CosmoLSS
Cosmology Large Scale Structure

Publications

A selection of relevant papers is listed in different fields

 

• Cosmology from BOSS using luminous red galaxies (LRGs)

1. Francisco-Shu Kitaura developed the method to do mock galaxy catalogues (PATCHY) and led the project of producing the BOSS mocks calibrating them to observational data:

Kitaura et al 2016 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2016MNRAS.456.4156K/abstract

2. Francisco-Shu Kitaura led the first measurement of baryon acoustic oscillations (BAOs) from cosmic voids (3.3 sigma detection):

Kitaura et al 2016 (Physical Review Letter)

https://ui.adsabs.harvard.edu/abs/2016PhRvL.116q1301K/abstract

3. and he led the reconstruction of the cosmic flows and cosmic web:

Ata, Kitaura et al 2017 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2017MNRAS.467.3993A/abstract

The PATCHY galaxy mocks helped to

4. measure the BAO from the 3-point correlation function:

Slepian (including Kitaura) et al 2017 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2017MNRAS.469.1738S/abstract

5. to find some evidence for a dynamical dark energy (3.5 sigma detection):

(Gong-bo) Zhao (Kitaura) et al 2017 (Nature Astronomy) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2017NatAs...1..627Z/abstract

6. to determine cosmological parameters:

BOSS collaboration (including Kitaura) 2017 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2017MNRAS.470.2617A/abstract

7. We recently showed that we can get the best BAO measurement from combining galaxies with cosmic voids (up to ~20% improvement):

(Cheng) Zhao et al (including Kitaura) 2019 (to be published in MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2018arXiv180203990Z/abstract

 

• Cosmology from eBOSS using quasars (QSOs)

1. Francisco-Shu Kitaura participated in the analysis producing mock quasar catalogs with EZmocks, a code he co-developed.

eBOSS collaboration (including Kitaura) 2018 (MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2018MNRAS.473.4773A/abstract

 

• Cross correlations of large scale structure data with the cosmic microwave background (CMB):

The cosmic web and cosmic flows reconstructions with SDSS and 2MRS data performed by Francisco-Shu Kitaura help for a number of projects:

1. Cross correlation 2MRS data with Planck to study the warm hot intergalactic medium (WHIM)

Génova-Santos, Atrio-Barandela, Kitaura et al 2015 (ApJ)

https://ui.adsabs.harvard.edu/abs/2015ApJ...806..113G/abstract

2. Evidence for missing baryons at low redshift using SDSS data

Hernández-Monteagudo, Ma, Kitaura et al 2015 (Physical Review Letter)

https://ui.adsabs.harvard.edu/abs/2015PhRvL.115s1301H/abstract

3. Detection of the kinematic Sunyaev Effect using SDSS and PLANCK data

Planck collaboration (including Kitaura as external collaborator) (A&A)

https://ui.adsabs.harvard.edu/abs/2016A%26A...586A.140P/abstract

 

• Cosmology and Galaxy evolution studies:

The cosmic web and cosmic flows reconstructions with 2MRS data performed by Francisco-Shu Kitaura help for a number of projects:

1. First application ever of a forward modelling Bayesian reconstruction of the dark matter and peculiar velocity field:

Kitaura et al 2012 (MNRAS Letter)

https://ui.adsabs.harvard.edu/abs/2012MNRAS.427L..35K/abstract

2. Unprecedented constrained N-body simulations of the Local Universe:

Heß, Kitaura, and Gottlöber 2013 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2013MNRAS.435.2065H/abstract

3. Unprecedented cosmic web reconstruction of the Local Universe including environmental studies for different types of galaxies:

Nuza, Kitaura et al 2014 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2014MNRAS.445..988N/abstract

4. Environmental studies for metal poor galaxies:

Filho et al (including Kitaura) 2015 (ApJ)

https://ui.adsabs.harvard.edu/abs/2015ApJ...802...82F/abstract

5. Environmental studies for metal poor galaxies:

Sánchez Almeida et al (including Kitaura) 2016 (ApJ)

https://ui.adsabs.harvard.edu/abs/2016ApJ...819..110S/abstract

6. Non-linear cosmic flows reconstruction to partially explain the tension in the Hubble measurements:

Heß and Kitaura 2016 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2016MNRAS.456.4247H/abstract

 

• Simulations for DESI

(high resolution N-body simulations with cosmic variance suppression):

In 2017 Chia-Hsun Chuang, Gustavo Yepes and F. S. Kitaura won a PRACE project with 25000 CPU hrs, which permitted them to produce simulations for the DESI project:

Chuang, Yepes, Kitaura et al 2019 (MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019MNRAS.487...48C/abstract

products available at: http://www.unitsims.org/

Chia-Hsun Chuang is currently the work package coordinator / leader for the cosmological simulations in DESI.

