Wavepacket treatment of reactor neutrino oscillation experiments and its implications on determining the neutrino mass hierarchy
Abstract
We derive the neutrino flavor transition probabilities with the neutrino treated as a wave packet. The decoherence and dispersion effects from the wavepacket treatment show up as damping and phaseshifting of the planewave neutrino oscillation patterns. If the energy uncertainty in the initial neutrino wave packet is larger than around 0.01 of the neutrino energy, the decoherence and dispersion effects would degrade the sensitivity of reactor neutrino experiments to mass hierarchy measurement to lower than 3 confidence level.
Keywords:
neutrino oscillation neutrino wave packet reactor neutrino∎
e1ylchan87@gmail.com \thankstexte2mcchu@phy.cuhk.edu.hk \thankstexte3kmtsui@icrr.utokyo.ac.jp \thankstexte4wongchf@mail.sysu.edu.cn \thankstexte5jyxu@phy.cuhk.edu.hk
1 Introduction
Much information regarding neutrino mixing have been revealed in the past few decades. However, in most oscillation data analyses, neutrinos are described as plane waves with definite energy and momentum. Since neutrino production and detection are spatially localized, a wavepacket description is more general and appropriate for a complete understanding of neutrino oscillations. Even if the planewave treatment is a good approximation for neutrino flavor transitions, the wavepacket decoherence and dispersion effects could still give rise to small corrections to oscillation parameters. We investigate the wavepacket treatment in detail, constrain the energy uncertainty of reactor antineutrinos, and calculate corrections to the mixing parameters by the Daya Bay DayaBay2013 () and KamLAND KamLand () reactor neutrino experiments.
According to our analyses, the wavepacket treatment does not produce significant modifications of the mixing parameters measured by current reactor neutrino experiments based on the planewave analysis. However, current experimental data allows a large possible range in the initial momentum width of the neutrino wave packet (). If the initial momentum/energy uncertainty of the neutrino wave packet is larger than around 0.02 of the neutrino energy, the decoherence and dispersion effects could have significant effects on future measurements of the neutrino mass hierarchy.
In this article, we apply a wavepacket treatment to neutrino oscillations Fuji:2006mq (); Blennow:2005yk (); Bernardini:2006ak (); Giunti:2007ry (); Naumov:2013vea (); Akhmedov:2009rb (); Naumov:2010um () and examine its phenomenological implications on reactor neutrino experiments at medium baseline.
The article is organized as follows: In Section 2, the wavepacket treatment is briefly reviewed. In Section 3, we use the survival probability derived in Section 2 to explore the sensitivity of potential measurements of mass hierarchy with medium baseline reactor experiments. We summarize and conclude in Section 4.
2 Wavepacket impact on current reactor neutrino experiments
2.1 Wavepacket treatment for neutrino oscillations
The planewave description of neutrino oscillation has been developed for almost 40 years Eliezer:1975ja (). In the standard calculation of planewave neutrino oscillations, the probability of detecting a neutrino flavor state with energy , evolved from a pure flavor state , at a distance from the production point is given by
(1) 
where denote the elements of the PMNS matrix and are the differences of the mass eigenvalues squared Maki:1962mu ().
However, as neutrino production and detection are spatially localized, there must be finite intrinsic energy/momentum uncertainties and a neutrino should be described by a wave packet. The wavepacket character of light has been discussed in details in Ref. JenkinsWhiteFundamentalsOfOptics1981 (). Based on similar arguments as in that reference, all particles are produced and detected as wave packets. In 1976, the wavepacket nature of propagating neutrinos was proposed Nussinov:1976uw (). A wavepacket description is expected to be more general and appropriate for a complete understanding of neutrino oscillations Kayser:1981ye (); Kiers:1995zj (); Giunti:2002xg (); Dolgov:2005vj (); Giunti:2007ry (); Akhmedov:2009rb (); Naumov:2010um (). Nevertheless, there are also arguments against the wavepacket treatment. Refs. Stodolsky:1998tc (); Lipkin:2002sq () argue that a wavepacket description is unnecessary as the oscillation system is stationary. However, it has been pointed out in Refs. Beuthe:2001rc (); Giunti:2003ax (); Farzan:2008eg (); Bilenky:2011pk (); priv:Dmitry () that the authors of Refs. Stodolsky:1998tc (); Lipkin:2002sq () have mixed the macroscopic stationarity with microscopic stationarity. The wavepacket description of neutrino oscillations is necessary at least in principle.
