arXiv subjects

Benjamin Joachimi

Publications and source records attributed to Benjamin Joachimi.

At least 55 records · Page 3Linked to original sources

arXiv2023

GLASS: Generator for Large Scale Structure

We present GLASS, the Generator for Large Scale Structure, a new code for the simulation of galaxy surveys for cosmology, which iteratively builds a light cone with matter, galaxies, and weak gravitational lensing signals as a sequence of nested shells. This allows us to create deep and realistic simulations of galaxy surveys at high angular resolution on standard computer hardware and with low resource consumption. GLASS also introduces a new technique to generate transformations of Gaussian random fields (including lognormal) to essentially arbitrary precision, an iterative line-of-sight integration over matter shells to obtain weak lensing fields, and flexible modelling of the galaxies sector. We demonstrate that GLASS readily produces simulated data sets with per cent-level accurate two-point statistics of galaxy clustering and weak lensing, thus enabling simulation-based validation and inference that is limited only by our current knowledge of the input matter and galaxy properties.

astro-ph.CO↗

arXiv2023

Optimising the shape of photometric redshift distributions with clustering cross-correlations

We present an optimisation method for the assignment of photometric galaxies into a chosen set of redshift bins. This is achieved by combining simulated annealing, an optimisation algorithm inspired by solid-state physics, with an unsupervised machine learning method, a self-organising map (SOM) of the observed colours of galaxies. Starting with a sample of galaxies that is divided into redshift bins based on a photometric redshift point estimate, the simulated annealing algorithm repeatedly reassigns SOM-selected subsamples of galaxies, which are close in colour, to alternative redshift bins. We optimise the clustering cross-correlation signal between photometric galaxies and a reference sample of galaxies with well-calibrated redshifts. Depending on the effect on the clustering signal, the reassignment is either accepted or rejected. By dynamically increasing the resolution of the SOM, the algorithm eventually converges to a solution that minimises the number of mismatched galaxies in each tomographic redshift bin and thus improves the compactness of their corresponding redshift distribution. This method is demonstrated on the synthetic LSST cosmoDC2 catalogue. We find a significant decrease in the fraction of catastrophic outliers in the redshift distribution in all tomographic bins, most notably in the highest redshift bin with a decrease in the outlier fraction from 57 per cent to 16 per cent.

astro-ph.CO↗

arXiv2023

Fast and realistic large-scale structure from machine-learning-augmented random field simulations

Producing thousands of simulations of the dark matter distribution in the Universe with increasing precision is a challenging but critical task to facilitate the exploitation of current and forthcoming cosmological surveys. Many inexpensive substitutes to full $N$-body simulations have been proposed, even though they often fail to reproduce the statistics of the smaller, non-linear scales. Among these alternatives, a common approximation is represented by the lognormal distribution, which comes with its own limitations as well, while being extremely fast to compute even for high-resolution density fields. In this work, we train a generative deep learning model, mainly made of convolutional layers, to transform projected lognormal dark matter density fields to more realistic dark matter maps, as obtained from full $N$-body simulations. We detail the procedure that we follow to generate highly correlated pairs of lognormal and simulated maps, which we use as our training data, exploiting the information of the Fourier phases. We demonstrate the performance of our model comparing various statistical tests with different field resolutions, redshifts and cosmological parameters, proving its robustness and explaining its current limitations. When evaluated on 100 test maps, the augmented lognormal random fields reproduce the power spectrum up to wavenumbers of $1 \ h \ \rm{Mpc}^{-1}$, and the bispectrum within 10%, and always within the error bars, of the fiducial target simulations. Finally, we describe how we plan to integrate our proposed model with existing tools to yield more accurate spherical random fields for weak lensing analysis.

astro-ph.CO↗

arXiv2023

KiDS-1000 Cosmology: Constraints from density split statistics

Context. Weak lensing and clustering statistics beyond two-point functions can capture non-Gaussian information about the matter density field, thereby improving the constraints on cosmological parameters relative to the mainstream methods based on correlation functions and power spectra. Aims. This paper presents a cosmological analysis of the fourth data release of the Kilo Degree Survey based on the density split statistics, which measures the mean shear profiles around regions classified according to foreground densities. The latter is constructed from a bright galaxy sample, which we further split into red and blue samples, allowing us to probe their respective connection to the underlying dark matter density. Methods. We use the state-of-the-art model of the density splitting statistics and validate its robustness against mock data infused with known systematic effects such as intrinsic galaxy alignment and baryonic feedback. Results. After marginalising over the photometric redshift uncertainty and the residual shear calibration bias, we measure for the full KiDS-bright sample a structure growth parameter of $S_8 = σ_8 \sqrt{Ω_\mathrm{m}/0.3} = 0.74^{+0.03}_{-0.02}$ that is competitive to and consistent with two-point cosmic shear results, a matter density of $Ω_\mathrm{m} = 0.28 \pm 0.02$, and a constant galaxy bias of $b = 1.32^{+0.12}_{-0.10}$.

