Atmospheric neutrino anomaly observed in water Cherenkov detectors
Results on atmospheric-neutrinos available before K2K are summarized in Table 1. The quantity \((\mu /e)_{exp}\) is the ratio of muons to electrons produced by atmospheric neutrinos with energies of the order of a GeV, and measures the flux ratio of \(\nu _{\mu }\) to \(\nu _e\) to be compared with the Monte Carlo prediction, \((\mu /e)_{MC}\). In addition to the Kamiokande result [9], the IMB experiment [15], which used a water Cherenkov, has also an indication of the \(\nu _{\mu }\) deficit. On the other hand, results of tracking detectors FREJUS [16] and NUSEX [17] are consistent with the predictions, though they suffered from low statistics. The results from SOUDAN [18, 19] were still preliminary in 1995.
Table 1 Results on atmospheric neutrino observations around 1990
One explanation of the anomaly was that there was a problem with water Cherenkov separation of muons and electrons. For this reason, before proposing a long baseline neutrino experiment, the particle identification capability in the water Cherenkov detector was tested by a charged particle beam at KEK. The lepton identification is based on the sharpness (or fuzziness) of the Cherenkov ring image. Figure 1 shows a sample of single Cherenkov-ring events in K2K. The particle identification capability of the water Cherenkov detector was addressed by a test experiment with a charged particle beam at KEK-PS [20]. The investigated momentum region was between 100 and 500 MeV/c.
A prototype water Cherenkov detector was set at the KEK PS K6 beamline in which charged particles were provided. Figure 2 shows the K6 beamline and the location of the detector. The K6 beamline consists of two bending magnets (D2, D3) and focusing quad magnets (Q7, Q8) with TOF and gas Cherenkov detectors for particle identification of the charged beam. This beamline provided the low energy secondary charged particles: pions, muons, and electrons with energies between 100 and 500 MeV, for the test of the water Cherenkov detector. An electron was identified by the time of flight (TOF) and gas Cherenkov detectors. A muon was identified by setting the second bending magnets to about 1/2 of the first bending magnet since the muon, which was emitted backward in the \(\pi \) rest frame, has an energy of \(m_{\mu }^2/m_{\pi }^2\times E_{\pi } \sim 0.5 E_{\pi }\). Figure 3 shows the TOF distribution at 500 MeV/c.
Figure 4 shows e-likelihood and \(\mu \)-likelihood at similar total Cherenkov light and Fig. 5 shows the result of misidentification probabilities for \(e\rightarrow \mu \) and \(\mu \rightarrow e\). It is evident that the mis-identification probabilities of about 1% level, are much smaller than the reported anomaly in atmospheric neutrino observations.
The oscillation physics to be explored by K2K
Kamiokande also measured the zenith angle dependence of the ratio \((\mu /e)_{exp}/(\mu /e)_{MC}\) for high energy neutrinos, where knowledge of the absolute initial flux was not necessary [10]. This is consistent with the picture where the different path lengths of the upward- and downward-going neutrinos produce different oscillation length. Figure 6 shows the path length of the neutrino from the creation in the atmosphere to the detection as a function of the zenith angle. The upward events are the interactions of neutrinos, which travel a substantial fraction of the earth diameter. On the other-hand, the downward events corresponding to neutrinos, which travel a distance on the order of an atmosphere thickness of several km. Figure 7 shows the ratio of \((\mu /e)_{exp}/(\mu /e)_{MC}\) as a function of the cosine of the zenith angle. Assuming 2-flavor mixing, the oscillation probability (P) from one neutrino to the other type is given by
$$\begin{aligned} P=\sin ^22\theta \times \sin ^2\frac{1.27 \cdot L(km) \cdot \varDelta m^2(eV^2)}{E(GeV)}, \end{aligned}$$
(5)
where E is the neutrino energy and L is the travel distance of the neutrino (baseline). Considering the fact that the dominant part of the atmospheric neutrinos are between sub-GeV and several GeV and the fact that the indicated deficiency starts near the horizon, the parameter region to be explored was in the \(\varDelta m^2\) region between \(10^{-2}\) and \(10^{-3}~\mathrm{eV}^2\). Also, the amount of the deficit increased as the zenith angle increased, to almost 0.5; this indicates a large neutrino mixing angle, \(\sin ^22\theta \sim 1\). The allowed parameter region under the assumption of neutrino oscillation is shown in Fig. 8 by Kamiokande [10]. The proposed accelerator-based experiment must be sensitive to small \(\varDelta m^2\) and look for a rather large effect, as opposed to accelerator programs searching for eV mass scales which require high background rejection. Instead, it required the highest possible intensity and the highest possible detector mass, since the event rate decreases as \(1/L^2\).
A feasibility study of the KEK-PS upgrade was performed in 1992–1993 [21]. The most critical aspect of the upgrade was to decrease beam losses, especially at the injection point and crossing transition energy point, to ensure sufficiently low radio-activity of the beam line. An important improvement was the introduction of white noise at the injection to ease the space charge effect. The planned upgrade of KEK-PS was achieved by rigorous efforts of the KEK accelerator department. Figure 9 shows the performance of the KEK-PS during the K2K experiment. The proton intensity per pulse was increased in the upgrade by more than a factor of two and the repetition rate was shortened to 0.5 Hz as shown in the feasibility study. In 5 years, it was possible to accumulate \(10^{20}\) protons on target.
In the proposal, the flux was estimated, using the Sanford-Wang parametrization for the pion production cross section. The GHEISHA [23] package has been used for secondary interactions of produced pions in the material (target, horn, and air, etc.). This calculation was compared with the observed neutrino flux at BNL E734 [24] simulating their experimental setup. Without oscillation effects, 500 charged-current (CC) events were expected in the 22.5 kton fiducial volume of the Super-Kamiokande detector with \(10^{20}\) protons-on-target (POT), while in the near detector with a 1.7 ton fiducial volume, 8000 CC events were expected.
