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  1. Cosmological Evolution of FR II Radio Galaxies Paramita Barai PhD Prospectus Talk Physics & Astronomy Georgia State University 19th Oct, 2004

  2. Contents • Introduction, Motivation, Goals & Procedures • Radio Observation Samples • Models of Radio Source Evolution • Multi-dimensional Monte-Carlo Simulation • Results -- Fits to Observation -- Statistical Tests • Future plans P. Barai, GSU

  3. Radio Galaxy Structure (Cygnus A) P. Barai, GSU

  4. Fanaroff Riley Class II Radio Galaxy • The separation between the points of peak intensity in the two lobes > (½ the total size of the source). • Bright lobes & hotspot. • P > 1025 W Hz-1 sr-1 • Synchrotron radiation  e-’s in the magnetic field in lobes • Unification paradigm for radio loud AGN  FR II’s are parent population of radio loud QSOs (different viewing angles) P. Barai, GSU

  5. FR Classes of Radio Galaxies P. Barai, GSU

  6. Motivation & Wider Implications • Expansion of Radio Galaxies • Impact on galaxy formation & evolution during the quasar era (1.5 < z < 3) • From multi-frequency observations : At z~1.5 • Star & Galaxy formation rate was greater • Comoving density of radio sources – 1000 times higher • Is there a causal connection? • How much volume of the relevant universe (baryonic filaments only) do radio lobes occupy? • Could the expanding lobes trigger star formation? • Rees – 1989; De-Young – 1991; Gopal-Krishna & Wiita – 2001 P. Barai, GSU

  7. Relevant Universe • Filaments containing baryonic material • Relevant Volume as a fraction of total Volume of Universe • 0.03 --- (z = 2: Quasar era) • 0.1 --- (z = 0) • Fig – Spatial distribution of warm / hot gas in the universe at present epoch -- Cen & Ostriker, 1999, ApJ, 514, 1 P. Barai, GSU

  8. Observational indication of radio lobes impacting star formation: High-z RGs in Optical & Radio(Bicknell et al., 2000)

  9. Numerical result – expanding radio lobes inducing star formation: Density contours at 0.8 Myr (left) and 1.1 Myr (right) after a RG bow shock struck a large elliptical cloud leaving dense cooling fragments (Mellema et al., 2002) P. Barai, GSU

  10. Youth Redshift Degeneracy • Only a small fraction of actual radio galaxies at high z can be observed • Various losses as radio sources evolve • Fig from Blundell et. al., 1999 • Grey bars – luminosity range to be detectable in 7C radio galaxy survey P. Barai, GSU

  11. Goals & Implications • Estimate: • Active life-time of radio galaxies. • The Relevant Volume Fraction of the Universe filled by Radio Lobes. • Model the Cosmological Evolution of the Radio Sources. P. Barai, GSU

  12. Procedures • Multi-dimensional Monte-Carlo Simulation • Primordial population of radio sources generated from some early time • Redshift distribution  observed Radio Luminosity Function (RLF) • Beam / Jet power distribution • Evolved by some ‘Radio Lobe Evolution Model’’  Impose survey flux limit  How many detected ? • Compare w/ radio observations: • Trends in P151 (Power at 151 MHz as obs.), D (linear size), z (redshift),  (spectral index) [P ~ –] • Best statistical fit  Best model of Radio Galaxy Evolution P. Barai, GSU

  13. Models • Models of evolution of radio lobes through the environment as they age: • Kaiser, Dennett-Thorpe & Alexander, 1997 (KDA) • Blundell, Rawlings & Willott, 1999 (BRW) • Manolakou & Kirk, 2002 (MK) • Best fit parameters for each model. • Sensitivity of the fits to the model parameters. • Compare these models – which is the best fit to observations? P. Barai, GSU

  14. Observed flux dominated by the radio lobe emission Complete Sample Single flux limit  P–z correlation To decouple P–z dependence use multiple complete samples at  fainter flux limits Selection frequency – (Cambridge Group) – 151 & 178 MHz Least synchrotron / IC losses, orientation biases High freq. (GHz) Core contribution in flux – Doppler boosting More Synchrotron, Adiabatic & IC losses Too low freq. (<100 MHz) Losses due to: Synchrotron self-absorption Free-free absorption Low energy cut off to relativistic particles which emit via synchrotron GHz peaked sources -- not detected Frequency Selection P. Barai, GSU

