Extinction correction is the quintessence of astronomy. To achieve precision astrophysics in plasma diagnostics as in the theme of the present Proceedings, one must perform extinction correction properly before executing any line diagnostics of line-emitting objects including planetary nebulae. By making use of the inseparable relationship between extinction correction and plasma diagnostics, we establish a novel method to determine the physical conditions of a line-emitting target and the extinction characteristics along the line of sight toward the target simultaneously and self-consistently. This approach is made possible by the exact analytical expressions for the extinction parameters in terms of the emission properties of the target and by statistical optimization of the extinction parameters to find the robust physical conditions of the target.

In this work we present a Bayesian approach to obtain the non-rotating stellar spectra from an observed spectrum of a rotating star. This is our first attempt to solve an inverse problem expressed in terms of a Fredholm integral. Our preliminary results with synthetic spectra are promising. More studies are required to compare our Bayesian approach with the standard method for real spectra.

During minima of solar activity, it is possible to estimate the influence of convection zone turbulence on the magnetic flux tubes forming active regions (ARs), because the toroidal field of the old cycle weakens, and the new toroidal field is still weak. We analyzed ARs of solar minima between 23-24 and 24-25 solar cycles. ARs were classified as regular, irregular and unipolar spots. Regular ARs follow the empirical laws consistent with the Babcock–Leighton dynamo theory. We found that regular ARs dominate by flux and by number during the solar minima. Irregular ARs are mainly represented by bipolar structures of deformed orientation and contribute only one-third in the total flux and one-third in the total number. Very complex multipolar ARs are extremely rare. So, during solar minima the global dynamo still guides the formation of ARs, whereas the turbulence only slightly affects the toroidal flux tubes orientation.

Abundance determinations in planetary nebulae (PNe) are crucial for understanding stellar evolution and the chemical evolution of the host galaxy.

We discuss the complications involved when the presence of a metal-rich phase is suspected in the nebula. We demonstrate that the presence of a cold region emitting mainly metal recombination lines necessitates a detailed treatment to obtain an accurate assessment of the enrichment of this cold gas phase.

We investigated a scenario where the presence of a broad absorption line (BAL) feature in quasars (QSOs) is contingent upon the line of sight being situated within an outflow cone emanating from the source. We examined the mechanism of dust-driven winds based on the failed radiatively accelerated dusty outflow (FRADO) model proposed by Czerny & Hryniewicz, letting it be responsible for the formation of massive outflow. We calculated the probability of observing the BAL effect from the geometry of outflow which is a function of global parameters of black hole mass (M•), Eddington ratio (αEdd), and metallicity (Z). We then compared the results with prevalence of BAL QSOs in a sample of observational data from SDSS. The consistency of our model with the data supports the interpretation of the BAL phenomenon as a result of source orientation, rather than a transitory stage in AGN evolution.

Binary Neutron star mergers (BNS) are of particular interest for the possibility of multi-messenger astronomy. We investigate if BNS gravitational waves trigger detection should be preferably conducted in a representation instead of another, we inspect time strains, Fourier transform, and spectrograms. We recreate the data stream of the LIGO detector, which could contain noise only or signals hidden in the noise. We train a binary classifier able to distinguish if a merger is present. We create two distinguished datasets, one with the LIGO detector instrumental noise and one whitened with respect to the detector PSD. We obtain the highest accuracy for whitened spectrograms - 93% correct classifications of an external validation dataset. Fourier transforms show similar behavior in both colored and whitened datasets with an accuracy of ∼77%. Whitened time strains have a substantial increase in accuracy from the colored to the whitened dataset, respectively 50% and 73%.

The grand minimum in the Sun’s activity is a distinctive mode characterized by a magnetic lull that almost completely lacks the emergence of sunspots on the solar surface for an extended duration. The factors driving this transition of an otherwise magnetically active star into a quiescent phase, the processes occurring within the solar interior and across the heliosphere during this period, and the mechanisms leading to the eventual resurgence of surface magnetic activity remain enigmatic. However, there have been sustained efforts in the past few decades to unravel these mysteries by employing a combination of observation, reconstruction and simulation of solar magnetic variability. Here, we summarize recent research on the solar grand minimum and highlight some outstanding challenges – both intellectual and practical – that necessitate further investigations.

