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(abridged) Accretion onto supermassive black holes (SMBHs) at close to the Eddington rate can influence the host galaxy via powerful winds. Theoretical models of such winds can explain observational correlations between SMBHs and their host galaxies and the powerful multi-phase outflows observed in a number of active galaxies. Analytic models usually assume spherical symmetry and a smooth gas distribution with an adiabatic equation of state. However, the interstellar medium in real galaxies is clumpy and cooling is important, complicating the analysis. We used a suite of idealised hydrodynamical simulations to isolate the effects of turbulence and cooling on the development and global properties of AGN wind-driven outflows on kiloparsec scales. We measured the outflow velocity, mass outflow rate and momentum and energy loading factors as the system evolved over 1.2 Myr and estimated plausible observationally derived values. We find that adiabatic simulations approximately reproduce the analytical estimates of outflow properties independently of turbulence or clumpiness. However, cooling reduces the outflow energy rate by 1-2 orders of magnitude in the smooth simulations and by up to one order of magnitude in the turbulent ones. The interplay between cooling and turbulence depends on AGN luminosity: in Eddington-limited AGN, turbulence enhances the coupling between the AGN wind and the gas, while the opposite happens in lower-luminosity simulations. This occurs because dense gas clumps are resilient to low-luminosity AGN feedback but get driven away by high-luminosity AGN feedback. The overall properties of multi-phase outflowing gas in our simulations qualitatively agree with observations of multi-phase outflows. We also find that using `observable' outflow properties leads to their parameters being underestimated by a factor of a few compared with real values., Comment: 17 pages, 18 figures; accepted for publication in Astronomy & Astrophysics

Understanding the impact of baryonic physics on cosmic structure formation is crucial for accurate cosmological predictions, especially as we usher in the era of large galaxy surveys with the Rubin Observatory as well as the Euclid and Roman Space Telescopes. A key process that can redistribute matter across a large range of scales is feedback from accreting supermassive black holes. How exactly these active galactic nuclei (AGN) operate from sub-parsec to Mega-parsec scales however remains largely unknown. To understand this, we investigate how different AGN feedback models in the Fable simulation suite affect the cosmic evolution of the matter power spectrum (MPS). Our analysis reveals that AGN feedback significantly suppresses clustering at scales $k \sim 10\,h\,cMpc^{-1}$, with the strongest effect at redshift $z = 0$ causing a reduction of $\sim 10\%$ with respect to the dark matter-only simulation. This is due to the efficient feedback in both radio (low Eddington ratio) and quasar (high Eddington ratio) modes in our fiducial Fable model. We find that variations of the quasar and radio mode feedback with respect to the fiducial Fable model have distinct effects on the MPS redshift evolution, with the radio mode being more effective on larger scales and later epochs. Furthermore, MPS suppression is dominated by AGN feedback effects inside haloes at $z = 0$, while for $z \gtrsim 1$ the matter distribution both inside and outside of haloes shapes the MPS suppression. Hence, future observations probing earlier cosmic times beyond $z \sim 1$ will be instrumental in constraining the nature of AGN feedback., Comment: Submitted to MNRAS. 17 pages, 10 (+2) figures. Fable MPS raw data is available at: https://github.com/MartinAlvarezSergio/Fable_MPS

Large language models (LLMs) have transformed AI and achieved breakthrough performance on a wide range of tasks that require human intelligence. In science, perhaps the most interesting application of LLMs is for hypothesis formation. A feature of LLMs, which results from their probabilistic structure, is that the output text is not necessarily a valid inference from the training text. These are 'hallucinations', and are a serious problem in many applications. However, in science, hallucinations may be useful: they are novel hypotheses whose validity may be tested by laboratory experiments. Here we experimentally test the use of LLMs as a source of scientific hypotheses using the domain of breast cancer treatment. We applied the LLM GPT4 to hypothesize novel pairs of FDA-approved non-cancer drugs that target the MCF7 breast cancer cell line relative to the non-tumorigenic breast cell line MCF10A. In the first round of laboratory experiments GPT4 succeeded in discovering three drug combinations (out of 12 tested) with synergy scores above the positive controls. These combinations were itraconazole + atenolol, disulfiram + simvastatin and dipyridamole + mebendazole. GPT4 was then asked to generate new combinations after considering its initial results. It then discovered three more combinations with positive synergy scores (out of four tested), these were disulfiram + fulvestrant, mebendazole + quinacrine and disulfiram + quinacrine. A limitation of GPT4 as a generator of hypotheses was that its explanations for them were formulaic and unconvincing. We conclude that LLMs are an exciting novel source of scientific hypotheses., Comment: 13 pages, 6 tables, 1 figure. Supplementary information available

