DiRAC 2.5 Operations 2017-2020
Lead Research Organisation:
University of Cambridge
Department Name: Institute of Astronomy
Abstract
Physicists across the astronomy, nuclear and particle physics communities are focussed on understanding how the Universe works at a very fundamental level. The distance scales with which they work vary by 50 orders of magnitude from the smallest distances probed by experiments at the Large Hadron Collider, deep within the atomic nucleus, to the largest scale galaxy clusters discovered out in space. The science challenges, however, are linked through questions such as: How did the Universe begin and how is it evolving? and What are the fundamental constituents and fabric of the Universe and how do they interact?
Progress requires new astronomical observations and experimental data but also new theoretical insights. Theoretical understanding comes increasingly from large-scale computations that allow us to confront the consequences of our theories very accurately with the data or allow us to interrogate the data in detail to extract information that has impact on our theories. These computations test the fastest computers that we have and push the boundaries of technology in this sector. They also provide an excellent environment for training students in state-of-the-art techniques for code optimisation and data mining and visualisation.
The DiRAC-2.5 project builds on the success of the DiRAC HPC facility and will provide the resources needed to support cutting edge research during 2017 in all areas of science supported by STFC.
In addition to the existing DiRAC-2 services, from April 2017 DiRAC-2.5 will provide:
1) A factor 2 increase in the computational power of the DiRAC supercomputer at the University of Durham, which is designed for simulations requiring large amounts of computer memory. The enhanced system will be used to:
(i) simulate the merger of pairs of black holes which generate gravitational waves such as those recently discovered by the LIGO consortium;
(ii) perform the most realistic simulations to date of the formation and evolution of galaxies in the Universe
(iii) carry out detailed simulations of the interior of the sun and of planetary interiors.
2) A new High Performance Computer at Cambridge whose particular architecture is well suited to the theoretical problems that we want to tackle that utilise large amounts of data, either as input or being generated at intermediate stages of our calculations. Two key challenges that we will tackle are those of:
(i) improving our understanding of the Milky Way through analysis of new data from the European
Space Agency's GAIA satellite and
(ii) improving the potential of experiments at CERN's Large Hadron Collider for discovery
of new physics by increasing the accuracy of theoretical predictions for rare processes involving the
fundamental constituents of matter known as quarks.
3) An additional 3500 compute cores on the DiRAC Complexity supercomputer at Leicester which will make it possible to
carry out simulations of some of the most complex physical situation in the Universe. These include:
(i) the formation of stars in clusters - for the first time it will be possible to follow the formation of stars many times more massive than the sun;
(ii) the accretion of gas onto supermassive black holes, the most efficient means of extracting energy from matter and the engine
which drives galaxy formation and evolution.
4) A team of three research software engineers who will help DiRAC researchers to ensure their scientific codes to extract
the best possible performance from the hardware components of the DiRAC clusters. These highly skilled programmers will
increase the effective computational power of the DiRAC facility during 2017.
Progress requires new astronomical observations and experimental data but also new theoretical insights. Theoretical understanding comes increasingly from large-scale computations that allow us to confront the consequences of our theories very accurately with the data or allow us to interrogate the data in detail to extract information that has impact on our theories. These computations test the fastest computers that we have and push the boundaries of technology in this sector. They also provide an excellent environment for training students in state-of-the-art techniques for code optimisation and data mining and visualisation.
The DiRAC-2.5 project builds on the success of the DiRAC HPC facility and will provide the resources needed to support cutting edge research during 2017 in all areas of science supported by STFC.
In addition to the existing DiRAC-2 services, from April 2017 DiRAC-2.5 will provide:
1) A factor 2 increase in the computational power of the DiRAC supercomputer at the University of Durham, which is designed for simulations requiring large amounts of computer memory. The enhanced system will be used to:
(i) simulate the merger of pairs of black holes which generate gravitational waves such as those recently discovered by the LIGO consortium;
(ii) perform the most realistic simulations to date of the formation and evolution of galaxies in the Universe
(iii) carry out detailed simulations of the interior of the sun and of planetary interiors.
