## Portfolio item number 1

** Published:**

Short description of portfolio item number 1

** Published:**

Short description of portfolio item number 1

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Short description of portfolio item number 2

Published in *MNRAS, 500(4):4639–4657*, 2020

Recommended citation: D.J. Bartlett, H. Desmond, J. Devriendt, P.G. Ferreira and A. Slyz. "Spatially offset black holes in the Horizon-AGN simulation and comparison to observations." *MNRAS, 500(4):4639–4657*.

Published in *Phys. Rev. D, 103(2):023523*, 2021

Recommended citation: D.J. Bartlett, H. Desmond and P.G. Ferreira (2021). "Constraints on Galileons from the positions of supermassive black holes" *Phys. Rev. D, 103(2):023523*.

Published in *Phys. Rev. D, 103(12):123502*, 2021

Recommended citation: D.J. Bartlett, H. Desmond and P.G. Ferreira (2021). "Calibrating galaxy formation effects in galactic tests of fundamental physics." *Phys. Rev. D, 103(12):123502*.

Published in *Phys. Rev. D, 104(8):084025*, 2021

Recommended citation: D.J. Bartlett, D. Bergsdal, H. Desmond, P.G. Ferreira and J. Jasche (2021). "Constraints on equivalence principle violation from gamma ray bursts." *Phys. Rev. D, 104(8):084025*.

Published in *Phys. Rev. D, 104(10):10351*, 2021

Recommended citation: D.J. Bartlett, H. Desmond, P.G. Ferreira and J. Jasche (2021). "Constraints on quantum gravity and the photon mass from gamma ray bursts." *Phys. Rev. D, 104(10):10351*.

Published in *Phys. Rev. D, 105(8):083514*, 2022

Recommended citation: A.N. Lasenby, W.J. Handley, D.J. Bartlett, and C.S. Negreanu (2022). "Perturbations and the future conformal boundary." *Phys. Rev. D, 105(8):083514*.

Published in *Phys. Rev. D, 105(8):083515*, 2022

Recommended citation: D.J. Bartlett, W.J. Handley and A.N. Lasenby (2022). "Improved cosmological fits with quantized primordial power spectra." *Phys. Rev. D, 105(8):083515*.

Published in *MNRAS, 514(3):4026–4045*, 2022

Recommended citation: R. Stiskalek, D.J. Bartlett, H. Desmond, and D. Anbajagane (2022). "The scatter in the galaxy–halo connection: a machine learning analysis." *MNRAS, 514(3):4026–4045*.

Published in *Phys. Rev. D, 106(10)110352*, 2022

Recommended citation: D.J. Bartlett, A. Kostic, H. Desmond, J. Jasche and G. Lavaux (2022). "Constraints on dark matter annihilation and decay from the large-scale structure of the nearby Universe." *Phys. Rev. D, 106(10)110352*.

Published in *MNRAS 521(2):1817-1831*, 2023

Recommended citation: H. Desmond, D.J. Bartlett and P.G. Ferreira (2023). "On the functional form of the radial acceleration relation." *MNRAS 521(2):1817-1831*.

Published in *Physical Review D (Submitted)*, 2023

Recommended citation: A. Kostic, D.J. Bartlett and H. Desmond (2023). "No evidence for p- or d-wave dark matter annihilation from local large-scale structure." *arXiv:2304.10301*.

Published in *IEEE Transactions on Evolutionary Computation 28, 950*, 2023

Recommended citation: D.J. Bartlett, H. Desmond and P.G. Ferreira (2023). "Exhaustive Symbolic Regression." *In IEEE Transactions on Evolutionary Computation 28, 950*.

Published in *Universe 2023, 9, 340*, 2023

Recommended citation: V. Vardanyan and D. J. Bartlett (2023). "Modeling and testing screening mechanisms in the laboratory and in space." *Universe 2023, 9, 340*.

Published in *The Genetic and Evolutionary Computation Conference (GECCO) 2023 Workshop on Symbolic Regression*, 2023

Recommended citation: D.J. Bartlett, H. Desmond and P.G. Ferreira (2023). "Priors for symbolic regression." *In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2402–2411*.