More than three decades after the pioneering imaging catalog by Balick (1987), where the small-scale and low-ionization structures (LISs) in planetary nebulae (PNe) became evident, the present study conducts a comprehensive statistical analysis of LISs’ physical-chemical and excitation properties. Gathering the largest dataset to date, we compare LISs with high-ionization components (rims/shells) across a diverse sample of PNe. Key findings include lower electron densities (NeS ii) in LISs than in adjacent rims/shells and comparable electron temperatures (Te N ii and O iii) between these two nebular componets. Various optical diagnostic diagrams, while revealing a clear excitation stratification between these groups of components, are not good enough to clearly pinpoint the main excitation mechanism behind the LISs line-emission. Photoionization and shock models highlight a notable overlap between mechanisms, emphasizing the coexistence of both excitation processes. Contrary to expectations, shocks are unlikely to be the primary excitation source for most of LISs in PNe.

Understanding the irregular variation of the solar cycle is crucial due to its significant impact on global climates and the heliosphere. Since the polar magnetic field determines the amplitude of the next solar cycle, variations in the polar field can lead to fluctuations in the solar cycle. We have explored the variability of the solar cycle at different levels of dynamo supercriticality. We observe that the variability depends on the dynamo operation regime, with the near-critical regime exhibiting more variability than the supercritical regime. Furthermore, we have explored the effects of the irregular BMR properties (emergence rate, latitude, tilt, and flux) on the polar field and the solar cycle. We find that they all produce considerable variation in the solar cycle; however, the variation due to the tilt scatter is the largest.

In a recent search (Kim et al. 2022), we looked for microlensing signature in gravitational waves from spectrograms of the binary black hole events in the first and second gravitational-wave transient catalogs. For the search, we have implemented a deep learning-based method (Kim et al. 2021) and figured out that one event, GW190707_093326, out of forty-six events, is classified into the lensed class. However, upon estimating the p-value of this event, we observed that the uncertainty of the p-value still includes the possibility of the event being unlensed. Therefore, we concluded that no significant evidence of beating patterns from the evaluated binary black hole events has found from the search. For a consequence study, we discuss the distinguishability between microlensed GWs and the signal from precessing black hole binaries.

. We show that an extragalactic jet with a velocity shear gives rise to Fermi like acceleration process for photons scattering withing the shear layers of the jet. Such photons then gain energy to produce a high energy power law. These power law spectra at high energies are frequently observed in several extragalactic objects such as Gamma Ray Bursts (GRBs). We implement the model on GRBs to show that the obtained range of the photon indices are well within their observed values. The analytic results are confirmed with numerical simulations following Monte Carlo approach.

The inherent stochastic and nonlinear nature of the solar dynamo makes the strength of the solar cycles vary in a wide range, making it difficult to predict the strength of an upcoming solar cycle. Recently, our work has shown that by using the observed correlation of the polar field rise rate with the peak of polar field at cycle minimum and amplitude of following cycle, an early prediction can be made. In a follow-up study, we perform SFT simulations to explore the robustness of this correlation against variation of meridional flow speed, and against stochastic fluctuations of BMR tilt properties that give rise to anti-Joy and anti-Hale type anomalous BMRs. The results suggest that the observed correlation is a robust feature of the solar cycle and can be utilized for a reliable prediction of peak strength of a cycle at least 2 to 3 years earlier than the minimum.

Both observations and theoretical studies have convincingly shown that outflows (i.e., wind and jet) are common phenomena from black hole accretion systems with various accretion rates, although the physical driving mechanisms are not exactly same for different accretion modes. Outflows are not only important in the dynamics of black hole accretion, but also play an important role in AGN feedback; therefore it is crucial to investigate their main physical properties including mass flux and velocity. In this paper we summarize recent studies in investigating the properties and driving mechanisms from black hole accretion flows with various accretion rates.

As surveys grow, the challenge is how to explore and interpret the increasing quantity of data. Integral field spectroscopic (IFS) galaxy surveys are a good example of how data have grown in complexity and in volume. In order to find complex relations between the spatially resolved structures of galaxies and their accretion histories, we combine IFS high-dimensionality data, deep learning and numerical simulations to infer the evolutionary paths of galaxies. In this work we generate 10,000 simulated galaxies from TNG50 hydro-cosmological simulation to compare with the 10,000 galaxies observed in MaNGA, thus generating a mock MaNGA sample. We then analyse how the simulated galaxies reproduce the properties of MaNGA galaxies and study how the evolutionary paths of the mock galaxies relate to their observable properties. We finish by outlining our next steps which include using contrastive learning.