astro-ph.CO↗

arXiv2022

Direct cosmological inference from three-dimensional correlations of the Lyman-$α$ forest

When performing cosmological inference, standard analyses of the Lyman-$α$ (Ly$α$) three-dimensional correlation functions only consider the information carried by the distinct peak produced by baryon acoustic oscillations (BAO). In this work, we address whether this compression is sufficient to capture all the relevant cosmological information carried by these functions. We do this by performing a direct fit to the full shape, including all physical scales without compression, of synthetic Ly$α$ auto-correlation functions and cross-correlations with quasars at effective redshift $z_{\rm{eff}}=2.3$, assuming a DESI-like survey, and providing a comparison to the classic method applied to the same dataset. Our approach leads to a $3.5\%$ constraint on the matter density $Ω_{\rm{M}}$, which is about three to four times better than what BAO alone can probe. The growth term $f σ_{8} (z_{\rm{eff}})$ is constrained to the $10\%$ level, and the spectral index $n_{\rm{s}}$ to $\sim 3-4\%$. We demonstrate that the extra information resulting from our `direct fit' approach, except for the $n_{\rm{s}}$ constraint, can be traced back to the Alcock-Paczyński effect and redshift space distortion information.

astro-ph.CO↗

arXiv2022

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 $\textit{any}$ 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 $10^4$ 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.

physics.geo-ph↗

arXiv2022

Rubin-Euclid Derived Data Products: Initial Recommendations

This report is the result of a joint discussion between the Rubin and Euclid scientific communities. The work presented in this report was focused on designing and recommending an initial set of Derived Data products (DDPs) that could realize the science goals enabled by joint processing. All interested Rubin and Euclid data rights holders were invited to contribute via an online discussion forum and a series of virtual meetings. Strong interest in enhancing science with joint DDPs emerged from across a wide range of astrophysical domains: Solar System, the Galaxy, the Local Volume, from the nearby to the primaeval Universe, and cosmology.

astro-ph.IM↗

arXiv2022

Three-point intrinsic alignments of dark matter halos in the IllustrisTNG simulation

We use the IllustrisTNG suite of cosmological simulations to measure intrinsic alignment (IA) bispectra of dark matter subhalos between redshifts 0 and 1. We decompose the intrinsic shear field into E- and B-modes and find that the bispectra $B_{δδ\mathrm{E}}$ and $B_{δ\mathrm{EE}}$, between the matter overdensity field, $δ$, and the E-mode field, are detected with high significance. We also model the IA bispectra analytically using a method consistent with the two-point non-linear alignment model. We use this model and the simulation measurements to infer the intrinsic alignment amplitude $A_\mathrm{IA}$ and find that values of $A_\mathrm{IA}$ obtained from IA power spectra and bispectra agree well at scales up to $k_\mathrm{max}= 2 \, h \mathrm{Mpc}^{-1}$, for example at $z=1$ $A_\mathrm{IA} = 2.13 \pm$ 0.02 from the cross power spectrum between the matter overdensity and E-mode fields and $A_\mathrm{IA} =2.11 \pm$ 0.03 from $B_{δδ\mathrm{E}}$. This demonstrates that a single physically motivated model can jointly model two-point and three-point statistics of intrinsic alignments, thus enabling a cleaner separation between intrinsic alignments and cosmological weak lensing signals.

astro-ph.CO↗

arXiv2022

The PAU Survey: Measurements of the 4000 Å spectral break with narrow-band photometry

