Measurement of the B-band galaxy Luminosity Function with Approximate Bayesian Computation

Tortorelli, Luca; Fagioli, Martina; Herbel, Jörg; Amara, Adam; Kacprzak, T.; Réfrégier, Alexandre

doi:10.1088/1475-7516/2020/09/048

Cited by 20 publications

(87 citation statements)

References 116 publications

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“…ABC, LFI and SBI 2 are all heterogeneous terms for approaches to problems where the likelihood is analytically intractable, but data can be simulated. Problems of this type are common in astronomy, and have led to a recent rise in work implementing SBI models, in a wide range of areas including cosmology (Jennings et al 2016; 2020), solar physics (Weiss et al 2021), supermassive black holes (Witzel et al 2020), exoplanets (Sandford et al 2019;Hsu et al 2020;Bryson et al 2020;Kunimoto & Bryson 2021), stellar astronomy (Cisewski-Kehe et al 2019;Kunimoto & Matthews 2020;Morris 2020), galactic astronomy (Mor et al 2019;Cheng et al 2020), dark matter studies (Hermans et al 2020), and extragalactic astronomy (Aufort et al 2020;Tortorelli et al 2020;He et al 2020;Enzi et al 2020).…”

Section: Simulation-based Inferencementioning

confidence: 99%

Accurate X-ray Timing in the Presence of Systematic Biases With Simulation-Based Inference

Huppenkothen,

Bachetti

2021

Preprint

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Because many of our X-ray telescopes are optimized towards observing faint sources, observations of bright sources like X-ray binaries in outburst are often affected by instrumental biases. These effects include dead time and photon pile-up, which can dramatically change the statistical inference of physical parameters from these observations. While dead time is difficult to take into account in a statistically consistent manner, simulating dead time-affected data is often straightforward. This structure makes the issue of inferring physical properties from dead time-affected observations fall into a class of problems common across many scientific disciplines. There is a growing number of methods to address them under the names of Approximate Bayesian Computation (ABC) or Simulation-Based Inference (SBI), aided by new developments in density estimation and statistical machine learning. In this paper, we introduce SBI as a principled way to infer variability properties from dead time-affected light curves. We use Sequential Neural Posterior Estimation to estimate the posterior probability for variability properties. We show that this method can recover variability parameters on simulated data even when dead time is variable, and present results of an application of this approach to NuSTAR observations of the galactic black hole X-ray binary GRS 1915+105.

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Section: Simulation-based Inferencementioning

confidence: 99%

Accurate X-ray Timing in the Presence of Systematic Biases With Simulation-Based Inference

Huppenkothen,

Bachetti

2021

Preprint

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“…This will add clustering to the galaxy forward model in UFig [61], which is both useful for clustering analysis and probe combination, as well as important for controlling systematics (e.g. [62,63]).…”

Section: Discussionmentioning

confidence: 99%

Rapid Simulations of Halo and Subhalo Clustering

Berner¹,

Réfrégier²,

Sgier³

et al. 2021

Preprint

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The analysis of cosmological galaxy surveys requires realistic simulations for their interpretation. Forward modelling is a powerful method to simulate galaxy clustering without the need for an underlying complex model. This approach requires fast cosmological simulations with a high resolution and large volume, to resolve small dark matter halos associated to single galaxies. In this work, we present fast halo and subhalo clustering simulations based on the Lagrangian perturbation theory code PINOCCHIO, which generates halos and merger trees. The subhalo progenitors are extracted from the merger history and the survival of subhalos is modelled. We introduce a new fitting function for the subhalo merger time, which includes an additional dependence on subhalo mass. The spatial distribution of subhalos within their hosts is modelled using a number density profile. We compare our simulations with the halo finder Rockstar applied to the full N-body code GADGET-2. We find a good agreement for the number ratio of subhalos, for halo masses down to 5.7 • 10 9 M /h. The subhalo mass function and the correlation function of halos and subhalos are also in good agreement. We investigate the effect of the chosen number density profile on the resulting subhalo clustering. Our simulation is approximate yet realistic and significantly faster compared to a full N-body simulation combined with a halo finder. The fast halo and subhalo clustering simulations offer good prospects for galaxy forward models using subhalo abundance matching.

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“…Consequently, ABC has been widely applied in various areas of astrophysics and cosmology (e.g. Schafer & Freeman 2012; Cameron & Pettitt 2012;Weyant et al 2013;Robin et al 2014;Lin & Kilbinger 2015;Akeret et al 2015;Jennings et al 2016;Hahn et al 2017;Davies et al 2018;Kacprzak et al 2018;He et al 2020;Tortorelli et al 2020Tortorelli et al , 2021.…”

Section: Basics Of Cluster Weak Lensingmentioning

confidence: 99%

Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology

Tam,

Umetsu,

Amara

2021

Preprint

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Likelihood-free inference provides a rigorous approach to preform Bayesian analysis using forward simulations only. The main advantage of likelihood-free methods is its ability to account for complex physical processes and observational effects in forward simulations. Here we explore the potential of likelihood-free forward modeling for Bayesian cosmological inference using the redshift evolution of the cluster abundance combined with weak-lensing mass calibration. We use two complementary likelihood-free methods, namely Approximate Bayesian Computation (ABC) and Density-Estimation Likelihood-Free Inference (DELFI), to develop an analysis procedure for inference of the cosmological parameters (Ω m , σ 8 ) and the mass scale of the survey sample. Adopting an eROSITA-like selection function and a 10% scatter in the observable-mass relation in a flat ΛCDM cosmology with Ω m = 0.286 and σ 8 = 0.82, we create a synthetic catalog of observable-selected NFW clusters in a survey area of 50 deg 2 . The stacked tangential shear profile and the number counts in redshift bins are used as summary statistics for both methods. By performing a series of forward simulations, we obtain convergent solutions for the posterior distribution from both methods. We find that ABC recovers broader posteriors than DELFI, especially for the Ω m parameter. For a weak-lensing survey with a source density of n g = 20 arcmin −2 , we obtain posterior constraints on S 8 = σ 8 (Ω m /0.3) 0.3 of 0.836 ± 0.032 and 0.810 ± 0.019 from ABC and DELFI, respectively. The analysis framework developed in this study will be particularly powerful for cosmological inference with ongoing cluster cosmology programs, such as the XMM-XXL survey and the eROSITA all-sky survey, in combination with wide-field weak-lensing surveys.

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Measurement of the B-band galaxy Luminosity Function with Approximate Bayesian Computation

Cited by 20 publications

References 116 publications

Accurate X-ray Timing in the Presence of Systematic Biases With Simulation-Based Inference

Accurate X-ray Timing in the Presence of Systematic Biases With Simulation-Based Inference

Rapid Simulations of Halo and Subhalo Clustering

Likelihood-free Forward Modeling for Cluster Weak Lensing and Cosmology

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