Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

Supermassive black holes (BH) accrete gas from their surroundings and coalesce with companions during galaxy mergers, and both processes change the BH mass and spin. By means of high-resolution hydrodynamical simulations of galaxies, either idealised or embedded within the cosmic web, we explore the effects of interstellar gas dynamics and external perturbations on BH spin evolution. All these physical quantities were evolved on-the-fly in a self-consistent manner. We use a 'maximal' model to describe the turbulence induced by stellar feedback to highlight its impact on the angular momentum of the gas accreted by the BH. Periods of intense star formation are followed by phases where stellar feedback drives large-scale outflows and hot bubbles. We find that BH accretion is synchronised with star formation, as only when gas is cold and dense do both processes take place. During such periods, gas motion is dominated by consistent rotation. On the other hand, when stellar feedback becomes substantial, turbulent motion randomises gas angular momentum. However BH accretion is strongly suppressed in that case, as cold and dense gas is lacking. In our cosmological simulation, at very early times (z>6), the galactic disc has not yet settled and no preferred direction exists for the angular momentum of the accreted gas, so the BH spin remains low. As the gas settles into a disc (6>z>3), the BH spin then rapidly reaches its maximal value. At lower redshifts (z<3), even when galaxy mergers flip the direction of the angular momentum of the accreted gas, causing it to counter-rotate, the BH spin magnitude only decreases modestly and temporarily. Should this be a typical evolution scenario for BH, it potentially has dramatic consequences regarding their origin and assembly, as accretion on maximally spinning BH embedded in thin Shakura-Sunyaev disc is significantly reduced.

Abstract:

Supermassive black holes (BH) accrete gas from their surroundings and coalesce with companions during galaxy mergers, and both processes change the BH mass and spin. By means of high-resolution hydrodynamical simulations of galaxies, either idealised or embedded within the cosmic web, we explore the effects of interstellar gas dynamics and external perturbations on BH spin evolution. All these physical quantities were evolved on-the-fly in a self-consistent manner. We use a 'maximal' model to describe the turbulence induced by stellar feedback to highlight its impact on the angular momentum of the gas accreted by the BH. Periods of intense star formation are followed by phases where stellar feedback drives large-scale outflows and hot bubbles. We find that BH accretion is synchronised with star formation, as only when gas is cold and dense do both processes take place. During such periods, gas motion is dominated by consistent rotation. On the other hand, when stellar feedback becomes substantial, turbulent motion randomises gas angular momentum. However BH accretion is strongly suppressed in that case, as cold and dense gas is lacking. In our cosmological simulation, at very early times (z>6), the galactic disc has not yet settled and no preferred direction exists for the angular momentum of the accreted gas, so the BH spin remains low. As the gas settles into a disc (6>z>3), the BH spin then rapidly reaches its maximal value. At lower redshifts (z<3), even when galaxy mergers flip the direction of the angular momentum of the accreted gas, causing it to counter-rotate, the BH spin magnitude only decreases modestly and temporarily. Should this be a typical evolution scenario for BH, it potentially has dramatic consequences regarding their origin and assembly, as accretion on maximally spinning BH embedded in thin Shakura-Sunyaev disc is significantly reduced.

Abstract:

Dark matter haloes are typically characterized by radial density profiles with fixed forms motivated by simulations (e.g. Navarro-Frenk-White [NFW]). However, simulation predictions depend on uncertain dark matter physics and baryonic modelling. Here, we present a method to constrain halo density profiles directly from observations using Exhaustive Symbolic Regression (ESR), a technique that searches the space of analytic expressions for the function that best balances accuracy and simplicity for a given dataset. We test the approach on mock weak lensing excess surface density (ESD) data of synthetic clusters with NFW profiles. Motivated by real data, we assign each ESD data point a constant fractional uncertainty and vary this uncertainty and the number of clusters to probe how data precision and sample size affect model selection. For fractional errors around 5%, ESR recovers the NFW profile even from samples as small as approximately 20 clusters. At higher uncertainties representative of current surveys, simpler functions are favoured over NFW, though it remains competitive. This preference arises because weak lensing errors are smallest in the outskirts, causing the fits to be dominated by the outer profile. ESR therefore provides a robust, simulation-independent framework both for testing mass models and determining which features of a halo's density profile are genuinely constrained by the data. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.

Abstract:

Physics seeks to uncover the laws of Nature and express them through mathematical equations . Despite the vast diversity of natural phenomena, physical equations exhibit structural regularities that set them apart from arbitrary mathematical expressions. While principles such as dimensional analysis have long guided the formulation of physical models, the exploration of more subtle statistical patterns within the equations of physics remains an open question. Here, by analysing four corpora of physics equations and applying advanced implicit-likelihood techniques, we find that the frequency of mathematical operators follows an exponential decay law, in contrast to Zipf's power law for word frequencies in natural languages. This reveals a statistical meta-law of physics, possibly reflecting a combination of communication efficiency and constraints imposed by Nature itself. The meta-law offers practical benefits for symbolic regression by drastically narrowing down the space of physically plausible expressions. More broadly, it may inform the development of language models that can generate coherent mathematical representations, advancing the automation of physical law discovery. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.