2) A new High Performance Computer at Cambridge whose particular architecture is well suited to the theoretical problems that we want to tackle that utilise large amounts of data, either as input or being generated at intermediate stages of our calculations. Two key challenges that we will tackle are those of:
(i) improving our understanding of the Milky Way through analysis of new data from the European
Space Agency's GAIA satellite and
(ii) improving the potential of experiments at CERN's Large Hadron Collider for discovery
of new physics by increasing the accuracy of theoretical predictions for rare processes involving the
fundamental constituents of matter known as quarks.
3) An additional 3500 compute cores on the DiRAC Complexity supercomputer at Leicester which will make it possible to
carry out simulations of some of the most complex physical situation in the Universe. These include:
(i) the formation of stars in clusters - for the first time it will be possible to follow the formation of stars many times more massive than the sun;
(ii) the accretion of gas onto supermassive black holes, the most efficient means of extracting energy from matter and the engine
which drives galaxy formation and evolution.
4) A team of three research software engineers who will help DiRAC researchers to ensure their scientific codes to extract
the best possible performance from the hardware components of the DiRAC clusters. These highly skilled programmers will
increase the effective computational power of the DiRAC facility during 2017.
Planned Impact
The expected impact of the DiRAC 2.5 HPC facility is fully described in the attached pathways to impact document and includes:
1) Disseminating best practice in High Performance Computing software engineering throughout the theoretical Particle Physics, Astronomy and Nuclear physics communities in the UK as well as to industry partners.
2) Working on co-design projects with industry partners to improve future generations of hardware and software.
3) Development of new techniques in the area of High Performance Data Analytics which will benefit industry partners and researchers in other fields such as biomedicine, biology, engineering, economics and social science, and the natural environment who can use this new technology to improve research outcomes in their areas.
4) Share best practice on the design and operation of distributed HPC facilities with UK National e-Infrastructure partners.
5) Training of the next generation of research scientists of physical scientists to tackle problems effectively on state-of-the-art of High Performance Computing facilities. Such skills are much in demand from high-tech industry.
6) Engagement with the general public to promote interest in science, and to explain how our ability to solve complex problems using the latest computer technology leads to new scientific capabilities/insights. Engagement of this kind also naturally encourages the uptake of STEM subjects in schools.
1) Disseminating best practice in High Performance Computing software engineering throughout the theoretical Particle Physics, Astronomy and Nuclear physics communities in the UK as well as to industry partners.
2) Working on co-design projects with industry partners to improve future generations of hardware and software.
3) Development of new techniques in the area of High Performance Data Analytics which will benefit industry partners and researchers in other fields such as biomedicine, biology, engineering, economics and social science, and the natural environment who can use this new technology to improve research outcomes in their areas.
4) Share best practice on the design and operation of distributed HPC facilities with UK National e-Infrastructure partners.
5) Training of the next generation of research scientists of physical scientists to tackle problems effectively on state-of-the-art of High Performance Computing facilities. Such skills are much in demand from high-tech industry.
6) Engagement with the general public to promote interest in science, and to explain how our ability to solve complex problems using the latest computer technology leads to new scientific capabilities/insights. Engagement of this kind also naturally encourages the uptake of STEM subjects in schools.
Organisations
Publications
Boyle P
(2023)
Simulating rare kaon decays K + ? p + l + l - using domain wall lattice QCD with physical light quark masses
in Physical Review D
Boyle P.
(2018)
Beyond the standard model Kaon mixing with physical masses.
in Proceedings of Science
Boyle P.A.
