Stochastic Modeling of the Time Variability of ALMA Calibrators

Characterizing the variability of the extragalactic sources used for calibration in the Atacama Large Millimeter/submillimeter Array (ALMA) is key to assess the flux scale uncertainty of science observations. To this end, we model the variability of 39 quasars which have been used by ALMA as secondary flux calibrators using continuous time stochastic processes. This formalism is specially adapted to the multi-frequency, quasi-periodic sampling which characterizes the calibration monitoring of ALMA. We find that simple mixtures of Ornstein–Uhlenbeck processes can describe well the flux and spectral index variability of these sources for Bands 3 and 7 (91.5 and 103.5, and 343.5 GHz, respectively). The spectral shape of the calibrators are characterized by negative spectral indices, mostly between −0.35 and −0.80, and with additional concavity. The model provides forecasts, interpolations, and uncertainty estimations for the observed fluxes that depend on the intrinsic variability of the source. These can be of practical use for the ALMA data calibrator survey and data quality assurance.

[1]  A. E. Guzman Kalman: Forecasts and interpolations for ALMA calibrator variability , 2019 .

[2]  Hong-tao Wang,et al.  The deviation of optical variability of radio-quiet quasars from damped random walk , 2019, Astrophysics and Space Science.

[3]  T. Takata,et al.  Modeling the Variability of Active Galactic Nuclei by an Infinite Mixture of Ornstein–Uhlenbeck (OU) Processes , 2018, The Astrophysical Journal.

[4]  S. Eyheramendy,et al.  An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves , 2018, Monthly Notices of the Royal Astronomical Society.

[5]  G. Jogesh Babu,et al.  Autoregressive Times Series Methods for Time Domain Astronomy , 2018, Front. Phys..

[6]  M. Zwaan,et al.  ALMACAL IV: a catalogue of ALMA calibrator continuum observations , 2018, 1805.00024.

[7]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[8]  Gopal-Krishna,et al.  Stochastic Modeling of Multiwavelength Variability of the Classical BL Lac Object OJ 287 on Timescales Ranging from Decades to Hours , 2017, The Astrophysical Journal.

[9]  J. Vanderplas Understanding the Lomb–Scargle Periodogram , 2017, 1703.09824.

[10]  Daniel Foreman-Mackey,et al.  Fast and Scalable Gaussian Process Modeling with Applications to Astronomical Time Series , 2017, 1703.09710.

[11]  A. Readhead,et al.  Bimodal radio variability in OVRO-40 m-monitored blazars , 2017, 1702.05493.

[12]  S. Kozłowski A Method to Measure the Unbiased Decorrelation Timescale of the AGN Variable Signal from Structure Functions , 2016, 1701.00005.

[13]  S. Kozłowski,et al.  Limitations on the recovery of the true AGN variability parameters using Damped Random Walk modeling , 2016, 1611.08248.

[14]  R. Xue,et al.  Curvature of the spectral energy distribution, the inverse Compton component and the jet in Fermi 2LAC blazars , 2016, 1609.05697.

[15]  India.,et al.  EVIDENCE FOR TWO LOGNORMAL STATES IN MULTI-WAVELENGTH FLUX VARIATION OF FSRQ PKS 1510-089 , 2016, 1604.04335.

[16]  M. Zwaan,et al.  ALMACAL I: FIRST DUAL-BAND NUMBER COUNTS FROM A DEEP AND WIDE ALMA SUBMILLIMETER SURVEY, FREE FROM COSMIC VARIANCE , 2015, 1508.05099.

[17]  Matthias Durr,et al.  Smoothness Priors Analysis Of Time Series , 2016 .

[18]  Christina Kluge,et al.  Data Reduction And Error Analysis For The Physical Sciences , 2016 .

[19]  George Marsaglia,et al.  Classical Goodness-of-Fit Tests for Univariate Distributions , 2015 .

[20]  G. Richards,et al.  Are the variability properties of the Kepler AGN light curves consistent with a damped random walk , 2015, 1505.00360.

[21]  H. Wickham Simple, Consistent Wrappers for Common String Operations , 2015 .

[22]  M. Perucho,et al.  Multi-frequency properties of synthetic blazar radio light curves within the shock-in-jet scenario , 2014, 1412.7194.

[23]  E. Pian,et al.  An optical view of BL Lacertae objects , 2014, 1407.7615.

[24]  J. Chiang,et al.  Detection of significant cm to sub-mm band radio and γ-ray correlated variability in Fermi bright blazars , 2014, 1403.4170.

[25]  N. Marcelino,et al.  The Calibration of ALMA using Radio Sources , 2014 .

