arXiv subjects

Arnau Pujol

Publications and source records attributed to Arnau Pujol.

11 recordsLinked to original sources

arXiv2022

ShapePipe: a new shape measurement pipeline and weak-lensing application to UNIONS/CFIS data

UNIONS is an ongoing collaboration that will provide the largest deep photometric survey of the Northern sky in four optical bands to date. As part of this collaboration, CFIS is taking $r$-band data with an average seeing of 0.65 arcsec, which is complete to magnitude 24.5 and thus ideal for weak-lensing studies. We perform the first weak-lensing analysis of CFIS $r$-band data over an area spanning 1700 deg$^2$ of the sky. We create a catalogue with measured shapes for 40 million galaxies, corresponding to an effective density of 6.8 galaxies per square arcminute, and demonstrate a low level of systematic biases. This work serves as the basis for further cosmological studies using the full UNIONS survey of 4800 deg$^2$ when completed. Here we present ShapePipe, a newly developed weak-lensing pipeline. This pipeline makes use of state-of-the-art methods such as Ngmix for accurate galaxy shape measurement. Shear calibration is performed with metacalibration. We carry out extensive validation tests on the Point Spread Function (PSF), and on the galaxy shapes. In addition, we create realistic image simulations to validate the estimated shear. We quantify the PSF model accuracy and show that the level of systematics is low as measured by the PSF residuals. Their effect on the shear two-point correlation function is sub-dominant compared to the cosmological contribution on angular scales <100 arcmin. The additive shear bias is below 5x$10^{-4}$, and the residual multiplicative shear bias is at most $10^{-3}$ as measured on image simulations. Using COSEBIs we show that there are no significant B-modes present in second-order shear statistics. We present convergence maps and see clear correlations of the E-mode with known cluster positions. We measure the stacked tangential shear profile around Planck clusters at a significance higher than $4σ$.

astro-ph.CO↗

arXiv2020

Shear measurement bias I: dependencies on methods, simulation parameters and measured parameters

We present a study of the dependencies of shear bias on simulation (input) and measured (output) parameters, noise, point-spread function anisotropy, pixel size, and the model bias coming from two different and independent galaxy shape estimators. We used simulated images from Galsim based on the GREAT3 control-space-constant branch, and we measured shear bias from a model-fitting method (gFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We show the bias dependencies found on input and output parameters for both methods, and we identify the main dependencies and causes. Most of the results are consistent between the two estimators, an interesting result given the differences of the methods. We also find important dependences on orientation and morphology properties such as flux, size, and ellipticity. We show that noise and pixelization play an important role in the bias dependencies on the output properties and galaxy orientation. We show some examples of model bias that produce a bias dependence on the Sersic index n as well as a different shear bias between galaxies consisting of a single Sersic profile and galaxies with a disc and a bulge. We also see an important coupling between several properties on the bias dependences. Because of this, we need to study several measured properties simultaneously in order to properly understand the nature of shear bias. This paper serves as a first step towards a companion paper that describes a machine learning approach to modelling shear bias as a complex function of many observed properties.

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arXiv2020

Shear measurement bias II: a fast machine learning calibration method

We present a new shear calibration method based on machine learning. The method estimates the individual shear responses of the objects from the combination of several measured properties on the images using supervised learning. The supervised learning uses the true individual shear responses obtained from copies of the image simulations with different shear values. On simulated GREAT3data, we obtain a residual bias after the calibration compatible with 0 and beyond Euclid requirements for a signal-to-noise ratio > 20 within ~15 CPU hours of training using only ~10^5 objects. This efficient machine-learning approach can use a smaller data set because the method avoids the contribution from shape noise. The low dimensionality of the input data also leads to simple neural network architectures. We compare it to the recently described method Metacalibration, which shows similar performances. The different methods and systematics suggest that the two methods are very good complementary methods. Our method can therefore be applied without much effort to any survey such as Euclid or the Vera C. Rubin Observatory, with fewer than a million images to simulate to learn the calibration function.

astro-ph.CO↗

arXiv2018

A highly precise shear bias estimator independent of the measured shape noise

We present a new method to estimate shear measurement bias in image simulations that significantly improves the precision with respect to current techniques. Our method is based on measuring the shear response for individual images. We generated sheared versions of the same image to measure how the galaxy shape changes with the small applied shear. This shear response is the multiplicative shear bias for each image. In addition, we also measured the individual additive bias. Using the same noise realizations for each sheared version allows us to compute the shear response at very high precision. The estimated shear bias of a sample of galaxies is then the average of the individual measurements. The precision of this method leads to an improvement with respect to previous methods concerned with the precision of estimates of multiplicative bias since our method is not affected by noise from shape measurements, which until now has been the dominant uncertainty. As a consequence, the method does not require shape-noise suppression for a precise estimation of shear multiplicative bias. Our method can be readily used for numerous applications such as shear measurement validation and calibration, reducing the number of necessary simulated images by a few orders of magnitude to achieve the same precision.

