scholarly journals The PAU survey: measurement of narrow-band galaxy properties with approximate bayesian computation

2021 ◽  
Vol 2021 (12) ◽  
pp. 013
Author(s):  
Luca Tortorelli ◽  
Malgorzata Siudek ◽  
Beatrice Moser ◽  
Tomasz Kacprzak ◽  
Pascale Berner ◽  
...  
Keyword(s):  
Population Model ◽  
Approximate Bayesian Computation ◽  
Narrow Band ◽  
Forward Model ◽  
Accelerating Universe ◽  
Bayesian Computation ◽  
The Galaxy ◽  
Spectral Coefficients ◽  
Approximate Bayesian ◽  
Galaxy Population

Abstract Narrow-band imaging surveys allow the study of the spectral characteristics of galaxies without the need of performing their spectroscopic follow-up. In this work, we forward-model the Physics of the Accelerating Universe Survey (PAUS) narrow-band data. The aim is to improve the constraints on the spectral coefficients used to create the galaxy spectral energy distributions (SED) of the galaxy population model in Tortorelli et al. 2020. In that work, the model parameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) data using Approximate Bayesian Computation (ABC). This led to stringent constraints on the B-band galaxy luminosity function parameters, but left the spectral coefficients only broadly constrained. To address that, we perform an ABC inference using CFHTLS and PAUS data. This is the first time our approach combining forward-modelling and ABC is applied simultaneously to multiple datasets. We test the results of the ABC inference by comparing the narrow-band magnitudes of the observed and simulated galaxies using Principal Component Analysis, finding a very good agreement. Furthermore, we prove the scientific potential of the constrained galaxy population model to provide realistic stellar population properties by measuring them with the SED fitting code CIGALE. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited (i < 22.5) sample of galaxies up to redshift z ∼ 0.8. We find that properties like stellar masses, star-formation rates, mass-weighted stellar ages and metallicities are in agreement within errors between observations and simulations. Overall, this work shows the ability of our galaxy population model to correctly forward-model a complex dataset such as PAUS and the ability to reproduce the diversity of galaxy properties at the redshift range spanned by CFHTLS and PAUS.

scholarly journals The multiplicity distribution of Kepler’s exoplanets

2019 ◽  
Vol 489 (3) ◽  
pp. 3162-3173 ◽  
Author(s):  
Emily Sandford ◽  
David Kipping ◽  
Michael Collins
Keyword(s):  
Population Model ◽  
Approximate Bayesian Computation ◽  
Multiplicity Distribution ◽  
Population Models ◽  
Bayesian Computation ◽  
Single Population ◽  
Model Following ◽  
Multiplicity Distributions ◽  
Approximate Bayesian

Abstract The true multiplicity distribution of transiting planet systems is obscured by strong observational biases, leading low-multiplicity systems to be overrepresented in the observed sample. Using the Kepler FGK planet hosts, we employ approximate Bayesian computation to infer the multiplicity distribution by comparing simulated catalogues to the observed one. After comparing a total of 10 different multiplicity distributions, half of which were two-population models, to the observed data, we find that a single-population model following a Zipfian distribution is able to explain the Kepler data as well as any of the dichotomous models we test. Our work provides another example of a way to explain the observed Kepler multiplicities without invoking a dichotomous planet population. Using our preferred Zipfian model, we estimate that an additional $2393_{-717}^{+904}$ planets likely reside in the 1537 FGK Kepler systems studied in this work, which would increase the planet count by a factor of $2.22_{-0.36}^{+0.46}$. Of these hidden worlds, $663_{-151}^{+158}$ are expected to reside in ostensibly single transiting planet systems, meaning that an additional planet(s) is expected for approximately 1-in-2 such Kepler systems.

scholarly journals Approximate Bayesian computation in large-scale structure: constraining the galaxy–halo connection