The D4000 spectral break index is one of the most important features in the visible spectrum, as it is a proxy for stellar ages and is also used in galaxy classification. However, its direct measurement has always been reserved to spectroscopy. Here, we present a general method to directly measure the D4000 with narrow-band (NB) photometry; it has been validated using realistic simulations, and then evaluated with PAUS NBs, cross-matched with VIPERS spectra ($i_{\rm AB} < 22.5$, $0.562 < z < 0.967$). We also reconstruct the D4000 with the SED-fitting code CIGALE; the use of PAUS NBs instead of broad bands significantly improves the SED fitting results. For D4000$_{\rm n}$, the direct measurement has $\rm \langle SNR \rangle \sim 4$, but we find that for $i_{\rm AB}<21$ all direct D4000 measurements have $\rm SNR>3$. The CIGALE D4000$_{\rm n}$ has $\rm \langle SNR \rangle \sim 20$, but underestimates the error by $>$50\%. Furthermore, the direct method recreates well the D4000-SFR relation, as well as the D4000-mass relation for blue galaxies (for red galaxies, selection effects impact the results). On the other hand, CIGALE accurately classifies galaxies into red and blue populations. We conclude that the direct measurement of D4000 with narrow-band photometry is a promising tool to determine average properties of galaxy samples, with results compatible with spectroscopy.

astro-ph.GA↗

arXiv2022

Forecasting the potential of weak lensing magnification to enhance LSST large-scale structure analyses

Recent works have shown that weak lensing magnification must be included in upcoming large-scale structure analyses, such as for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), to avoid biasing the cosmological results. In this work we investigate whether including magnification has a positive impact on the precision of the cosmological constraints, as well as being necessary to avoid bias. We forecast this using an LSST mock catalog and a halo model to calculate the galaxy power spectra. We find that including magnification has little effect on the precision of the cosmological parameter constraints for an LSST galaxy clustering analysis, where the halo model parameters are additionally constrained by the galaxy luminosity function. In particular, we find that for the LSST gold sample ($i < 25.3$) including weak lensing magnification only improves the galaxy clustering constraint on $Ω_{\rm{m}}$ by a factor of 1.03, and when using a very deep LSST mock sample ($i<26.5$) by a factor of 1.3. Since magnification predominantly contributes to the clustering measurement and provides similar information to that of cosmic shear, this improvement would be reduced for a combined galaxy clustering and shear analysis. We also confirm that not modelling weak lensing magnification will catastrophically bias the cosmological results from LSST. Magnification must therefore be included in LSST large-scale structure analyses even though it does not significantly enhance the precision of the cosmological constraints.

astro-ph.CO↗

arXiv2022

Explaining deep learning of galaxy morphology with saliency mapping

We successfully demonstrate the use of explainable artificial intelligence (XAI) techniques on astronomical datasets in the context of measuring galactic bar lengths. The method consists of training convolutional neural networks on human classified data from Galaxy Zoo in order to predict general galaxy morphologies, and then using SmoothGrad (a saliency mapping technique) to extract the bar for measurement by a bespoke algorithm. We contrast this to another method of using a convolutional neural network to directly predict galaxy bar lengths. These methods achieved correlation coefficients of 0.76 and 0.59, and root mean squared errors of 1.69 and 2.10 respective to human measurements. We conclude that XAI methods outperform conventional deep learning in this case, which could be reasonably explained by the larger datasets available when training the models. We suggest that our XAI method can be used to extract other galactic features (such as the bulge-to-disk ratio) without needing to collect new datasets or train new models. We also suggest that these techniques can be used to refine deep learning models as well as identify and eliminate bias within training datasets.

astro-ph.IM↗

arXiv2022

Joint constraints on cosmology and the impact of baryon feedback: combining KiDS-1000 lensing with the thermal Sunyaev-Zeldovich effect from Planck and ACT

We conduct a pseudo-$C_\ell$ analysis of the tomographic cross-correlation between 1000 deg$^2$ of weak lensing data from the Kilo-Degree Survey (KiDS-1000) and the thermal Sunyaev-Zeldovich (tSZ) effect measured by Planck and the Atacama Cosmology Telescope (ACT). Using HMx, a halo-model-based approach that consistently models the gas, star, and dark matter components, we are able to derive constraints on both cosmology and baryon feedback for the first time from these data, marginalising over redshift uncertainties, intrinsic alignment of galaxies, and contamination by the cosmic infrared background (CIB). We find our results to be insensitive to the CIB, while intrinsic alignment provides a small but significant contribution to the lensing--tSZ cross-correlation. The cosmological constraints are consistent with those of other low-redshift probes and prefer strong baryon feedback. The inferred amplitude of the lensing--tSZ cross-correlation signal, which scales as $σ_8(Ω_\mathrm{m}/0.3)^{0.2}$, is low by $\sim 2\,σ$ compared to the primary cosmic microwave background constraints by Planck. The lensing--tSZ measurements are then combined with pseudo-$C_\ell$ measurements of KiDS-1000 cosmic shear into a novel joint analysis, accounting for the full cross-covariance between the probes, providing tight cosmological constraints by breaking parameter degeneracies inherent to both probes. The joint analysis gives an improvement of 40% on the constraint of $S_8=σ_8\sqrt{Ω_\mathrm{m}/0.3}$ over cosmic shear alone, while providing constraints on baryon feedback consistent with hydrodynamical simulations, demonstrating the potential of such joint analyses with baryonic tracers such as the tSZ effect. We discuss remaining modelling challenges that need to be addressed if these baryonic probes are to be included in future precision-cosmology analyses.

