We show that, contrary to simple predictions, most AGNs show at best only a small increase of lags with increasing wavelength in the J, H, K, and L bands. We suggest that a possible cause of this near simultaneity from the near-IR to the mid-IR is that the hot dust is in a hollow bi-conical outflow of which we preferentially see the near side. In the proposed model sublimation or re-creation of dust (with some delay relative luminosity variations) along our line of sight in the hollow cone as the flux varies could be a factor in explaining the AGN changing-look phenomenon (CL). Variations in the dust obscuration can help explain changes in relationship of Hβ time delay on Luv variability. The relative wavelength independence of IR lags simplifies the use of IR lags for estimating cosmological parameters.

Since the last 1990s, Doppler spectroscopy has been one of the most prolific methods of detecting and characterizing exoplanets (Fischer et al. 2016). The latest generation of stabilized spectrographs can achieve impressive levels of precision and stability, approaching that needed to detect the motion of a Sun-like star due to the gravity of an Earth-mass planet in its habitable zone (Crass et al. 2021). However, the exoplanet detection power of modern radial velocity (RV) exoplanet surveys is typically limited by the spectral variability of the target star. Machine learning (ML) has the potential to significantly improve the ability of RV exoplanet surveys to distinguish planets for stellar variability. Astronomers have begun making applying a wide variety of ML techniques, from principal component analysis and multilinear regression to convolutional neural networks. This paper reviews the state of the field for mitigating stellar variability in RV exoplanet surveys from a ML perspective. Early results show that relatively simple ML techniques paired with well-engineered features often perform comparable to much more complex ML models, while providing improved interpretability and explainability. These are likely to be critical factors for establishing the credibility and robustness of any future detections of potentially Earth-like planets.

The canonical undestanding of stellar convection has recently been put under doubt due to helioseismic results and global 3D convection simulations. This “convective conundrum” is manifested by much higher velocity amplitudes in simulations at large scales in comparison to helioseismic results, and the difficulty in reproducing the solar differential rotation and dynamo with global 3D simulations. Here some aspects of this conundrum are discussed from the viewpoint of hydrodynamic Cartesian 3D simulations targeted at testing the rotational influence and surface forcing on deep convection. More specifically, the dominant scale of convection and the depths of the convection zone and the weakly subadiabatic – yet convecting – Deardorff zone are discussed in detail.

We review recent results that utilize planetary nebulae (PNe) to constrain galactic dynamical and chemical evolution in different environments. Planetary nebulae have become essential probes in both these aspects of galaxy evolution, and novel observations illustrate their potential. Dynamical evolutionary models need observational constraints, and PNe uniquely probe the environment far from the galactic centers. In chemical evolution, the PN progenitor population spans a broad range of formation epochs; the oldest progenitors of local PNe are contemporary with H II regions in z ∼ 2 star-forming galaxies (SFGs). The future will bring even more possibilities in these fields, especially with the advent of extremely large telescopes.

The magnetic network is a typical magnetic structure of the quiet Sun. Investigating its cycle dependence is crucial for understanding its evolution. We aim to identify and analyze the spatial scales of the magnetic network within magnetic power spectra derived from high-resolution Solar and Heliospheric Observatory (SOHO)/Michelson Doppler Imager (MDI) and Solar Dynamics Observatory (SDO)/ Helioseismic and Magnetic Imager (HMI) synoptic magnetograms. The data sets cover the entirety of solar cycles 23, 24, and part of cycle 25. We find that the identified magnetic network sizes identified range from 26 Mm to 41 Mm. There seems to be no obvious dependence on the solar cycle, and the sizes are distributed uniformly within the identification range.

Planetary nebulae (PNe) are essential tracers of the kinematics of the diffuse halo and intracluster light where stellar spectroscopy is unfeasible, due to their strong emission lines. However, that is not all they can reveal about the underlying stellar population. In recent years, it has also been found that PNe in the metal-poor halos of galaxies have different properties (specific frequency, luminosity function), than PNe in the more metal-rich galaxy centers. A more quantitative understanding of the role of age and metallicity in these relations would turn PNe into valuable stellar-population tracers. In order to do that, a full characterization of PNe in regions where the stellar light can also be analysed in detail is necessary. In this work, we make use of integral-field spectroscopic data covering the central regions of galaxies, which allow us to measure both stellar ages and metallicities as well as to detect PNe. This analysis is fundamental to calibrate PNe as stellar population tracers and to push our understanding of galaxy properties at unprecedented galactocentric distances.

Flows originating from black hole magnetospheres via Blandford-Znajek (BZ) process start highly relativistically, with very large Lorentz factors γ01, imprinted into the flow during pair production within the gaps. As a result, BZ-driven outflows would produce spine-brightened images, contrary to observations of the edge-brightened jet in M87. We conclude that M87 jet is not BZ-driven.

Many problems in astronomy and physics lend themselves to solutions from machine learning methods for the detection and classification of astronomical signals, and model inference from those signals. The historic presentation of machine learning methods as ‘black boxes’ has generated push back from some in the the physics/astronomy communities regarding how useful they are to truly uncover the physical laws that govern our world. Skepticism about the applicability of new computational methods in scientific inference is not new; we highlight connections between the machine learning contexts and previous computational paradigm shifts in astronomy. Moreover, several advances in methodologies challenge the assumption that machine learning ‘gives us answers that we can use but do not understand’ to standing physics questions. We summarize some astronomical machine learning data challenges used in astronomy and how we can use challenges on different scales to test different parts/use cases of our analysis methods.