The KEK to Kamioka long-baseline neutrino oscillation experiment (K2K) was proposed in April 1995 [25, 26] and approved in 1996 at KEK as cooperation of three organizations: KEK, ICRR, and INS. The K2K collaboration started with three countries: Japan, the USA and Korea, and later included Canada, France, Italy, Poland, Russia, Spain, and Switzerland. The construction completed in 1999. Data were accumulated from 2000 to 2005. The complete description of the experiment can be found in the K2K final paper [22]. The experiment was proposed to be sensitive to the oscillations, \(\varDelta m^2 \ge 3\times 10^{-3} \mathrm{eV}^2\) and \(\sin ^22\theta \ge 0.1\), at more than the \(3\sigma \) confidence level. The goals were: (1) confirm the disappearance of \(\nu _{\mu }\) in accelerator neutrinos with similar distance and energy as Kamiokande atmospheric neutrino results and (2) to show the deficiency is due to neutrino oscillation. This could be shown by the energy dependence of the disappearance rate. The neutrino energy could be reconstructed by using charged current quasi-elastic neutrino interaction \(\nu _{\mu }+n\rightarrow \mu +p\) with two-body kinematics. With proper beam monitoring, it can be shown that either the \(\nu _{\mu }\) flux is decreased and/or the \(\nu _e\) flux is increased. If the \(\nu _{\mu } \rightarrow \nu _e\) oscillation causes the atmospheric neutrino anomaly, the \(\nu _{\mu }\) flux will be decreased and the \(\nu _e\) flux will be increased. With \(\nu _{\mu } \rightarrow \nu _{\tau }\) oscillation, the \(\nu _{\mu }\) flux would reduce while keeping the \(\nu _e\) flux unchanged.
K2K experimental design principle and challenges
The experimental principle of long baseline neutrino experiments with an accelerator beam and Super-Kamiokande, is summarized in this section.
Super-Kamiokande is the most massive neutrino detector in the world. The cosmic-ray background, which is especially difficult in a low counting-rate experiment, is negligible thanks to the underground site of the detector; there is no need for sending timing signals over long distances. The synchronization with the accelerator is required only in the analysis stage in terms of a common timing signal recorded at KEK and at Super-Kamiokande by a GPS time stamp at both locations.
The neutrino beam energy expected from KEK-PS is well suited to the Water-Cherenkov detector technique. Since the neutrino beam energy is typically 1 GeV, a large fraction of the neutrino reactions are either quasi-elastic or single-pion production, which are easily identified in a water-Cherenkov detector. An electron, which is the signal for \(\nu _{\mu }\rightarrow \nu _e\) oscillations, can be identified in the low-energy region, thanks to the small \(\pi ^0\) energy and low multiplicity of the events. Also, we do not expect hadronic jets that would otherwise swamp the electron signal.
In an accelerator beam compared to atmospheric neutrino production, the decay volume to produce neutrinos from \(\pi \) and K decays is much smaller than that of atmospheric neutrinos (on the order of 10 km, the thickness of earth atmosphere). This reduces the \(\mu \) decay probability, suppresses the \(\nu _e\) component to the order of percent level and makes an almost pure (\(\sim 99\%\) purity) \(\nu _{\mu }\) beam. The other advantage compared to atmospheric neutrinos is that the initial neutrino beam, before the oscillation, can be measured by placing a detector just after its production, which can monitor the \(\nu _{\mu }\) flux and the \(\nu _e\) contamination and measure the neutrino spectrum.
The experimental challenges are as follows. The oscillation effect to be searched for is the change of the neutrino spectrum over a long distance. One of the main points of the experiment is how precisely we predict the observed quantities at a far distance with and without oscillation effects. Then, the predictions should be compared with the observation.
Three pieces of information are needed. First, the neutrino spectrum at Super-Kamiokande must be predicted reliably. Second, the cross sections for low energy neutrinos in water must be modeled properly. Figure 10 shows the compilation (by the SOUDAN collaboration) of neutrino cross section measurements (see also the recent data in [27]). Finally, the detector response to the neutrino interactions must be taken into account due to different spectrum shapes that are expected in the near and far detectors.
In principle, the neutrino spectrum can be calculated, once the \(\pi ^{\pm }\) and \(K^{\pm }\) momentum and angular distributions in the decay volume are known. However, at the time of designing K2K, \(\pi \) production data at the relevant energy regions were only available for a thin target in a limited kinematic range. The Cho model [28] (a pion production model) with the Sanford–Wang formula [23, 29] is used to parameterize data . In addition, substantial effects must be taken into account to predict the neutrino flux. First, the production target (two interaction lengths of aluminum in K2K) is thick, that causes sizeable secondary interactions. Second, the horn focusing efficiency depends on the position of meson production along the beam axis. This depends on the proton beam profile, divergence, and attenuation of proton beam due to the total absorption cross section of 12 GeV proton and the target material.
Another difficulty is that the neutrino flux at two different locations does not scale as \((1/L)^2\) where L is the distance. This is because the acceptance of the two detectors are functions of the parent meson energy (angular divergence in the decay process), decay point (geometrical acceptance), and direction (focusing is not perfect, production of mesons is via strong interaction with \(\sim \)100MeV energy spread). Thus, the ratio of the neutrino fluxes at the far and near detectors (Far/Near ratio) depends on the energy of the neutrinos.
Furthermore, in the neutrino experiment, the observable is event rates, i.e. the product of neutrino flux and cross section as a function of the reconstructed neutrino energy. The predicted neutrino cross sections suffer from ambiguities due to low energy hadronic effects of target nucleons and cannot be calculated from first principles. Both cross section and flux must be determined simultaneously in a self-consistent way as described in Sect. 2.8.
K2K experimental setup
A schematic view of the K2K experiment is shown in Fig. 11. The figure shows the \(\pi \) and K production target, horn, pion monitor, decay volume, muon monitor and near detectors (ND). After 250 km of baseline, Super-Kamiokande (SK) locates and detects the neutrino events. Figure 12 shows the neutrino beam flux at ND and SK predicted by the MC simulation. The GPS information was used for identifying the accelerator produced events in SK. The fully contained events in the fiducial volume, which are used in the oscillation analysis of K2K, are selected and reconstructed by using the same methods as have been used in the atmospheric neutrino analysis. The GPS was also used for the alignment of the 250 km long experimental setup by a long-baseline GPS survey between KEK and Kamioka. The estimated precision of the beam orientation was 0.01 mrad, based on the accuracy (1 mm) of the GPS position survey.Footnote 1 The construction precision of the beam line and detectors at the near site were about 10 mm, that resulted in a precision of the beam orientation better than 0.1 mrad.