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arXiv2017

Cosmic CARNage I: on the calibration of galaxy formation models

We present a comparison of nine galaxy formation models, eight semi-analytical and one halo occupation distribution model, run on the same underlying cold dark matter simulation (cosmological box of co-moving width 125$h^{-1}$ Mpc, with a dark-matter particle mass of $1.24\times 10^9 h^{-1}$ Msun) and the same merger trees. While their free parameters have been calibrated to the same observational data sets using two approaches, they nevertheless retain some 'memory' of any previous calibration that served as the starting point (especially for the manually-tuned models). For the first calibration, models reproduce the observed z = 0 galaxy stellar mass function (SMF) within 3-σ. The second calibration extended the observational data to include the z = 2 SMF alongside the z~0 star formation rate function, cold gas mass and the black hole-bulge mass relation. Encapsulating the observed evolution of the SMF from z = 2 to z = 0 is found to be very hard within the context of the physics currently included in the models. We finally use our calibrated models to study the evolution of the stellar-to-halo mass (SHM) ratio. For all models we find that the peak value of the SHM relation decreases with redshift. However, the trends seen for the evolution of the peak position as well as the mean scatter in the SHM relation are rather weak and strongly model dependent. Both the calibration data sets and model results are publicly available.

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arXiv2017

nIFTy Cosmology: the clustering consistency of galaxy formation models

We present a clustering comparison of 12 galaxy formation models (including Semi-Analytic Models (SAMs) and Halo Occupation Distribution (HOD) models) all run on halo catalogues and merger trees extracted from a single ΛCDM N-body simulation. We compare the results of the measurements of the mean halo occupation numbers, the radial distribution of galaxies in haloes and the 2-Point Correlation Functions (2PCF). We also study the implications of the different treatments of orphan (galaxies not assigned to any dark matter subhalo) and non-orphan galaxies in these measurements. Our main result is that the galaxy formation models generally agree in their clustering predictions but they disagree significantly between HOD and SAMs for the orphan satellites. Although there is a very good agreement between the models on the 2PCF of central galaxies, the scatter between the models when orphan satellites are included can be larger than a factor of 2 for scales smaller than 1 Mpc/h. We also show that galaxy formation models that do not include orphan satellite galaxies have a significantly lower 2PCF on small scales, consistent with previous studies. Finally, we show that the 2PCF of orphan satellites is remarkably different between SAMs and HOD models. Orphan satellites in SAMs present a higher clustering than in HOD models because they tend to occupy more massive haloes. We conclude that orphan satellites have an important role on galaxy clustering and they are the main cause of the differences in the clustering between HOD models and SAMs.

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arXiv2016

What determines large scale galaxy clustering: halo mass or local density?

Using dark matter simulations we show how halo bias is determined by local density and not by halo mass. This is not totally surprising, as according to the peak-background split model, local density is the property that constraints bias at large scales. Massive haloes have a high clustering because they reside in high density regions. Small haloes can be found in a wide range of environments which determine their clustering amplitudes differently. This contradicts the assumption of standard Halo Occupation Distribution (HOD) models that the bias and occupation of haloes is determined solely by their mass. We show that the bias of central galaxies from semi-analytic models of galaxy formation as a function of luminosity and colour is not correctly predicted by the standard HOD model. Using local density instead of halo mass the HOD model correctly predicts galaxy bias. These results indicate the need to include information about local density and not only mass in order to correctly apply HOD analysis in these galaxy samples. This new model can be readily applied to observations and has the advantage that the galaxy density can be directly observed, in contrast with the dark matter halo mass.

astro-ph.CO↗

arXiv2016

A new method to measure galaxy bias by combining the density and weak lensing fields

