arXiv subjects

Pau Tallada-Crespí

Publications and source records attributed to Pau Tallada-Crespí.

13 recordsLinked to original sources

arXiv2025

Halo Lightcones with Optimised Orientation and Interpolation in Cosmological Simulations -- an application to mock H$α$ selected galaxies

A critical step to create realistic mock catalogs that support large-scale photometric and spectroscopic sky surveys is the production of cosmological simulations that accurately model the survey observables taking into account the redshift-dependent galaxy formation and evolution processes. Here we develop an efficient framework, HOLCon (Halo Optimised Lightcone Constructor), for post-facto construction of dark matter halo lightcones from simulations and use them to generate a mock galaxy catalogue. HOLCon includes a module to optimise the lightcone's orientation within the simulation box, minimising repeated structures when the survey volume exceeds a single box -- a common challenge in modern surveys. A linear interpolation scheme tracks the evolution of halo properties across snapshots. Applied to the publicly available Uchuu simulation, we construct a lightcone of 50 ${\rm deg}^2$ and extending up to $z = 10$, providing representative coverage of deep fields of Stage IV surveys. We validate the lightcone for cosmological applications by comparing the dark matter halo clustering in the lightcone with those from the original simulation snapshots. Subsequently, we make the galaxy-halo connection on the lightcone with a redshift extended version of the SciPIC algorithm producing a comprehensive set of descriptive galaxy attributes. HOLCon leverages Dask, a scalable parallel computing pythonic framework for fast construction of dark matter halo lightcones enabling rapid creation of multiple statistical realizations essential for robust cosmological inference. The produced galaxy mock makes predictions for clustering of H$α$ emitters, making it a useful cosmology resource.

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arXiv2024

ANNZ+: an enhanced photometric redshift estimation algorithm with applications on the PAU Survey

ANNZ is a fast and simple algorithm which utilises artificial neural networks (ANNs), it was known as one of the pioneers of machine learning approaches to photometric redshift estimation decades ago. We enhanced the algorithm by introducing new activation functions like tanh, softplus, SiLU, Mish and ReLU variants; its new performance is then vigorously tested on legacy samples like the Luminous Red Galaxy (LRG) and Stripe-82 samples from SDSS, as well as modern galaxy samples like the Physics of the Accelerating Universe Survey (PAUS). This work focuses on testing the robustness of activation functions with respect to the choice of ANN architectures, particularly on its depth and width, in the context of galaxy photometric redshift estimation. Our upgraded algorithm, which we named ANNZ+, shows that the tanh and Leaky ReLU activation functions provide more consistent and stable results across deeper and wider architectures with > 1 per cent improvement in root-mean-square error ($σ_{\textrm{RMS}}$) and 68th percentile error ($σ_{68}$) when tested on SDSS data sets. While assessing its capabilities in handling high dimensional inputs, we achieved an improvement of 11 per cent in $σ_{\textrm{RMS}}$ and 6 per cent in $σ_{68}$ with the tanh activation function when tested on the 40-narrowband PAUS dataset; it even outperformed ANNZ2, its supposed successor, by 44 per cent in $σ_{\textrm{RMS}}$. This justifies the effort to upgrade the 20-year-old ANNZ, allowing it to remain viable and competitive within the photo-z community today. The updated algorithm ANNZ+ is publicly available at https://github.com/imdadmpt/ANNzPlus.