In the past years, the results obtained by the WISSH quasar project provided a novel general picture on the distinctive multi-band properties of hyper-luminous (Lbol > 1047 erg/s) quasars at high redshift (z ∼ 2-4), unveiling interesting relations among active galactic nuclei, winds and interstellar medium, in these powerful sources at cosmic noon. Since 2022, we are performing a systematic and statistically-significant VLA study of the radio properties of WISSH. We carried out high-resolution VLA observations aiming at: 1) identifying young radio source from the broad-band spectral shape of these objects; 2) sample an unexplored high redshift/high luminosity regime, tracking possible evolutionary effects on the radio-loud/radio-quiet dichotomy; 3) quantifying orientation effects on the observed winds/outflows properties.

We summarize the main results from the survey of Planetary Nebulae (PNe) in M 31 with Megacam@CFHT and subsequent spectroscopy with Hectospec@MMT. We identified ∼5000 PNe in M 31 (∼1200 with spectroscopy; ∼200 with chemical abundances). We find a PN Luminosity Function faint-end rise, linked to a percentage of older stars in the parent population. We utilize PN extinction to distinguish young and old PNe. We find that the [Ar/H] vs [O/Ar] plane for emission-line nebulae is analogous to the [Fe/H] vs [α/Fe] plane for stars, and exploration of the M 31 disc PNe in this plane allowed us to constrain its chemical enrichment history. We find the kinematically and chemically distinct thin and thick discs of M 31, and that the G1-clump substructure is formed from perturbed disc material. We infer that M 31 has had a wet major (mass-ratio∼1:5) merger ∼2.5-4 Gyr ago, and obtain important constraints on the cannibalized satellite properties.

In this contribution, I present a selected overview of optical interferometry imaging results that brought insights on stellar activity and mass loss in evolved stars. I briefly introduce the STELLIM project that aims to characterize stellar surfaces and circumstellar environments by producing fast and reliable interferometric images.

Could Machine Learning (ML) make fundamental discoveries and tackle unsolved problems in Cosmology? Detailed observations of the present contents of the universe are consistent with the Cosmological Constant ʌ & Cold Dark Matter model, subject to some unresolved inconsistencies (‘tensions’) among observations of the Hubble Constant and the clumpiness factor. To understand these issues further, large surveys of billions of galaxies and other probes require new statistical approaches. In recent years the power of ML, and in particular ‘Deep Learning’, has been demonstrated for object classification, photometric redshifts, anomaly detection, enhanced simulations, and inference of cosmological parameters. It is argued that the more traditional ‘shallow learning’ (i.e. with pre-processing feature extraction) is actually quite deep, as it brings in human knowledge, while ‘deep learning’ might be perceived as a black box, unless supplemented by explainability tools. The ‘killer applications’ of ML for Cosmology are still to come. New ways to train the next generation of scientists for the Data Intensive Science challenges ahead are also discussed. Finally, the chatbot ChatGPT is challenged to address the question posed in this article’s title.1

The study of the differential rotation in the chromosphere of the Sun is of significant importance as it provides valuable insights into the rotational behaviour of the solar atmosphere at higher altitudes and the coupling mechanism between the various layers of the solar atmosphere. In this work, we employed the image correlation technique, explicitly focusing on plages, intending to estimate the chromospheric differential rotation. For this purpose, we have utilized Ca ii K spectroheliograms (1907 – 2007) from the Kodaikanal Solar Observatory (KoSO), recently calibrated with a better technique to ensure accuracy. Our analysis indicates that plages in the chromosphere exhibit faster rotation and a smaller latitudinal gradient when compared to the rotation rate obtained through sunspot tracking. Furthermore, we investigate the temporal analysis of the chromospheric differential rotation parameters across various solar cycles.

The radiative mode of AGN feedback, operated through outflows, plays an essential role in the evolution of galaxies. Quasar outflows are detected as blue-shifted broad absorption lines in the UV/optical spectra of quasars. Thanks to the Sloan digital sky survey, 100,000 broad absorption line quasars are available now for ensemble statistical studies. This rich dataset has also enabled us to identify some peculiar cases of these sources. By quantifying the BAL fraction in radio-loud BAL quasars, our studies demonstrate a clear trend of increasing BAL fraction as the viewing angle approaches an edge-on orientation, favoring the orientation model of BAL quasars. Also, by contrasting the properties of BAL quasars with appearing and disappearing BAL troughs, our analysis suggests that the extreme variations in BAL troughs are driven by ionization changes.

We compare different estimates of distances to planetary nebulae (PNe), namely, Gaia parallaxes and statistical values, in order to determine the most reliable distance for each PN. In numerous instances, we find that the distances derived from the Gaia parallaxes are not the most reliable, and that better estimates can be obtained from the median of the available statistical values. Our resulting distances imply that the distributions of distances from the Galactic plane of PNe with [WR] central stars is different from the distributions of both non-[WR] hydrogen-poor central stars and hydrogen-rich central stars.

With the volume and availability of astronomical data growing rapidly, astronomers will soon rely on the use of machine learning algorithms in their daily work. This proceeding aims to give an overview of what machine learning is and delve into the many different types of learning algorithms and examine two astronomical use cases. Machine learning has opened a world of possibilities for us astronomers working with large amounts of data, however if not careful, users can trip into common pitfalls. Here we’ll focus on solving problems related to time-series light curve data and optical imaging data mainly from the Deeper, Wider, Faster Program (DWF). Alongside the written examples, online notebooks will be provided to demonstrate these different techniques. This guide aims to help you build a small toolkit of knowledge and tools to take back with you for use on your own future machine learning projects.