Galaxy formation and evolution

Abstract:

We present a sample of large double radio sources hosted by spiral galaxies (spiral double radio active galactic nuclei, SDRAGNs). Candidates were initially selected through the Radio Galaxy Zoo project and subsequently refined using Sloan Digital Sky Survey images. The most promising were targeted in the Zoo Gems Hubble Space Telescope (HST) program, yielding images for 36 candidates. We assess the likelihood that each spiral galaxy is the genuine host of the radio emission, finding 15 new high-probability SDRAGNs. The hosts are seen preferentially close to edge-on. SDRAGNs predominantly show type II Fanaroff–Riley (FR II) radio structures and optical pseudobulges. After accounting for sample selection effects, the radio-jet axes lie preferentially near the poles of the galactic disks; we find a constant probability distribution for intrinsic pole–jet angles ϕ < 30°, declining to zero at ϕ = 60°. We have obtained optical spectra for all these newly identified SDRAGNs. Among both previously known and new SDRAGN samples, 8/25 show Seyfert 2 signatures, 6/25 show central star formation, and 5/25 show low-ionization nuclear emission-line region emission strong enough to indicate active galactic nuclei (AGN) activity or shock ionization, broadly similar to radio galaxies in elliptical hosts but with the addition of star formation (diluting or masking weak AGN signatures). SDRAGNs include FR II sources seen at unusually low radio powers, and preferentially occur in significant galaxy overdensities on 1 Mpc scales. Our “false alarms”—systems where HST data show the spiral is not the actual host galaxy—include radio sources seen through large portions of foreground spiral disks, potentially providing useful probes for Faraday rotation studies of disk magnetic fields.

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'.

Abstract:

In cosmology, emulators play a crucial role by providing fast and accurate predictions of complex physical models, enabling efficient exploration of high-dimensional parameter spaces that would be computationally prohibitive with direct numerical simulations. Symbolic emulators have emerged as promising alternatives to numerical approaches, delivering comparable accuracy with significantly faster evaluation times. While previous symbolic emulators were limited to relatively narrow prior ranges, we expand these to cover the parameter space relevant for current cosmological analyses. We introduce approximations to hypergeometric functions used for the Λ cold dark matter (ΛCDM) comoving distance and linear growth factor which are accurate to better than 0.001% and 0.05%, respectively, for all redshifts and for Ωm∈[0.1,0.5]. We show that integrating symbolic emulators into a Dark Energy Survey Year 1 (DES-Y1)-like 3×2 pt analysis produces cosmological constraints consistent with those obtained using standard numerical methods. Our symbolic emulators offer substantial improvements in speed and memory usage, demonstrating their practical potential for scalable, likelihood-based inference. This article is part of the discussion meeting issue 'Symbolic regression in the physical sciences'.