(2018)
QED corrections to leptonic decay rates
in Proceedings of Science
Bozorgnia N
(2020)
The dark matter component of the Gaia radially anisotropic substructure
in Journal of Cosmology and Astroparticle Physics
Bozorgnia N
(2019)
On the correlation between the local dark matter and stellar velocities
in Journal of Cosmology and Astroparticle Physics
Brady R
(2024)
Numerical Equivalence of Diabatic and Adiabatic Representations in Diatomic Molecules
in Journal of Chemical Theory and Computation
Brady RP
(2022)
An ab initio study of the rovibronic spectrum of sulphur monoxide (SO): diabatic vs. adiabatic representation.
in Physical chemistry chemical physics : PCCP
Brady S
(2023)
Solving the initial conditions problem for modified gravity theories
in Physical Review D
Braspenning J
(2024)
The FLAMINGO project: galaxy clusters in comparison to X-ray observations
in Monthly Notices of the Royal Astronomical Society
Braspenning J
(2023)
Sensitivity of non-radiative cloud-wind interactions to the hydrodynamic solver
in Monthly Notices of the Royal Astronomical Society
Brooks R
(2023)
The north-south asymmetry of the ALFALFA H i velocity width function
in Monthly Notices of the Royal Astronomical Society
Brough S
(2024)
Preparing for low surface brightness science with the Vera C. Rubin Observatory: a comparison of observable and simulated intracluster light fractions
in Monthly Notices of the Royal Astronomical Society
Brown S
(2024)
ARTEMIS emulator: exploring the effect of cosmology and galaxy formation physics on Milky Way-mass haloes and their satellites
in Monthly Notices of the Royal Astronomical Society
Brown S
(2022)
Towards a universal model for the density profiles of dark matter haloes
in Monthly Notices of the Royal Astronomical Society
Brown S
(2020)
Connecting the structure of dark matter haloes to the primordial power spectrum
in Monthly Notices of the Royal Astronomical Society
Broxterman J
(2024)
The FLAMINGO project: baryonic impact on weak gravitational lensing convergence peak counts
in Monthly Notices of the Royal Astronomical Society
Buie E
(2020)
Interpreting Observations of Absorption Lines in the Circumgalactic Medium with a Turbulent Medium
in The Astrophysical Journal
Buividovich P
(2024)
Out-of-equilibrium chiral magnetic effect from simulations on Euclidean lattices
in Physical Review D
Buividovich P
(2023)
Real-time simulations of quantum spin chains: Density of states and reweighting approaches
in Physical Review B
Buividovich P
(2021)
Static magnetic susceptibility in finite-density $$SU\left( 2\right) $$ lattice gauge theory
in The European Physical Journal A
Buividovich P
(2021)
Numerical study of the chiral separation effect in two-color QCD at finite density
in Physical Review D
Buividovich P
(2020)
Numerical Study of the Chiral Separation Effect in Two-Color QCD at Finite Density
Buividovich P
(2021)
Numerical study of the chiral separation effect in two-color QCD at finite density
Buividovich P
(2020)
Electric conductivity in finite-density S U ( 2 ) lattice gauge theory with dynamical fermions
in Physical Review D
Buividovich P
(2022)
Quantum chaos in supersymmetric quantum mechanics: An exact diagonalization study
in Physical Review D
Bulla M
(2020)
White dwarf deflagrations for Type Iax supernovae: polarisation signatures from the explosion and companion interaction
in Astronomy & Astrophysics
Burba J
(2024)
Sensitivity of Bayesian 21 cm power spectrum estimation to foreground model errors
in Monthly Notices of the Royal Astronomical Society
Cabayol-Garcia L
(2023)
A neural network emulator for the Lyman-a forest 1D flux power spectrum
in Monthly Notices of the Royal Astronomical Society
Cabayol-Garcia L
(2023)
A neural network emulator for the Lyman-$a$ 1D flux power spectrum
Callan F
(2024)
Including a luminous central remnant in radiative transfer simulations for Type Iax supernovae
in Monthly Notices of the Royal Astronomical Society
Callingham T
(2022)
The chemo-dynamical groups of Galactic globular clusters
in Monthly Notices of the Royal Astronomical Society
Callingham T
(2019)
The mass of the Milky Way from satellite dynamics
in Monthly Notices of the Royal Astronomical Society
Campargue A
(2020)
Observation of electric-quadrupole infrared transitions in water vapor
in Physical Review Research
Campargue A
(2020)
Detection of electric-quadrupole transitions in water vapour near 5.4 and 2.5 µm.
