248 research outputs found
KiDS-1000 Cosmology::machine learning - accelerated constraints on Interacting Dark Energy with COSMOPOWER
We derive constraints on a coupled quintessence model with pure momentum
exchange from the public 1000 deg cosmic shear measurements from the
Kilo-Degree Survey and the 2018 Cosmic Microwave Background data.
We compare this model with CDM and find similar and
log-evidence values. We accelerate parameter estimation by sourcing
cosmological power spectra from the neural network emulator COSMOPOWER. We
highlight the necessity of such emulator-based approaches to reduce the
computational runtime of future similar analyses, particularly from Stage IV
surveys. As an example, we present MCMC forecasts on the same coupled
quintessence model for a -like survey, revealing degeneracies
between the coupled quintessence parameters and the baryonic feedback and
intrinsic alignment parameters, but also highlighting the large increase in
constraining power Stage IV surveys will achieve. The contours are obtained in
a few hours with COSMOPOWER, as opposed to the few months required with a
Boltzmann code.Comment: 5 pages, 2 min. summary video available at
https://youtu.be/c2x8hzApAgE. Emulators available in the COSMOPOWER GitHub
repository, https://github.com/alessiospuriomancini/cosmopower. Matches
version published in MNRAS Letter
Accelerating Bayesian microseismic event location with deep learning
We present a series of new open-source deep-learning algorithms to accelerate Bayesian full-waveform point source inversion of microseismic
events. Inferring the joint posterior probability distribution of moment tensor components and source location is key for rigorous uncertainty
quantification. However, the inference process requires forward modelling of microseismic traces for each set of parameters explored by the sampling
algorithm, which makes the inference very computationally intensive. In this paper we focus on accelerating this process by training deep-learning
models to learn the mapping between source location and seismic traces for a given 3D heterogeneous velocity model and a fixed isotropic moment
tensor for the sources. These trained emulators replace the expensive solution of the elastic wave equation in the inference process.
We compare our results with a previous study that used emulators based on Gaussian processes to invert microseismic events. For fairness of
comparison, we train our emulators on the same microseismic traces and using the same geophysical setting. We show that all of our models provide
more accurate predictions, ∼ 100 times faster predictions than the method based on Gaussian processes, and a (105) speed-up
factor over a pseudo-spectral method for waveform generation. For example, a 2 s long synthetic trace can be generated in ∼ 10 ms on a
common laptop processor, instead of ∼ 1 h using a pseudo-spectral method on a high-profile graphics processing unit card. We also
show that our inference results are in excellent agreement with those obtained from traditional location methods based on travel time estimates. The
speed, accuracy, and scalability of our open-source deep-learning models pave the way for extensions of these emulators to generic source mechanisms
and application to joint Bayesian inversion of moment tensor components and source location using full waveforms.</p
Towards fast machine-learning-assisted Bayesian posterior inference of microseismic event location and source mechanism
Bayesian inference applied to microseismic activity monitoring allows the accurate location of microseismic events from recorded seismograms and the estimation of the associated uncertainties. However, the forward modelling of these microseismic events, which is necessary to perform Bayesian source inversion, can be prohibitively expensive in terms of computational resources. A viable solution is to train a surrogate model based on machine learning techniques, to emulate the forward model and thus accelerate Bayesian inference. In this paper, we substantially enhance previous work, which considered only sources with isotropic moment tensors. We train a machine learning algorithm on the power spectrum of the recorded pressure wave and show that the trained emulator allows complete and fast event locations for any source mechanism. Moreover, we show that our approach is computationally inexpensive, as it can be run in less than 1 hour on a commercial laptop, while yielding accurate results using less than 104 training seismograms. We additionally demonstrate how the trained emulators can be used to identify the source mechanism through the estimation of the Bayesian evidence. Finally, we demonstrate that our approach is robust to real noise as measured in field data. This work lays the foundations for efficient, accurate future joint determinations of event location and moment tensor, and associated uncertainties, which are ultimately key for accurately characterising human-induced and natural earthquakes, and for enhanced quantitative seismic hazard assessments
Fast emulation of anisotropies induced in the cosmic microwave background by cosmic strings
Cosmic strings are linear topological defects that may have been produced
during symmetry-breaking phase transitions in the very early Universe. In an
expanding Universe the existence of causally separate regions prevents such
symmetries from being broken uniformly, with a network of cosmic string
