Dr Deaglan Bartlett

Abstract:

If dark matter annihilates into standard model particles with a cross section which is velocity dependent, then Local Group dwarf galaxies will not be the best place to search for the resulting gamma ray emission. A greater flux would be produced by more distant and massive halos, with larger velocity dispersions. We construct full-sky predictions for the gamma ray emission from galaxy- and cluster-mass halos within $\sim 200\mathrm{Mpc}$ using a suite of constrained $N$ -body simulations () based on the Bayesian Origin Reconstruction from Galaxies algorithm. Comparing to observations from the Large Area Telescope and marginalizing over reconstruction uncertainties and other astrophysical contributions to the flux, we obtain constraints on the cross section which are 2 (7) orders of magnitude tighter than those obtained from dwarf spheroidals for $p$ -wave ( $d$ -wave) annihilation. We find no evidence for either type of annihilation from dark matter particles with masses in the range $m_{\chi }=2–500\mathrm{GeV}/c^2$ , for any channel. As an example, for annihilations producing bottom quarks with $m_{\chi }=10\mathrm{GeV}/c^2$ , we find $a_1<2.4\times {10}^{-21}{\mathrm{cm}}^3{\mathrm{s}}^{-1}$ and $a_2<3.0\times {10}^{-18}{\mathrm{cm}}^3{\mathrm{s}}^{-1}$ at 95% confidence, where the product of the cross section, $\sigma$ , and relative particle velocity, $v$ , is given by $\sigma v=a_{\ell }(v/c{)}^{2\ell }$ and $\ell =1$ , 2 for $p$ - and $d$ -wave annihilation, respectively. Our bounds, although failing to exclude the thermal relic cross section for velocity-dependent annihilation channels, are among the tightest to date.

Abstract:

Abstract Symbolic regression (SR) has emerged as a powerful method for uncovering interpretable mathematical relationships from data, offering a novel route to both scientific discovery and efficient empirical modelling. This article introduces the Special issue on symbolic regression for the physical sciences, motivated by the Royal Society discussion meeting held in April 2025. The contributions collected here span applications from automated equation discovery and emergent-phenomena modelling to the construction of compact emulators for computationally expensive simulations. The introductory review outlines the conceptual foundations of SR, contrasts it with conventional regression approaches and surveys its main use cases in the physical sciences, including the derivation of effective theories, empirical functional forms and surrogate models. We summarize methodological considerations such as search-space design, operator selection, complexity control, feature selection and integration with modern AI approaches. We also highlight ongoing challenges, including scalability, robustness to noise, overfitting and computational complexity. Finally, we emphasize emerging directions, particularly the incorporation of symmetry constraints, asymptotic behaviour and other theoretical information. Taken together, the papers in this Special issue illustrate the accelerating progress of SR and its growing relevance across the physical sciences. This article is part of the discussion meeting issue ‘Symbolic regression in the physical sciences’.