in Physical chemistry chemical physics : PCCP
Campos Estrada B
(2024)
On the likely magnesium-iron silicate dusty tails of catastrophically evaporating rocky planets
in Monthly Notices of the Royal Astronomical Society
Camps P
(2022)
High-resolution synthetic UV-submm images for Milky Way-mass simulated galaxies from the ARTEMIS project
in Monthly Notices of the Royal Astronomical Society
Can K
(2020)
Lattice QCD evaluation of the Compton amplitude employing the Feynman-Hellmann theorem
in Physical Review D
Cardoso V
(2023)
Curvature and dynamical spacetimes: can we peer into the quantum regime?
in Classical and Quantum Gravity
Carlberg R
(2024)
Star Stream Velocity Distributions in Cold Dark Matter and Warm Dark Matter Galactic Halos
in The Astrophysical Journal
Carrillo A
(2024)
Can we really pick and choose? Benchmarking various selections of Gaia Enceladus/Sausage stars in observations with simulations
in Monthly Notices of the Royal Astronomical Society
Cataldi P
(2022)
Fingerprints of modified gravity on galaxies in voids
in Monthly Notices of the Royal Astronomical Society
Cataneo M
(2019)
On the road to percent accuracy: non-linear reaction of the matter power spectrum to dark energy and modified gravity
in Monthly Notices of the Royal Astronomical Society
Cataneo M
(2022)
The matter density PDF for modified gravity and dark energy with Large Deviations Theory
in Monthly Notices of the Royal Astronomical Society
Cautun M
(2019)
The aftermath of the Great Collision between our Galaxy and the Large Magellanic Cloud
in Monthly Notices of the Royal Astronomical Society
Cayuso R
(2024)
Scalar emission from neutron star-black hole binaries in scalar-tensor theories with kinetic screening
in Physical Review D
Cayuso R
(2023)
Self-Consistent Modeling of Gravitational Theories beyond General Relativity
in Physical Review Letters
| Description | Many new discoveries about the formation and evolution of galaxies, star formation, planet formation and particle physics theory have been made possible by the award. |
| Exploitation Route | Many international collaborative projects are supported by the HPC resources provided by DiRAC |
| Sectors | Aerospace Defence and Marine Creative Economy Digital/Communication/Information Technologies (including Software) Education Healthcare |
| URL | http://www.dirac.ac.uk |
| Title | 25 hot-Jupiter properties from HST & Spitzer |
| Description | VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Five key exoplanet questions answered via the analysis of 25 hot-Jupiter atmospheres in eclipse.' (bibcode: 2022ApJS..260....3C) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJS/260/3 |
| Title | Bayesian evidence for the tensor-to-scalar ratio r and neutrino masses m_nu: Effects of uniform vs logarithmic priors (supplementary inference products) |
| Description | These are the nested sampling inference products and input files that were used to compute results for arXiv:2102.11511. Example plotting scripts (as .ipynb or as .html files) and figures from the papers are included to demonstrate usage. Filename conventions: lcdm: Concordance cosmological model called \(\Lambda\mathrm{CDM}\) (without extension this assumes \(r=0\) and a single massive neutrino with mass \(m_\nu=0.06\,\mathrm{eV}\)). _r: \(\Lambda\mathrm{CDM}\) with variable tensor-to-scalar ratio \(r\). _nu: \(\Lambda\mathrm{CDM}\) with three massive neutrinos, sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\). mcmc: Cobaya's Markov Chain Monte Carlo Metropolis sampler.https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 pc#d###: PolyChord run with #d repeats per parameter block (where d is the number of parameters in that block) and with ### live points.https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 _class: theory code CLASS.https://github.com/lesgourg/class_public/releases/tag/v2.9.4 _p18_TTTEEElowTE_SZ: Planck 2018 TT,TE,EE+lowl+lowE data.https://pla.esac.esa.int/pla/#cosmology _nufit50: NuFIT 5.0 data.http://www.nu-fit.org/?q=node/228 _NH