Therefore, a neutrino is described by a wave packet as it propagates freely Kiers:1995zj (); Akhmedov:2009rb (); Naumov:2010um ():
(2)  
(3) 
where is an energy eigenstate with energy , is the mean momentum, is the width of the wave packet in momentum space^{1}^{1}1Here, is the effective uncertainty, with , which has included both the production and detection neutrino energy uncertainties Giunti:2002xg (); Giunti:2003ax (); Beuthe:2001rc (). Moreover, we would like to point out that represents the energy uncertainty of detection at the microscopic level, ie., that of the inversebeta decay reaction. This is different from the detector energy resolution, which is determined by macroscopic parameters such as the performance of PMTs and geometry of the antineutrino detector, etc. In principle, the detector resolution is irrelevant for the size of the neutrino wave packets., assumed to be independent of the neutrino energy here, and is a neutrino flavor state. Fig. 1 pictorially describes the wavepacket effects on the propagations of neutrino mass eigenstates.
In order to calculate the integral in Eq. (2.1), we expand the energy around the mean momentum up to second order
(4) 
where , is the group velocity of wave packet. We use Eq. (2.1), (3) and (4) to calculate the neutrino flavor transition probabilities at baseline :
(5)  
where  
Detailed derivation of Eq. (5) is shown in Appendix A. In Eq. (5), the terms in the first bracket correspond to the standard planewave oscillation probabilities, and those in the second bracket represent the modifications from the wavepacket treatment. The exp term corresponds to the decoherence effect due to the fact that different mass states propagate at different speeds and they gradually separate and stop to interfere with each other, resulting in a damping of oscillations. The terms depending on come from the quadratic correction in Eq. (4); they describe the dispersion effects and are dependent on the dispersion length(s)^{2}^{2}2The “dispersion length” in this report represents the distance where the dispersion effect becomes important in the neutrino oscillation. A different definition of dispersion length and more detailed discussion of dispersion can be found in Beuthe:2001rc (); Naumov:2013vea (); Dolgov:2005vj (). . Furthermore, are proportional to , while only. Therefore, if , the dispersion effect is expected to be more suppressed and negligible. Dispersion has two effects on the oscillations. On the one hand the spreading of the wave packet compensates for the spatial separation of the mass states, hence restoring parts of their interferences. On the other hand, dispersion reduces the overlapping fraction of the wave packets, and thus the interference or oscillation effects cannot be fully restored. Moreover, it also modifies the flavor oscillation phases:
(6) 
with deviations from the standard planewave oscillation phase written in the parentheses. If = 0, then just reduce to the standard planewave oscillation phases.
Additional discussions about the phenomenological consequences of the wavepacket treatment and details of the derivation of Eq. (5) can be found in OurPaper (); Bernardini:2006ak (); Naumov:2013vea (); Beuthe:2001rc (); Akhmedov:2009rb ().
In this paper, we focus on the analyses of reactor neturino experiments. According to our wavepacket treatment, in reactor neutrino experiments, the antielectron neutrino survival probability is
(7) 
2.2 The constraints from current reactor experiments
To date, the value of the parameter has not yet been determined. If is not negligible, the wavepacket effects could be significant and have important implications on current and future neutrino oscillation experiments. In this article, we constrain by analyzing the published Daya Bay and KamLAND data shown in references An:2015rpe () and KamLand (), considering statistical errors only. Figs. 2 and 3 show the data points from Daya Bay experiment An:2015rpe (), along with the oscillation curves corresponding to different values of . Fig. 4 shows the data points from KamLAND KamLand () and the oscillation curves of planewave and wavepacket treatments. In Figs. 2 to 4, are the fluxweighted average reactor baselines^{3}^{3}3In Daya Bay, the effective baselines are calculated for all three experimental halls., and the error bars just show the statistical uncertainties.