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arXiv2024

The PAU Survey: The quasar UV and Ly$α$ luminosity functions at $2.7<z<5.3$

We present the Lyman-$α$ (Ly$α$) and ultraviolet (UV) luminosity function (LF), in bins of redshift, of quasars selected in the Physics of the Accelerating Universe Survey (PAUS). A sample of 915 objects was selected at $2.7 10^{44}$ ${\rm erg\,s}$$^{-1}$. In order to obtain corrections for the LF estimation, and assess the accuracy of our selection method, we produced mock catalogs of $0<z<4.3$ quasars and galaxies that mimic our target population and their main contaminants. Our results show a clear evolution of the Ly$α$ and UV LFs, with a declining tendency in the number density of quasars towards increasing redshifts. In addition, the faint-end power-law slope of the Ly$α$ LF becomes steeper with redshift, suggesting that the number density of Ly$α$-bright quasars declines faster than that of fainter emitters. By integrating the Ly$α$ LF we find that the total Ly$α$ emitted by bright quasars per unit volume rapidly declines with increasing redshift, being sub-dominant to that of star-forming galaxies by several orders of magnitude by $z\sim 4$. Finally, we stack the NB pseudo-spectra of a visually selected "golden sample" of 591 quasars to obtain photometric composite SEDs in bins of redshift, enabling to measure the mean IGM absorption by the Lyman-$α$ forest as a function of redshift.

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arXiv2024

The PAU Survey: galaxy stellar population properties estimates with narrowband data

Narrowband galaxy surveys have recently gained interest as a promising method to achieve the necessary accuracy on the photometric redshift estimate of individual galaxies for stage-IV cosmological surveys. One key advantage is the ability to provide higher spectral resolution information about galaxies that should allow a more accurate and precise estimation of galaxy stellar population properties. However, the impact of adding narrow-band photometry on the stellar population properties estimate is largely unexplored. The scope of this work is two-fold: on one side, leveraging the predictive power of broad-band and narrow-band data to infer galaxy physical properties such as stellar masses, ages, star formation rates and metallicities. On the other hand, evaluating the improvement of performance in estimating galaxy properties when we use narrow-band data instead of broad-band. In this work we measure the stellar population properties of a sample of galaxies in the COSMOS field for which both narrowband and broadband data are available. In particular, we employ narrowband data from PAUS and broad-band data from CFHTLS. We use two different spectral energy distribution fitting codes to measure galaxy properties, namely CIGALE and Prospector. We find that the increased spectral resolution of narrow-band photometry does not yield a substantial improvement on constraining galaxy properties using spectral energy distribution fitting. Still we find that we obtain a more diverse distribution of metallicities and dust optical depths with cigale when employing the narrowband data. The effect is not as prominent as expected, which we relate this to the low narrowband SNR of a majority of the galaxies, the respective drawbacks of both codes as well as the coverage only in the optical regime. The measured properties are afterwards compared to the COSMOS2020 catalogue, showing good agreement.

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arXiv2023

The PAU survey: Estimating galaxy photometry with deep learning

With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed Lumos for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artefacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10% to 2%, comparing to aperture photometry. Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with the Deepz machine learning photo-z code and the photo-z outlier rate by 20%. The photo-z improvement is lower than expected from the SNR increment, however currently the photometric calibration and outliers in the photometry seem to be its limiting factor.

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arXiv2022

Modeling Intrinsic Galaxy Alignment in the MICE Simulation

The intrinsic alignment (IA) of galaxies is potentially a major limitation in deriving cosmological constraints from weak lensing surveys. In order to investigate this effect we assign intrinsic shapes and orientations to galaxies in the light-cone output of the MICE simulation, spanning $\sim5000\,{\rm deg}^2$ and reaching redshift $z=1.4$. This assignment is based on a 'semi-analytic' IA model that uses photometric properties of galaxies as well as the spin and shape of their host halos. Advancing on previous work, we include more realistic distributions of galaxy shapes and a luminosity dependent galaxy-halo alignment. The IA model parameters are calibrated against COSMOS and BOSS LOWZ observations. The null detection of IA in observations of blue galaxies is accounted for by setting random orientations for these objects. We compare the two-point alignment statistics measured in the simulation against predictions from the analytical IA models NLA and TATT over a wide range of scales, redshifts and luminosities for red and blue galaxies separately. We find that both models fit the measurements well at scales above $8\,h^{-1}{\rm Mpc}$, while TATT outperforms NLA at smaller scales. The IA parameters derived from our fits are in broad agreement with various observational constraints from red galaxies. Lastly, we build a realistic source sample, mimicking DES Year 3 observations and use it to predict the IA contamination to the observed shear statistics. We find this prediction to be within the measurement uncertainty, which might be a consequence of the random alignment of blue galaxies in the simulation.