and _IH: normal and inverted neutrino hierarchy. _logr##: logarithmic sampling of tensor-to-scalar ratio \(r\) with lower log bound given .by log10r=-##. _mdD: sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\) (medium and heavy neutrino mass are derived parameters) with mass units in eV. _logmdD##: logarithmic (instead of uniform) sampling of the lightest neutrino mass \(m_\mathrm{light}\) with lower log bound given by log10mlight=-##. Datasets used for the nested sampling runs: Planck 2018 TT,TE,EE+lowl+lowE: https://pla.esac.esa.int/pla/#cosmology NuFIT 5.0: http://www.nu-fit.org/?q=node/228 Software used: Cobaya: https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 CLASS: https://github.com/lesgourg/class_public/releases/tag/v2.9.4 PolyChord: https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 Anesthetic: https://github.com/lukashergt/anesthetic/tree/138299739544e888cc318746be087c898f1aff15 For more details see Cobaya's (https://cobaya.readthedocs.io/en/latest/index.html) and Anesthetic's (https://anesthetic.readthedocs.io/en/latest/) documentation. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.4556360 |
| Title | Bayesian evidence for the tensor-to-scalar ratio r and neutrino masses m_nu: Effects of uniform vs logarithmic priors (supplementary inference products) |
| Description | These are the nested sampling inference products and input files that were used to compute results for arXiv:2102.11511. Example plotting scripts (as .ipynb or as .html files) and figures from the papers are included to demonstrate usage. Filename conventions: lcdm: Concordance cosmological model called \(\Lambda\mathrm{CDM}\) (without extension this assumes \(r=0\) and a single massive neutrino with mass \(m_\nu=0.06\,\mathrm{eV}\)). _r: \(\Lambda\mathrm{CDM}\) with variable tensor-to-scalar ratio \(r\). _nu: \(\Lambda\mathrm{CDM}\) with three massive neutrinos, sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\). mcmc: Cobaya's Markov Chain Monte Carlo Metropolis sampler.https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 pc#d###: PolyChord run with #d repeats per parameter block (where d is the number of parameters in that block) and with ### live points.https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 _class: theory code CLASS.https://github.com/lesgourg/class_public/releases/tag/v2.9.4 _p18_TTTEEElowTE_SZ: Planck 2018 TT,TE,EE+lowl+lowE data.https://pla.esac.esa.int/pla/#cosmology _nufit50: NuFIT 5.0 data.http://www.nu-fit.org/?q=node/228 _NH and _IH: normal and inverted neutrino hierarchy. _logr##: logarithmic sampling of tensor-to-scalar ratio \(r\) with lower log bound given .by log10r=-##. _mdD: sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\) (medium and heavy neutrino mass are derived parameters) with mass units in eV. _logmdD##: logarithmic (instead of uniform) sampling of the lightest neutrino mass \(m_\mathrm{light}\) with lower log bound given by log10mlight=-##. Datasets used for the nested sampling runs: Planck 2018 TT,TE,EE+lowl+lowE: https://pla.esac.esa.int/pla/#cosmology NuFIT 5.0: http://www.nu-fit.org/?q=node/228 Software used: Cobaya: https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 CLASS: https://github.com/lesgourg/class_public/releases/tag/v2.9.4 PolyChord: https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 Anesthetic: https://github.com/lukashergt/anesthetic/tree/138299739544e888cc318746be087c898f1aff15 For more details see Cobaya's (https://cobaya.readthedocs.io/en/latest/index.html) and Anesthetic's (https://anesthetic.readthedocs.io/en/latest/) documentation. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.4556359 |
| Title | Costless correction of chain based nested sampling parameter estimaion in gravitational wave data and beyond (supplementary material) |