We use Eq. (2.1) to fit the data points in Figs. 2 and 3 to get the constraint of from the Daya Bay Experiment. In these figures, the black (solid) curves correspond to = 0, sin = 0.084 and = eV, same as those of the standard planewave treatment. The brown (dashed) curve represents the wavepacket result with = 0.1, sin = 0.084 and = eV (the bestfit mixing parameters when = 0.1). The green (dashed) curve shows the oscillation pattern with = 0.3, sin = 0.096 and = eV (the bestfit mixing parameters when = 0.3). The blue (dotdashed) curve corresponds to = 0.5, sin = 0.108 and = eV (the bestfit mixing parameters when = 0.5). The data points in Figs. 2 and 3 show clearly the existence of neutrino oscillation, and the data agree with the derived by the planewave approach (Eq. (2.1)) for a certain set of mixing parameters. This is not surprising since the baselines of the Daya Bay Experiment are short compared to the coherence length, so that the oscillations are not washed out yet.
Moreover, the decoherence and dispersion effects are dependent on the baseline , and thus the wavepacket impact in the far hall of Daya Bay is expected to be more significant than in the near halls. In the plots in Fig. 3, the black (solid) and brown (dashed) curves overlap almost completely, while the differences between the black, green and blue curves are also small compared to the error bars. However, for the far hall EH3, the wavepacket impact becomes significant for 0.3. It is because in Eq. (2.1), the term depends on the baseline . Since the effective baseline of EH3 is longer, the damping of oscillation (decoherence effect) in EH3 is more significant.
The result of our data analysis is shown in Fig. 5. Here, we have just considered the statistical errors. The constraints on the parameters could become worse with the systematic errors taken into account. Fig. 5 shows that the wavepacket impact is not significant in Daya Bay experiment and it hardly affects the measurement of . The vertical black line in the figure represents the bestfit value of sin in the planewave model. A larger value of , or larger decoherence effect, implies that the true value of should be larger^{4}^{4}4If is nonnegligible but we still see oscillation effect from the data, it means that the true value of the mixing angle is actually larger than we expected in planewave assumption.. From our analysis, there is no strong evidence to suggest nonzero . Moreover, our result suggests that the modification of is not significant even when the wavepacket framework is considered. The bestfit value of sin from planewave analysis (vertical black line) is not ruled out even with 1 C.L. We believe that it is because the effective baselines of the Daya Bay Experiment are short compared to the coherence length.
We perform the similar analysis with KamLAND data from Fig. 4. The result of our data analysis is shown in Fig. 6. Again, the systematic errors are not considered.
Eq. (5) shows that , which implies that the dispersion length is much longer than coherence length if is negligible. This means that the decoherence effect from separation of wave packets is expected to be more significant than the dispersion effect. However, the dispersion effect is also important because it partly restores the oscillation, especially in the case of large . If the dispersion effect is not considered, the modifications of the true values of mixing angles in Figs. 5 and 6 would be more significant. The bounds in Figs. 5 and 6 come from the combination of decoherence and dispersion effects, but the contribution from decoherence is expected to be dominant.
Figs. 5 and 6 show that wavepacket effects are not significant in the current reactor neutrino experiments. The 1 upper bound of the energy uncertainty , which is larger than some previous theoretical estimations () SigmaxRange (); Giunti:2007ry (); Akhmedov:2009rb (); Giunti:2006fr (). Although our analyses have not considered systematic errors, our study on the current reactor experiments suggest that can be around for reactor experiments. We emphasize that in this article is an effective parameter which include both the production and detection neutrino energy uncertainties. The estimation of the value of this parameter or the size of neutrino wave packet has not come to a strong conclusion yet. Conventionally, it has been argued that should be much smaller than 1 Giunti:2006fr (). However, there are still no experimental support for such an assumption. In this paper we do not calculate or suggest the theoretical value of . We point out that the wavepacket impact is not significant for current reactor neutrino experiments. Nevertheless, the 1 C.L. allowed range of is , within which the potential wavepacket impact could lead to significant effects and additional challenges in future neutrino oscillation experiments.
3 Measuring neutrino mass hierarchy in reactor neutrino experiments
3.1 Measurement of neutrino mass hierarchy
The signs for and have not yet been determined. Normal Hierarchy (NH) corresponds to positive and with as the lightest mass state. Inverted Hierarchy (IH) corresponds to negative and with as the lightest mass state TheoryOfNu ().