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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.

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arXiv2022

New catalogue of dark-matter halo properties identified in MICE-GC -- I. Analysis of density profile distributions

Constraints on dark matter halo masses from weak gravitational lensing can be improved significantly by using additional information about the morphology of their density distribution, leading to tighter cosmological constraints derived from the halo mass function. This work is the first of two in which we investigate the accuracy of halo morphology and mass measurements in 2D and 3D. To this end, we determine several halo physical properties in the MICE-Grand Challenge dark matter only simulation. We present a public catalogue of these properties that includes density profiles and shape parameters measured in 2D and 3D, the halo centre at the peak of the 3D density distribution as well as the gravitational and kinetic energies and angular momentum vectors. The density profiles are computed using spherical and ellipsoidal radial bins, taking into account the halo shapes. We also provide halo concentrations and masses derived from fits to 2D and 3D density profiles using NFW and Einasto models for halos with more than $1000$ particles ($\gtrsim 3 \times 10^{13} h^{-1} M_{\odot}$). We find that the Einasto model provides better fits compared to NFW, regardless of the halo relaxation state and shape. The mass and concentration parameters of the 3D density profiles derived from fits to the 2D profiles are in general biased. Similar biases are obtained when constraining mass and concentrations using a weak-lensing stacking analysis. We show that these biases depend on the radial range and density profile model adopted in the fitting procedure, but not on the halo shape.

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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.

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arXiv2021

The PAU Survey: narrowband photometric redshifts using Gaussian processes

We study the performance of the hybrid template-machine-learning photometric redshift (photo-$z$) algorithm Delight, which uses Gaussian processes, on a subset of the early data release of the Physics of the Accelerating Universe Survey (PAUS). We calibrate the fluxes of the $40$ PAUS narrow bands with $6$ broadband fluxes ($uBVriz$) in the COSMOS field using three different methods, including a new method which utilises the correlation between the apparent size and overall flux of the galaxy. We use a rich set of empirically derived galaxy spectral templates as guides to train the Gaussian process, and we show that our results are competitive with other standard photometric redshift algorithms. Delight achieves a photo-$z$ $68$th percentile error of $σ_{68}=0.0081(1+z)$ without any quality cut for galaxies with $i_\mathrm{auto}<22.5$ as compared to $0.0089(1+z)$ and $0.0202(1+z)$ for the BPz and ANNz2 codes, respectively. Delight is also shown to produce more accurate probability distribution functions for individual redshift estimates than BPz and ANNz2. Common photo-$z$ outliers of Delight and BCNz2 (previously applied to PAUS) are found to be primarily caused by outliers in the narrowband fluxes, with a small number of cases potentially indicating spectroscopic redshift failures in the reference sample. In the process, we introduce performance metrics derived from the results of BCNz2 and Delight, allowing us to achieve a photo-$z$ quality of $σ_{68}<0.0035(1+z)$ at a magnitude of $i_\mathrm{auto}<22.5$ while keeping $50$ per cent objects of the galaxy sample.

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arXiv2021

The PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies

We present the first measurements of the projected clustering and intrinsic alignments (IA) of galaxies observed by the Physics of the Accelerating Universe Survey (PAUS). With photometry in 40 narrow optical passbands ($450\rm{nm}-850\rm{nm}$), the quality of photometric redshift estimation is $σ_{z} \sim 0.01(1 + z)$ for galaxies in the $19\,\rm{deg}^{2}$ Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) W3 field, allowing us to measure the projected 3D clustering and IA for flux-limited, faint galaxies ($i < 22.5$) out to $z\sim0.8$. To measure two-point statistics, we developed, and tested with mock photometric redshift samples, `cloned' random galaxy catalogues which can reproduce data selection functions in 3D and account for photometric redshift errors. In our fiducial colour-split analysis, we made robust null detections of IA for blue galaxies and tentative detections of radial alignments for red galaxies ($\sim1-3σ$), over scales of $0.1-18\,h^{-1}\rm{Mpc}$. The galaxy clustering correlation functions in the PAUS samples are comparable to their counterparts in a spectroscopic population from the Galaxy and Mass Assembly survey, modulo the impact of photometric redshift uncertainty which tends to flatten the blue galaxy correlation function, whilst steepening that of red galaxies. We investigate the sensitivity of our correlation function measurements to choices in the random catalogue creation and the galaxy pair-binning along the line of sight, in preparation for an optimised analysis over the full PAUS area.