inevitably forming as a result. To faithfully generate observables of such
processes requires computationally expensive numerical simulations, which
prohibits many types of analyses. We propose a technique to instead rapidly
emulate observables, thus circumventing simulation. Emulation is a form of
generative modelling, often built upon a machine learning backbone. End-to-end
emulation often fails due to high dimensionality and insufficient training
data. Consequently, it is common to instead emulate a latent representation
from which observables may readily be synthesised. Wavelet phase harmonics are
an excellent latent representations for cosmological fields, both as a summary
statistic and for emulation, since they do not require training and are highly
sensitive to non-Gaussian information. Leveraging wavelet phase harmonics as a
latent representation, we develop techniques to emulate string induced CMB
anisotropies over a 7.2 degree field of view, with sub-arcminute resolution, in
under a minute on a single GPU. Beyond generating high fidelity emulations, we
provide a technique to ensure these observables are distributed correctly,
providing a more representative ensemble of samples. The statistics of our
emulations are commensurate with those calculated on comprehensive Nambu-Goto
simulations. Our findings indicate these fast emulation approaches may be
suitable for wide use in, e.g., simulation based inference pipelines. We make
our code available to the community so that researchers may rapidly emulate
cosmic string induced CMB anisotropies for their own analysis
class_sz I: Overview
class_sz is a versatile and robust code in C and Python that can compute
theoretical predictions for a wide range of observables relevant to
cross-survey science in the Stage IV era. The code is public at
https://github.com/CLASS-SZ/class_sz along with a series of tutorial notebooks
(https://github.com/CLASS-SZ/notebooks). It will be presented in full detail in
paper II. Here we give a brief overview of key features and usage.Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble
(France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of
conferences, EDP Science
A measurement of the CMB temperature power spectrum and constraints on cosmology from the SPT-3G 2018 TT/TE/EE Data Set
We present a sample-variance-limited measurement of the temperature power spectrum () of the cosmic microwave background (CMB) using observations of a field made by SPT-3G in 2018. We report multifrequency power spectrum measurements at 95, 150, and 220GHz covering the angular multipole range . We combine this measurement with the published polarization power spectrum measurements from the 2018 observing season and update their associated covariance matrix to complete the SPT-3G 2018 data set. This is the first analysis to present cosmological constraints from SPT , , and power spectrum measurements jointly. We blind the cosmological results and subject the data set to a series of consistency tests at the power spectrum and parameter level. We find excellent agreement between frequencies and spectrum types and our results are robust to the modeling of astrophysical foregrounds. We report results for CDM and a series of extensions, drawing on the following parameters: the amplitude of the gravitational lensing effect on primary power spectra , the effective number of neutrino species , the primordial helium abundance , and the baryon clumping factor due to primordial magnetic fields . We find that the SPT-3G 2018 data are well fit by CDM with a probability-to-exceed of . For CDM, we constrain the expansion rate today to and the combined structure growth parameter to . The SPT-based results are effectively independent of Planck, and the cosmological parameter constraints from either data set are within of each other. (abridged)..
A Measurement of the CMB Temperature Power Spectrum and Constraints on Cosmology from the SPT-3G 2018 TT/TE/EE Data Set
We present a sample-variance-limited measurement of the temperature power
spectrum () of the cosmic microwave background (CMB) using observations of
a field made by SPT-3G in 2018. We report
multifrequency power spectrum measurements at 95, 150, and 220GHz covering the
angular multipole range . We combine this
measurement with the published polarization power spectrum measurements from
the 2018 observing season and update their associated covariance matrix to
complete the SPT-3G 2018 data set. This is the first analysis to
present cosmological constraints from SPT , , and power spectrum
measurements jointly. We blind the cosmological results and subject the data
set to a series of consistency tests at the power spectrum and parameter level.
We find excellent agreement between frequencies and spectrum types and our
results are robust to the modeling of astrophysical foregrounds. We report
results for CDM and a series of extensions, drawing on the following
parameters: the amplitude of the gravitational lensing effect on primary power
spectra , the effective number of neutrino species
, the primordial helium abundance , and the
baryon clumping factor due to primordial magnetic fields . We find that the
SPT-3G 2018 data are well fit by CDM with a
probability-to-exceed of . For CDM, we constrain the expansion
rate today to and the
combined structure growth parameter to . The SPT-based
results are effectively independent of Planck, and the cosmological parameter
constraints from either data set are within of each other.