| Description | These are the nested sampling inference products used to obtain the results for arXiv:2404.16428. The chains are given as pickled dataframes as there are over 1000 runs provided here. The script for reproducing the plots in the paper is also given. Folders: outdir_simulated_BBH - contains 200 runs on the same simulated signal. 'samples' contains the full sets of weighted samples from each run and 'full_dfs' contains the dataframes with the weighted samples AND the phantom points from the run. The samples in 'full_dfs' with chain_no=0 are the weighted samples, and all others are phantoms. outdir_test - contains the single run on the above simulated signal which was performed with num_repeats=25*ndims=100. outdir_coverage - contains 1000 runs on different simulated signals, with parameters drawn from the prior. The drawn values are saved as '{}_params.npy' for each run. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.10911043 |
| Title | Costless correction of chain based nested sampling parameter estimaion in gravitational wave data and beyond (supplementary material) |
| Description | These are the nested sampling inference products used to obtain the results for arXiv:2404.16428. The chains are given as pickled dataframes as there are over 1000 runs provided here. The script for reproducing the plots in the paper is also given. Folders: outdir_simulated_BBH - contains 200 runs on the same simulated signal. 'samples' contains the full sets of weighted samples from each run and 'full_dfs' contains the dataframes with the weighted samples AND the phantom points from the run. The samples in 'full_dfs' with chain_no=0 are the weighted samples, and all others are phantoms. outdir_test - contains the single run on the above simulated signal which was performed with num_repeats=25*ndims=100. outdir_coverage - contains 1000 runs on different simulated signals, with parameters drawn from the prior. The drawn values are saved as '{}_params.npy' for each run. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.10911044 |
| Title | Data underpinning: Can Multi-Threaded Flux Tubes in Coronal Arcades Support a Magnetohydrodynamic Avalanche? |
| Description | Here is attached the relevant source code used for the numerical simulations undertaken in connection with the work underpinned. Here, these simulations use a numerical MHD code produced by Tony Arber, now at the University of Warwick, and others, adapted and configured in order to reflect the particular model at hand. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://risweb.st-andrews.ac.uk/portal/en/datasets/data-underpinning-can-multithreaded-flux-tubes-in... |
| Title | Data underpinning: Linking computational models to follow the evolution of heated coronal plasma |
| Description | Here is attached the source code used for performing the one-dimensional, thermodynamic simulations described in the paper. The files are embargoed until 09/07/2022. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://research-portal.st-andrews.ac.uk/en/datasets/data-underpinning-linking-computational-models-... |
| Title | Data underpinning: Self-consistent nanoflare heating in model active regions: MHD avalanches |
| Description | |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://risweb.st-andrews.ac.uk/portal/en/datasets/data-underpinning-selfconsistent-nanoflare-heatin... |
| Title | ExoMol molecular line lists. XLIV. SiO |
| Description | VizieR online Data Catalogue associated with article published in journal Monthly Notices of the Royal Astronomical Society with title ' ExoMol line lists. XLIV. IR and UV line list for silicon monoxide (^28^Si^15^O).' (bibcode: 2022MNRAS.510..903Y) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/510/903 |
| Title | Inference products for "Finite inflation in curved space" |