Beam line components
For studies in a wide range of \(\varDelta m^2\), a wideband beam is most suitable. K2K adopted a horn magnet system to obtain the wideband beam with the highest possible intensity. A schematic view of the horn magnets is shown in Fig. 13.
First, the horn focuses a wide angular and momentum range of pions. At low energy, the production target must be inside of the horn magnet, because of the large angular distributions of produced pions. K2K used a 2 interaction length aluminum rod as the production target of the first horn. The aluminum target was used as the inner conductor of the horn, which reduced the radial size of the horn while maintaining a high magnetic field and containing the proton beam.
A large number of secondary charged particles, mainly pions, focused by the horn system enter the decay volume and decay to muons and neutrinos (\(\pi ^+ \rightarrow \mu ^+ + \nu _\mu \)). The momentum distributions of the pions, muons and neutrinos are shown in Fig. 14.
The muon profile from \(\pi \rightarrow \mu + \nu \) decay is measured by the muon monitor (MUMON) at the end of the decay volume after the beam dump. The muons measured by MUMON after the beam dump have energies above 5.5 GeV, thus their parents have more than 6 GeV. The muon profile measured by MUMON is used to monitor the neutrino beam direction. There are two methods to monitor the neutrino beam. One is by MUMON as mentioned and the other is the neutrino event distribution profile in ND. The reason for having two measurements of profiles is because of the two horn system. Low energy \(\pi \)’s are emitted at relatively large angles and are most efficiently focussed by the first horn. On the contrary, high energy \(\pi \)’s are emitted preferably in the forward direction, thus the second horn focusing effect is larger for higher energy pions. The relative alignment of the two horns must be monitored at both low and high energy. Otherwise, the possible misalignment of the two horns produces an artificial distortion of the neutrino spectrum, which may mimic neutrino oscillation.
The \(\pi ^{\pm }\) and \(K^{\pm }\) momentum and angular distributions in the decay volume were estimated based on two methods in K2K: an in-situ measurement by the pion monitor (PIMON) and from hadron production measurements by the CERN HARP experiment [31]. The in-situ measurement of the \(\pi ^{\pm }\) and \(K^{\pm }\) momentum and angular distribution was performed in PIMON. A schematic view of PIMON is shown in Fig. 16. The original idea was adapted from a technique used in the high energy \(\gamma \) ray astronomy. In some of the high \(\gamma \) ray searches from stars, the direction and the angular divergence of the air shower are measured, using air Cerenkov radiation with spherical mirror and array of photo-sensors at the focal plane of the mirror. Secondary particles from the production target are proton, \(\pi \), K and \(e^{\pm }\). Since the secondary proton energy must be below 12 GeV (incident energy) in K2K, a gas Cherenkov detector can be used, which is sensitive only to \(\pi ^{\pm }\) above 2 GeV/c (same \(\beta \) as 12 GeV proton) and \(e^{\pm }\). Furthermore, by changing the refractive index of the gas, a large part of the \(\pi ^{\pm }\) direction and momentum distribution can be extracted. Unfortunately, with a higher proton beam energy like T2K, where secondary particles contain much high energy proton up to 30 GeV, the PIMON approach is not possible.
Near detectors
Neutrinos are detected first by a set of near detectors (ND) located approximately 300 m from the neutrino production target and then by SK 250 km away. The ND consists of a 1 kton water Cherenkov detector (1KT) and a fine-grained detector system. The schematic view of ND is shown in Fig. 15. By having a water Cherenkov detector at the near site, the neutrino interaction cross section, nuclear final state interactions in the detector medium, the detector response to neutrino events, such as the muon identification efficiency and event type selection criteria, will cancel to first order in the far detector measurements. (There will remain effects due to the size of the detector, environment, etc.) The effects due to different spectrum shape in the near and the far detectors are estimated by having a fine grain tracking detector, which has a better capability of counting multiplicity of the high energy event component. A complete description of ND is in [22].
Neutrino flux prediction and the far-to-near flux ratio with hadron production data
Pion monitor: a in-situ measurement of the pion beam
The pion monitor (PIMON) was a gas Cherenkov imaging detector that consists of a gas vessel, a spherical mirror, and an array of 20 photo-multiplier tubes. The Cherenkov photons emitted by pions passing through the gas vessel are reflected toward and focused onto the PMT array by the spherical mirror. Then, the PMT array on the focal plane detects the image of Cherenkov photons. Thanks to the characteristics of the spherical mirror, photons propagating in the same direction are focused on the same position on the focal plane. Thus the spatial distribution is that of the direction of the Cherenkov light. The pion momentum distribution is also obtained from the size of the Cherenkov ring. Furthermore, a momentum scan can be done by modifying the refractive index of the inner gas at several points.
PIMON was operated periodically just downstream the horn magnets to measure the momentum (\(p_{\pi }\)) versus divergence (\(\theta _{\pi }\)) 2-dimensional distribution of pions entering the decay volume. As shown in Fig. 16, a pie-shaped mirror is used as the spherical mirror to measure only 1/30 part of the beam assuming azimuthal symmetry of the distribution. The top was aligned to be on the beam center. The reflection angle with respect to beam direction is \(30^{\circ }\).
An array of 20 PMTs (modified R5600-01Q made by Hamamatsu Corporation) is set 3 m away from the beam center to avoid beam associated radiation. The reflected photons are focused on the plane of the PMT array and detected by PMTs. They are arranged vertically with 35 mm intervals. The array can be moved by a half pitch of the interval along with the array, and hence 40 data points (one point for every 1.75 cm) are taken for Cherenkov light distribution. The relative gain among 20 PMTs was calibrated using a Xe lamp before the measurements. The gain ratio between neighboring PMTs was also checked using Cherenkov photons during the run. The error on the relative gain calibration is estimated to be 10% for June 1999 run and 5% for November 1999 run. The larger uncertainty in June 1999 was due to a saturation effect of PMTs, which was corrected by a 2nd polynomial function.
The gas vessel is filled with freon gas R-318 (\(\mathrm{C}_4\mathrm{F}_8\)). Its refractive index n is varied by changing the gas pressure using an external gas system. The data are taken at several refractive indices ranging between \(n=1.00024\!-\!1.00242\) to make PIMON sensitive to different pion momenta. Beyond \(n=1.00242\), the primary protons also emit Cherenkov photons which are a significant background. This corresponds to setting a momentum threshold of 2 GeV/c for pions.Footnote 2 The absolute refractive index is calibrated by the Cherenkov photon distribution from 12 GeV primary protons with the refractive index set at \(n=1.00294\).