 

• Papers on mock galaxy catalogs:

 

• original novel methods:

1. Perturbation theory based gravity solver for fast computations:

Kitaura and Heß 2013 (MNRAS Letter)

https://ui.adsabs.harvard.edu/abs/2013MNRAS.435L..78K/abstract

2. Non-linear and stochastic bias modelling:

Kitaura, Yepes and Prada 2014 (MNRAS Letter)

https://ui.adsabs.harvard.edu/abs/2014MNRAS.439L..21K/abstract

3. First method constraining the bias parameters to fit the 3 point statistics:

Kitaura et al 2015 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2015MNRAS.450.1836K/abstract

4. Non-local and non-linear dependence of the halo mass on the environment:

(in this study we found the superior accuracy of PATCHY w.r.t. EZmocks):

Zhao, Kitaura et al 2015 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2015MNRAS.451.4266Z/abstract

5. Fast generation of mocks imposing the halo univariate PDF:

Chuang, Kitaura et al 2015 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2015MNRAS.446.2621C/abstract

6. MCMC calibration of bias parameters based on reference N-body simulations:

Vakili, Kitaura et al 2017 (MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2017MNRAS.472.4144V/abstract

7. Learning the full bias relation (multi-variate PDF) from N-body simulations:

Balaguera-Antolínez, Kitaura et al 2019 (MNRAS Letter) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019MNRAS.483L..58B/abstract

8. Computing covariance matrices based on a single reference simulation:

Balaguera-Antolínez, Kitaura et al 2019 (to be published in MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019arXiv190606109B/abstract

 

• participation in mock comparison projects:

1. We organised an international competition including Pinocchio, PThalos, Halogen, EZmocks, PATCHY, and the N-body based ICE-COLA. Our methods (EZmocks, PATCHY) were the only ones at the level of (or superior to) ICE-COLA:

Chuang et al (including Kitaura) 2015 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2015MNRAS.452..686C/abstract

2. Here we investigated fast gravity solvers and ALPT, developed by myself among them:

Munari et al (including Kitaura) 2017 (JCAP) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2017JCAP...07..050M/abstract

We made a series of 3 comparison studies including: correlation functions, power spectra, bis-spectra, and covariance matrices (participating codes: Halogen, PeakPatch, Pinocchio, ICE-COLA, and PATCHY. PATCHY turned out to be the most efficient and accurate one.):

3. Lippich et al (including Kitaura) 2019 (MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.1786L/abstract

4. Colavincenzo et al (including Kitaura) 2019 (MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.4883C/abstract

5. Blot et al (including Kitaura) 2019 (MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019MNRAS.485.2806B/abstract

6. In this study, we compared parametric to non-parametric codes and demonstrated the high accruacy of our BAM method for DESI (clearly beating PATCHY):

Pellejero-Ibañez, Balaguera-Antolínez, Kitaura et al 2019 (to be published in MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019arXiv191013164P/abstract

 

• Papers on Bayesian inference methodology developments in cosmology:

1. First paper presenting a general Bayesian approach for large scale structure analysis beyond the Gaussian case from previous works:

Kitaura and Enßlin 2008 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2008MNRAS.389..497K/abstract

2. Particular solution for the lognormal Poisson posterior distribution function

Kitaura et al 2010 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2010MNRAS.403..589K/abstract

3. Hamiltonian Monte Carlo Sampling of the lognormal Poisson model

Jasche and Kitaura 2010 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2010MNRAS.407...29J/abstract

4. Further development sampling velocity fields and power spectra:

Kitaura et al 2012 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2012MNRAS.420...61K/abstract

5. Method to recover initial conditions from a galaxy distribution:

Kitaura 2013 (MNRAS Letter)

https://ui.adsabs.harvard.edu/abs/2013MNRAS.429L..84K/abstract

6. Introduction of complex non-linear bias models with non-Poisson distribution functions:

Ata, Kitaura et al 2015 (MNRAS)

https://ui.adsabs.harvard.edu/abs/2015MNRAS.446.4250A/abstract

7. Redshift space distortions corrections within a Bayesian framework:

Kitaura et al 2016 (MNRAS Letter)

https://ui.adsabs.harvard.edu/abs/2016MNRAS.457L.113K/abstract

8. Bayesian method, ideal to deal with galaxy cluster data:

Bos, Kitaura and Weygaert 2019 (MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019MNRAS.488.2573B/abstract

9. Publication of the code:

Bos and Kitaura 2019 (GitHub) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2018ascl.soft10002B/abstract

10. Bayesian method ready to be used on deep surveys including selection effects, complex biasing, etc

Kitaura et al 2019 (to be published in MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019arXiv191100284K/abstract

11. Novel Hamiltonian Monte Carlo Method for large scale structure analysis, which is crucially faster than previous ones:

Hernandez-Sanchez, Kitaura et al 2019 (to be published in MNRAS) IAC affiliation

https://ui.adsabs.harvard.edu/abs/2019arXiv191102667H/abstract