On The Nature of High Energy Correlations

Transcription

1 On The Nature of High Energy Correlations Daniel Kocevski Kavli Institute for Particle Astrophysics and Cosmology SLAC - Stanford University

2 High Energy Correlations Epk vs. Eiso Amati et al νf ν peak (Epk) is related to the total isotropic equivalent radiated energy (Eiso) Seen in data from several spacecraft, including Fermi Has been used to determine z and hence Eiso by proxy Interesting GRB Physics? GRB Cosmology? Epk,src ~ Eiso 0.5 Amati 2009

3 Contentious History Nakar & Piran 2005 Band & Preece 2005 Amati 2007 Cabrera et al Butler, Kocevski, Bloom 2007 Ghirlanda et al Nava et al Butler, Kocevski, Bloom 2009

4 Malmquist Type Correlations σ 20σ 10σ 5σ Eiso (erg cmï ) Daniel Kocevski - NASA Goddard, Oct 29th Luminosity Distance 1029

5 Missing Bursts?? Why don t we see high fluence bursts with low Epk?

6 Simulate The Entire Process Assume source frame properties ϕ(l), Δtsrc, Epk distributions Populate GRBs at various redshifts Assume ρ(z) ~ SFR Simulate a detector response Sensitivity, energy window Calculate observed quantities Epk, Eiso, Δtobs

7 Spectral Evolution ergs cm-2 s-1 High z GRBs are fainter, redshifted, and evolve slower i F i (photons kev cm s ) How does the time integrated spectrum of GRBs depend on redshift? How does this effect T90 and Eiso? Energy (kev) Energy (kev) z= ergs cm s i F i (photons kev-2 cm 2-1 s 1) What is the net effect on the time integrated Epk and estimated Eiso? z=

8 Step 1: Simulate the GRB Assume a spectral shape Use Batse α, β, Epk distributions Assume some spectral evolution νfν Consistent with relativistic curvature Epk ~ t -1 ; Flux ~ Epk 2 Assume a luminosity function Flux Broken power law (Butler et al. 2010) Assume source frame T90 distribution Energy Time

9 Step 2: Distribute the GRBs Assign a comoving rate density Assume ρ(z) ~ Modified SFR Assume CDM Cosmology This gives a GRB rate Peaks at z ~ 2 Matches the observed distribution Rare GRBs are most probable at z ~ 2

10 Step 3: Simulate the detector Run simulated time-integrated spectrum through a detector response function BATSE Data Response Matrix Gives us the sensitivity vs. energy Produce counts light curves Assume background noise level Determine if spacecraft would have triggered on GRB If so, find duration using Bayesian Block algorithm

11 z = 1 T90 = 35 Eiso = 4.21e z = 2 T90 = 32 Eiso = 4.10e+53 Counts Counts Time (sec) Time (sec) z = 3 T90 = 31 Eiso = 3.88e z = 4 T90 = 24 Eiso = 3.37e Counts 2200 Counts Time (sec) Time (sec)

12 T90 vs. Redshift Swift 1000 Simulated Duration (sec) Duration (sec) LGRBs Redshift 344 LGRBs Redshift No evidence for time dilation in GRB durations vs. redshift Similar to problems of measuring galaxy size vs distance

13 Eiso (ergs) Luminosity (photons cmï2) Simulation Demographics z z Assuming ϕ(l), Δtsrc, and a trigger criteria (5σ) we can reproduce the observed luminosity and Eiso distribution as a function of redshift Dim bursts are only seen at low redshift, bright (rare) bursts occur at high redshift

14 Results We can plot Epk-Eiso for all simulated GRBs (regardless of whether they triggered the detector)

15 Results: Luminosity Intrinsic luminosity increases from left to right

16 Results: Burst Duration Higher Epk and Eiso GRBs appear longer

17 Results: Trigger Significance Amati 2002 Very distinct pattern of trigger significance that matches the shape of the Epk-Eiso correlation

18 Results: Redshift Distribution High Eiso GRBs only occur at high redshift Low Epk, High Eiso GRBs are redshifted out of the bandpass

19 Simulated Epk-Eiso Relation Low Observed Flux Swift Pre-Swift Low Observed Epk Detector thresholds convolved with probability of seeing bright (rare) GRBs can reproduce the observed relation

20 Conclusions The Epk-Eiso correlation appears to be due to a combination of complex threshold effects and a population cutoff Very simple FRED model demonstrates the effects How do the Epk-Eiso correlations seen within individual pulses fit into this picture? How do X-ray flares fit into this picture? Nice example (and warning): It s not enough to understand just your detector thresholds Must also consider how objects are distributed in space

21 Results We can plot Epk-Eiso for all simulated GRBs (regardless of whether they triggered the detector) Daniel Kocevski - NASA Goddard, Oct 29th 2010

22 Results: Luminosity Intrinsic luminosity increases from left to right Daniel Kocevski - NASA Goddard, Oct 29th 2010

23 Results: Burst Duration Higher Epk and Eiso GRBs appear longer Daniel Kocevski - NASA Goddard, Oct 29th 2010

24 Results: Trigger Significance Very distinct pattern of trigger significance that matches the shape of the Epk-Eiso correlation Daniel Kocevski - NASA Goddard, Oct 29th 2010

25 Results: Redshift Distribution High Eiso GRBs only occur at high redshift Low Epk, High Eiso GRBs are redshifted out of the bandpass Daniel Kocevski - NASA Goddard, Oct 29th 2010

26 Simulated Epk-Eiso Relation Low Observed Flux Low Observed Epk Detector thresholds convolved with probability of seeing bright (rare) GRBs can reproduce the observed relation Daniel Kocevski - NASA Goddard, Oct 29th 2010

27 Conclusions The Epk-Eiso correlation appears to be due to a combination of complex threshold effects and a population cutoff Very simple FRED model demonstrates the effects How do the Epk-Eiso correlations seen within individual pulses fit into this picture? How do X-ray flares fit into this picture? Nice example (and warning): It s not enough to understand just your detector thresholds Must also consider how objects are distributed in space Daniel Kocevski - NASA Goddard, Oct 29th 2010