For the background subtraction, beam associated radiation and electro-magnetic showers, which mainly come from the decay of neutral pions, \(\pi ^0\;\rightarrow \;2\gamma \), are considered. For the first category of background, a measurement with the mirror directed off from the direction of the PMT array is performed. For the electro-magnetic shower category, the subtraction is done by using the distribution measured at the lowest refractive index. After all the backgrounds are subtracted, the distribution of the Cherenkov light emitted from pions is obtained as shown in Fig. 17. The prediction of the beam Monte Carlo (MC) simulation is superimposed as well.
A \(\chi ^2\)-fitting is employed to extract \((p_\pi ,\theta _\pi )\) 2-dimensional distributions with various reflective indices. The \((p_\pi ,\theta _\pi )\)-plane is binned into \(5\times 10\) bins; 5 bins in \(p_\pi \) above 2 GeV/c with 1 GeV/c slice (the last bin is integrated over \(p_\pi >6\ \mathrm{GeV}/c\)) and 10 bins in \(\theta _\pi \) from \(-50\) to 50 mrad with 10 mrad slice. Templates of the Cherenkov light distributions emitted by pions in these bins are produced for each refractive index using an MC simulation. The weight of contribution from each bin was the fitting parameter. Figure 18 show the fit result.
The neutrino energy spectra at ND and SK are derived from the sum of the contributions of pions in each \((p_\pi ,\theta _\pi )\) bin in neutrino energy bins, which depend mainly on the pion kinematics and the geometry of the decay volume. The resulting ratio of the neutrino spectra at SK to that at the ND yields the far-to-near ratio correction. The shape of flux ratio reflects the profile of the neutrino flux
Neutrino flux prediction with the HARP measurements
The HARP experiment [31] was conducted in 2001 and 2002 in the CERN PS T9 beam-line, in order to study in a systematic and accurate way hadron production. The experiment studied a variety of produced hadrons (pions and kaons in particular) and with large phase space coverage, as a function of incident beam particle type (protons, pions), beam momentum (from 1.5 to 15 GeV/c), nuclear target material (from hydrogen to lead), and nuclear target thickness (from 2% to more than 100% hadronic interaction length fraction). Secondary tracks are efficiently reconstructed in the HARP forward spectrometer via a set of drift chambers located upstream and downstream with respect to a dipole magnet. Particle identification for forward tracks is obtained with a time-of-flight system, a Cherenkov threshold detector, and an electromagnetic calorimeter.
In particular, as part of the HARP program, the pion production measurement at the beam energy of 12 GeV on aluminum target was performed. In the forward region, the kinematical region of \(30<\theta _{\pi }<210\) mrad and \(0.75<p_{\pi }<6.5\) GeV/c was covered, which matches well with the pion production phase space to be responsible for the dominant fraction of the K2K neutrino beam flux.
The HARP results are adopted in the beam MC simulation to estimate the neutrino spectra at ND and SK. The calculated neutrino flux at the far and near sites in the absence of neutrino oscillation is shown in Fig. 19.
The uncertainties in the flux prediction using HARP data are from primary interactions, primary proton beam optics, thick target effects such as secondary interactions of produced mesons, and the horn magnetic fields.
First, primary beam optics affect the effective interaction length of the target and material to be transversed by produced mesons before getting into the horn magnetic field. An uncertainty of 1.2 mm and 2.0 mrad in the mean transverse impact point on the target and in the mean injection angle, respectively, are assumed based on long-term beam stability studies [30].
Second, the contributions due to uncertainties in the multiplicity and kinematics of \(\pi ^+\) production in the proton-aluminum hadronic interactions, its interaction length, and the overall normalization of charged and neutral kaon production are considered as sources of the systematic uncertainties in the primary hadronic interactions. The HARP data were incorporated in terms of Sanford–Wang parameters. The uncertainties and correlations measured in HARP are propagated into flux uncertainties using standard error matrix propagation methods. An uncertainty of about 30% is assumed for the uncertainty in the proton-aluminum hadronic interaction length. The uncertainty in the overall charged and neutral kaon production normalization is assumed to be 50%.
Third, given the two-interaction length production target and other material present in the target area, such as the magnetic horns, re-interaction effects of the secondaries produced in primary hadronic interactions (e.g., \(\pi ^+\) absorption in aluminum) have an impact on the neutrino flux prediction. The systematic uncertainty is estimated by changing the hadron interaction models to describe secondary hadronic interactions in the beam MC simulation. In particular, we consider that the relatively large differences in the GCALOR/GFLUKA [32,33,34] and GHEISHA [35] models assumptions, also in comparison to available experimental data, are representative of the uncertainties associated with secondary hadronic interactions.
Finally, we estimate the uncertainties due to the imperfect knowledge of the magnetic field in the horn system. The uncertainties related to the magnetic field strength and to the field homogeneity in azimuth are taken into account. We assume a 10% uncertainty in the absolute field strength, which is well within the experimental uncertainty on the magnetic field strength and the horn current measured using inductive coils during the horn testing phase [36]. Furthermore, a periodic perturbation in azimuth of up to \(\pm 15\%\) amplitude with respect to the nominal field-strength is assumed as the uncertainty in the field homogeneity, which is also based on the experimental accuracy achieved in the measurement of the magnetic field mapping in azimuth during horn testing [37].
Far-to-near flux ratio
The far-to-near flux ratio, \(\varPhi ^{\mathrm{SK}}/\varPhi ^{\mathrm{ND}}\), predicted by the HARP \(\pi ^+\) production measurement for primary hadronic interactions with the systematic error evaluation discussed in Sect. 2.5.2 in the absence of neutrino oscillations, is shown in Fig. 20 as a function of neutrino energy. The energy dependence of the far-to-near flux ratio is due to the acceptance difference between ND and SK with the divergence of the pion and neutrino beams. The ND has a larger acceptance for the off-axis beam described in Sect. 3.3, resulting in more low energy flux. In addition, the ND has a larger acceptance for the forward emitted neutrinos (\(\theta = 0\) in Eq. 15) in the pion decay where the pion beam has a divergence. This results in the more high energy neutrino flux at ND. We estimate that the flux ratio uncertainty in this analysis is at the 2–3% level below 1 GeV neutrino energy, while it is of the order of 4–9% above 1 GeV. As mentioned above, we find that the dominant contribution to the uncertainty in the far-to-near ratio comes from the HARP \(\pi ^+\) measurement itself. In particular, the uncertainty in the flux ratio prediction integrated over all neutrino energies is 2.0%, where the contribution of the HARP \(\pi ^+\) production uncertainty is 1.4%.