astro-ph.CO↗

arXiv2021

The PAU Survey: Measurement of Narrow-band galaxy properties with Approximate Bayesian Computation

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 \textsc{CIGALE}. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited ($\mathrm{i}<22.5$) sample of galaxies up to redshift $\mathrm{z \sim 0.8}$. 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.

astro-ph.GA↗

arXiv2021

Geometry vs growth: Internal consistency of the flat ΛCDM model with KiDS-1000

We carry out a multi-probe self-consistency test of the flat $Λ$CDM model with the aim of exploring potential causes of the reported tensions between high- and low-redshift cosmological observations. We divide the model into two theory regimes determined by the smooth background (geometry) and the evolution of matter density fluctuations (growth), each governed by an independent set of Lambda Cold Dark Matter ($Λ$CDM) cosmological parameters. This extended model is constrained by a combination of weak gravitational lensing measurements from the Kilo-Degree Survey, galaxy clustering signatures extracted from Sloan Digital Sky Survey campaigns and the Six-Degree Field Galaxy Survey, and the angular baryon acoustic scale and the primordial scalar fluctuation power spectrum measured in $\textit{Planck}$ cosmic microwave background (CMB) data. We find strong consistency between the geometry and growth parameters, and with the posterior of standard $Λ$CDM analysis. Tension in the amplitude of matter density fluctuations as measured by the parameter $S_8$ persists at around 3$σ$, with a $1.5\,\%$ constraint of $S_8 = 0.776_{-0.008}^{+0.016}$ for the combined probes. We also observe a less significant preference (at least $2σ$) for higher values of the Hubble constant, $H_0 = 70.5^{+0.7}_{-1.5}\,{\rm km\, s^{-1} Mpc^{-1}}$, as well as for lower values of the total matter density parameter $Ω_{\rm{m}} = 0.289^{+0.007}_{-0.005}$ compared to the full $\textit{Planck}$ analysis. Including the subset of the CMB information in the probe combination enhances these differences rather than alleviate them, which we link to the discrepancy between low and high multipoles in $\textit{Planck}$ data.

astro-ph.CO↗

arXiv2021

KiDS-1000: Constraints on the intrinsic alignment of luminous red galaxies

We constrain the luminosity and redshift dependence of the intrinsic alignment (IA) of a nearly volume-limited sample of luminous red galaxies selected from the fourth public data release of the Kilo-Degree Survey (KiDS-1000). To measure the shapes of the galaxies, we used two complementary algorithms, finding consistent IA measurements for the overlapping galaxy sample. The global significance of IA detection across our two independent luminous red galaxy samples, with our favoured method of shape estimation, is $\sim10.7σ$. We find no significant dependence with redshift of the IA signal in the range $0.2<z<0.8$, nor a dependence with luminosity below $L_r\lesssim 2.9 \times 10^{10} h^{-2} L_{r,\odot}$. Above this luminosity, however, we find that the IA signal increases as a power law, although our results are also compatible with linear growth within the current uncertainties. This behaviour motivates the use of a broken power law model when accounting for the luminosity dependence of IA contamination in cosmic shear studies.