  15. Complete Observational Samples • 3CRR – • S178 > 10.9 Jy (S151 > 12.4 Jy), 4.23 sr, 145 sources. • Declination (B1950),   10 & at  10 from galactic plane • 6CE – • 2.00  S151 3.93 Jy, 0.102 sr, 58 sources. • 08h20m30s < R.A. < 13h01m30s , +3401'00" <  < +4000'00" • 7CRS – (I+II+III) • S151 > 0.5 Jy, 0.022 sr, 128 sources. • 7CI – 2h < R.A. < 2.4h , 29.5° <  < 34.3° • 7CII – 8.1h < R.A. < 8.4h , 24.3° <  < 29.6° • 7CIII – Within 3° radius of 18h +66° P. Barai, GSU

  16. P – D – z Planes for Complete Samples

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  20. Simplified Model of a Radio Source P. Barai, GSU

  21. Environment Density Profile BRW & MK 0 = 1.6710–23 kg m–3  = 1.5 a0 = 10 kpc KDA 0 = 7.210–22 kg m–3  = 1.9 a0 = 2 kpc Total separation between hotspots t : Age of source Q0 : Power of each jet Radio Lobe Size Evolution P. Barai, GSU

  22. Shared Physics in the 3 Models • Cylindrical jet moving out & accelerating particles (e–' s) at termination shock • Transport of relativistic particles (e– 's) from head to lobe -- emitting (in radio) via Synchrotron mechanism • Power Losses: • Adiabatic loss (as source expands) • Inverse Compton scattering off CMB photons • Synchrotron radiation • Intricacies of the models are different P. Barai, GSU

  23. e– 's initially accelerated First–order Fermi process at termination shock Energy distribution is power-law function of initial Lorentz factor Injection index: p = 2.14 uCMB ~ (z+1)4 Taken Constant for a source born at z Cocoon (lobe): pc ~ t(–4–)/(5–) : density exponent in external atmosphere ph / pc ~ 4RT2 RT = Axial Ratio = Length / Width = 2 5  RT  1.3 Key Assumptions of KDA Model P. Barai, GSU

  24. phead/plobe = 6 Injection index (p) governed by break frequencies Main diff. from KDA Break in frequency spectrum of synchrotron emission  break in energy spectrum of freshly injected particles Longest & shortest times particles are lingering in hotspot before injection into lobe -- Slow & Fast Break Frequencies -- bs & bf BRW Lobe Luminosity P. Barai, GSU

  25. Adiabatic losses in head compensated (by some turbulent re-acceleration process) during transport b/w jet termination shock & lobe Parameter  describes the transport  = 1 --- Diffusion Range of escape times for particles from high-loss head region MK Power Evolution P. Barai, GSU

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  27. tbir , tage , (zobs), Q0 For each source Sources born every 106 years – from z = 10  tbir tmax-age = 500 Myr Relevant Comoving Volume Volume of shell where source must be present s.t. we see it today w/in a max age of 500 MYr R(t) – Scale factor of Universe  = r (FLAT space time) Generating Primordial Radio Source Population P. Barai, GSU

  28. Relevant Comoving Volume(H0 = 71 km s–1 Mpc–1)

  29. Redshift distribution # per unit comov. vol: z0 = 2.2 z0 = 0.6 V  (z)  # of Sources Sources allocated radial coord. () -- uniform distribution within comoving volume tage = tobs - tbir Initial Power distribution if, Qmin < Q0 < Qmax Qmin = 51037 W Qmax = 51042 W x = 2.6 Multi-Dimensional Monte-Carlo Simulation P. Barai, GSU

  30. Radio Luminosity Function (Willott et. al. 2001) • Willott et. al. P. Barai, GSU

  31. Newest RLF Grimes et. al., 2004 (GRW) z2a = 1.684 z2b = 0.447 RLF from GRW P. Barai, GSU

  32. Some plot showing the sources generated (a small sample) ?? • Tage vs. z • Q vs z • Tbir vs. z P. Barai, GSU

  33. Model Comparison Results

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  35. Fraction of Relevant Universe filled by Radio Lobes (Alternative model) • Cosmological evolution of the ambient gas density • Case 1: Power law ambient density • Case 2: Constant density medium: • Total mass contained within a sphere of R Mpc = N  Mass within the standard z = 0 extended halo. • For z = 2.5, N = 30 is reasonable (since relevant volume fraction = 0.10 and about 50 % of the mass is in the filaments at z = 0). P. Barai, GSU

  36. Volumes filled by Radio Lobes over time, for the power-law model (Case 1: V1) & the dense IGM model (Case 2: V2, N=30, R=5Mpc).

  37. Comparison of Actual & Simulated P – D – z –  Planes

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  50. Kolmogorov – Smirnov (K–S) statistics to test fits of the 3 models : P(K–S)Fractional Probability that the quantities of model prediction and observation are drawn from same cumulative distribution population