publications

2023

1. 

   Bayesian Computation in Astronomy: Novel methods for parallel and gradient-free inference

   Minas Karamanis

   arXiv preprint arXiv:2303.16134, 2023

   Abs arXiv HTML PDF

   The goal of this thesis is twofold; introduce the fundamentals of Bayesian inference and computation focusing on astronomical and cosmological applications, and present recent advances in probabilistic computational methods developed by the author that aim to facilitate Bayesian data analysis for the next generation of astronomical observations and theoretical models. The first part of this thesis familiarises the reader with the notion of probability and its relevance for science through the prism of Bayesian reasoning, by introducing the key constituents of the theory and discussing its best practices. The second part includes a pedagogical introduction to the principles of Bayesian computation motivated by the geometric characteristics of probability distributions and followed by a detailed exposition of various methods including Markov chain Monte Carlo (MCMC), Sequential Monte Carlo (SMC), and Nested Sampling (NS). Finally, the third part presents two novel computational methods (Ensemble Slice Sampling and Preconditioned Monte Carlo) and their respective software implementations (zeus and pocoMC). [abridged]

2. JAX-COSMO: An End-to-End Differentiable and GPU Accelerated Cosmology Library

   Jean-Eric Campagne, François Lanusse, Joe Zuntz, and 7 more authors

   The Open Journal of Astrophysics, 2023

   Abs arXiv HTML PDF

   We present jax-cosmo, a library for automatically differentiable cosmological theory calculations. It uses the JAX library, which has created a new coding ecosystem, especially in probabilistic programming. As well as batch acceleration, just-in-time compilation, and automatic optimization of code for different hardware modalities (CPU, GPU, TPU), JAX exposes an automatic differentiation (autodiff) mechanism. Thanks to autodiff, jax-cosmo gives access to the derivatives of cosmological likelihoods with respect to any of their parameters, and thus enables a range of powerful Bayesian inference algorithms, otherwise impractical in cosmology, such as Hamiltonian Monte Carlo and Variational Inference. In its initial release, jax-cosmo implements background evolution, linear and non-linear power spectra (using halofit or the Eisenstein and Hu transfer function), as well as angular power spectra with the Limber approximation for galaxy and weak lensing probes, all differentiable with respect to the cosmological parameters and their other inputs. We illustrate how autodiff can be a game-changer for common tasks involving Fisher matrix computations, or full posterior inference with gradient-based techniques. In particular, we show how Fisher matrices are now fast, exact, no longer require any fine tuning, and are themselves differentiable. Finally, using a Dark Energy Survey Year 1 3x2pt analysis as a benchmark, we demonstrate how jax-cosmo can be combined with Probabilistic Programming Languages to perform posterior inference with state-of-the-art algorithms including a No U-Turn Sampler, Automatic Differentiation Variational Inference,and Neural Transport HMC. We further demonstrate that Normalizing Flows using Neural Transport are a promising methodology for model validation in the early stages of analysis.

2022

1. Overview of the instrumentation for the Dark Energy Spectroscopic Instrument

   B Abareshi, J Aguilar, S Ahlen, and 8 more authors

   The Astronomical Journal, 2022

   Abs arXiv HTML PDF

   The Dark Energy Spectroscopic Instrument (DESI) has embarked on an ambitious five-year survey to explore the nature of dark energy with spectroscopy of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the Baryon Acoustic Oscillation method to measure distances from the nearby universe to z > 3.5, as well as measure the growth of structure and probe potential modifications to general relativity. In this paper we describe the significant instrumentation we developed for the DESI survey. The new instrumentation includes a wide-field, 3.2-deg diameter prime-focus corrector that focuses the light onto 5020 robotic fiber positioners on the 0.812 m diameter, aspheric focal surface. The positioners and their fibers are divided among ten wedge-shaped petals. Each petal is connected to one of ten spectrographs via a contiguous, high-efficiency, nearly 50 m fiber cable bundle. The ten spectrographs each use a pair of dichroics to split the light into three channels that together record the light from 360 - 980 nm with a resolution of 2000 to 5000. We describe the science requirements, technical requirements on the instrumentation, and management of the project. DESI was installed at the 4-m Mayall telescope at Kitt Peak, and we also describe the facility upgrades to prepare for DESI and the installation and functional verification process. DESI has achieved all of its performance goals, and the DESI survey began in May 2021. Some performance highlights include RMS positioner accuracy better than 0.1", SNR per \sqrtÅ > 0.5 for a z > 2 quasar with flux 0.28e-17 erg/s/cm^2/A at 380 nm in 4000s, and median SNR = 7 of the [OII] doublet at 8e-17 erg/s/cm^2 in a 1000s exposure for emission line galaxies at z = 1.4 - 1.6. We conclude with highlights from the on-sky validation and commissioning of the instrument, key successes, and lessons learned. (abridged)

2. 