Figures 19 and 20 show the results of the extracted neutrino spectra and the far-to-near flux ratio from the PIMON data taken in November 1999. In the figures, the results from the HARP measurements and the predictions of the Beam-MC are also overlaid. The results of PIMON measurements are consistent with both of them, confirming the validity of the far-to-near estimations.
All three predictions on the far-to-near ratio are consistent with each other within their uncertainties. Among them, K2K used the one predicted by the HARP measurements in the neutrino oscillation analysis described in this paper. It gives the most accurate measurements on the hadron production.
The central values for the far-to-near flux ratio as a function of neutrino energy obtained from the HARP results of \(\pi ^+\) production, \(\overline{R}_{i}\), is given in Table 2, where the index i denotes an energy bin number. The total systematic uncertainties on the far-to-near flux ratio as a function of neutrino energy are given in Table 3, together with the uncertainty correlations among different energy bins, expressed in terms of the fractional error matrix \(\langle \delta R_i \delta R_j \rangle / (\overline{R}_i \overline{R}_j)\), where i, j label neutrino energy bins. The far-to-near central values and its error matrix are used in the analysis for neutrino oscillation.
Table 2 Predictions for the far-to-near muon neutrino flux ratio as a function of neutrino energy, with the HARP measurement for \(\pi ^+\) production in primary hadronic interactions. The neutrino energy binning is also indicated
Table 3 Fractional error matrix \(\langle \delta R_i \delta R_j \rangle / (\overline{R}_i \overline{R}_j)\) obtained from the systematic uncertainties on the far-to-near flux predictions. The neutrino energy binning is same as in Table 2. The values are written in the unit of \(10^{-3}\)
Prediction of neutrino events at SK based on the measurements at ND
As already mentioned, knowledge of the neutrino flux itself is not precise enough to predict the events observed at SK. So, we use the combination of the far-to-near flux ratio (\(\varPhi ^{SK}/\varPhi ^{ND}\)) and the flux measurements at the near detector (\(\varPhi ^{ND}\)) for a prediction of the SK events. The neutrino spectrum at the near detector can be extracted from ND data with neutrino interaction models implemented in the NEUT library [38]. The expected events in SK will be generated by the same neutrino interaction models in NEUT, where some parameters were tuned in the ND measurements.
The near detector complex consists of a 1 kton water Cherenkov detector (1KT) and a fine-grained detector system. The schematic view of ND in K2K-IIbFootnote 3 is shown in Fig. 15. The 1KT has the same neutrino interaction target as that in SK, water. The detection efficiency is also similar to that in SK. At the same time, to complement the weaknesses of the water Cherenkov detector (such as detection of multi-particle final states), two tracking detectors were constructed (SciFi and SciBar).
The signatures of neutrino oscillation appear as a reduction in the number of observed \(\nu _{\mu }\) events and distortion in the \(\nu _{\mu }\) energy spectrum. To study the \(\nu _{\mu }\) energy spectrum, 1 ring \(\mu \)-like events (\(1R \mu \)) are used. The \(\nu _{\mu }\) energy can be reconstructed as \(E_\nu ^\mathrm{rec}\) for Charged-Current Quasi-Elastic (CC QE, \(\nu _\mu + n \rightarrow \mu + p\)) events. Neglecting Fermi momentum:
$$\begin{aligned} E_\nu ^\mathrm{rec}=\frac{m_NE_\mu -m^2_\mu /2}{m_N-E_\mu +P_\mu \cos \theta _\mu }, \end{aligned}$$
(6)
where \(m_N\), \(E_\mu \), \(m_\mu \), \(P_\mu \) and \(\theta _\mu \) are the nucleon mass, muon energy, the muon mass, the muon momentum and the scattering angle relative to the neutrino beam direction, respectively. The CCQE events have a signature of 1 ring \(\mu \)-like events (\(1R \mu \)), because most of the recoil protons are below the Cherenkov threshold of water. The background to this selection is due to charged current inelastic events (CC nonQE, \(\nu _\mu + N \rightarrow \mu + N + \pi 's\), where \(\pi \)’s are missed). Obviously, \(E_{\nu }\) with the CCQE assumption will be under-estimated for CC nonQE events. The issue is how to estimate the neutrino spectrum from the observed neutrino events in the presence of some contamination from CC nonQE events.
The event selection used to extract the spectrum is as follows. Figure 21 shows the likelihood distribution of the number of Cherenkov rings in K2K for both data and the MC simulation. The selection of a single ring is well reproduced by MC. Figure 22 shows the comparison of the distributions of the particle identification likelihood for data and the MC simulation for single ring events. The lepton identification capability is well understood. To measure the \(\mu \) energy, the events are required to be contained in the detector. The definition of the containment is checked as follows. Figure 23 shows the comparison of the distributions of the largest charge in a PMT for an event for data. Events that have a charge less than 200 photo-electrons (p.e.) are considered as Fully Contained (FC) events. The rest are partially contained (PC) events, because exiting particles emit Cherenkov light near the PMT around the exit point, which is likely to produce a large amount of light recorded by one PMT. Figure 24 shows the selection efficiency between all fiducial volume events and FC1R\(\mu \) events as a function of momentum.
The spectrum can be decomposed into overall normalization and its shape. The spectrum shape was obtained by fitting the observed two-dimensional distributions of \(p_{\mu }\) versus \(\theta _{\mu }\) for 1 ring \(\mu \)-like events (\(1R \mu \)) by a weighted sum of templates of CC QE and of CC nonQE, which were generated by the MC simulation for eight energy bins. The neutrino energies are divided into eight bins as shown in Table 4. Figure 25 shows the (\(p_{\mu }\), \(\theta _{\mu }\)) distribution for FC1R\(\mu \) events in the 1KT detector. For the MC expectation, the \((p_{\mu }, \theta _{\mu })\) distribution is prepared for each \(E_{\nu }\) bin and separately for CC QE and CC nonQE interactions; \(8\times 2\) distributions are prepared in total for each event sample. Some of the templates are shown in Fig. 26.