Published in *Physical Review D*, 2023

Recommended citation: A. Paopiamsap, D. Alonso, D.J. Bartlett and M. Bilicki (2023). "Constraints on dark matter and astrophysics from tomographic $\gamma$-ray cross-correlations." *Phys. Rev. D 109, 103517*.

Published in *Physical Review D*, 2023

Recommended citation: Tomas Sousa, D.J. Bartlett, H. Desmond and P.G. Ferreira (2024). "Optimal inflationary potentials." *Phys. Rev. D 109, 083524*.

Published in *Open Journal of Astrophysics*, 2023

Recommended citation: D.J. Bartlett and H. Desmond (2023). "Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors." *The Open Journal of Astrophysics, 6, 11 2023.*.

Published in *Open Journal of Astrophysics*, 2024

Recommended citation: M. Ho, D.J. Bartlett et al. (2024). "ltu-ili: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology." *The Open Journal of Astrophysics, 7, 7 2024*.

Published in *Parallel Problem Solving from Nature (PPSN) Conference 2024*, 2024

Recommended citation: G. Kronberger, Fabricio Olivetti de Franca, H. Desmond, D. J. Bartlett, and L. Kammerer (2024). "The Inefficiency of Genetic Programming for Symbolic Regression." *arXiv:2404.17292*.

Published in *A&A (Submitted)*, 2024

Recommended citation: D.J. Bartlett, M. Ho and B.D. Wandelt (2024). "Bye bye, local bias: the statistics of the halo field are not determined by the local mass density." *arXiv:2405.00635*.

Published in *A&A*, 2024

Recommended citation: D.J. Bartlett, B.D. Wandelt, M. Zennaro, P.G. Ferreira and H. Desmond (2024). "syren-halofit: A fast, interpretable, high-precision formula for the $\Lambda$CDM nonlinear matter power spectrum." *A&A 686:A150*.

Published in *A&A*, 2024

Recommended citation: D.J. Bartlett, L. Kammerer, G. Kronberger, H. Desmond, P.G. Ferreira, B.D. Wandelt, B. Burlacu, D. Alonso and M. Zennaro (2023). "A precise symbolic emulator of the linear matter power spectrum." *A&A 686:A209*.

Published in *arXiv preprint*, 2024

Recommended citation: A. Constantin, D.J. Bartlett, H. Desmond and P.G. Ferreira (2024). "Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature." *arXiv:2408.11065*.

Published in *arXiv preprint*, 2024

Recommended citation: W.J. Wolf, C. García-García, D.J. Bartlett and P.G. Ferreira (2024). "Scant evidence for thawing quintessence." *arXiv:2408.17318*.

Published in *arXiv preprint*, 2024

Recommended citation: D.J. Bartlett, M. Chiarenza, L. Doeser and F. Leclercq (2024). "COmoving Computer Acceleration (COCA): N-body simulations in an emulated frame of reference." *arXiv:2409.02154*.

** Published:**

Non-standard physics, such as Lorentz Invariance Violation in Quantum Gravity (models or a non-zero photon mass, can lead to an energy-dependent propagation speed for photons, such that photons of different energies from a distant source would arrive at different times, even if they were emitted simultaneously. The short durations and large distances to high energy astrophysical transients therefore allow us to test the fundamental assumptions of the standard models of cosmology and particles physics by considering the energy-dependent time delay between photon arrival time (spectral lag) of Gamma Ray Bursts (GRBs). Many previous attempts to do place constraints on such theories are obtained using a handful of GRBs, do not propagate uncertainties in the redshifts of sources, or suffer from uncertain systematics in the model for other contributions to the spectral lag (noise). In this talk I will discuss recent work in which we looked to overcome these challenges and hence were able to constrain the quantum gravity energy scale and photon mass by constructing probabilistic source-by-source forward models of the time delays for a large sample of GRBs and we demonstrate that these constraints are robust to the choice of noise model.