| Description | These are the MCMC and nested sampling inference products and input files that were used to compute results for the paper "Finite inflation in cuved space" by L. T. Hergt, F. J. Agocs, W. J. Handley, M. P. Hobson, and A. N. Lasenby from 2022. Example plotting scripts (as \(\texttt{.ipynb}\) or as \(\texttt{.html}\) files) and figures from the paper are included to demonstrate usage. We used the following python packages for the genertion of MCMC and nested sampling chains: Package Version anesthetic 2.0.0b12 classy 2.9.4 cobaya 3.0.4 GetDist 1.3.3 primpy 2.3.6 pyoscode 1.0.4 pypolychord 1.20.0 Filename conventions: \(\texttt{mcmc}\): MCMC run \(\texttt{pcs#d####}\): PolyChord run (in synchronous mode) with \(\texttt{#d}\) repeats per parameter block (where \(\texttt{d}\) is the number of parameters in that block) and with \(\texttt{####}\) live points. \(\texttt{_cl_hf}\): Using Boltzmann theory code CLASS with nonlinearities code halofit. \(\texttt{_p18}\): Using Planck 2018 CMB data. \(\texttt{_TTTEEE}\): Using the high-l TTTEEE likelihood. \(\texttt{_TTTEEElite}\): Using the lite version of the high-l TTTEEE likelihood. \(\texttt{_lowl_lowE}\): Using the low-l likelihoods for temperature and E-modes. \(\texttt{_BK15}\): Using data from the 2015 observing season of Bicep2 and the Keck Array. \(\texttt{lcdm}\): Concordance cosmological model called LCDM (standard 6 cosmological sampling parameters, no tensor perturbations, zero spatial curvature) \(\texttt{_r}\): Extension with a variable tensor-to-scalar ratio \(r\). \(\texttt{_omegak}\): Extension with a variable curvature density parameter \(\Omega_K \). \(\texttt{_H0}\): Sampling over \(H_0\) instead of \(\theta_\mathrm{s}\). \(\texttt{_omegakh2}\): Extension with a variable curvature density parameter, but sampling over \(H_0\) instead of \(\theta_\mathrm{s}\) and over \(\omega_K\equiv\Omega_Kh^2\) instead of \(\Omega_K \). \(\texttt{_mn2}\): Using a quadratic monomial potential for the computation of the primordial universe. \(\texttt{_nat}\): Using the natural inflation potential for the computation of the primordial universe. \(\texttt{_stb}\): Using the Starobinsky potential for the computation of the primordial universe. \(\texttt{_AsfoH}\): Using the primordial sampling parameters {`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`}. \(\texttt{_perm}\): Assuming a permissive reheating scenario. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.6547872 |
| Title | Inference products for "Finite inflation in curved space" |
| Description | These are the MCMC and nested sampling inference products and input files that were used to compute results for the paper "Finite inflation in cuved space" by L. T. Hergt, F. J. Agocs, W. J. Handley, M. P. Hobson, and A. N. Lasenby from 2022. Example plotting scripts (as \(\texttt{.ipynb}\) or as \(\texttt{.html}\) files) and figures from the paper are included to demonstrate usage. We used the following python packages for the genertion of MCMC and nested sampling chains: Package Version anesthetic 2.0.0b12 classy 2.9.4 cobaya 3.0.4 GetDist 1.3.3 primpy 2.3.6 pyoscode 1.0.4 pypolychord 1.20.0 Filename conventions: \(\texttt{mcmc}\): MCMC run \(\texttt{pcs#d####}\): PolyChord run (in synchronous mode) with \(\texttt{#d}\) repeats per parameter block (where \(\texttt{d}\) is the number of parameters in that block) and with \(\texttt{####}\) live points. \(\texttt{_cl_hf}\): Using Boltzmann theory code CLASS with nonlinearities code halofit. \(\texttt{_p18}\): Using Planck 2018 CMB data. \(\texttt{_TTTEEE}\): Using the high-l TTTEEE likelihood. \(\texttt{_TTTEEElite}\): Using the lite version of the high-l TTTEEE likelihood. \(\texttt{_lowl_lowE}\): Using the low-l likelihoods for temperature and E-modes. \(\texttt{_BK15}\): Using data from the 2015 observing season of Bicep2 and the Keck Array. \(\texttt{lcdm}\): Concordance cosmological model called LCDM (standard 6 cosmological sampling parameters, no tensor perturbations, zero spatial curvature) \(\texttt{_r}\): Extension with a variable tensor-to-scalar ratio \(r\). \(\texttt{_omegak}\): Extension with a variable curvature density parameter \(\Omega_K \). \(\texttt{_H0}\): Sampling over \(H_0\) instead of \(\theta_\mathrm{s}\). \(\texttt{_omegakh2}\): Extension with a variable curvature density parameter, but sampling over \(H_0\) instead of \(\theta_\mathrm{s}\) and over \(\omega_K\equiv\Omega_Kh^2\) instead of \(\Omega_K \). \(\texttt{_mn2}\): Using a quadratic monomial potential for the computation of the primordial universe. \(\texttt{_nat}\): Using the natural inflation potential for the computation of the primordial universe. \(\texttt{_stb}\): Using the Starobinsky potential for the computation of the primordial universe. \(\texttt{_AsfoH}\): Using the primordial sampling parameters {`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`}. \(\texttt{_perm}\): Assuming a permissive reheating scenario. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/doi/10.5281/zenodo.6547871 |
| Title | Renormalisation of the energy-momentum tensor in three-dimensional scalar SU(N) theories using the Wilson flow -- data release |
| Description | This repository contains the lattice two-point function measurements required to reproduce the results of the paper "Renormalisation of the energy-momentum tensor in three-dimensional scalar SU(N) theories using the Wilson flow" (https://arxiv.org/abs/2009.14767). The code required to perform the data analysis can be found in https://github.com/josephleekl/scalar_emt_analysis. For any questions please get in touch: joseph.lee@ed.ac.uk |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4290392 |
| Title | Research data supporting "Finite-Temperature Effects on the X-ray Absorption Spectra of Crystalline Aluminas from First Principles" |
| Description | The data in this submission consists of all files generated using the plane-wave density-functional theory code, CASTEP. It contains the structure files for alpha and gamma alumina crystal structures as well as the structure files from monte-carlo sampling. In addition, it contains the electronic density of states calculations and core-hole X-Ray absorption data for alpha and gamma alumina at 0 K and 300 K. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://www.repository.cam.ac.uk/handle/1810/349670 |
| Title | Spectra and light curves presented in Double detonations: variations in Type Ia supernovae due to different core and He shell masses - II. Synthetic observables |
| Description | Dataset containing simulated spectra and light curves presented in the paper "Double detonations: variations in Type Ia supernovae due to different core and He shell masses - II. Synthetic observables" (ADS). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/7997387 |
| Title | Spectra and light curves presented in Double detonations: variations in Type Ia supernovae due to different core and He shell masses - II. Synthetic observables |
| Description | Dataset containing simulated spectra and light curves presented in the paper "Double detonations: variations in Type Ia supernovae due to different core and He shell masses - II. Synthetic observables" (ADS). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/8192770 |
| Title | Spectra presented in Double detonations: variations in Type Ia supernovae due to different core and He shell masses - II. Synthetic observables |
| Description | Dataset containing simulated spectra presented in the paper "Double detonations: variations in Type Ia supernovae due to different core and He shell masses - II. Synthetic observables" (ADS). |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/7997388 |
| Title | VLT/X-SHOOTER & Keck/ESI spectra of z~5 QSOs |
| Description | VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Chasing the tail of cosmic reionization with dark gap statistics in the Ly{alpha} forest over 5 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| URL | https://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/923/223 |
| Title | Z-dependent yields of double detonations |
| Description | VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'Metallicity-dependent nucleosynthesis yields of Type Ia supernovae originating from double detonations of sub-MCh white dwarfs.' (bibcode: 2021A&A...656A..94G) |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://cdsarc.cds.unistra.fr/viz-bin/cat/J/A+A/656/A94 |