   Accelerating astronomical and cosmological inference with preconditioned Monte Carlo

   Minas Karamanis, Florian Beutler, John A Peacock, and 2 more authors

   Monthly Notices of the Royal Astronomical Society, 2022

   Abs arXiv HTML PDF

   We introduce preconditioned Monte Carlo (PMC), a novel Monte Carlo method for Bayesian inference that facilitates efficient sampling of probability distributions with non-trivial geometry. PMC utilizes a Normalizing Flow (NF) in order to decorrelate the parameters of the distribution and then proceeds by sampling from the preconditioned target distribution using an adaptive Sequential Monte Carlo (SMC) scheme. The results produced by PMC include samples from the posterior distribution and an estimate of the model evidence that can be used for parameter inference and model comparison, respectively. The aforementioned framework has been thoroughly tested in a variety of challenging target distributions achieving state-of-the-art sampling performance. In the cases of primordial feature analysis and gravitational wave inference, PMC is approximately 50 and 25 times faster, respectively, than nested sampling (NS). We found that in higher dimensional applications, the acceleration is even greater. Finally, PMC is directly parallelisable, manifesting linear scaling up to thousands of CPUs.

3. pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology

   Minas Karamanis, David Nabergoj, Florian Beutler, and 2 more authors

   Journal of Open Source Software, 2022

   Abs arXiv HTML PDF

   pocoMC is a Python package for accelerated Bayesian inference in astronomy and cosmology. The code is designed to sample efficiently from posterior distributions with non-trivial geometry, including strong multimodality and non-linearity. To this end, pocoMC relies on the Preconditioned Monte Carlo algorithm which utilises a Normalising Flow to decorrelate the parameters of the posterior. It facilitates both tasks of parameter estimation and model comparison, focusing especially on computationally expensive applications. It allows fitting arbitrary models defined as a log-likelihood function and a log-prior probability density function in Python. Compared to popular alternatives (e.g. nested sampling) pocoMC can speed up the sampling procedure by orders of magnitude, cutting down the computational cost substantially. Finally, parallelisation to computing clusters manifests linear scaling.

4. MATRYOSHKA: halo model emulator for the galaxy power spectrum

   Jamie Donald-McCann, Florian Beutler, Kazuya Koyama, and 1 more author

   Monthly Notices of the Royal Astronomical Society, 2022

   Abs arXiv HTML PDF

   We present 𝚖𝚊𝚝𝚛𝚢𝚘𝚜𝚑𝚔𝚊, a suite of neural network based emulators and accompanying Python package that have been developed with the goal of producing fast and accurate predictions of the nonlinear galaxy power spectrum. The suite of emulators consists of four linear component emulators, from which fast linear predictions of the power spectrum can be made, allowing all nonlinearities to be included in predictions from a nonlinear boost component emulator. The linear component emulators includes an emulator for the matter transfer function that produces predictions in ∼0.0004 s, with an error of <0.08 per cent (at 1σ level) on scales 10−4 h Mpc−1<k<101 h Mpc−1. In this paper we demonstrate 𝚖𝚊𝚝𝚛𝚢𝚘𝚜𝚑𝚔𝚊 by training the nonlinear boost component emulator with analytic training data calculated with HALOFIT, that has been designed to replicate training data that would be generated using numerical simulations. Combining all the component emulator predictions we achieve an accuracy of <0.75 per cent (at 1σ level) when predicting the real space nonlinear galaxy power spectrum on scales 0.0025 h Mpc−1<k<1 h Mpc−1. We use 𝚖𝚊𝚝𝚛𝚢𝚘𝚜𝚑𝚔𝚊 to investigate the impact of the analysis setup on cosmological constraints by conducting several full shape analyses of the real space galaxy power spectrum. Specifically we investigate the impact of the minimum scale (or kmax), finding an improvement of ∼1.8× in the constraint on σ8 by pushing kmax from kmax=0.25 h Mpc−1 to kmax=0.85 h Mpc−1, highlighting the potential gains when using clustering emulators such as 𝚖𝚊𝚝𝚛𝚢𝚘𝚜𝚑𝚔𝚊 in cosmological analyses.