The spectrum shape is obtained by fitting the observed \((p_{\mu }, \theta _{\mu })\) distribution with sixteen templates that are prepared for eight neutrino energy bins and separately for QE and non-QE interactions. The fitting parameters are the neutrino energy spectrum parameters for eight energy bins (\(f^{\phi }_{1}\sim f^{\phi }_{8} \)), and a parameter, \(R_{nQE}\), representing the cross section ratio of CC nonQE to CC QE.
The contents in each \((p_{\mu }, \theta _{\mu })\) bin, \(N^{MC}_{i,j}\), are expressed with the 16 templates and the fitting parameters as,
$$\begin{aligned} N^{MC}_{m,n} \equiv C_{norm}\cdot \sum _{i=1}^{8} f^{\phi }_i \cdot \left[ N^{MC(QE)}_{m,n,i} + R_{nQE} \cdot N^{MC(nQE)}_{m,n,i} \right] , \end{aligned}$$
(7)
Table 4 The \(E_{\nu }\) interval of each flux bin
where \(C_{norm}\), \(N^{MC(QE)}_{m,n,i}\) and \(N^{MC(nQE)}_{m,n,i}\) are a normalization parameter, the number of expected contents in (m,n)-th bin for QE interaction and nonQE interaction for i-th neutrino energy bin. The \(\chi ^{2}\) between the observed distributions, \(N^{data}_{m,n}\) , and the MC expectation, \(N^{MC}_{m,n}\), is calculated as:
$$\begin{aligned} \chi ^2 \equiv \sum _{m,n} \frac{(N^{obs}_{m,n}-N^{MC}_{m,n})^2}{\sigma ^2_{m,n}}+\frac{(1-\epsilon )^2}{\sigma ^2_{energy}} \end{aligned}$$
(8)
where \(\epsilon \) is the fitting parameter for energy scale (nominally \(\sim \) 1), \(\sigma _{energy}\) is the estimated uncertainty of the energy scale (+3/-4%).
The measurements of other near detectors, SciFi and SciBar, are also included to calculate the total \(\chi ^{2}\). Finally, the set of fitting parameters (\(f^{\phi }_i, R_{nQE}\)) is found by minimizing the \(\chi ^{2}\) function. The best fit values, their error sizes and the correlations between them are listed in [22].
SK data
The SK events in K2K are collected from June 1999 to November 2004 as shown in Fig. 9. There is a gap of data taking period in 2002 because the SK detector had an accident of PMT explosion and the following repairing work. We called the former period as K2K-I and the latter as K2K-II. More detailed definitions are found in Table 5. In the periods of K2K-I and K2K-II, sub-periods (a, b, and c) are further defined with the experimental conditions. SK has full PMT photo-coverage of 40% (SK-I) in K2K-I and only 19% in K2K-II (SK-II).
Table 5 Summary of the number of protons on target for each period. All the numbers are in units of \(10^{18}\) protons on target (POT)
The accelerator produced events in the SK detector are selected by using GPS information. The fully contained events in the fiducial volume, which are used in the oscillation analysis in K2K, are selected and reconstructed by using the similar methods used in the atmospheric neutrino analysis at SK.
Neutrino interactions from the accelerator are selected by comparing two Universal Time Coordinated time stamps from GPS, \(T_{KEK}\) for the KEK-PS beam spill start time and \(T_{SK}\) for the SK trigger time. The time difference \(\varDelta T = T_{SK} - T_{KEK} -TOF\), where TOF is a time of flight, is distributed from 0 and 1.1 \(\upmu \)s to match the width of the beam spill of the KEK-PS. The uncertainty of the synchronization for two sites is measured to be less than 200 nanosecond by using the atomic clock. Thus, \(\varDelta T\) for the K2K events are required to be from \(-0.2\) to 1.3 \(\upmu \)s.
In addition, the following cuts are required;
- (1)
no pre-activity within 30 \(\upmu \)s before the event to remove the decay electron events from cosmic muons which stopped in the detector before the beam window,
- (2)
no activity in the outer detector (Fully Contained event),
- (3)
no flashing PMT. A PMT sometimes begins to flash because of discharge around the dynode. These events have unique characteristics such as a timing distribution which is much broader than neutrino events and a repeated ring pattern,
- (4)
at least 30 MeV energy deposited assuming to be electron events in the inner detector,
- (5)
a reconstructed vertex point in the 22.5 kton fiducial volume, at least 2 m inside from the inner detector wall.
Table 6 SK event reduction summary
Table 6 shows the event reduction summary for K2K-I and K2K-II. The difference in the number of events after \(|\Delta \mathrm{T}|<500\mu \mathrm{s}\) cut comes from the difference of the detector trigger threshold for SK-1 and SK-2. In total, 112 accelerator-produced fully contained events are observed in the K2K experiment and 58 events are reconstructed as 1-ring \(\mu \)-like. Table 7 shows the event summary of observed events. Efficiency for these cuts is 77.2% for K2K-I and 77.9% for K2K-II. A major contribution of inefficiency comes from NC interaction events.
Figure 27 shows the \(\varDelta T\) distribution at each of the reduction steps. A clear peak at \(\varDelta T=0\) is seen after the fiducial volume cut. Three off-timing fully contained events are observed within \(\pm 500\mu \mathrm{s}\) timing window while two background events are expected from atmospheric neutrinos. Nine micro bunch structures can be seen clearly in the \(\varDelta T\) distribution with finer binning for all fully contained events in SK as shown in Fig. 28. Figure 29 shows the event rate as a function of POT. The event rate is consistent with the assumption that it is proportional to POT. A Kolmogorov–Smirnov (KS)-test to that assumption has a probability of 79%.
Table 8 Systematic errors for \({\mathrm{\mathrm N}_{\mathrm{SK}}}\)
Table 9 Systematic errors for reconstructed neutrino energy spectrum
Systematic uncertainties on the estimation of \(\mathrm{N}_{\mathrm{SK}}\) and reconstructed neutrino energy in SK are evaluated using the atmospheric neutrinos as a control sample. Table 8 shows the systematic errors for \(\mathrm{N}_{\mathrm{SK}}\). The dominant uncertainty for \(\mathrm{N}_{\mathrm{SK}}\) comes from the vertex reconstruction in a fiducial volume. It is evaluated by comparing the number of events for atmospheric neutrino data with the MC expectation in the fiducial volume using the two different vertex reconstruction programs. Uncertainties in the neutrino interaction model from events in the different acceptances between ND and SK are also taken into account in the oscillation analysis. Systematic errors for the reconstructed neutrino energy spectrum from event reconstruction are shown in Table 9. Uncertainties coming from the ring counting and particle identification are evaluated by comparing the likelihood distribution for data and MC. Figures 30 and 31 show the ring counting and particle identification likelihood distributions of atmospheric neutrino data compared with the MC expectation in SK-II.
The MC expectation reproduces the data well. The uncertainties for the energy scale are also estimated by using cosmic ray muon, \(\pi ^0\) invariant mass, and decay electron samples. They are taken into account in the oscillation analysis (2.0% for K2K-I and 2.1% for K2K-II).
Oscillation analysis
The K2K data were taken over the period from June 1999 to November 2004, which is divided into five periods corresponding to different experimental configurations as shown in Table 5. The horn current was 200 kA (250 kA) and the diameter of the production target was 2 cm (3 cm) in the Ia (other) period. The SK PMTs were full density for Ia and Ib, but were half density for IIa, IIb, and IIc. The Lead–glass calorimeter was placed between the SciFi and MRD in Ia and Ib, which was replaced by the SciBar for IIa, IIb, and IIc periods. Only the first four layers of the SciBar detector were installed for IIa while it was complete for IIb and IIc. Furthermore, the water target in SciFi was replaced by aluminum rods during IIc. The numbers of protons delivered to the target and for the physics analysis are summarized in Table 5. In total, \(92.23\times 10^{18}\) POT are used in physics analysis.
Neutrino oscillation causes both a suppression in the total number of events observed at SK and distortion of the energy spectrum. Therefore, all of the beam-induced neutrino events observed within the fiducial volume of SK are used to measure the overall suppression and the subset of these events for which the incoming neutrino energy can be reconstructed (FC1R\(\mu \) events) are separately studied in order to study the spectral distortion.
The likelihood function is defined as the product of the likelihoods for the observed number of events in the SK fiducial volume (\( \mathcal {L}_{norm}\)), the shape of the \(E_{rec}\) spectrum (\(\mathcal {L}_{shape}\)) and a systematic constraint likelihood term (\(\mathcal {L}_{syst}\)) where they are varied within their uncertainties as,
$$\begin{aligned} \mathcal {L}= & {} \mathcal {L}_{norm} \times \mathcal {L}_{shape} \times \mathcal {L}_{syst} \end{aligned}$$
(9)
We perform neutrino oscillation analysis in an assumption of two-flavor mixing. The oscillation parameters, (\(\sin ^22\theta , \varDelta m^2\)), are obtained by maximizing the likelihood function.
One-hundred twelve FC events and 58 FC1R\(\mu \) events are used in \(\mathcal {L}_{norm}\) and \(\mathcal {L}_{shape}\), respectively. The systematic parameters consist of the neutrino energy spectrum at ND, the far-to-near ratio, the neutrino cross section uncertainties, and SK energy scale and overall normalization. The expected number of events in SK with neutrino oscillation (\(N_{exp}\)) is calculated from the number of observed events in 1KT (\(N^{obs}_{1KT}\)) for oscillation parameters (\(\sin ^22\theta , \varDelta m^2\));
$$\begin{aligned} N_{exp}(\sin ^22\theta , \varDelta m^2) = N^{obs}_{1KT}\cdot \frac{N^{SK}_{MC}}{N^{1KT}_{MC}} \cdot \frac{M_{SK}}{M_{1KT}} \cdot \frac{POT_{SK}}{POT_{1KT}} \cdot C_{\nu _e}, \end{aligned}$$
where \(N^{SK(1KT)}_{MC}\) is the expected number of events in MC as
$$\begin{aligned} N^{SK (1KT)}_{exp}=\sum _i \int dE_{\nu }\varPhi ^{SK (1KT)}(E_{\nu }) \cdot \sigma ^i (E_{\nu }) \cdot \epsilon ^{SK,1KT} (E_{\nu }). \end{aligned}$$
The neutrino flux at SK (\(\varPhi ^{SK}\)) is predicted from ND analysis based on the flux at 1KT (\(\varPhi ^{1KT}\)) as
$$\begin{aligned} \varPhi ^{SK}= R^{F/N}(E_\nu ) \cdot \varPhi ^{1KT}(E_{\nu }) \cdot (1-P(E_{\nu }, \sin ^22\theta , \varDelta m^2)). \end{aligned}$$
In the above expression, the neutrino oscillation probability is
$$\begin{aligned} P(E_{\nu }, \sin ^22\theta , \varDelta m^2)=\sin ^22\theta \times \sin ^2\frac{1.27 \cdot L(km) \cdot \varDelta m^2(eV^2)}{E_{\nu }(GeV)}, \end{aligned}$$
and, \(R^{F/N}(E_\nu )\) is the near-to-far flux ratio based on HARP and Pion Monitor analysis, \(\sigma ^i (E_{\nu })\) is interaction cross section with water, ’i’ runs for CC QE, CC nonQE, and NC interactions constrained by ND data. The \(\epsilon _{SK}\) (\(\epsilon _{1KT}\)) is SK (1KT) detection efficiency of FC events, \(M_{SK}\) (\(M_{1KT})\) is the fiducial mass of SK (1KT), \(POT_{SK}\) (\(POT_{1KT})\) is the number of protons on target which corresponds to the analyzed data in SK (1KT) and \(C_{\nu _e}\) is the correction for the electron neutrino component in the neutrino beam estimated to be 0.996 by the MC simulation.
The normalization term, \(\mathcal {L}_{norm}\), is expressed in terms of a Poisson probability of the observe events (\(N_{obs}\)) where the expected number of events is \(N_{exp}\):
$$\begin{aligned} \mathcal {L}_{norm} = \frac{(N_{exp})^{N_{obs}}}{N_{obs}!}e^{-N_{exp}} \end{aligned}$$
(10)
Table 10 Summary of the oscillation parameters at the best fit point for each fit condition
The shape term \( \mathcal {L}_{shape} \) is defined as the product of the probability for each FC\(1R\mu \) event to be observed at a reconstructed energy \(E_\nu ^{rec}\). The expected spectrum shape of reconstructed energy at SK is
$$\begin{aligned} \phi ^{SK}_{exp} =\int dE_{\nu }\varPhi ^{SK}(E_{\nu }) \cdot \sigma (E_{\nu }) \cdot \epsilon ^{SK}_{1R\mu }(E_{\nu }) \cdot r(E_\nu ;E_\nu ^{rec}) \nonumber \\ \end{aligned}$$
(11)
where \(\epsilon ^{SK}_{1R\mu }(E_{\nu })\) is the detection efficiency of \(1R\mu \) events and \(r(E_\nu ;E_\nu ^{rec}) \) is the probability of reconstructing an event with true energy \(E_{\nu }\) as \(E_\nu ^{rec}\), based on neutrino interaction and detector simulation. The probability density function is
$$\begin{aligned} \mathcal {L}_{shape}=\prod ^{N_{1R\mu }} \phi ^{SK}_{exp}(E_{\nu }^{rec} , \sin ^22\theta , \varDelta m^2)). \nonumber \\ \end{aligned}$$
(12)
The systematic parameters are treated as fit parameters which are assumed to follow a Gaussian distribution and are constrained within the associated uncertainties. Details on the systematic errors are discussed in [22].
The number of FC events without neutrino oscillation is estimated to be \(158.4^{+9.4}_{-8.7}\). The major contribution to the uncertainty on the normalization (4.8%), is due to the fiducial volume uncertainty from the vertex reconstruction at both 1KT and SK.
Result
The results are summarized as follows;
The maximum likelihood points in the \(\sin ^{2}2\theta \) – \(\varDelta m^{2} \) space for all or physical regions are summarized in Table 10 with full and partial data sets.
The best fit point in the physical region is found to be at \((\sin ^{2}2\theta , \varDelta m^{2}) = (1.00, 2.8 \times 10^{-3} eV^{2})\). With the oscillation parameters, the expected number of events is 107.2, which agrees well with 112 observed within statistical uncertainty.
The observed \(E_{rec}\) distribution is shown in Fig. 32 with the expected distributions for the best-fit parameters, and the expectation without oscillations.
The consistency between the observed and fitted \(E_{rec}\) distributions is checked using a KS test. For the best fit parameters, the KS probability is 37%, while that for the null oscillation hypothesis is 0.07%. Thus, the observation is consistent with the oscillation hypothesis.
Although the point with the highest likelihood is outside of the physical region, \((\sin ^{2}2\theta , \varDelta m^{2}) = (1.2, 2.6\times 10^{-3} eV^{2})\), the probability that \(\sin ^{2}2\theta \ge 1.2\) if the true parameters are at the best fit physical parameters is 26.2%, based on MC virtual experiments.
The probability that the observations are due to a statistical fluctuation instead of neutrino oscillation is estimated by computing the log-likelihood ratio of the null oscillation case to the best fit point. The null oscillation probability is calculated to be 0.0015% (4.3\(\sigma \)). When only normalization (shape) information is used, the probability is 0.06% (0.42%).
The null oscillation probability was calculated separately for two periods of K2K-I and K2K-II. The probabilities for each sub-sample is summarized in Table 11.
Table 12 shows the effect of each systematic uncertainty on the null oscillation probability.
The effect is tested by applying each uncertainty individually. As shown in the table, the dominant contribution to the probabilities with the normalization information are from the far-to-near flux ratio and normalization error, while energy scale is the dominant error source for the probability with the \(E^{\mathrm {rec}}_{\mu }\) shape information.
Table 11 Summary of the null oscillation probability. Each row is classified by used likelihood, and each column represents the data set
Table 12 Effect of each systematic uncertainty on the null oscillation probability. The numbers in the table are null oscillation probabilities with only the systematic uncertainty in the first column applied, and “stat” means when all systematic sources are turned off
Allowed region of oscillation parameters are evaluated based on the difference of log-likelihood between each point and the best fit point as
$$\begin{aligned} \varDelta {\ln \mathcal {L}}(\sin ^22\theta ,\varDelta m^2)\equiv & {} \ln \left( \frac{\mathcal {L}_{\mathrm {max}}^{\mathrm {phys}}}{\mathcal {L}(\sin ^22\theta ,\varDelta m^2)} \right) \nonumber \\= & {} \ln \mathcal {L}_{\mathrm {max}}^{\mathrm {phys}} - {\ln \mathcal {L}}(\sin ^22\theta ,\varDelta m^2),\nonumber \\ \end{aligned}$$
(13)
where \(\mathcal {L}_{\mathrm {max}}^{\mathrm {phys}}\) is the best fit point and \(\mathcal {L}(\sin ^22\theta ,\varDelta m^2)\) is the likelihood at \((\sin ^22\theta ,\varDelta m^2)\) with systematic parameters that maximize the likelihood at that point. Figure 33 is the allowed region corresponding to the 68%, 90% and 99% confidence levels (CL). They are defined to be \(\ln \mathcal {L}= \ln \mathcal {L}_{\mathrm {max}}^{\mathrm {phys}} - 1.37, -2.58 ~\mathrm {and}~-4.91\), respectively. These intervals are derived by using the 2-dimensional Gaussian approximation from the maximum in the unphysical region [39]. The 90% CL contour crosses the \(\sin ^{2}2\theta = 1\) axis at \(\varDelta m^{2} = 1.9~\mathrm {and}~3.5 \times 10^{-3}~eV^{2}\). Figure 34 shows the \(\ln \mathcal {L}_{\mathrm {max}}^\mathrm {phys} - \ln \mathcal {L}(\sin ^22\theta ,\varDelta m^2)\) distributions as a function of \(\sin ^{2}2\theta \) and \(\varDelta m^{2}\), with a slice at \(\varDelta m^{2} = 2.8 \times 10^{-3} eV^{2}\) and \(\sin ^{2}2\theta = 1.00\). Another consistency check was performed, by comparing the analyses using only the total number of events and only with shape analysis (Fig. 35). Also, the allowed regions calculated with only K2K-I/K2K-II data was checked for the consistency.
In conclusion, the K2K experiment, by using a neutrino beam derived from an accelerator proton beam, confirms neutrino oscillations first observed in atmospheric neutrinos.