Hideyuki Fuke

This corrects the article DOI: 10.1103/PhysRevLett.130.211001.

The General Antiparticle Spectrometer (GAPS) is a balloon-borne experiment that aims to measure low-energy cosmicray antiparticles. GAPS has developed a new antiparticle identification technique based on exotic atom formation caused by incident particles, which is achieved by ten layers of Si(Li) detector tracker in GAPS. The conventional analysis uses the physical quantities of the reconstructed incident and secondary particles. In parallel with this, we have developed a complementary approach based on deep neural networks. This paper presents a new convolutional neural network (CNN) technique. A three-dimensional CNN takes energy depositions as three-dimensional inputs and learns to identify their positional/energy correlations. The combination of the physical quantities and the CNN technique is also investigated. The findings show that the new technique outperforms existing machine learning-based methods in particle identification.

This study developed a novel thermal control system to cool detectors of the General AntiParticle Spectrometer (GAPS) before its flights. GAPS is a balloon-borne cosmic-ray observation experiment. In its payload, GAPS contains over 1000 silicon detectors that must be cooled below −40℃. All detectors are thermally coupled to a unique heat-pipe system (HPS) that transfers heat from the detectors to a radiator. The radiator is designed to be cooled below −50℃ during the flight by exposure to space. The pre-flight state of the detectors is checked on the ground at 1 atm and ambient room temperature, but the radiator cannot be similarly cooled. The authors have developed a ground cooling system (GCS) to chill the detectors for ground testing. The GCS consists of a cold plate, a chiller, and insulating foam. The cold plate is designed to be attached to the radiator and cooled by a coolant pumped by the chiller. The payload configuration, including the HPS, can be the same as that of the flight. The GCS design was validated by thermal tests using a scale model. The GCS design is simple and provides a practical guideline, including a simple estimation of appropriate thermal insulation thickness, which can be easily adapted to other applications.

We present the measurement of the energy dependence of the boron flux in cosmic rays and its ratio to the carbon flux in an energy interval from 8.4 GeV/n to 3.8 TeV/n based on the data collected by the Calorimetric Electron Telescope (CALET) during ∼6.4 yr of operation on the International Space Station. An update of the energy spectrum of carbon is also presented with an increase in statistics over our previous measurement. The observed boron flux shows a spectral hardening at the same transition energy E0∼200 GeV/n of the C spectrum, though B and C fluxes have different energy dependences. The spectral index of the B spectrum is found to be γ=-3.047±0.024 in the interval 25<E<200 GeV/n. The B spectrum hardens by ΔγB=0.25±0.12, while the best fit value for the spectral variation of C is ΔγC=0.19±0.03. The B/C flux ratio is compatible with a hardening of 0.09±0.05, though a single power-law energy dependence cannot be ruled out given the current statistical uncertainties. A break in the B/C ratio energy dependence would support the recent AMS-02 observations that secondary cosmic rays exhibit a stronger hardening than primary ones. We also perform a fit to the B/C ratio with a leaky-box model of the cosmic-ray propagation in the Galaxy in order to probe a possible residual value λ0 of the mean escape path length λ at high energy. We find that our B/C data are compatible with a nonzero value of λ0, which can be interpreted as the column density of matter that cosmic rays cross within the acceleration region.

Abstract The CALorimetric Electron Telescope (CALET) on the International Space Station consists of a high-energy cosmic-ray CALorimeter (CAL) and a lower-energy CALET Gamma-ray Burst Monitor (CGBM). CAL is sensitive to electrons up to 20 TeV, cosmic-ray nuclei from Z = 1 through Z ∼ 40, and gamma rays over the range 1 GeV–10 TeV. CGBM observes gamma rays from 7 keV to 20 MeV. The combined CAL-CGBM instrument has conducted a search for gamma-ray bursts (GRBs) since 2015 October. We report here on the results of a search for X-ray/gamma-ray counterparts to gravitational-wave events reported during the LIGO/Virgo observing run O3. No events have been detected that pass all acceptance criteria. We describe the components, performance, and triggering algorithms of the CGBM—the two Hard X-ray Monitors consisting of LaBr₃(Ce) scintillators sensitive to 7 keV–1 MeV gamma rays and a Soft Gamma-ray Monitor BGO scintillator sensitive to 40 keV–20 MeV—and the high-energy CAL consisting of a charge detection module, imaging calorimeter, and the fully active total absorption calorimeter. The analysis procedure is described and upper limits to the time-averaged fluxes are presented.

The Calorimetric Electron Telescope (CALET) was launched on the International Space Station in 2015 and since then has collected a large sample of cosmic-ray charged particles over a wide energy. Thanks to a couple of layers of segmented plastic scintillators placed on top of the detector, the instrument is able to identify the charge of individual elements from proton to iron (and above). The imaging tungsten scintillating fiber calorimeter provides accurate particle tracking and the lead tungstate homogeneous calorimeter can measured the energy with a wide dynamic range. One of the CALET scientific objectives is to measure the energy spectra of cosmic rays to shed light on their acceleration and propagation in the Galaxy. By the observation in first five years, a precise measurement of the iron spectrum is now available in the range of kinetic energy per nucleon from 10 GeV/n to 2 TeV/n. The CALET's result with a description of the analysis and details on systematic uncertainties will be illustrated. Also, a comparison with previous experiments' results is given.

This study developed a novel thermal control system to cool detectors of the General AntiParticle Spectrometer (GAPS) before its flights. GAPS is a balloon-borne cosmic-ray observation experiment. In its payload, GAPS contains over 1000 silicon detectors that must be cooled below −40℃. All detectors are thermally coupled to a unique heat-pipe system (HPS) that transfers heat from the detectors to a radiator. The radiator is designed to be cooled below −50℃ during the flight by exposure to space. The pre-flight state of the detectors is checked on the ground at 1 atm and ambient room temperature, but the radiator cannot be similarly cooled. The authors have developed a ground cooling system (GCS) to chill the detectors for ground testing. The GCS consists of a cold plate, a chiller, and insulating foam. The cold plate is designed to be attached to the radiator and cooled by a coolant pumped by the chiller. The payload configuration, including the HPS, can be the same as that of the flight. The GCS design was validated by thermal tests using a scale model. The GCS design is simple and provides a practical guideline, including a simple estimation of appropriate thermal insulation thickness, which can be easily adapted to other applications.

The General Antiparticle Spectrometer (GAPS) is a balloon-borne experiment that aims to measure low-energy cosmicray antiparticles. GAPS has developed a new antiparticle identification technique based on exotic atom formation caused by incident particles, which is achieved by ten layers of Si(Li) detector tracker in GAPS. The conventional analysis uses the physical quantities of the reconstructed incident and secondary particles. In parallel with this, we have developed a complementary approach based on deep neural networks. This paper presents a new convolutional neural network (CNN) technique. A three-dimensional CNN takes energy depositions as three-dimensional inputs and learns to identify their positional/energy correlations. The combination of the physical quantities and the CNN technique is also investigated. The findings show that the new technique outperforms existing machine learning-based methods in particle identification.