Implications of Correlated GRB Pulse Properties

1. Implications of Correlated GRB Pulse Properties Presented at Los Alamos August 29, 2011 2nd GTAC Jon Hakkila Collaborators: Rob Preece, Tom Loredo, Carlo Graziani, Tim Giblin, Robert Wolpert, Alex Greene.

2. Synchrotron shock model (e.g. Rees & Meszaros, ApJL, 1994, 430, 94) or jitter radiation (Medvedev 2000, ApJ 540, 704. General Prompt GRB Properties Light Curves Spectra  Epk  25-50 keV, 50-100 keV, 100-300 keV, 300 keV-1 MeV

3. Hypernova Central Engine Model of Long GRBs Merging Compact Objects Central Engine Model of Short GRBs GRB Classes Short Long Intermediate? The Intermediate class - statistically identified in BATSE and Swift data, although not unambiguously associated with a separate source population.

4. Many GRB bulk properties (e.g. lag, Epeak, variability) correlate with luminosity. How can the intrinsic effects leading to these correlations be separated from other effects (e.g. class differences and selection biases)? • Inverse square law: distant GRBs appear fainter than similar nearby ones • GRB complexity results from overlapping pulses. • Long and Short GRBs appear to be inherently different. • Relativistic cosmology alters GRB observed properties: • Time dilation: distant GRBs have longer durations and temporal structures than similar nearby ones. • Energy shift: distant GRBs have their fluxes shifted to lower observed energies than similar nearby ones. • Energy shift: distant GRBs have their fluxes shifted to lower observed energies than similar nearby ones.

5. Black - summed 4-channel emission Green - high energy channel emission Red - low energy channel emission GRB pulse properties can help disentangle GRB complexity, via Observable Pulse Properties - obtained using semi-automated 4-parameter pulse model (Norris et al. 2005 ApJ, 627, 324; Hakkila et al. 2008 ApJ 677, L81): • Inherent time asymmetry (longer decay than rise rates), • Hard-to-soft spectral evolution, and • Longer pulse durations at lower energies. • Pulse peak flux (p256) - peak flux of summed multichannel data (black) measured on 256 ms timescale. • Pulse duration - time span when flux is e-3 of pulse peak flux. • Pulse peak lag- time span between channel 3 peak (100-300 keV; green) and channel 1 peak (25-50 keV; red). • Fluence - time-integrated flux. • Hardness - ratio of channel 3 fluence to channel 1 fluence. • Asymmetry - pulse shape measure; 0 is symmetric and 1 is asymmetric.

6. 300 keV - 1 MeV 100 keV - 300 keV 25 keV - 1 MeV 50 keV - 100 keV 3 2 1 25 keV - 50 keV Pulse-fitting example: GRB 950325a (BATSE 3480)

7. 300 keV - 1 MeV 100 keV - 300 keV 50 keV - 100 keV 1 2 3 4 25 keV - 1 MeV 25 keV - 50 keV Pulse-fitting example: GRB 910930 (BATSE 0840)

8. 100 keV - 300 keV 50 keV - 100 keV 1 2 25 keV - 1 MeV 25 keV - 50 keV Pulse-fitting example: GRB 930123 (BATSE 2600)

9. original burst CCF; short lag 1 2 25 keV - 1 MeV CCF of reconstructed pulse 1: long lag CCF of reconstructed pulse 2: short lag CCF of reconstructed pulses 1+2: short lag GRB 930123 (BATSE 2600) What does GRB lag measure (as obtained from the CCF)? The CCF lag is dominated by large amplitude, short pulses with short lags. Longer-lag pulses and pulse overlap can smear out this behavior.

10. Peak luminosity (L) vs. duration (w) and lag (l0) for BATSE GRBs: Pulse relations replace bulk prompt emission relations. Pulse Property Correlations (1390 pulses in 646 BATSE GRBs) The GRB lag vs. luminosity relation (Norris, Marani, & Bonnell 2000, ApJ 534, 248) is actually a pulse lag vs. pulse luminosity relation (Hakkila et al. 2008, ApJ 677, L81). Pulse duration also correlates with pulse lag, so pulse duration also indicates luminosity.

11. Supportive Pulse Observations from HETE-2 and Swift X-ray flares share many -ray pulse characteristics. However, flare energetics decrease and pulse durations increase with time after the trigger, indicating that less energy is present as flares develop (Margutti et al. 2010, MNRAS, 406, 2149). Pulse lag and pulse duration vs. pulse luminosity relations are found in HETE-2 (Arimoto et al. 2010, PASJ, 62, 487) and Swift (Chincarini et al. (MNRAS, 2010, 406, 2113).

12. More Pulse Property Correlations 1338 pulses in 610 BATSE GRBs: (Hakkila and Preece, 2011 ApJ (in press))

13. Long (upper) and Short (lower) GRB Pulse Correlations GRB pulse property correlations: short duration pulses have shorter lags, are brighter, are harder, and are more time symmetric than long duration pulses (Hakkila and Preece, ApJ 2011 (in press)).

14. GRB classification using pulse properties The pulses in single-pulsed Long GRBs are demonstrably longer than those in single-pulsed Short GRBs (Hakkila et al., 2010 BAAS). However, double- and triple-pulsed GRBs have less distinctive pulse differences (Adams et al., 2010 BAAS). -> The pulse distribution can help determine whether a GRB belongs to the Long or Short burst class, even though individual pulses are likely formed by a progenitor-independent process.