2017 ◽  
Vol 469 (3) ◽  
pp. 2791-2805 ◽  
Author(s):  
ChangHoon Hahn ◽  
Mohammadjavad Vakili ◽  
Kilian Walsh ◽  
Andrew P. Hearin ◽  
David W. Hogg ◽  
...  
Keyword(s):  
Large Scale ◽  
Approximate Bayesian Computation ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Bayesian Computation ◽  
The Galaxy ◽  
Galaxy Halo ◽  
Approximate Bayesian

scholarly journals Weighted approximate Bayesian computation via Sanov’s theorem

2021 ◽  
Author(s):  
Cecilia Viscardi ◽  
Michele Boreale ◽  
Fabio Corradi
Keyword(s):  
Large Deviations ◽  
Posterior Distribution ◽  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Information Theoretic ◽  
Discrete Random Variables ◽  
Positive Weights ◽  
Approximate Bayesian ◽  
Information Theoretic Method ◽  
Computational Resources

AbstractWe consider the problem of sample degeneracy in Approximate Bayesian Computation. It arises when proposed values of the parameters, once given as input to the generative model, rarely lead to simulations resembling the observed data and are hence discarded. Such “poor” parameter proposals do not contribute at all to the representation of the parameter’s posterior distribution. This leads to a very large number of required simulations and/or a waste of computational resources, as well as to distortions in the computed posterior distribution. To mitigate this problem, we propose an algorithm, referred to as the Large Deviations Weighted Approximate Bayesian Computation algorithm, where, via Sanov’s Theorem, strictly positive weights are computed for all proposed parameters, thus avoiding the rejection step altogether. In order to derive a computable asymptotic approximation from Sanov’s result, we adopt the information theoretic “method of types” formulation of the method of Large Deviations, thus restricting our attention to models for i.i.d. discrete random variables. Finally, we experimentally evaluate our method through a proof-of-concept implementation.

Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

2021 ◽  
Vol 62 (2) ◽  
Author(s):  
Jason D. Christopher ◽  
Olga A. Doronina ◽  
Dan Petrykowski ◽  
Torrey R. S. Hayden ◽  
Caelan Lapointe ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Absorption Spectroscopy ◽  
Approximate Bayesian Computation ◽  
Flow Parameter ◽  
Bayesian Computation ◽  
Laser Absorption Spectroscopy ◽  
Laser Absorption ◽  
Approximate Bayesian

scholarly journals Approximate Bayesian Computation for Discrete Spaces

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 312
Author(s):  
Ilze A. Auzina ◽  
Jakub M. Tomczak
Keyword(s):  
Approximate Bayesian Computation ◽  
Likelihood Function ◽  
Real Life ◽  
Random Variables ◽  
Bayesian Computation ◽  
Inference Problem ◽  
Markov Kernel ◽  
Inference Problems ◽  
Binary Neural Network ◽  
Approximate Bayesian

Many real-life processes are black-box problems, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be defined. For continuous random variables, likelihood-free inference problems can be solved via Approximate Bayesian Computation (ABC). However, an optimal alternative for discrete random variables is yet to be formulated. Here, we aim to fill this research gap. We propose an adjusted population-based MCMC ABC method by re-defining the standard ABC parameters to discrete ones and by introducing a novel Markov kernel that is inspired by differential evolution. We first assess the proposed Markov kernel on a likelihood-based inference problem, namely discovering the underlying diseases based on a QMR-DTnetwork and, subsequently, the entire method on three likelihood-free inference problems: (i) the QMR-DT network with the unknown likelihood function, (ii) the learning binary neural network, and (iii) neural architecture search. The obtained results indicate the high potential of the proposed framework and the superiority of the new Markov kernel.

scholarly journals Complex genetic admixture histories reconstructed with Approximate Bayesian Computation

2021 ◽  
Author(s):  
Cesar A. Fortes‐Lima ◽  
Romain Laurent ◽  
Valentin Thouzeau ◽  
Bruno Toupance ◽  
Paul Verdu
Keyword(s):  
Approximate Bayesian Computation ◽  
Genetic Admixture ◽  
Bayesian Computation ◽  
Approximate Bayesian

scholarly journals Cophylogeny Reconstruction via an Approximate Bayesian Computation

2014 ◽  
Vol 64 (3) ◽  
pp. 416-431 ◽  
Author(s):  
C. Baudet ◽  
B. Donati ◽  
B. Sinaimeri ◽  
P. Crescenzi ◽  
C. Gautier ◽  
...  
Keyword(s):  
Approximate Bayesian Computation ◽  
Bayesian Computation ◽  
Approximate Bayesian
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