As indicated by the recent data obtained by the Daya Bay Experiment, An:2015rpe (). With such a (relatively) large value of , it is possible to determine the neutrino mass hierarchy (MH) in reactor neutrino experiments at medium baseline Petcov (); Yifang ().
For a detector at baseline , the observed antielectron neutrino flux of visible energy is given by
(8) 
is the reactor neutrino energy spectrum, and is the inverse beta decay cross section. is the detector response function with energy resolution , which will be discussed in more detail in the next subsection.
It is known that at a baseline of around 50 km, which corresponds to the first minimum of oscillation for reactor neutrinos, the sensitivity for measuring MH is maximal. The upper panel in Fig. 7 shows at = 53 km for NH and IH, with standard oscillation parameters in the planewave treatment ( in Eq. (2.1)).
Lower panel: Same as in upper panel, but with = 0.1. The two curves for NH and IH completely overlap due to the wavepacket impacts.
3.2 Impact of wavepacket treatment
Our wavepacket treatment shows that the amplitudes of neutrino oscillations will be reduced. In particular, damping of the oscillations will be significant for an intermediate baseline reactor neutrino experiment if is . The lower panel in Fig. 7 shows the neutrino visible energy spectrum at a baseline of 53 km, with standard neutrino mixing parameters and = 0.1. If is large, the neutrino spectra for NH and IH are indistinguishable from each other.
We modify the GLoBES software GLoBES_04 (); GLoBES_07 () to perform numerical simulations of a 53 km baseline reactor neutrino experiment, using a similar setup as in Qian:2012xh () and Yifang (): a 20 kton detector with 3% energy resolution, reactors with a total thermal power of 40 GW and a nominal running time of six years. In the absence of oscillations, the total number of events is about 10. As this paper focuses on the wavepacket impact, the systematic errors were not taken into account in the following simulations^{5}^{5}5We have also performed simulations with the following systematic errors Yifang (): 2% correlated reactor uncertainty, 0.8% uncorrelated reactor uncertainty, 1% spectral uncertainty, and 1% detectorrelated uncertainty. The results are similar to what we present here.. We took the oscillation parameter values from global analysis Global_analysis () as = 7.54 10 eV, ( + )/2 = 2.43 10 eV, sin = 0.307 and sin = 0.0241.
To distinguish between NH and IH, we quantify the sensitivity of the MH measurement by employing the leastsquares method, based on a function:
(9) 
where is the measured neutrino events in the th energy bin, and is the predicted number of neutrino events with oscillations taken into account (without considering the systematic errors)^{6}^{6}6Without loss of generality, in our simulations, NH is assumed to be the true mass hierarchy. The result is identical if we assume IH to be true.. The number of bins used is 164, equally spaced between 1.8 and 10 MeV.
We fit the hypothetical data set with as the free variable, defined as
(10) 
The capability to resolve the mass hierarchy is then given by the difference between the minimum value for IH and NH:
(11) 
is used to explore the wavepacket effects as well as the impact of statistics and systematics in measuring the MH.^{7}^{7}7Note that and can be located at different values of . If , which corresponds to the planewave treatment, we get 19.5, implying that we could distinguish the MH with a confidence level of nearly 4 , as shown in the upper panel of Fig. 8. However, if , the sensitivity will be reduced due to the damping of oscillations and will drop to around 3.35, as shown in the bottom panel of Fig. 8.
The solid (black) curve in Fig. 9 further shows the variation of as a function of . It shows that drops rapidly with , to become smaller than 3 C.L. as . In this case, it is difficult to determine MH.
In the rest of this section we will investigate how to increase the sensitivity to MH by improving the experimental setup.
3.3 Consequences of energy resolution and statistics
^{8}^{8}8The xaxes in most of the following figures just represent the “truevalue” of .References Zhan:2009rs (); Petcov (); Takaesu:2013wca () suggest that the sensitivity of MH measurement depends on the detector resolution. As shown in Eq. (3.1), the observed flux depends on the detector response function and energy resolution , which are defined as:
(12)  
where the detector energy resolution is parameterized as  
(13) 
In the previous subsection, we assumed that = 0.03 and = 0 in order to achieve a 3% detector resolution.