The microstructure of metals and foams can be effectively modelled with anisotropic power diagrams (APDs), which provide control over the shape of individual grains. One major obstacle to the wider adoption of APDs is the computational cost that is associated with their generation. We propose a novel approach to generate APDs with prescribed statistical properties, including fine control over the size of individual grains. To this end, we rely on fast optimal transport algorithms that stream well on Graphics Processing Units (GPU) and handle non-uniform, anisotropic distance functions. This allows us to find large APDs that best fit experimental data and generate synthetic high-resolution microstructures in (tens of) seconds. This unlocks their use for computational homogenisation, which is especially relevant to machine learning methods that require the generation of large collections of representative microstructures as training data. The paper is accompanied by a Python library, PyAPD, which is freely available at: www.github.com/mbuze/PyAPD., Comment: 25 pages, 6 figures

Understanding how galaxies quench their star formation is crucial for studies of galaxy evolution. Quenching is related to the cold gas decrease. In the first paper we showed that the dust removal timescale in early-type galaxies (ETGs) is about 2.5 Gyr. Here we present carbon monoxide (CO) and 21 cm hydrogen (H I) line observations of these galaxies and measure the timescale of removal of the cold interstellar medium (ISM). We find that all the cold ISM components (dust, molecular and atomic gas) decline at similar rates. This allows us to rule out a wide range of potential ISM removal mechanisms (including starburst-driven outflows, astration, a decline in the number of asymptotic giant branch stars), and artificial effects like stellar mass-age correlation, environmental influence, mergers, and selection bias, leaving ionization by evolved low-mass stars and ionization/outflows by supernovae Type Ia or active galactic nuclei as viable mechanisms. We also provide evidence for an internal origin of the detected ISM. Moreover, we find that the quenching of star formation in these galaxies cannot be explained by a reduction in gas amount alone, because the star formation rates (SFRs) decrease faster (on a timescale of about 1.8 Gyr) than the amount of cold gas. Furthermore, the star formation efficiency of the ETGs (SFE = SFR/MH2) is lower than that of star-forming galaxies, whereas their gas mass fractions (fH2 = MH2/M*) are normal. This may be explained by the stabilization of gas against fragmentation, for example due to morphological quenching, turbulence, or magnetic fields., Comment: Astrophysical Journal, published; 29 pages, 13 figures, 6 tables; V2: matched the published version and typos in the SFR and outflow equations fixed - no change in the conclusions

It is well established that supermassive black hole (SMBH) feedback is crucial for regulating the evolution of massive, if not all, galaxies. However, modelling the interplay between SMBHs and their host galaxies is challenging due to the vast dynamic range. Previous simulations have utilized simple subgrid models for SMBH accretion, while recent advancements track the properties of the unresolved accretion disc, usually based on the thin $\alpha$-disc model. However, this neglects accretion in the radiatively inefficient regime, expected to occur through a thick disc for a significant portion of an SMBH's lifetime. To address this, we present a novel 'unified' accretion disc model for SMBHs, harnessing results from the analytical advection-dominated inflow-outflow solution (ADIOS) model and state-of-the-art GR(R)MHD simulations. Going from low to high Eddington ratios, our model transitions from an ADIOS flow to a thin $\alpha$-disc via a truncated disc, incorporating self-consistently SMBH spin evolution due to Lense-Thirring precession. Utilizing the moving mesh code AREPO, we perform simulations of single and binary SMBHs within gaseous discs to validate our model and assess its impact. The disc state significantly affects observable luminosities, and we predict markedly different electromagnetic counterparts in SMBH binaries. Crucially, the assumed disc model shapes SMBH spin magnitudes and orientations, parameters that gravitational wave observatories like LISA and IPTA are poised to constrain. Our simulations emphasize the importance of accurately modelling SMBH accretion discs and spin evolution, as they modulate the available accretion power, profoundly shaping the interaction between SMBHs and their host galaxies., Comment: Accepted for publication in MNRAS; 29 pages, 17 figures, 1 appendix; doi:10.1093/mnras/stae1422

