The confluence of data from the Murchison Widefield Array and an imaging pipeline tailored for spectroscopic snapshot images of the Sun at low radio frequencies have led to enormous improvements in the imaging quality of the Sun. These developments have lowered the detection thresholds by up to two orders of magnitude as compared to earlier studies, and have enabled the discovery of Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs). Their spatial distribution and various other properties are consistent with being the radio signatures of coronal nanoflares hypothesized by Parker (1988) to explain coronal heating in the quiet Sun emissions. We present the status of the multiple projects we have been pursuing to improve the detection and characterisation of WINQSEs, ranging from looking for them in multiple independent datasets using independent detection techniques to looking for their counter parts to estimate the energy associated with them and understanding their morphologies.

In the interstellar medium, inelastic collisions are so rare that they cannot maintain a local thermodynamical equilibrium (LTE). Atomic and molecular populations therefore do not follow a simple Boltzmann distribution and non-LTE spectra are the rule rather than the exception. In such conditions, accurate state-to-state collisional data are crucial for a quantitative interpretation of spectra. In recent years, considerable progress has been made in quantum calculations of inelastic cross sections for a variety of targets, types of transitions and projectiles. For a few benchmark species, detailed comparisons between theory and experiment were also carried out at the state-to-state level and in the quantum regime. In this article, we highlight such comparisons for three important molecules: CO, H2O and CH+. We also describe current computational efforts to extend these advances to ever larger targets, new transition types, and new environments (e.g. stellar envelopes or cometary atmospheres).

We develop an adaptive method to automatically identify ARs from radial synoptic maps observed by SOHO/MDI and SDO/HMI, calibrate the detections between HMI and MDI data based on identified ARs flux and area and further derive a homogeneous dataset including ARs’ area and flux over the last two solar cycles. The data are compared with sunspot number, USAF/NOAA sunspot area, SMARPs and SHARPs and BARD area and flux, which show reasonable agreement. The identified ARs during the overlap period of MDI and HMI have the same areas as a whole while the AR flux based on MDI maps is about 1.36 times as large as that of HMI maps. Based on our dataset, we find strong ARs (|flux| > 1022Mx) contribute most to the difference between cycles 23 and 24 while other ARs (|flux| < 1022Mx) are similar in the two cycles in both area and flux.

MHD avalanches involve small, narrowly localized instabilities spreading across neighbouring areas in a magnetic field. Cumulatively, many small events release vast amounts of stored energy. Straight cylindrical flux tubes are easily modelled, between two parallel planes, and can support such an avalanche: one unstable flux tube causes instability to proliferate, via magnetic reconnection, and then an ongoing chain of like events. True coronal loops, however, are visibly curved, between footpoints on the same solar surface. With 3D MHD simulations, we verify the viability of MHD avalanches in the more physically realistic, curved geometry of a coronal arcade. MHD avalanches thus amplify instability across strong solar magnetic fields and disturb wide regions of plasma. Contrasting with the behaviour of straight cylindrical models, a modified ideal MHD kink mode occurs, more readily and preferentially upwards in the new, curved geometry. Instability spreads over a region far wider than the original flux tubes and than their footpoints. Consequently, sustained heating is produced in a series of ‘nanoflares’ collectively contributing substantially to coronal heating. Overwhelmingly, viscous heating dominates, generated in shocks and jets produced by individual small events. Reconnection is not the greatest contributor to heating, but is rather the facilitator of those processes that are. Localized and impulsive, heating shows no strong spatial preference, except a modest bias away from footpoints, towards the loop’s apex. Remarkable evidence emerges of ‘campfire’ like events, with simultaneous, reconnection-induced nanoflares at separate sites along coronal strands, akin to recent results from Solar Orbiter. Effects of physically realistic plasma parameters, and the implications for thermodynamic models, with energetic transport, are discussed.

Using tree-ring radiocarbon 14C data, solar cycles are now reconstructed for the last millennium, more than doubling the previously known statistic of direct solar observations and giving a new opportunity to validate basic empirical rules connecting solar cycle lengths, strengths and intensities. This includes the Waldmeier rule relating the cycle’s strength to the length of its ascending phase, and the Gnevyshev-Ohl rule suggesting that cycles are paired so that the intensity of an odd solar cycle is higher than that of the preceding even cycle. Using the extended solar-cycle statistic, we found that the Waldmeier rule remains robust for the well-defined solar cycles implying that it is an intrinsic feature of the solar cycle. However, the validity of the Gnevyshev-Ohl rule is not confirmed at any reasonable statistical level, indicating that either the insufficient accuracy of the reconstructed solar cycles or that this rule is not a robust feature.

We carry out the first statistical study that investigates the flare-coronal mass ejections (CMEs) association rate as function of the flare intensity and the total unsigned magnetic flux (ΦAR) of ARs that produces the flare. Our results show that flares of the same GOES class but originating from an AR of larger ΦAR, are much more likely confined. This implies that ΦAR is a decisive quantity describing the eruptive character of a flare, as it provides a global parameter relating to the strength of the background field confinement. We also calculated the mean twist values α in regions close to the polarity inversion line and proposed a new parameter α / ΦAR to measure the probability for a large flare to be associated with a CME. We find that the new parameter α/ ΦAR is well able to distinguish eruptive flares from confined flares.