As mentioned in the footnote in Section 2, the detector resolution is different from the energy uncertainty of detection at the microscopic level. The detector resolution is determined by the properties of the macroscopic detector, which should be taken into account even in the planewave assumption. Similar to the decoherence effect due to , a poor energy resolution can also destroy the measured oscillation effect. At = 53 km, the oscillation could be smeared out with a finite energy resolution, and the MH information could be destroyed, particularly in the case of large . Fig. 10 shows that if , which is allowed by the Daya Bay and KamLAND data, the detector resolution (or the parameter in Eq. 13), must be better than 3% in order to determine the MH with a C.L. of more than 2 .
On the other hand, we can also improve the MH sensitivity by collecting more data. Fig. 11 shows the impact of statistics on the measurement of MH as a function of , assuming a detector energy resolution of 3%, as suggested by references Yifang (); Zhan:2009rs (). Much longer run time would be required for a 2 C.L. measurement if is larger than 0.01.
3.4 The optimal baseline
Without considering the wavepacket impact, 50 60 km is the ideal location to measure mass hierarchy for reactor neutrino experiments Qian:2012xh (); Yifang (). However, in the presence of significant wavepacket impact, longer baseline would lead to larger damping of the oscillation amplitude. Although reducing the baseline will lead to a loss of maximal phase difference between the NH and IH oscillation curves, this can save back part of the oscillation. Therefore, the optimal baseline of measuring MH could be shorter than 50 km, depending on the value of .
Fig. 12 shows the as a function of baseline for different values of . In the case of the planewave limit (), the MH can be distinguished with a confidence level of nearly 4 at 53 km. However, as increases, the maximum shifts to shorter baseline. Fig. 13 further shows the value of optimal baseline as a function of .
4 Conclusion
The wavepacket impacts on current reactor neutrino oscillation and neutrino mass hierarchy measurements have been discussed. Our analyses show that the wavepacket treatment would not lead to significant modifications of the oscillation parameters from the Daya Bay and KamLAND results based on planewave assumptions. Moreover, our analyses also suggest that the energy uncertainty parameter can be .
The decoherence and dispersion effects depend not only on the initial neutrino energy uncertainty, but also the values of and baseline. Since the measurement of the neutrino MH in medium baseline reactor neutrino experiments relies on the fast oscillations, the decoherence and dispersion effects could be significant and make it more difficult. We found that even if is just around 0.02, the sensitivity of MH measurement would be largely reduced. The optimal baseline shifts to smaller value as increases, due to the damping of oscillation amplitudes by wavepacket effects.
We have to emphasize that we are not suggesting that the wavepacket impact would be so large as to make the mass hierarchy measurement in medium baseline reactor neutrino experiments impossible. We point out that the planewave model of neutrno oscillation is only an approximation, and the wavepacket treatment is more general. While the wavepacket effect is insignificant in the current reactor neutrino oscillation experiments, its impact on future oscillation experiments needs to be determined.
Acknowledgement
The authors thank to Dmitry Naumov, Maxim Gonchar, Emilio Ciuffoli, Jarah Evslin, Kam Biu Luk, Yu Feng Li and Alex E. Bernardini for discussions and suggestions.
This work is partially supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Nos. CUHK 1/07C and CUHK3/CRF/10), and the CUHK Research Committee Group Research Scheme 3110067.
Appendix A Derivation of the transition probabilities
In order to derive the neutrino oscillation probability, we substitute Eq. (4) into Eq. (2.1) to obtain
(14)  
(15)  
which is a moving Gaussian wave packet with dispersion. Here, we have used the approximation , where is the neutrino energy measured by the detector. Eq. (15) shows that the propagating state exhibits dispersion – a spreading of the wave packet in space. Both the group velocity and the rate of dispersion depend on the neutrino mass. The heavier the mass, the smaller is the group velocity and the higher is the rate of dispersion.
Let () represent the neutrino flavor states. Since the flavor eigenstates are superpositions of mass eigenstates , from Eq. (15), the time evolution of a flavor state is given by
(16) 
Then the transition probability of at a distance and time is given by
(17) 
which is a function of both time () and distance ().