(abridged)Comment: 35 Pages, 17 Figures, 11 Table
Improvements in cosmological constraints from breaking growth degeneracy
The key probes of the growth of a large-scale structure are its rate f and amplitude σ8. Redshift space distortions in the galaxy power spectrum allow us to measure only the combination fσ8, which can be used to constrain the standard cosmological model or alternatives. By using measurements of the galaxy-galaxy lensing cross-correlation spectrum or of the galaxy bispectrum, it is possible to break the fσ8 degeneracy and obtain separate estimates of f and σ8 from the same galaxy sample. Currently there are very few such separate measurements, but even this allows for improved constraints on cosmological models
Euclid preparation. XXIV. Calibration of the halo mass function in CDM cosmologies
Euclid's photometric galaxy cluster survey has the potential to be a very
competitive cosmological probe. The main cosmological probe with observations
of clusters is their number count, within which the halo mass function (HMF) is
a key theoretical quantity. We present a new calibration of the analytic HMF,
at the level of accuracy and precision required for the uncertainty in this
quantity to be subdominant with respect to other sources of uncertainty in
recovering cosmological parameters from Euclid cluster counts. Our model is
calibrated against a suite of N-body simulations using a Bayesian approach
taking into account systematic errors arising from numerical effects in the
simulation. First, we test the convergence of HMF predictions from different
N-body codes, by using initial conditions generated with different orders of
Lagrangian Perturbation theory, and adopting different simulation box sizes and
mass resolution. Then, we quantify the effect of using different halo-finder
algorithms, and how the resulting differences propagate to the cosmological
constraints. In order to trace the violation of universality in the HMF, we
also analyse simulations based on initial conditions characterised by
scale-free power spectra with different spectral indexes, assuming both
Einstein--de Sitter and standard CDM expansion histories. Based on
these results, we construct a fitting function for the HMF that we demonstrate
to be sub-percent accurate in reproducing results from 9 different variants of
the CDM model including massive neutrinos cosmologies. The calibration
systematic uncertainty is largely sub-dominant with respect to the expected
precision of future mass-observation relations; with the only notable exception
of the effect due to the halo finder, that could lead to biased cosmological
inference.Comment: 24 pages, 21 figures, 5 tables, 3 appendixes
Euclid preparation. XXXI. Performance assessment of the NISP Red-Grism through spectroscopic simulations for the Wide and Deep surveys
This work focuses on the pilot run of a simulation campaign aimed at
investigating the spectroscopic capabilities of the Euclid Near-Infrared
Spectrometer and Photometer (NISP), in terms of continuum and emission line
detection in the context of galaxy evolutionary studies. To this purpose we
constructed, emulated, and analysed the spectra of 4992 star-forming galaxies
at using the NISP pixel-level simulator. We built the
spectral library starting from public multi-wavelength galaxy catalogues, with
value-added information on spectral energy distribution (SED) fitting results,
and from Bruzual and Charlot (2003) stellar population templates. Rest-frame
optical and near-IR nebular emission lines were included using empirical and
theoretical relations. We inferred the 3.5 NISP red grism spectroscopic
detection limit of the continuum measured in the band for star-forming
galaxies with a median disk half-light radius of \ang{;;0.4} at magnitude ABmag for the Euclid Wide Survey and at ABmag for the Euclid Deep Survey. We found a very good
agreement with the red grism emission line detection limit requirement for the
Wide and Deep surveys. We characterised the effect of the galaxy shape on the
detection capability of the red grism and highlighted the degradation of the
quality of the extracted spectra as the disk size increases. In particular, we
found that the extracted emission line signal to noise ratio (SNR) drops by
45 when the disk size ranges from \ang{;;0.25} to \ang{;;1}. These
trends lead to a correlation between the emission line SNR and the stellar mass
of the galaxy and we demonstrate the effect in a stacking analysis unveiling
emission lines otherwise too faint to detect.Comment: 23 pages, 21 figure
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