[26]  Brandon C. Kelly,et al.  FLEXIBLE AND SCALABLE METHODS FOR QUANTIFYING STOCHASTIC VARIABILITY IN THE ERA OF MASSIVE TIME-DOMAIN ASTRONOMICAL DATA SETS , 2014, 1402.5978.

[27]  Dae-Won Kim,et al.  Assessment of stochastic and deterministic models of 6304 quasar lightcurves from SDSS Stripe 82 , 2013, 1304.2863.

[28]  J. Scargle,et al.  KEPLER OBSERVATIONS OF RAPID OPTICAL VARIABILITY IN THE BL LACERTAE OBJECT W2R1926+42 , 2013, 1302.4445.

[29]  J. S. Stuart,et al.  CHARACTERIZING THE OPTICAL VARIABILITY OF BRIGHT BLAZARS: VARIABILITY-BASED SELECTION OF FERMI ACTIVE GALACTIC NUCLEI , 2012, 1209.3770.

[30]  G. Jogesh Babu,et al.  Modern Statistical Methods for Astronomy: With R Applications , 2012 .

[31]  S. Corder,et al.  How ALMA is calibrated: I Antenna-based pointing, focus and amplitude calibration , 2012, 1210.1899.

[32]  C. Koen Estimation of the coherence time of stochastic oscillations from modest samples , 2012 .

[33]  Noriyuki Kawaguchi,et al.  An origin of the radio jet in M87 at the location of the central black hole , 2011, Nature.

[34]  Garching,et al.  The long-term millimeter activity of active galactic nuclei , 2011, 1107.5456.

[35]  Brandon C. Kelly,et al.  A STOCHASTIC MODEL FOR THE LUMINOSITY FLUCTUATIONS OF ACCRETING BLACK HOLES , 2010, 1009.6011.

[36]  M. Böttcher,et al.  TIME-DEPENDENT RADIATION TRANSFER IN THE INTERNAL SHOCK MODEL SCENARIO FOR BLAZAR JETS , 2010, 1011.3113.

[37]  E. Bullock,et al.  MODELING THE TIME VARIABILITY OF SDSS STRIPE 82 QUASARS AS A DAMPED RANDOM WALK , 2010, 1004.0276.

[38]  Usa,et al.  QUANTIFYING QUASAR VARIABILITY AS PART OF A GENERAL APPROACH TO CLASSIFYING CONTINUOUSLY VARYING SOURCES , 2009, 0909.1326.

[39]  Brandon C. Kelly,et al.  ARE THE VARIATIONS IN QUASAR OPTICAL FLUX DRIVEN BY THERMAL FLUCTUATIONS? , 2009, 0903.5315.

[40]  S. Ravi Bayesian Logical Data Analysis for the Physical Sciences: a Comparative Approach with Mathematica® Support , 2007 .

[41]  T. Bastian,et al.  Accurate Amplitude and Flux Calibrationof the MMAM , 2007 .

[42]  Chris Koen,et al.  The analysis of irregularly observed stochastic astronomical time-series—I. Basics of linear stochastic differential equations , 2005 .

[43]  P. Gregory Bayesian Logical Data Analysis for the Physical Sciences: A Comparative Approach with Mathematica® Support , 2005 .

[44]  Philip C. Gregory,et al.  Bayesian Logical Data Analysis for the Physical Sciences: Acknowledgements , 2005 .

[45]  I. McHardy,et al.  Synchrotron Self-Compton Model for Rapid Nonthermal Flares in Blazars with Frequency-dependent Time Lags , 2004, astro-ph/0406235.

[46]  S. Guilloteau,et al.  An Amplitude Calibration Strategy for ALMA , 2002 .

[47]  C. Megan Urry,et al.  VARIABILITY OF ACTIVE GALACTIC NUCLEI , 1997 .

[48]  Richard A. Davis,et al.  Introduction to time series and forecasting , 1998 .

[49]  G. Kitagawa Smoothness priors analysis of time series , 1996 .

[50]  Stefan J. Wagner,et al.  Intraday Variability in Quasars and BL LAC Objects , 1995 .

[51]  James D. Hamilton Time Series Analysis , 1994 .

[52]  Martin J. Rees,et al.  Theory of extragalactic radio sources , 1984 .

[53]  Matts Roos,et al.  MINUIT-a system for function minimization and analysis of the parameter errors and correlations , 1984 .

[54]  J. Scargle Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data , 1982 .

[55]  J. Scargle Studies in astronomical time series analysis. I - Modeling random processes in the time domain , 1981 .

[56]  Roger D. Blandford,et al.  Relativistic jets as compact radio sources , 1979 .

[57]  Clive W. J. Granger,et al.  Time Series Modelling and Interpretation , 1976 .