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arXiv2021

The PAU survey: Ly$α$ intensity mapping forecast

In this work, we explore the application of intensity mapping to detect extended Ly$α$ emission from the IGM via cross-correlation of PAUS images with Ly$α$ forest data from eBOSS and DESI. Seven narrow-band (FWHM=13nm) PAUS filters have been considered, ranging from 455 to 515 nm in steps of 10 nm, which allows the observation of Ly$α$ emission in a range $2.7<z<3.3$. The cross-correlation is simulated first in an area of 100 deg$^2$ (PAUS projected coverage), and second in two hypothetical scenarios: a deeper PAUS (complete up to $i_{\rm AB}<24$ instead of $i_{\rm AB}<23$, observation time x6), and an extended PAUS coverage of 225 deg$^2$ (observation time x2.25). A hydrodynamic simulation of size 400 Mpc/h is used to simulate both extended Ly$α$ emission and absorption, while the foregrounds in PAUS images have been simulated using a lightcone mock catalogue. Using an optimistic estimation of uncorrelated PAUS noise, the total probability of a non-spurious detection is estimated to be 1.8\% and 4.5\% for PAUS-eBOSS and PAUS-DESI , from a run of 1000 simulated cross-correlations with different realisations of instrumental noise and quasar positions. The hypothetical PAUS scenarios increase this probability to 15.3\% (deeper PAUS) and 9.0\% (extended PAUS). With realistic correlated noise directly measured from PAUS images, these probabilities become negligible. Despite these negative results, some evidences suggest that this methodology may be more suitable to broad-band surveys.

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arXiv2020

The PAU Survey: An improved photo-$z$ sample in the COSMOS field

We present -- and make publicly available -- accurate and precise photometric redshifts in the ACS footprint from the COSMOS field for objects with $i_{\mathrm{AB}}\leq 23$. The redshifts are computed using a combination of narrow band photometry from PAUS, a survey with 40 narrow bands spaced at $100Å$ intervals covering the range from $4500Å$ to $8500Å$, and 26 broad, intermediate, and narrow bands covering the UV, visible and near infrared spectrum from the COSMOS2015 catalogue. We introduce a new method that models the spectral energy distributions (SEDs) as a linear combination of continuum and emission line templates and computes its Bayes evidence, integrating over the linear combinations. The correlation between the UV luminosity and the OII line is measured using the 66 available bands with the zCOSMOS spectroscopic sample, and used as a prior which constrains the relative flux between continuum and emission line templates. The flux ratios between the OII line and $\mathrm{H}_α$, $\mathrm{H}_β$ and $\mathrm{OIII}$ are similarly measured and used to generate the emission line templates. Comparing to public spectroscopic surveys via the quantity $Δ_z\equiv(z_{\mathrm{photo}}-z_{\mathrm{spec}})/(1+z_{\mathrm{spec}})$, we find the photometric redshifts to be more precise than previous estimates, with $σ_{68}(Δ_z) \approx (0.003, 0.009)$ for galaxies at magnitude $i_{\mathrm{AB}}\sim18$ and $i_{\mathrm{AB}}\sim23$, respectively, which is $3\times$ and $1.66\times$ tighter than COSMOS2015. Additionally, we find the redshifts to be very accurate on average, yielding a median of the $Δ_z$ distribution compatible with $|\mathrm{median}(Δ_z)|\leq0.001$ at all redshifts and magnitudes considered. Both the added PAUS data and new methodology contribute significantly to the improved results.

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