We present a new method to measure the redshift-dependent galaxy bias by combining information from the galaxy density field and the weak lensing field. This method is based on Amara et al. (2012), where they use the galaxy density field to construct a bias-weighted convergence field kg. The main difference between Amara et al. (2012) and our new implementation is that here we present another way to measure galaxy bias using tomography instead of bias parameterizations. The correlation between kg and the true lensing field k allows us to measure galaxy bias using different zero-lag correlations, such as / or / . Our method measures the linear bias factor on linear scales under the assumption of no stochasticity between galaxies and matter. We use the MICE simulation to measure the linear galaxy bias for a flux-limited sample (i < 22.5) in tomographic redshift bins using this method. This paper is the first that studies the accuracy and systematic uncertainties associated with the implementation of the method, and the regime where it is consistent with the linear galaxy bias defined by projected 2-point correlation functions (2PCF). We find that our method is consistent with linear bias at the percent level for scales larger than 30 arcmin, while nonlinearities appear at smaller scales. This measurement is a good complement to other measurements of bias, since it does not depend strongly on sigma8 as the 2PCF measurements. We apply this method to the Dark Energy Survey Science Verification data in a follow-up paper.

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arXiv2015

nIFTy Cosmology: Comparison of Galaxy Formation Models

We present a comparison of 14 galaxy formation models: 12 different semi-analytical models and 2 halo-occupation distribution models for galaxy formation based upon the same cosmological simulation and merger tree information derived from it. The participating codes have proven to be very successful in their own right but they have all been calibrated independently using various observational data sets, stellar models, and merger trees. In this paper we apply them without recalibration and this leads to a wide variety of predictions for the stellar mass function, specific star formation rates, stellar-to- halo mass ratios, and the abundance of orphan galaxies. The scatter is much larger than seen in previous comparison studies primarily because the codes have been used outside of their native environment within which they are well tested and calibrated. The purpose of the `nIFTy comparison of galaxy formation models' is to bring together as many different galaxy formation modellers as possible and to investigate a common approach to model calibration. This paper provides a unified description for all participating models and presents the initial, uncalibrated comparison as a baseline for our future studies where we will develop a common calibration framework and address the extent to which that reduces the scatter in the model predictions seen here.

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arXiv2014

Are the halo occupation predictions consistent with large scale galaxy clustering?

We study how well we can reconstruct the 2-point clustering of galaxies on linear scales, as a function of mass and luminosity, using the halo occupation distribution (HOD) in several semi-analytical models (SAMs) of galaxy formation from the Millennium Simulation. We find that HOD with Friends of Friends groups can reproduce galaxy clustering better than gravitationally bound haloes. This indicates that Friends of Friends groups are more directly related to the clustering of these regions than the bound particles of the overdensities. In general we find that the reconstruction works at best to 5% accuracy: it underestimates the bias for bright galaxies. This translates to an overestimation of 50% in the halo mass when we use clustering to calibrate mass. We also found a degeneracy on the mass prediction from the clustering amplitude that affects all the masses. This effect is due to the clustering dependence on the host halo substructure, an indication of assembly bias. We show that the clustering of haloes of a given mass increases with the number of subhaloes, a result that only depends on the underlying matter distribution. As the number of galaxies increases with the number of subhaloes in SAMs, this results in a low bias for the HOD reconstruction. We expect this effect to apply to other models of galaxy formation, including the real universe, as long as the number of galaxies incresases with the number of subhaloes. We have also found that the reconstructions of galaxy bias from the HOD model fails for low mass haloes with M = 3-5x10^11 Msun/h. We find that this is because galaxy clustering is more strongly affected by assembly bias for these low masses.

astro-ph.CO↗

arXiv2014

Subhaloes gone Notts: the clustering properties of subhaloes

We present a study of the substructure finder dependence of subhalo clustering in the Aquarius Simulation. We run 11 different subhalo finders on the haloes of the Aquarius Simulation and we study their differences in the density profile, mass fraction and 2-point correlation function of subhaloes in haloes. We also study the mass and vmax dependence of subhalo clustering. As the Aquarius Simulation has been run at different resolutions, we study the convergence with higher resolutions. We find that the agreement between finders is at around the 10% level inside R200 and at intermediate resolutions when a mass threshold is applied, and better than 5% when vmax is restricted instead of mass. However, some discrepancies appear in the highest resolution, underlined by an observed resolution dependence of subhalo clustering. This dependence is stronger for the smallest subhaloes, which are more clustered in the highest resolution, due to the detection of subhaloes within subhaloes (the sub-subhalo term). This effect modifies the mass dependence of clustering in the highest resolutions. We discuss implications of our results for models of subhalo clustering and their relation with galaxy clustering.

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