Biological data in digital form has become a, if not the, driving force behind innovations in biology, medicine, and the environment. No study and no model would be complete without access to digital data (including text) collected by others and available in public repositories. With this ascent in the fundamental importance of data for reproducible scientific progress has come a troubling paradox., Comment: 12 pages 5852 words 1 figure

The gyro-kinetic model is an approximation of the Vlasov-Maxwell system in a strongly magnetized magnetic field. We propose a new algorithm for solving it combining the Semi-Lagrangian (SL) method and the Arakawa (AKW) scheme with a time-integrator. Both methods are successfully used in practice for different kinds of applications, in our case, we combine them by first decomposing the problem into a fast (parallel) and a slow (perpendicular) dynamical system. The SL approach and the AKW scheme will be used to solve respectively the fast and the slow subsystems. Compared to the scheme in [1], where the entire model is solved using only the SL method, our goal is to replace the method used in the slow subsystem by the AKW scheme, in order to improve the conservation of the physical constants.

In this work, we use hydrodynamical simulations to explore the effects of kinetic AGN jet feedback on the progression and outcome of the major merger of two isolated, gas-rich galaxies. We present simulations that use the moving-mesh code AREPO to follow the progression of the merger through first passage and up to the final coalescence, modelling the black holes at the centres of both of the merging galaxies using our prescription for black hole accretion via an $\alpha$-disc and feedback in the form of a spin-driven jet. We find that the jets drive large-scale, multiphase outflows which launch large quantities of cold gas out to distances greater than 100 kpc and with velocities that reach $\sim 2500 \, {\rm km \, s^{-1}}$. Gas in the outflows that decelerates, cools and falls back on the galaxies can provide a rich source of fuel for the black hole, leading to intense episodes of jet activity in which the jet can become significantly misaligned. The presence of AGN jets affects the growth of the stellar component: star formation is moderately suppressed at all times during the merger and the peak of the star formation rate, attained during the final coalescence of the galaxies, is reduced by a factor of $\sim 2$. Analysis of simulations such as these will play a central role in making precise predictions for multimessenger investigations of dual radio-AGN, which next-generation observational facilities such as LISA, Athena and SKA will make possible., Comment: 20 pages, 10 figures, accepted for publication by MNRAS

We propose a fold change visualization that demonstrates a combination of properties from log and linear plots of fold change. A useful fold change visualization can exhibit: (1) readability, where fold change values are recoverable from datapoint position; (2) proportionality, where fold change values of the same direction are proportionally distant from the point of no change; (3) symmetry, where positive and negative fold changes are equidistant to the point of no change; and (4) high dynamic range, where datapoint values are discernable across orders of magnitude. A linear visualization has readability and partial proportionality but lacks high dynamic range and symmetry (because negative direction fold changes are bound between [0, 1] while positive are between [1, $\infty$]). Log plots of fold change have partial readability, high dynamic range, and symmetry, but lack proportionality because of the log transform. We outline a new transform and visualization, named mirrored axis distortion of fold change (MAD-FC), that extends a linear visualization of fold change data to exhibit readability, proportionality, and symmetry (but still has the limited dynamic range of linear plots). We illustrate the use of MAD-FC with biomedical data using various fold change charts. We argue that MAD-FC plots may be a more useful visualization than log or linear plots for applications that require a limited dynamic range (approximately $\pm$2 orders of magnitude or $\pm$8 units in log2 space).