We do know that planetary nebulae (PNe) are ionized gaseous clouds of material ejected by evolved dying stars. The intense ultraviolet radiation field of these stars leads to the dissociation of the cold molecular gas and then to the ionization/excitation of the resultant atomic gas. The chemical composition, ionization structure, physical conditions and formation process of nebular shells, rims, and halos are well comprehended. On the contrary, the origin of low-ionization structures (LISs) frequently found in PNe break the overall picture, and it still remains poorly understood. The latest discoveries of molecular hydrogen (H2) in LISs have changed how we think about their origin. Besides the detection of H2 emission, the [Fe ii] 1.644μm and [C i] 8727Å lines have also been detected in LISs. These results add new pieces to the puzzling problem of LISs opening a new window to enrich our knowledge and understanding on these microstructures.

Planetary nebulae (PNe) are remnants of evolved stars, fundamental for understanding stellar life cycles and galactic enrichment. In this work, we present a summary of our recent work using three-dimensional models and spatially resolved constraints to examine the physical and chemical properties of PNe, with a particular focus on preliminary results for the PN NGC 3132. Our results indicate that a star of about 3M⊙, surrounded by a shell, wind, or disk with approximately 5.0×10−6M⊙ and extending to about 300AU is necessary to adequately reproduce the observations, consistent with recent JWST findings. We also discuss the importance of this methodology in studying the properties of the progenitor stars and making abundance determinations.

Galaxy mergers are central to our current understanding of the universe. These events are known to change the morphologies of the merging galaxies, accompanied by changes to physical processes Galaxy mergers are rare events and to better study them we need larger, statistical samples.

Greater use of machine learning and artificial intelligence has seen galaxy mergers become a target for these techniques. Recently, application of these techniques has gone from proof of concept to using machine learning galaxy merger catalogues for science.

In this proceedings, we will examine how extraction of morphological parameters by traditional methods can be used to aid neural networks in classification of galaxy mergers. We will see how current networks may not be extracting all the information from imaging data. We will also discuss how samples of rare objects can be contaminated and the impact this has on our science. This contribution is based on Pearson et al. (2022).

Photon-driven flows have been studied for almost a century, and a quantitative description of the radiative forces on atoms and ions is important for understanding a wide variety of systems, including active galactic nuclei (AGN). The colloquially-termed “radiation pressure” of line-driven winds plays an important role in driving outflows in these environments. Quantifying the associated forces is crucial to understanding how these flows enable interactive mechanisms within these environments, such as AGN feedback. Here we provide new calculations of the dimensionless line strength parameter due to radiation driving. For representative AGN, we calculate the photoionization balance at each step along the line of sight (LOS) to the proposed wind-launching region above the accretion disk. We then use a recently compiled database of approximately 5.6 million spectral lines to compute the strength of the line-driving force on the gas and the mass-loss rates resulting from these outflows. We also introduce a “shielding factor’’ that increases the magnitude of the accretion disk column density prior to the launch radius. This shielding factor simulates a proposed inner “failed wind” region that is thought to shield the outflowing gas from becoming over-ionized by the central source. We also revisit and formalize the role of the commonly-used ionization parameter in setting the properties of the accelerating gas.

Statistical study of 3047 active regions (ARs) from 1996 to 2021 was performed using the catalog of the magneto-morphological classes (MMC) of ARs CrAO. According to the magneto-morphological classification of ARs proposed earlier, all ARs, except for unipolar spots, were sorted out between two categories: regular (bipolar groups obeying the Hale’s polarity law, the Joy’s law, etc.) and irregular ARs (all the rest). We analyzed the number and fluxes of ARs depending on their location in different (relative to the equator) hemispheres. We found that the trends for fluxes are more pronounced. For ARs of both MMC types, they demonstrate signs of both a multi-peak and double-peak structure. Some peaks coincide with the main maxima of the cycle. The second main maximum is mainly formed by the irregular ARs in the S-hemisphere. This might be due to the interaction of the dipole and quadrupole components of the global magnetic field.

Most of our knowledge about the Universe comes from the careful analysis of light that reaches us. Spectroscopy, which is the most detailed method of spectrum analysis, when applied to stars provides information on the parameters of their atmospheres, including effective temperature, acceleration, velocity fields, and their chemical composition. Stellar classification brought forth the understanding of what physical parameters are critical in shaping stellar atmospheres. It is a key element that has linked efforts related to numerical modelling of atmospheres with observations. We present preliminary results on the method of stellar spectra classification based on large-scale unsupervised pre-training. The applied deep neural network of the auto-encoder type, thanks to the use of differentiable elements of physical modelling in the decoder, allows to work with medium to high-resolution spectra, is insensitive to normalization errors, and different radial and rotational velocity, and operates in a wide range of signal-to-noise ratio.