2021

1. hankl: A lightweight Python implementation of the FFTLog algorithm for Cosmology

   Minas Karamanis, and Florian Beutler

   arXiv preprint arXiv:2106.06331, 2021

   Abs arXiv PDF

   We introduce hankl, a lightweight Python implementation of the FFTLog algorithm for Cosmology. The FFTLog algorithm is an extension of the Fast Fourier Transform (FFT) for logarithmically spaced periodic sequences. It can be used to efficiently compute Hankel transformations, which are paramount for many modern cosmological analyses that are based on the power spectrum or the 2-point correlation function multipoles. The code is well-tested, open source, and publicly available.

2. 

   zeus: a python implementation of ensemble slice sampling for efficient Bayesian parameter inference

   Minas Karamanis, Florian Beutler, and John A Peacock

   Monthly Notices of the Royal Astronomical Society, 2021

   Abs arXiv Bib HTML PDF

   We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov chain Monte Carlo (MCMC) algorithm specifically designed to tackle the computational challenges posed by modern astronomical and cosmological analyses. In particular, the method requires only minimal hand-tuning of 1−2 hyperparameters that are often trivial to set; its performance is insensitive to linear correlations and it can scale up to 1000s of CPUs without any extra effort. Furthermore, its locally adaptive nature allows to sample efficiently even when strong non-linear correlations are present. Lastly, the method achieves a high performance even in strongly multimodal distributions in high dimensions. Compared to emcee, a popular MCMC sampler, zeus performs 9 and 29 times better in a cosmological and an exoplanet application, respectively.

   @article{karamanis2021zeus,
     title = {zeus: a python implementation of ensemble slice sampling for efficient Bayesian parameter inference},
     author = {Karamanis, Minas and Beutler, Florian and Peacock, John A},
     journal = {Monthly Notices of the Royal Astronomical Society},
     volume = {508},
     number = {3},
     pages = {3589--3603},
     year = {2021},
     publisher = {Oxford University Press},
     doi = {10.1093/mnras/stab2867},
     url = {https://doi.org/10.1093/mnras/stab2867},
   }

3. 

   Ensemble Slice Sampling: Parallel, black-box and gradient-free inference for correlated & multimodal distributions

   Minas Karamanis, and Florian Beutler

   Statistics and Computing, 2021

   Abs Bib HTML PDF

   Slice sampling has emerged as a powerful Markov Chain Monte Carlo algorithm that adapts to the characteristics of the target distribution with minimal hand-tuning. However, Slice Sampling’s performance is highly sensitive to the user-specified initial length scale hyperparameter and the method generally struggles with poorly scaled or strongly correlated distributions. This paper introduces Ensemble Slice Sampling (ESS), a new class of algorithms that bypasses such difficulties by adaptively tuning the initial length scale and utilising an ensemble of parallel walkers in order to efficiently handle strong correlations between parameters. These affine-invariant algorithms are trivial to construct, require no hand-tuning, and can easily be implemented in parallel computing environments. Empirical tests show that Ensemble Slice Sampling can improve efficiency by more than an order of magnitude compared to conventional MCMC methods on a broad range of highly correlated target distributions. In cases of strongly multimodal target distributions, Ensemble Slice Sampling can sample efficiently even in high dimensions. We argue that the parallel, black-box and gradient-free nature of the method renders it ideal for use in scientific fields such as physics, astrophysics and cosmology which are dominated by a wide variety of computationally expensive and non-differentiable models.

   @article{karamanis2021ensemble,
     title = {Ensemble Slice Sampling: Parallel, black-box and gradient-free inference for correlated \& multimodal distributions},
     author = {Karamanis, Minas and Beutler, Florian},
     journal = {Statistics and Computing},
     volume = {31},
     pages = {1--18},
     year = {2021},
     publisher = {Springer},
     url = {https://doi.org/10.1007/s11222-021-10038-2},
   }