Experimental studies are key to investigating the physical and chemical processes that drive cloud and haze formation from gas and solid phase molecular precursors in (exo)planetary environments, and validating the theoretical calculations used in models of (exo)planetary atmospheres. They allow characterizing the physical, optical, and chemical properties of laboratory-generated analogs, hence providing critical input parameters to models for observational data analysis. In this paper, we present examples of (1) experiments performed with different facilities to produce analogs of Titan and exoplanet atmospheric aerosols from gas phase molecular precursors, and (2) the characterization of these analogs to provide information on their composition, morphology, and optical constants to the scientific community. We also introduce the recently launched NASA Center for Optical Constants (NCOC), which will provide this critical data to the scientific community for (exo)planetary-relevant ices and organic refractory materials produced in the laboratory from the irradiation of gas and ice precursors.

For the last 20 years, Galactic Surveys have been revolutionizing our vision of the universe and broadening our understanding of the vastness of space that surrounds us. Galactic Surveys such as APOGEE, Gaia-ESO, GALAH, WEAVE and the currently-under-development 4MOST are teaching us a great deal about the chemical composition of stellar atmospheres, the formation and evolution of galaxies and how elements are synthesised in the universe. However, many questions remain unanswered and the current focus of ongoing and future surveys. Answering each of these questions requires the collection of data, normally as spectra, as most of the information we receive from the universe is electromagnetic radiation. Following the very expensive acquisition of astronomical spectra, another crucial task lies ahead: the analysis of these spectra to extract the priceless information they carry. High-quality atomic data of many neutral and ionised species is essential to conduct this analysis.

Alfvénic waves are regarded as an important process in understanding coronal heating, solar wind acceleration, and the fractionization of low first-ionization-potential (FIP) elements. Recently, significant progresses have been made in the detection of propagating Alfvénic waves in the solar chromosphere using two different methods: the imaging method and the spectroscopic method. The imaging method detects Alfvénic waves that oscillate in the direction perpendicular to the line of sight, and the spectroscopic method, those that oscillates in the line of sight direction. We have applied the spectroscopic method to the imaging spectral data taken by the FISS on GST at Big Bear. As a result, we detected a number of propagating Alfvénic wave packets, and found that there are two distinct groups: three-minute period waves, and ten-minute period waves.

In this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.

The butterfly diagram of the solar cycle is the equatorward migration of sunspot’s emergence latitudes as the solar cycle evolves, which was attributed to the equatorward flow at the base of the convection zone. However, helioseismological studies indicate controversial forms of the flow, and even present poleward flow at the base, which poses a big challenge to the wide-accepted mechanism. So we aim to propose a new mechanism, that is the latitude-dependent radial flux transport.

During 2017, when the Sun was moving toward the minimum phase of solar cycle 24, an exceptionally eruptive active region (AR) NOAA 12673 emerged on the Sun during August 28-September 10. During the highest activity level, the AR turned into a δ-type sunspot region, which manifests the most complex configuration of magnetic fields from the photosphere to the coronal heights. The AR 12673 produced four X-class and 27 M-class flares, along with numerous C-class flares, making it one of the most powerful ARs of solar cycle 24. Notably, it produced the largest flare of solar cycle 24, namely, the X9.3 event on 2017 September 6. In this work, we highlight the results of our comprehensive analysis involving multi-wavelength imaging and coronal magnetic field modeling to understand the evolution and eruptivity from AR 12673. We especially focus on the morphological, spectral and kinematical evolution of the two X-class flares on 6 September 2017. We explore various large- and small-scale magnetic field structures of the active region which are associated with the triggering and subsequent outbursts during the powerful solar transients.

The paper corresponds to the session organised by the IAU inter-commission B2-B5 working group “Laboratory Astrophysics Data Compilation, Validation and Standardization: from the Laboratory to FAIR usage in the Astronomical Community” at the IAU 2022 General Assembly. The session included talks about the usage and implementation of FAIR concepts in VAMDC and in the IVOA, then domain specific talks oriented towards planetology, dust and ices. The program (doi.org/10.5281/zenodo.7050654) and the various talks can be found in the ZENODO “cb5-labastro” community (zenodo.org/communities/cb5-labastro).

Most (yet not all) results of atomic physics research of Charlotte Moore Sitterly (CMS), which was closely connected to astrophysics, are now incorporated in online databases, one of which is the Atomic Spectra Database of the National Institute of Standards and Technology. The use of this database extends far beyond astrophysics, but this review focuses on astrophysical applications. The impact of CMS’s work on modern atomic physics and other sciences is discussed, and problems that urgently need solutions are outlined.

The magnetic power spectrum analysis provides an effective way to understand the observed distribution of the photospheric magnetic fields and their interaction with plasma motions. We aim to investigate the power spectra using spherical harmonic decomposition of SOHO/MDI and SDO/HMI synoptic magnetograms for cycles 23 and 24. We find that the calibration factor between MDI and HMI power spectral density is spatial scale-dependent. The magnetic power spectra show two peaks at the AR scale (l≈30) and supergranular scale (l≈120). The power law indices between these show a good anti-correlation with the amplitude of magnetic activity.

The signatures of small-scale features in the solar atmosphere are severely degraded by limited angular resolution of the observations. The Deep Solar ALMA Neural Network Estimator (Deep-SANNE) is trained towards synthetic observables from 3D magnetohydrodynamic simulations to recognize the small-scale dynamic features in data at limited observational resolution, and provide maps of correction factors across the field of view. The correction factors can be used to acquire deconvolved refined images with significantly improved brightness temperature contrasts, where the strength of brightening events are reproduced to an accuracy of 94.0% instead of the 43.7% at observational resolution. Deep-SANNE can also provide masks of the most probable locations with large accuracies, and estimations on the radiation formation heights in connection to the small-scale features. The Deep-SANNE refined images and estimations of radiation formation heights allow for larger accuracy and meaningful analysis of solar ALMA data.