evolution of prompt gamma-ray spectra, we can generate a simulated population of single pulsed GRBs at a variety of redshifts and observe how their light curves would appear to a gamma-ray detector here on Earth. We find that the observer frame duration of individual pulses does not increase as a function of redshift as one would expect from the cosmological expansion of a Friedman-Lemaitre-Robertson-Walker Universe. In fact, the duration of individual pulses is seen to decrease as their signal-to-noise decreases with increasing redshift, as only the brightest portion of a high redshift GRBs light curve is accessible to the detector. The results of our simulation are consistent with the fact that a systematic broadening of GRB durations as a function of redshift has not materialized in either the Swift or Fermi detected GRBs with known redshift. We show that this fundamental duration bias implies that the measured durations and associated Eiso estimates for GRBs detected near an instruments detection threshold should be considered lower limits to their true values. We conclude by predicting that the average peak-to-peak time for a large number of multi-pulsed GRBs as a function of redshift may eventually provide the evidence for time dilation that has so far eluded detection.
We describe the selection of the James Webb Space Telescope (JWST) North Ecliptic Pole (NEP) Time-Domain Field (TDF), a ~14 diameter field located within JWSTs northern Continuous Viewing Zone (CVZ) and centered at (RA, Dec)_J2000 = (17:22:47.896, +65:49:21.54). We demonstrate that this is the only region in the sky where JWST can observe a clean (i.e., free of bright foreground stars and with low Galactic foreground extinction) extragalactic deep survey field of this size at arbitrary cadence or at arbitrary orientation, and without a penalty in terms of a raised Zodiacal background. This will crucially enable a wide range of new and exciting time-domain science, including high redshift transient searches and monitoring (e.g., SNe), variability studies from Active Galactic Nuclei (AGN) to brown dwarf atmospheres, as well as proper motions of possibly extreme scattered Kuiper Belt and Inner Oort Cloud Objects, and of nearby Galactic brown dwarfs, low-mass stars, and ultracool white dwarfs. A JWST/NIRCam+NIRISS GTO program will provide an initial 0.8--5.0micron spectrophotometric characterization to m_AB ~ 28.8+/-0.3 mag of four orthogonal spokes within this field. The multi-wavelength (radio through X-ray) context of the field is in hand (ground-based near-UV--visible--near-IR), in progress (VLA 3GHz, VLBA 5GHz, HST UV--visible, Chandra X-ray, IRAM30m 1.3 and 2mm), or scheduled (JCMT 850micron). We welcome and encourage ground- and space-based follow-up of the initial GTO observations and ancillary data, to realize its potential as an ideal JWST time-domain community field.
Microlensing is a powerful tool for discovering cold exoplanets, and the The Roman Space Telescope microlensing survey will discover over 1000 such planets. Rapid, automated classification of Romans microlensing events can be used to prioritize follow-up observations of the most interesting events. Machine learning is now often used for classification problems in astronomy, but the success of such algorithms can rely on the definition of appropriate features that capture essential elements of the observations that can map to parameters of interest. In this paper, we introduce tools that we have developed to capture features in simulated Roman light curves of different types of microlensing events, and evaluate their effectiveness in classifying microlensing light curves. These features are quantified as parameters that can be used to decide the likelihood that a given light curve is due to a specific type of microlensing event. This method leaves us with a list of parameters that describe features like the smoothness of the peak, symmetry, the number of peaks, and width and height of small deviations from the main peak. This will allow us to quickly analyze a set of microlensing light curves and later use the resulting parameters as input to machine learning algorithms to classify the events.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا