VARIABLE ALTITUDE MUON DETECTION AND ENERGY DEPENDENCE OF COSMIC RAY MUONS

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1 VARIABLE ALTITUDE MUON DETECTION AND ENERGY DEPENDENCE OF COSMIC RAY MUONS Andrew T. McNichols Department of Physics and Astronomy University of Hawai i at Hilo Hilo, HI ABSTRACT This study investigates cosmic ray muon flux and energy with variable altitude. A portable muon detection apparatus was used at 400-meter intervals between sea level and 4,200 meters to measure and parameterize muon incidence rates with altitude and the characteristic energies of as many muon strikes as possible in order to determine the rate and kinetic energy at which cosmic ray muons traverse the lower atmosphere. The results show a distinct increase in muon flux and that overall muon energy increases with detection altitude. INTRODUCTION In Fall 2013, our research extended the relationship between cosmic ray muon flux and altitude to high elevations (~4200 meters), and determined the relationship between the altitudes at which cosmic ray muons are detected and their individual particle energy. In Spring 2014 we characterized muon flux as a function of azimuthal and polar angle (see Robert Pipes report for details) and improved the statistical result of our characterization of muon energy with altitude. This study is important to NASA because muons are ionizing particles, having the potential to affect the functionality of electronics- such as microchips in embedded systems- due to the large trails of ions that high-energy muons leave behind as they travel from the upper atmosphere down to elevations populated with scientific instruments. As muons lose energy due to collisions and, more frequently, radiative processes in the atmosphere (Beringer p.334, 2012), they may be captured on nuclei accompanied by energy release. If energy release events occur in a circuit board, it has the potential to adversely impact sensitive onboard equipment, such as measurement devices and control systems. This project provides an informed conclusion about the nature of atmospheric sciences and instrumentation with regard to ionizing particle decays and interactions in accordance with NASA s second goal as outlined in the 2011 version of the strategic plan: "Expanding scientific understanding of the Earth and the Universe in which we live." Our research group has enabled improved predictive capability for cosmic ray events and improved understanding of the fundamental physical processes of the space environment and how to maximize the productivity of technological undertakings at increasing altitude in the atmosphere. This is in support of NASA s strategic objectives 2.1.2, 2.1.7, and

2 BACKGROUND Muons are elementary ionizing particles with a long decay time. Classified as leptons, they are among the lightest and most fundamental elementary particles, in that they have no substructure. They are similar to electrons (they have equivalent charge and spin angular momentum), but with a rest mass ~207 times greater (Beringer p.30, 2012). They can be produced in a variety of ways, but this study is concerned with muons that are produced in interactions between incident primary cosmic rays and Earth s upper atmosphere. Since muons produced in cosmic ray showers are relativistic, high-energy (~ 4 GeV at sea-level), ionizing particles they have no trouble making their way through mountains, but dense enough materials can still noticeably affect their path. This was the first experiment of its kind conducted at the University of Hawai i Hilo (UH Hilo). There have, however, been similar scientific investigations performed (Easwar & MacIntire, 1991), (Frisch & Smith, 1963), but only for a limited range of altitudes (up to approximately 600 meters, compared to the 4,200-meter summit of Mauna Kea). Flux characterization experiments have been performed beyond 4000m (Beringer p. 306, 2012), but were intended primarily to investigate the upper atmosphere (>10km). Our team s experiment was more focused on the impact of cosmic rays on high-altitude ground-based human activity; due to Mauna Kea s propensity for high-quality astronomical observing, our data were collected in an area with a high concentration of sensitive scientific equipment. EXPERIMENT METHODS This study was divided into two complementary sections, undertaken concurrently. Investigating the flux rate as a function of altitude could be performed with passive operation of our research team s detector equipment, allowing us to investigate relative energy of incident cosmic ray muons using actively controlled software. The flux experiment detected ionizing particles with pulse signals above a given threshold setting that passed through our detector. Since muons are the most numerous charged particles at sea level (Beringer p. 306, 2012), we can be reasonably sure that we are predominantly detecting muons produced by charged pion decays resulting from primary cosmic ray particle showers (Coan& Ye, 2003). Especially given the large number of particle detections (>660,000) during our more than 60 hours of detection time, results from this experiment reveal the behavior of muon flux activity in our altitude regime. The energy experiment involved connecting an oscilloscope to our detection equipment so we could represent the photon pulses from the photomultiplier tube in the detector in order to statistically characterize muon energy at corresponding detection elevations. The energy investigations consisted of a preliminary exploration phase and a secondary bulk collection phase. During the first phase we used an oscilloscope with strictly manual waveform saving capability. During the bulk collection phase we used a newer digital oscilloscope with the capability to automatically save waveforms. The exploration phase revealed that our 34

3 experimental methods were limited by the speed of our sampling, and the bulk collection phase was designed to remedy this by improving our statistics through continuous sampling. DESCRIPTION OF APPARATUS Equipment Summary: Our equipment for this experiment included a GW Instek GDS-2102 digital oscilloscope, a Tektronix TDS 2024C digital oscilloscope, a TeachSpin muon detector box, a TeachSpin particle detector, a Dell laptop computer running Ubuntu 12.04, the LabView SignalExpress VI (running on Windows 7), and a Honda EU2000i gas generator to provide remote power. Detector: The particle detector was manufactured by Teachspin Company. Its active volume is a plastic scintillator in the shape of a right circular cylinder of 15 cm diameter and 12.5 cm height placed at the bottom of the black anodized aluminum alloy tube. The properties of the polyvinyltoluene-based scintillator are summarized in the reference (Coan & Ye p. 6, 2003). The muon detector sensitivity is controlled via two settings - photomultiplier tube (PMT) voltage adjustment and discriminator (HVA) threshold control (Coan et. al. p.2, 2005). The PMT voltage adjustment is responsible for governing the potential difference across the photomultiplier in the muon detector body, which is in turn the source of signal output power. The HVA threshold is what governs the cutoff range beyond which the detector box registers a particle pass-through as a muon event and not random noise. Manual Oscilloscope: The GW Instek GDS-2102 digital oscilloscope was used to display and save individual muon event pulses during the first phase of the experiment in Fall It provided us with 25,000 or 2,500 data points per waveform in each individual saved file, the higher setting allowing far greater detail of the waveforms pulse structure. The vertical axis of each waveform pulse corresponds to an analog unit for energy; integration of the pulse (done via separate software) represents the cumulative energy imparted to the detector by the incident particle. Because saving 2,500 points per file resulted in a shorter time to read each file to memory, we chose to use the lower number of data points to increase the amount of particles that could be sampled. This did not noticeably impact the quality of the pulse data. Automated Oscilloscope: We used the Tektronix TDS 2024C digital oscilloscope to display and save muon energy analog pulses during Spring This oscilloscope is restricted to 2,500 data points per waveform in each saved file, and this setting could not be changed to reduce read-out time. The benefit of replacing the GW Instek model with the Tektronix was that the Textronix connects with LabView SignalExpress, a proprietary software that can automatically save a pulse waveform whenever the oscilloscope displays a new one (i.e., when the detector threshold is 35

4 crossed) if it is not in the process of reading out another file. This capability dramatically improved our team s collection efficiency. Shielding: Thick steel or iron pipe provides an adequate attenuating material for high-energy muons (Beringer p. 334, 2012). We planned to shield our detector to observe and quantify the effect that this has on the energy distribution of muon events to compare with unshielded data as altitude varies. We acquired two old ductile iron pipe casings of ¾ thickness to serve as a particle shield for our detector. We used a ½ thick iron pipe cap as a cap during the first run, and a threequarter inch steel sheet metal slab as a cap during the second run. PROCEDURE In order to record meaningful data with our equipment, our team made calibration adjustments to our oscilloscopes, detector box, and the detector. The most important calibration was setting the PMT and discriminator controls at a voltage that corresponded to the actual muon flux passing through the detector. This was a foundational adjustment that determined the results of the flux investigations for all subsequent tests. We determined that calibrating the detector s settings to register 1-3 muon strikes per second near sea level would be an appropriate baseline that would contend for the increased flux rate of muons at higher elevations. This is a reasonably accurate flux rate for the volume of scintillator that we used (Beringer p. 306, 2012). Detector specifications (Coan & Ye p. 29, 2003) allow a wide range of functional PMT settings, so we set the voltage at V and kept this setting consistent during the entire experiment. We decided our HVA voltage ( V) by modifying our threshold discriminator until it yielded the desired 1-3 strikes per second at our lab (elevation approximately 11 meters above sea level). It was necessary to optimize the PMT and HVA voltage settings in order to conduct the flux experiment, but our equipment was incapable of saving energy data without the use of an externally connected digital oscilloscope. We achieved optimal granularity in our waveform data by scaling our oscilloscope to (25 nanoseconds/division, 20 millivolts/division) on the (x, y) axes. We used a timing trigger of 30 mv. Calibration for both energy experiments involved ensuring that our equipment settings were as close as possible to the measured settings that we had established during our control run at sea level elevation. We used our voltmeter to measure and record the settings for our PMT output and HVA adjustment controls before we began any measurement run. The altitude locations for our sampling were based on several criteria. These included ease of access, proximity to our desired elevation intervals (~400 meters during the first flux and energy phase, ~1400 meters including sea level and summit during the second energy phase), distance from culturally and ecologically sensitive areas, and local terrain. Please see Fig. 1 for all of our measurement and calibration sites. 36

5 To make the best use of the limited time available for data collection, we selected fewer altitudes at which to perform our experiment, but increased the amount of time spent at each elevation. We distributed our measurement altitudes based on the highest altitude that we could access (the true summit of Mauna Kea at 4205 meters). We stepped down in approximately 1400-meter increments to express high (summit), medium-high, medium-low, and low (sea level) intervals of our available measurement regime. Shielding of the detector helped us corroborate our developing hypotheses. To act as a useful sanity check data set, we added our shielding device and then duplicated our energy sampling experiments at each measurement location. We applied the shielding and performed a second run after we had first recorded data with the detector unshielded. During the second set of energy experiments, we applied a new, thicker steel cap to have a more uniform distribution of metal surrounding the detector. Whenever our team performed flux/energy and flux/angle data collection experiments at the same location on the same day (see Pipes report), we performed the angular distribution experiments first, and then reset our equipment for the energy and flux measurement experiments in the manner described above. Figure 1: A satellite image of all the locations where our team collected flux and energy data. The triangles represent sites where we used manual waveform saving; the circles represent locations where we used automated energy sampling. 37

6 DATA ANALYSIS To analyze our data, we almost exclusively used a self-generated software suite that we developed in several languages, including C++/ROOT for lifetime histograms and energy histograms, Excel for some basic plotting work done in the field, and Python for numerical integration and file manipulation. We used the MatPlotLib library to create plots in Python. We found that muon flux rate clearly increases with elevation. See Fig. 3 for a graphical representation of all of our flux measurement data. The flux rate for a shielded detector is generally higher, especially when the thicker steel cap is applied (the lower fit curve). These data exhibit an essentially linear upward trend with only minor variation, corroborating the work of other recent studies (Beringer p. 334, 2012), which observed a more or less flat slope increase from (0-5 km). We were able to express this effect into a larger measurement regime featuring higher altitudes than have previously been studied by ground-based muon detection experiments, simultaneously providing a link to studies performed in the upper atmosphere. The data also suggest a strong correlation between muon altitude and muon energy. We found a steady, slow linear increase in the relative mean energy of muons as the measurement altitude is increased. There is also an across-the-board decrease in the total muon flux that we observed during the second phase of the experiment, which can best be described by the application of the thicker steel cap. Figure 2: Two energy distribution histograms constructed using data from the automated energy sampling experiment (Spring 2014). Notice the wider spread of the Gaussian curve at the higher elevation, especially in the shielded data sets (the darker line). Energy analog values are normalized such that the highest energy analog at each altitude is equal to 1. Fig. 2 suggests a pronounced spread in the distribution of muon energy, of which the higher energies are better represented when iron shielding surrounds the detector. These histograms display the overall distribution of muon energies for a given altitude. The elevations shown here are characteristic of the general trend towards a more pronounced spread at higher elevations, particularly with shielding applied. 38

