Correlation in Spontaneous Fission of Cf252

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1 Correlation in Spontaneous Fission of Cf252 Stefano Marin 1, Matthew J. Marcath 1, Patricia F. Schuster 1, Shaun D. Clarke 1, and Sara A. Pozzi 1 1. Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 31 st October, 2018

2 Team members and collaborators CVT Team University of Michigan S. Marin M. J. Marcath (LANL) Lab Collaboration P. F. Schuster S. D. Clarke Prof. S. A. Pozzi V. A. Protopopescu P. Talou R. Vogt

3 Introduction and Motivation Why study fission?: High energy density Self sustaining Fission is ubiquitous in all fields of nuclear engineering: Neutron source for experiments Fissile material in reactor Fissile material in weapon 3

4 Mission Relevance Fundamental nuclear data are used to inform simulations and create new technologies. Characteristics of fission become signatures of fission: Improve accuracy of current detection methods, such as multiplicity counting and imaging. Prompts the creation of new verification technologies. 4 Fig. 1: Fast neutron multiplicity counter

5 What is Fission? 5

6 What is Fission? 6

7 Fission actually 7

8 Classical Characterization of Fission Emission For each particle we need to provide: - Type of particle - Position and time of origin - Energy and direction of motion - Multiplicity Fission events from: - Data sampling of MCNPX-PoliMi [1] - Fission event generator FREYA [2] - Fission event generator CGMF [2] INPUT: Nuclear data, distributions PROCESS: Fission event generators OUTPUT: Fission events Sampling of data 8

9 DIRECTION Fig. 3: Fission neutron angular distribution for 235 U. Bowman, 1962 [4] Fig. 2: Fission neutron multiplicity distribution for several isotopes. Terrel, 1957 [3] MULTIPLICITY Fig. 4: Fission neutron energy distribution for 235 U. Watt, 1952 [5] ENERGY 9

10 Correlations from Fission Fig. 5: Neutron energy spectrum as a function of neutron multiplicity as simulated by MCNPX-PoliMi for 252 Cf [1] Fig. 6: Neutron average energy dependency on multiplicity, from theoretical models. Lemaire, 2006 [6]. 10

11 Correlations from Fission Fig 7: Measured neutron-neutron angular distribution from 252 Cf, as a function of lower energy threshold. Marcath, 2016 [7] Fig 8: Simulated neutron-fission axis angle for several values of emission anisotropies. Chietera, 2018 [8] 11

12 Correlations from Fission Multiplicity Correlation? Photon Multiplicity Fig 9: average total gamma ray energy as a function of average fragment s neutron multiplicity. Talou, 2018 [2]. 12

13 Complete Distributions We want to infer the joint probability distributions: - How does one multiplicity affect the other? Fig. 10: A model for a joint probability distribution. The relationship between marginal and joint distributions is shown explicitly. 13

Experimental Distributions Fig. 12: Joint probability distributions from three fission event generators. Fig. 11: Multiplicities of photons and neutrons from the spontaneous fission of 252 Cf.

14 Experimental Distributions Fig. 12: Joint probability distributions from three fission event generators. Fig. 11: Multiplicities of photons and neutrons from the spontaneous fission of 252 Cf. From MCNPX-PoliMi evaluated data [1]. 14

15 Methods Requirements: - Simultaneous detection of both particle types. - Event-by-event analysis, i.e. fast response. Organic Scintillators! Fig 13: CAD model of the Chi-Nu detector array at the LANSCE facility in Los Alamos 15

16 Detector characterization System characteristics: - We calibrated each 7 x2 EJ-309 scintillator and optimized PSD parameters. - Combining the total geometric coverage (~8%), and intrinsic efficiency to the fission spectrum (<50%), only a 3% efficiency is achieved. Fig. 14: PSD characterization of the detectors. The discrimination line is Marcath, 2018[9]. Fig. 15: Light output response of EJ-309 and intrinsic efficiency as a function of energy. Marcath, 2018 [9]. 16

Investigating correlations We collect data, clean it from background, and use simulation to estimate the effect of response; better fission data would improve this step as well.

17 Investigating correlations We collect data, clean it from background, and use simulation to estimate the effect of response; better fission data would improve this step as well. We collected data on an event-byevent basis, recording the frequency of each multiplicity event. Fig 16: Collected data in the form of a joint multiplicity distribution. Marginal distributions show the unlikeliness of high multiplicities. Fig 17: Effects of detection efficiency on shape of distributions. 17

18 Finding correlations Correlations are often hard to determine, especially global correlation, i.e. those spanning the entire emission of the fission event. We use linear models to estimate the correlation, and quadratic models to estimate errors. We have found a competition in the fission emission of 252 Cf. Fig 18: Regression plots showing the dependency between the multiplicity of neutrons and photons. Fig. 19: Regression plots for fission event generators, for the fission of 252 Cf. Linear and quadratic fits of the data are shown. 18

19 CVT impact Physics models Correlations in nuclear data Verification techniques and technologies The needs of the verification and nonproliferation community are changing based on the most recent technological development. Nuclear data and nuclear models will help present and future developments. 19

20 Conclusion Correlations are the missing piece in nuclear data, and can put a limit to the accuracy of simulation. Correlations are the product of unobserved physical events, which produce observable quantities. Physics based simulations predict correlations. Experimentally, we can determine correlations, but care must be used to eliminate all sources of bias. 20

21 Acknowledgements The (CVT) would like to thank the NNSA and DOE for the continued support of these research activities. This work was funded by the under Department of Energy National Nuclear Security Administration award number DE-NA

22 References [1] S. A. Pozzi et al. MCNPX-PoliMi for nuclear nonproliferation applications" Nuclear Instruments and Methods in Physics Research A 694 (2012) [2] P. Talou et al. Correlated prompt ssion data in transport simulations" European Physical Journal A 54.1 (2018). [3] J. Terrell Distributions of Fission Neutron Numbers Physical Review 108 (1957) [4] H.R. Bowman et al.: Velocity and Angular Distributions of Prompt Neutrons from Spontaneous Fission of Cf252 Physical Review 126 (1962) [5] B. E. Watt Energy Spectrum of Neutrons from Thermal Fission of U235 Physical Review 87 (1952) [6] S. Lemaire et al. Monte Carlo approach to sequential neutron emission from fission fragments Physical Review C 72 (2005) [7] M. J. Marcath et al. Neutron angular distribution in plutonium-240 spontaneous fission Nuclear Instruments and Methods in Physics Research A 830 (2016) [8] A. Chietera et al. Angular correlations in the prompt neutron emission of spontaneous fission of 252 Cf European Physical Journal A 54 (2018). [9] M. J. Marcath et al. Measured and simulated 252Cf(sf) prompt neutron-photon competition Physical Review C 97 (2018)

23 Questions?

Cf-252 spontaneous fission prompt neutron and photon correlations

Cf-252 spontaneous fission prompt neutron and photon correlations M. J. Marcath 1,*, P. Schuster 1, P. Talou 2, I. Stetcu 2, T. Kawano 2, M. Devlin 2, R. C. Haight 2, R. Vogt 3,4, J. Randrup 5, S. D. Clarke