astro-ph.CO↗

arXiv2021

Cosmology beyond BAO from the 3D distribution of the Lyman-$α$ forest

We propose a new method for fitting the full-shape of the Lyman-$α$ (Ly$α$) forest three-dimensional (3D) correlation function in order to measure the Alcock-Paczynski (AP) effect. Our method preserves the robustness of baryon acoustic oscillations (BAO) analyses, while also providing extra cosmological information from a broader range of scales. We compute idealized forecasts for the Dark Energy Spectroscopic Instrument (DESI) using the Ly$α$ auto-correlation and its cross-correlation with quasars, and show how this type of analysis improves cosmological constraints. The DESI Ly$α$ BAO analysis is expected to measure $H(z_\mathrm{eff})r_\mathrm{d}$ and $D_\mathrm{M}(z_\mathrm{eff})/r_\mathrm{d}$ with a precision of $\sim0.9\%$ each, where $H$ is the Hubble parameter, $r_\mathrm{d}$ is the comoving BAO scale, $D_\mathrm{M}$ is the comoving angular diameter distance and the effective redshift of the measurement is $z_\mathrm{eff}\simeq2.3$. By fitting the AP parameter from the full shape of the two correlations, we show that we can obtain a precision of $\sim0.5-0.6\%$ on each of $H(z_\mathrm{eff})r_\mathrm{d}$ and $D_\mathrm{M}(z_\mathrm{eff})/r_\mathrm{d}$. Furthermore, we show that a joint full-shape analysis of the Ly$α$ auto-correlation and its cross-correlation with quasars can measure the linear growth rate times the amplitude of matter fluctuations in spheres of $8\;h^{-1}$Mpc, $fσ_8(z_\mathrm{eff})$. Such an analysis could provide the first ever measurement of $fσ_8(z_\mathrm{eff})$ at redshift $z_\mathrm{eff}>2$. By combining this with the quasar auto-correlation in a joint analysis of the three high-redshift two-point correlation functions, we show that DESI could be able to measure $fσ_8(z_\mathrm{eff}\simeq2.3)$ with a precision of $5-12\%$, depending on the smallest scale fitted.

astro-ph.CO↗

arXiv2021

Characterising Improvements in Photometric Redshift Probability Density Functions with Galaxy Morphology

In this work, we studied the impact of galaxy morphology on photometric redshift (photo-$z$) probability density functions (PDFs). By including galaxy morphological parameters like the radius, axis-ratio, surface brightness and the Sérsic index in addition to the $ugriz$ broadbands as input parameters, we used the machine learning photo-$z$ algorithm ANNz2 to train and test on galaxies from the Canada-France-Hawaii Telescope Stripe-82 (CS82) Survey. Metrics like the continuous ranked probability score (CRPS), probability integral transform (PIT), Bayesian odds parameter, and even the width and height of the PDFs were evaluated, and the results were compared when different number of input parameters were used during the training process. We find improvements in the CRPS and width of the PDFs when galaxy morphology has been added to the training, and the improvement is larger especially when the number of broadband magnitudes are lacking.

astro-ph.CO↗

arXiv2021

Magnification bias in galaxy surveys with complex sample selection functions

Gravitational lensing magnification modifies the observed spatial distribution of galaxies and can severely bias cosmological probes of large-scale structure if not accurately modelled. Standard approaches to modelling this magnification bias may not be applicable in practice as many galaxy samples have complex, often implicit, selection functions. We propose and test a procedure to quantify the magnification bias induced in clustering and galaxy-galaxy lensing (GGL) signals in galaxy samples subject to a selection function beyond a simple flux limit. The method employs realistic mock data to calibrate an effective luminosity function slope, $α_{\rm{obs}}$, from observed galaxy counts, which can then be used with the standard formalism. We demonstrate this method for two galaxy samples derived from the Baryon Oscillation Spectroscopic Survey (BOSS) in the redshift ranges $0.2 < z \leq 0.5$ and $0.5 < z \leq 0.75$, complemented by mock data built from the MICE2 simulation. We obtain $α_{\rm{obs}} = 1.93 \pm 0.05$ and $α_{\rm{obs}} = 2.62 \pm 0.28$ for the two BOSS samples. For BOSS-like lenses, we forecast a contribution of the magnification bias to the GGL signal between the multipole moments, $\ell$, of 100 and 4600 with a cumulative signal-to-noise ratio between 0.1 and 1.1 for sources from the Kilo-Degree Survey (KiDS), between 0.4 and 2.0 for sources from the Hyper Suprime-Cam survey (HSC), and between 0.3 and 2.8 for ESA Euclid-like source samples. These contributions are significant enough to require explicit modelling in future analyses of these and similar surveys. Our code is publicly available within the \textsc{MagBEt} module (\url{https://github.com/mwiet/MAGBET}).

astro-ph.CO↗