The solar transition region is a thin region between the chromosphere and the corona, where temperatures rise sharply. The formation temperature of the 500–1600 Å ultraviolet (UV) spectra emitted by this region is estimated to be 20–800 kK. Interface Region Imaging Spectrograph (IRIS), launched in 2013, enabled detailed diagnostics of the UV spectra originating from this region. IRIS observations of solar active regions have revealed the existence of small, sudden brightening events (UV Bursts) similar to Ellerman Bombs observed in the Hα wing of the photosphere. However, Ellerman Bombs and UV Bursts are considered physical phenomena with different formation altitudes and temperature conditions. It has been suggested that the magnetic reconnection mechanism that causes UV Bursts may not be uniform. Therefore, it is necessary to classify events commonly referred to as UV Bursts into different physical classes based on their spectral shapes and to statistically clarify the magnetic and plasma conditions associated with each. In this study, we focused on solar active region 11850 on September 24, 2013, where the presence of UV Bursts was reported by Peter et al. (2014), and attempted to detect UV Bursts from IRIS spectroscopic observation data using Variational AutoEncoder (VAE), a machine learning model. In this presentation, we will discuss the physical characteristics of UV Bursts.

Understanding when and how water was delivered to or generated on the Moon is crucial for deciphering the history of the Moon’s formation and evolution. Several previous missions suggested the presence of water on the lunar surface. Infrared spectroscopic observations with the M3 onboard Chandrayaan-1 indicated the existence of water ice in the permanent shadow of the polar regions. Furthermore, observations by the Stratospheric Observatory for Infrared Astronomy (SOFIA) have detected a water molecule emission line at 6.1 μm in the high-latitude surface layers of the Moon. These observations highly suggest the presence of water in the permanent shadows of the Moon.
 In this study, we attempt to determine the H2O ion loss for each lunar region by directly comparing H2O ion detection amounts globally, using data from the MAP-PACE instrument onboard KAGUYA(SELENE). We identified regions where H2O ions are produced and lost and the underlying causes. Using MAP-PACE and MAP-LMAG data obtained at an altitude of ~100km, we calculated the convection electric field at the observation points to determine the surface location where the detected ions were generated. We selected data from periods when the Moon was outside the Earth’s magnetotail to analyze the interaction between the solar wind and the lunar surface. We compared counts corresponding to the time-of-flight of H2O ions.
In this analysis, we selected data obtained when the Moon was outside the Earth’s magnetotail and specifically targeted ions originating from selected lunar surface regions. To accurately separate and identify H2O ions from the mass spectra, we implemented a Markov Chain Monte Carlo (MCMC) method based on Bayesian inference. This approach enabled a more reliable estimation of H2O ion counts from the time-of-flight data and allowed us to characterize the lunar surface distribution of H2O ions with higher resolution than previously achieved.

Solar flares are impulsive brightenings that occur in the solar atmosphere. Various active phenomena associated with solar flares, such as flare ribbons, postflare loops, and filament/prominence eruptions, can be directly observed in imaging data. On stars other than the Sun, impulsive brightenings known as stellar flares are also observed.

Recent spectroscopic observations suggest that stellar flares are not simply characterized by an increase in stellar brightness, but involve a variety of active phenomena. However, unlike the Sun, it is difficult to identify what kinds of phenomena occur on the surfaces of distant stars because their surfaces cannot be spatially resolved. To overcome this difficulty, I investigate what information can be extracted from spatially integrated data obtained in stellar observations by analyzing detailed solar observations from a Sun-as-a-star (spatially integrated) perspective. In particular, I focus on Hα observations, which can capture various flare-associated phenomena and are available for both solar and stellar studies. In this talk, I will introduce recent progress in Sun-as-a-star studies of filament/prominence eruptions and postflare loops.

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For understanding coronal heating, it is important to clarify how heat and plasma flows are transported within coronal loops. However, actual observations provide only two-dimensional information obtained by integrating three-dimensional structures along the line of sight, making it difficult to determine what kinds of structures and motions are reflected in the observed spectra. In particular, Doppler velocity and non-thermal velocity derived from EUV spectroscopy are widely used to discuss coronal dynamics, but it has not yet been quantitatively clarified what kinds of structures contribute to these observables. To address this unresolved issue, we use a three-dimensional radiative MHD simulation to investigate how the underlying loop structures and dynamics quantitatively contribute to EUV observables. As a first step toward understanding how loop dynamics contribute to EUV spectra, it is necessary to define what should be identified as a loop in the simulation. In this presentation, I will introduce our current method for defining loops and show initial results on how well the defined loops account for bright coronal structures. I will also discuss how this approach can provide a basis for interpreting EUV spectra in terms of coronal loop dynamics.

Since the Apollo missions, the Moon had long been considered a dry body. However, recent observations by various missions (M3, SOFIA, LRO, LADEE, and LCROSS) have revealed an active water (OH/H2O) cycle on the Moon.
Sources of lunar water include micrometeorites and comets. Furthermore, recent D/H ratio analyses highlight ongoing chemical synthesis driven by interactions between solar wind protons and surface oxygen. Driven by thermal gradients, highly volatile water molecules desorb, travel via ballistic flights, and are ultimately trapped in permanently shadowed regions (PSR) at the poles.
To investigate the origin and transport process of lunar water, we analyze mass spectra obtained by the plasma particle analyzer (the electrostatic analyzer and time-of-flight mass spectrometer) aboard the Kaguya spacecraft. We apply the Markov chain Monte Carlo (MCMC) method to analyze low-count data, which is difficult to analyze using conventional fitting methods that assume sufficient statistics.

The solar transition region is a thin region between the chromosphere and the corona, where temperatures rise sharply. The formation temperature of the 500–1600 Å ultraviolet (UV) spectra emitted by this region is estimated to be 20–800 kK, assuming an optically thin ionization equilibrium. Interface Region Imaging Spectrograph (IRIS), launched in 2013, enabled detailed diagnostics of the UV spectra originating from this region. IRIS observations of solar active regions have revealed the existence of small, sudden brightening events (UV Bursts) similar to Ellerman Bombs observed in the Hα wing of the photosphere. However, Ellerman Bombs and UV Bursts are considered physical phenomena with different formation altitudes and temperature conditions. It has been suggested that the magnetic reconnection mechanism that causes UV Bursts may not be uniform. Therefore, it is necessary to classify events commonly referred to as UV Bursts into different physical classes based on their spectral shapes and to statistically clarify the magnetic and plasma conditions associated with each. In this study, we focused on solar active region 11850 on September 24, 2013, where the presence of UV Bursts was reported by Peter et al. (2014), and attempted to detect UV Bursts from IRIS spectroscopic observation data using Variational AutoEncoder (VAE), a machine learning model.