** Published:**

Conventional probes of fundamental physics tend to consider one of three regimes: small scales, cosmological scales or the strong-field regime. Since LCDM is known to have several galactic-scale issues and novel physics (modified gravity, non-cold dark matter etc.) can alter galactic dynamics and morphology, tests of fundamental physics on astrophysical scales can provide tight constraints which are complementary to traditional techniques. By forward-modelling observational signals on a source-by-source basis and marginalising over models describing other astrophysical and observational processes, it is possible to harness the constraining power of galaxies whilst accounting for their complexity. In this talk I will demonstrate how these Bayesian Monte Carlo-based forward models can be used to constrain a variety of gravitational theories and outline ways to assess their robustness to baryonic effects.

** Published:**

This talk is based on the following paper.

** Published:**

High energy astrophysical transients at cosmological distances allow us to test the fundamental assumptions of the standard models of cosmology and particles physics, such as Lorentz invariance, the massless nature of the photon or the weak equivalence principle. If any of these assumptions are incorrect, photons of different energies propagate differently through spacetime, which could be observable in the spectral lags of Gamma Ray Bursts. Constraining such violations can challenging in the presence of uncertainties in the redshifts of sources, uncertain systematics in the model for other contributions to the spectral lag, and the long range of the gravitational potential. In this talk I will discuss how one can overcome these hurdles by constructing probabilistic source-by-source forward models and by combining constrained realisations of the local density field with unconstrained large-scale modes to obtain some of the tightest constraints on these models to date.

** Published:**

Symbolic Regression (SR) algorithms learn analytic expressions which fit data accurately and in a highly interpretable manner. As such, these methods can be used to help uncover “physical laws” from data or provide simple and interpretable effective descriptions of complex, non-linear phenomena. Conventional SR suffers from two fundamental issues which I address here. First, typical SR methods search the space stochastically and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and poorly motivated. I will introduce a new method for SR – Exhaustive Symbolic Regression (ESR) - which addresses both of these issues. To illustrate the power of ESR, I will apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding ~40 functions (out of 5.2 million considered) that fit the data more economically than the Friedmann equation. I will then employ ESR to learn the form of the radial acceleration relation (RAR) of galaxy dynamics and therefore assess the claim that its asymptotic limits provide evidence for a new law of nature, namely Modified Newtonian Dynamics.

** Published:**

Accurate peculiar velocity field maps are critical for various cosmological analyses, including Hubble constant determinations and density field reconstructions. In this talk, I will discuss the challenges faced when reconstructing the peculiar velocity field from distance tracers, as well as the collective efforts of the Aquila Consortium in developing a physical, Bayesian hierarchical model for this task. We employ Bayesian hierarchical models, connecting the initial matter density with peculiar velocity data to reconstruct the final density and velocity fields. Utilising the BORG algorithm, this approach outperforms traditional methods, even in the face of model mis-specification. I will discuss the importance of the inhomogeneous Malmquist bias for obtaining an unbiased velocity field reconstructions and will present results from simulations and peculiar velocity datasets demonstrating our model’s accuracy. In the latter part of the presentation, I will introduce a novel unified pipeline, which facilitates cosmological parameter sampling. This integration allows for the inclusion of peculiar velocity data in initial condition reconstructions which produce accurate and self-consistent density and velocity fields. This advancement holds significant implications for cosmology and astrophysics and could provide valuable insights into the S8 tension.

** Published:**

The matter power spectrum of cosmology, $P(k)$, is of fundamental importance in cosmological analyses, yet solving the Boltzmann equations can be computationally prohibitive if required several thousand times, e.g. in a MCMC. Emulators for $P(k)$ as a function of cosmology have therefore become popular, whether they be neural network or Gaussian process based. Yet one of the oldest emulators we have is an analytic, physics-informed fit proposed by Eisenstein and Hu (E&H). Given this is already accurate to within a few percent, does one really need a large, black-box, numerical method for calculating $P(k)$, or can one simply add a few terms to E&H? In this talk I demonstrate that Symbolic Regression can obtain such a correction, yielding sub-percent level predictions for $P(k)$.

Undergraduate course, *University 1, Department*, 2014

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Workshop, *University 1, Department*, 2015

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