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Register a new userThe PAU Survey: Measurement of Narrow-band galaxy properties with Approximate Bayesian Computation
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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 CIGALE. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited ($mathrm{i}<22.5$) sample of galaxies spanning the redshift range $mathrm{0<z<1.0}$. 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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Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution spectra from narrow band photometry, provided by the PAUS (Physics of the Accelerating Universe) survey. We find that, with the photometric fluxes from the 40 narrow band filters and without including morphological information, it is possible to separate stars and galaxies to very high precision, 98.4% purity with a completeness of 98.8% for objects brighter than I = 22.5. This precision is obtained with a Convolutional Neural Network as a classification algorithm, applied to the objects spectra. We have also applied the method to the ALHAMBRA photometric survey and we provide an updated classification for its Gold sample.
We present a mock catalogue for the Physics of the Accelerating Universe Survey (PAUS) and use it to quantify the competitiveness of the narrow band imaging for measuring spectral features and galaxy clustering. The mock agrees with observed number count and redshift distribution data. We demonstrate the importance of including emission lines in the narrow band fluxes. We show that PAUCam has sufficient resolution to measure the strength of the 4000AA{} break to the nominal PAUS depth. We predict the evolution of a narrow band luminosity function and show how this can be affected by the OII emission line. We introduce new rest frame broad bands (UV and blue) that can be derived directly from the narrow band fluxes. We use these bands along with D4000 and redshift to define galaxy samples and provide predictions for galaxy clustering measurements. We show that systematic errors in the recovery of the projected clustering due to photometric redshift errors in PAUS are significantly smaller than the expected statistical errors. The galaxy clustering on two halo scales can be recovered quantatively without correction, and all qualitative trends seen in the one halo term are recovered. In this analysis mixing between samples reduces the expected contrast between the one halo clustering of red and blue galaxies and demonstrates the importance of a mock catalogue for interpreting galaxy clustering results. The mock catalogue is available on request at https://cosmohub.pic.es/home.
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 ($450rm{nm}-850rm{nm}$), the quality of photometric redshift estimation is $sigma_{z} sim 0.01(1 + z)$ 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 $zsim0.8$. 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 ($sim1-3sigma$), 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.
We study the consistency of the physical properties of galaxies retrieved from SED-fitting as a function of spectral resolution and signal-to-noise ratio (SNR). Using a selection of physically motivated star formation histories, we set up a control sample of mock galaxy spectra representing observations of the local universe in high-resolution spectroscopy, and in 56 narrow-band and 5 broad-band photometry. We fit the SEDs at these spectral resolutions and compute their corresponding the stellar mass, the mass- and luminosity-weighted age and metallicity, and the dust extinction. We study the biases, correlations, and degeneracies affecting the retrieved parameters and explore the r^ole of the spectral resolution and the SNR in regulating these degeneracies. We find that narrow-band photometry and spectroscopy yield similar trends in the physical properties derived, the former being considerably more precise. Using a galaxy sample from the SDSS, we compare more realistically the results obtained from high-resolution and narrow-band SEDs (synthesized from the same SDSS spectra) following the same spectral fitting procedures. We use results from the literature as a benchmark to our spectroscopic estimates and show that the prior PDFs, commonly adopted in parametric methods, may introduce biases not accounted for in a Bayesian framework. We conclude that narrow-band photometry yields the same trend in the age-metallicity relation in the literature, provided it is affected by the same biases as spectroscopy; albeit the precision achieved with the latter is generally twice as large as with the narrow-band, at SNR values typical of the different kinds of data.
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 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 artifacts, 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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