The tremendous new capabilities of JWST already enable great discoveries across broad areas of astrophysics, spanning scales from exoplanets to cosmology. We have also found some puzzling results that require re-examination of our prior assumptions and are a rich source for future investigations. JWST is remarkably complex, and the mission is possible because of the work of thousands of people around the world. Despite the complexity, it remains an open mission, with opportunity for researchers at all career stages, across a variety of institutions, and around the world. We hope to apply the lessons of our experiences to future ambitious astrophysics projects that we will pursue together.

Stellar streams unveil galactic histories through gravitational interactions, mergers, and tidal disruptions. To explore the complex morphology of streams, we use a ∼47 million halo main sequence stars catalogue using Gaia DR3 proper motion and photometry information, the combination of which renders the reduced proper motion parameter. This sample with reliable photometric distances reaches out to ∼21 kpc thereby probing much further out than would be possible using reliable Gaia parallaxes. Binned velocity moments on-sky pop up several known streams in the inner halo - particularly retrograde structures, due to the kinematic selection. We select and characterise the streams GD-1, Jhelum and Phlegethon. The faint signs of disequilibrium in the form of kinks and density variations in these thin streams will paint a more detailed picture of the existence and properties of the dark matter sub-haloes that perturb them and in turn, the mass distribution of our Galaxy.

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.

Life on Earth can be (has been) affected by various phenomena of non-biological or extraterrestrial origin, such as worldwide volcanic eruptions, the impacts of asteroids and comets, solar storms, the evolution of the Sun, supernova explosion, cosmic ray showers, …

These phenomena and the associated catastrophes can be grouped into different categories depending on its origin: terrestrial, solar system, galactic, and extragalactic, and the final destiny of the Universe. We will shortly described many of the identified risks and compare them by the degree of affectation for Life and Humanity.

The time scales and the area on Earth affected by each phenomenon vary considerably among them. We list the phenomena that can affect a region the size of a country, a continent, or a global catastrophe. However, we note that, given humanity’s degree of global economic and social interdependence, a local-scale phenomenon can even have global consequences.

The risks can be further classified in random and deterministic. Random threats are those associated with an event that has a certain probability of occurrence on a time scale, but we do not the exact date in the future, i.e.: an asteroid impact or a supernova explosion. Deterministic threats are those that will surely occur in a range of time in the future, i.e.: the evolution of the Sun into a red giant.

This comparative study will analyze what the “certainties” are (in statistical terms) about the different phenomena of extraterrestrial origin that will affect life on Earth on different geographical and temporal scales.

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.

We use oxygen and argon abundances for planetary nebulae (PNe) with low internal extinction (progenitor ages of > 4.5 Gyr) and high extinction (progenitor ages < 2.5 Gyr), as well as those of the HII regions, to constrain the chemical enrichment and star formation efficiency in the thin and thicker discs of M31. As the argon element is produced in larger fractions by Type Ia supernovae compared to oxygen, we found that the mean log(O/Ar) values of PNe as a function of their argon abundances [12 + log(Ar/H)] trace the interstellar medium (ISM) conditions at the time of birth of the M31 disc PN progenitors. Thus, the chemical enrichment and star formation effciency information encoded in the [α/Fe] versus [Fe/H] distribution of stars is also imprinted in the oxygen-to-argon abundance ratio log(O/Ar) versus argon abundance for the nebular emissions of the different stellar evolution phases. The chemical evolution model that reproduces the mean log(O/Ar) values as a function of argon abundance for the high- and low-extinction PNe requires a second infall of metal-poorer gas during a gas-rich (wet) satellite merger for the M31 disc region within 14 kpc. A strong starburst is ongoing in the intermediate radial range (14 < RGC< 18 kpc). In the outer M31 disc (RGC > 18 kpc), the log(O/Ar) versus argon abundance distribution of the younger high-extinction PNe indicates that they too were formed in a burst, though mostly from the metal-poorer gas. In M31, the thin disc is younger and less radially extended, formed stars at a higher star formation effciency, and had a faster chemical enrichment timescale than the more extended thicker disc. Both the thin and thicker discs in M31 reach similar high argon abundances (12 + log(Ar/H))∼ 6.7. The chemical and structural properties of the thin and thicker discs in M31 are thus remarkably different from those determined for the Milky Way thin and thick discs.

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.

The Pan-STARRS telescopes, located on Haleakala, Maui, Hawaii, are conducting a long-term search of the night sky for Near-Earth Objects. The Pan-STARRS survey is now one of the leading NEO surveys, accounting for more than 40% of all new NEO discoveries, and over 50% of discoveries of larger NEOs. Pan-STARRS is also a prolific comet discovery telescope, and discovered the first interstellar object, ‘Oumuamua.

Massive quiescent galaxies (MQGs) at redshifts z > 3 present a profound challenge to our understanding of galaxy formation and evolution. These galaxies undergo rapid and intense star formation followed by swift quenching very early in cosmic history, resulting in the formation of compact galaxies with half-light radii smaller than 1 kpc. New observations, particularly from the James Webb Space Telescope (JWST), have rapidly advanced our knowledge of their abundance, possible formation pathways, and physical properties. However, current theoretical models still struggle to replicate the observed number densities and characteristics of these galaxies. This review attempts to consolidate recent observational breakthroughs, discusses the comparison with theoretical models, and highlights key unresolved questions. An especially interesting development is the discovery of sub-populations of even older and early forming (at z > 7) quiescent galaxies which pose particular challenges and add to the ledger of the ‘too much too soon’ galaxy and AGN problems revealed by JWST. I outline my own views on key future research directions to unravel the mysteries of MQGs and their role in the broader context of cosmic evolution.

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.

New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning. Here we present an end-to-end strategy for recovering a free-form analytical potential from a mere snapshot of stellar positions and velocities. First we show how auto-differentiation can be used to capture an agnostic map of the gravitational potential and its underlying dark matter distribution in the form of a neural network. However, in the context of physics, neural networks are both a plague and a blessing as they are extremely flexible for modeling physical systems but largely consist in non-interpretable black boxes. Therefore, in addition, we show how a complementary symbolic regression approach can be used to open up this neural network into a physically meaningful expression. We demonstrate our strategy by recovering the potential of a toy isochrone system.

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.