15. Correlated pulse properties are measured in the observer’s frame, indicating that effects of relativistic cosmology, including the inverse-square law, are of only secondary importance to intrinsic effects. Relates to the GRB pulse luminosity function!!! Peak luminosity (L) vs. duration (w)for BATSE GRBs.

16. BATSE 0111; z ≈ 0.9 BATSE 0332; z ≈ 0.9 Some low-z BATSE bursts BATSE 0563; z ≈ 0.8 BATSE 1406; z ≈ 0.8 Correlated pulse properties can be used to estimate Long GRB redshifts (Hakkila, Fragile, & Giblin, 2009, AIP Conf. 1133, 479) . BATSE 0214; z ≈ 4.3 BATSE 0237; z ≈ 5.3 BATSE 0594; z ≈ 5.4 BATSE 0803; z ≈ 4.6 Some high-z BATSE bursts

17. Short Swift GRBs (Norris, Gehrels, and Scargle 2011, ApJ 735, 23) extend the peak flux vs. duration relation to ms timescales. > Short and Long burst observable pulse properties appear to indicate a single distribution. GRB pulses have similar correlated properties independent of GRB class (and potentially of progenitor, environment, etc.). How can peak flux correlations be reconciled with differing luminosities of Short and Long GRBs? > Short and Long GRB pulse peak luminosities do not differ dramatically when the number of pulses per burst is taken into account (data from Ghirlanda et al. 2009, A&A 496, 585).

18. Many GRB prompt emission properties that correlate with luminosity are actually properties of convolved pulses: • The lag vs. peak luminosity relation (Norris et al. 2000, ApJ 534, 248): burst lag is convolved from pulse lags; peak luminosity is most often based on peak flux of the brightest pulse. • Variability vs. luminosity (Reichart et al. 2001, ApJ 552, 57): variability is a convolved measure of pulse duration and number of pulses; peak luminosity is most often based on peak flux of the brightest pulse. • Epeak vs. Eiso (Amati et al. 2002, A&A 390, 81): Epeak is the time-integrated peak of the combined spectra of all pulses; Eiso is the summed fluence luminosity of all pulses. • The internal luminosity function (Hakkila et al. 2007, ApJS, 169,62): the distribution of luminosity within a burst is a convolution of the luminosity within each pulse and the number of pulses.

19. Mean properties of pulses within GRBs (Hakkila and Preece, ApJ 2011 (in press)).

20. Peng et al. (2009, ApJ 698, 417) Correlative properties are bulk measurements of hard-to-soft (H2S) pulse evolution • Pulses start near-simultaneously at all energies (Hakkila and Nemiroff 2009, ApJ 705, 372). • Pulse Epeak values appear to decay from the pulse onset (e.g. Peng et al. 2009, ApJ 698, 417).

21. Intensity Tracking pulse: BATSE trigger 1886 Intensity tracking GRB pulsesapparently do not exist • Detailed study of 27 GRB pulses (14 hard-to-soft, 10 Intensity tracking, 3 ambiguous) pulses common to BATSE pulse database (Peng et al. 2009, ApJ 698, 417; Peng et al. 2010 NA, 332, 92; Lu et al. 2010 ApJ, 1146, 1154). • Intensity tracking pulses typically originate in complex, multi-pulsed GRBs, and most of these are composed of overlapping pulses. No IT pulses can be unambiguously fitted by a single pulse model, while 7 hard-to-soft pulses can. • There appears to be a single pulse type.

22. A Conundrum • Pulse peak intensity indicates the maximum photon rate detected from the decaying pulse spectrum. Yet instrumental spectral response, initial pulse spectrum, and GRB redshift all alter the pulse peak intensity. How does the detector sample observable pulse properties so that they always correlate with what should be a distorted peak intensity? • Occam’s Razor: A pulse should be defined by its hard-to-soft spectral evolution rather than by its intensity evolution. Since pulse intensity increases while the energy decays, peak intensity and pulse rise are symptoms of hard-to-soft evolution, rather than the climax. • Energy is injected at the beginning of the pulse, and the pulse we observe is simply the energy decay resulting from this initial injection. This appears to be true for all GRB pulses, regardless of class.

23. Implications of Correlated GRB Pulse Properties: • Pulse physics appears ubiquitous and is easily replicated across a variety of GRB environments. Properties of individual pulses within Long and Short GRBs may not be appropriate classification tools whereas pulse distributions within a GRB may be. • Theoretical pulse model needs not have many free parameters. • Standard model: Kinematic energy injection into a medium via relativistic shocks; the medium cools. Standard spectral model is preferentially a synchrotron spectrum (e.g. Rees & Meszaros, ApJL, 1994, 430, 94) or a thermal plus power law spectrum (e.g. Goodman 1986, ApJ 308, L47; Daigne & Mochkovitch 2002, MNRAS 336, 1271). • Correlative pulse relations are not a direct and simple consequence of the standard synchrotron shock model, which has no time-dependent component (Boci, Hafizi, & Mochkovitch 2010, A&A, 519, 76). • Jitter radiation appears to produce reasonable spectra coupled with short intensity tracking time histories that do not have correlated pulse properties (e.g. Medvedev, Pothapragada, & Reynolds 2009, ApJL, 702 L91). • Curvature in a relativistic outflow can explain some, evolving pulse correlations (e.g. Qin et al., Phys. Rev. D, 2006), but is model-dependent (rise vs. decay).