7 During the second part of our energy experiment, we encountered a sampling issue even when using a digital oscilloscope with an interface designed to perform a continuous signal readout. This effect can be recognized in Fig. 2, where the shielded detector seems to produce muons of higher energy (in comparison with the unshielded detector); notice that the separation between shielded and unshielded muon energies increases at the higher altitude. Since the oscilloscope software itself did not allow for a reduction in the number of data points per save (which was rigid at 2,500), our instrument was only saving waveforms (above our target threshold) every few seconds, not at the ideal, continuous rate. This stringency ensured that instead of collecting every waveform pulse emanating from a particle that passed through the detector (as would be ideal), we were limited to sampling only at the capacity that the oscilloscope and LabView could save individual waveforms. RESULTS AND DISCUSSION Since our team repurposed measurement equipment for a use that it was not intended, our sampling effectiveness was drastically reduced for the energy portion of our experiment. In the first part of our energy investigations, this manifested itself as a statistical issue- we could not reasonably acquire enough particle waveforms to make very precise statistical inferences using a manual read-out process. Our detector encountered a signal pulse 1-3 times per second at sea level due to the consistent controls settings that we invoked. Even with this realistic detection rate, which manifests itself as extremely small error in Fig. 3, we were still only saving the waveforms of 8-12% of muons detected while operating at sea level. Being that muon flux rate increases with altitude, the rate of saved signals was higher at the top of the mountain. Since our iron shielding limits the overall amount of particles incident on the detection surface and the interactional cross-section is higher for lower-energy particles, the read-out rate was also higher when the shielding was applied. 39

8 Figure 3: A plot displaying the cumulative total muon detection rate at different elevations over the course of our entire experiment. The upper series consists of data from Fall 2013; the lower, Spring Although the mean energy for atmospheric cosmic ray muons peaks at around 4 MeV (Beringer p. 306, 2012), the precise shape of the energy curve is not well known. Increasing the discriminator threshold on our detector ensures that PMT output pulses have higher amplitude in order to be registered (i.e., more kinetic energy). Setting the discriminator to a value ~30% of an average pulse height ensures that we are examining the properties of almost exclusively muon detection events. Having such a high discriminator value also systematically ignores a large quantity of cosmic ray muons. This systematic bias introduces the misleading effect in the energy histograms (Fig. 2) that makes it appear as though shielding produces muons at higher energies. This effect could have been corrected with either continuous waveform sampling (which our team was unable to achieve with our equipment) or far more active detector time than is practical or necessary. We also attempted to correct for this effect by increasing the number of samples taken, using the automated oscilloscope during second phase of our experiment. 40

9 FUTURE WORK AND CONCLUSIONS Researchers who work with this apparatus or experimental setup in the future might wish to electrically ground the metal shielding that surrounds the detector and may also find it helpful to apply a correction for the detector saturation effect introduced above. The acquisition of a second detector would allow for more advanced investigations into the distribution of energetic particles in the local environment- enabling an experiment to connect the angular and energy components of cosmic ray muon flux. Further limiting of the threshold setting could account for the missing muons during the unshielded energy collection. If the discriminator threshold is brought to a level where only the events registering during the shielded runs are accepted as muon strikes, then enough detector time would show these higher energy muons during an unshielded run, as there would be no saturation effect from the flooding of the detector with lower energy muons. We believe we have assembled a useful guide to ionizing particle behavior for a location where there are numerous possibilities for research using sensitive electronic instruments at high altitude. This, in addition to the software suite that we compiled, enables future researchers to conduct experiments that are beneficial to NASA and the community as a whole. ACKNOWLEDGEMENTS Numerous parties contributed their time, effort, and resources to this project: John Coney, Dr. Jacob Hudson, Dr. Kathy Cooksey, The County of Hawai i Department of Water Supply, Aloha Construction & Welding Co., Office of Mauna Kea Management, and the UH Hilo Astrophysics Club. I d also like to especially thank the NASA Hawai i Space Grant Consortium, my co-author Robert Pipes, and my mentor Dr. Jesse Goldman. REFERENCES Beringer J. et al. (2012) Lepton summary table; Cosmic rays; Passage of particles through matter: muon loss at high energy. Particle Data Group PR D86, , 30; 306; 334; Coan T.E., Liu T., and Ye J. (2006) A compact apparatus for muon lifetime and time dilation demonstration in the undergraduate laboratory. Am. J. Phys. 74, 161. Coan T.E. and Ye J. (2003) Muon Physics Manual. v ; 15-18; 29. Easwar N. and MacIntire D. (1991) Study of the effect of relativistic time dilation on cosmic ray muon flux: An undergraduate modern physics experiment. Am. J. Phys. 59 (7), Frisch D. H. and Smith J. H. (1963) Measurement of the relativistic time dilation using μ- mesons. Am. J. Phys. 31 (5),