In an oscillation experiment, the neutrino is detected at a fixed baseline but the time is not measured. In order to obtain the oscillation probability as a function of the baseline, the time has to be integrated out in Eq. (A). Since reactor neutrinos propagate almost at the speed of light, is nonzero only around . The transition probability is nonzero only within a small time window around a time where
(18) 
On the other hand, the size of is constrained by the spatial width of the wave packet which is typically much smaller than the baseline , which means that . Moreover,
(19)  
(20) 
Eq. (19) shows that the factor () changes slowly with the variable . Within a small , these terms can be treated as constants. Therefore, within the small integration region which is constrained by the width of the wave packet, it is acceptable to approximate for this factor,
(21) 
However, since Eq. (20) shows that the derivative of is not negligible even in a small region. We did not use the same approximation in the factors and . Therefore, the integral of Eq. (A) can be approximated as,
(22) 
In the last step of Eq. (A), we have ignored the terms proportional to , since they are expected to be negligible for ultrarelativistic neutrinos. In the case of reactor neutrino experiments, the survival probability is given by
(23)  
where  
The damping factor corresponds to an decoherence effect from delocalization, which is neglected in the main article. This is because in most circumstances, . The details of and the delocalization decoherence effect will be discussed in Appendix B.
Appendix B The decoherence effect from delocalization
The decoherence effect discussed in Section 2 is due to the separation of different neutrino wave packets. With larger values of , the decoherence effect would be more significant. On the other hand, there exists another decoherence effect which is due to the delocalization of the production and detection processes. Different from what we have studied above, the decoherence effect from delocalization will become significant only if is extremely small, or the spatial uncertainty is large. In fact, in neutrino oscillations, one of the coherence conditions is that the intrinsic production (and also detection) energy uncertainties are much larger than the energy differences between different mass eigenstates ( is the energy of mass eigenstate ) Akhmedov:2012uu (), namely,
(24) 
Eq. (B) implies that in order to measure the interferences between different mass eigenstates, the spatial uncertainty , has to be much smaller than the oscillation length .
Eq. (5) is just an approximate neutrino oscillation probability formula and it does not describe the decoherence effect from delocalization. More precisely, the flavor transition probability in Eq. (5) should be multiplied by an additional factor
(25)  
With these delocalization terms also taken into account, a more complete survival probability should be written as Eq. (A) in the previous section. The damping factor in Eq. (25) corresponds to the decoherence effect from delocalization. If 0, the modifications from delocalization are negligible. In this case Eq. (A) just reduces to Eq. (5). is proportional to , and so the decoherence effect from delocalization matters only when , about 1 km in the measurement of oscillation. In this case is extremely small.
If we use Eq. (A) rather than Eq. (5) to do analysis, we will find that the term will offer a lower bound on and the delocalization effect is significant only when is extremely small, which implies a large . Fig. 14 shows our analysis of the Daya Bay data, which is similar to Fig. 5, but this time the delocalization term is considered.
From Fig. 14 we can see that in a large range of the parameter space, there are no modifications on the true value of sin. This means that in most of the parameter region, wavepacket impact can be safely neglected. Moreover, Fig. 14 also suggests that only if , which means that (1 km), the decoherence effect from delocalization is significant.
At this point, we can discuss the wavepacket impact in two different regimes. If is large (), since , the damping factors exp in Eq. (5) become significant and the decoherence effect from the separation of wave packets cannot be neglected. On the other hand, if or even smaller, then the additional damping factor in Eq. (25) starts to dominate since . In this case the decoherence effect from delocalization becomes important. Nevertheless, in most reactor neutrino experiments, the dimensions of the reactor cores and detectors are just around a few meters. It is unlikely that the spatial width of the initial neutrino wave packet would be larger than 1 km.
However, in most reactor experiments, including current ones such as Daya Bay and KamLAND, and also the proposed measurements of neutrino mass hierarchy at medium baseline, the delocalization terms can be neglected.
This is because (or, the spatial uncertainty ).
In this case, Eq. (25) just reduces to Eq. (5). Therefore, we neglected the decoherence effect from delocalization in our study and performed the simulations and analyses with Eq. (5).
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