arXiv subjects

Raul E. Angulo

Publications and source records attributed to Raul E. Angulo.

At least 55 records · Page 3Linked to original sources

arXiv2022

The MillenniumTNG Project: Inferring cosmology from galaxy clustering with accelerated N-body scaling and subhalo abundance matching

We introduce a novel technique for constraining cosmological parameters and galaxy assembly bias using non-linear redshift-space clustering of galaxies. We scale cosmological N-body simulations and insert galaxies with the SubHalo Abundance Matching extended (SHAMe) empirical model to generate over 175,000 clustering measurements spanning all relevant cosmological and SHAMe parameter values. We then build an emulator capable of reproducing the projected galaxy correlation function at the monopole, quadrupole and hexadecapole level for separations between $0.1\,h^{-1}{\rm Mpc}$ and $25\,h^{-1}{\rm Mpc}$. We test this approach by using the emulator and Monte Carlo Markov Chain (MCMC) inference to jointly estimate cosmology and assembly bias parameters both for the MTNG740 hydrodynamic simulation and for a semi-analytical galaxy formation model (SAM) built on the MTNG740-DM dark matter-only simulation, obtaining unbiased results for all cosmological parameters. For instance, for MTNG740 and a galaxy number density of $n\sim 0.01 h^{3}{\rm Mpc}^{-3}$, we obtain $σ_{8}=0.799^{+0.039}_{-0.044}$ ($σ_{8,{\rm MTNG}} =$ 0.8159), and $Ω_\mathrm{M}h^2= 0.138^{+ 0.025}_{- 0.018}$ ($Ω_{\mathrm{M}} h^2_{\rm MTNG} =$ 0.142). For fixed Hubble parameter ($h$), the constraint becomes $Ω_\mathrm{M}h^2= 0.137^{+ 0.011}_{- 0.012}$. Our method performs similarly well for the SAM and for other tested sample densities. We almost always recover the true amount of galaxy assembly bias within one sigma. The best constraints are obtained when scales smaller than $2\,h^{-1}{\rm Mpc}$ are included, as well as when at least the projected correlation function and the monopole are incorporated. These methods offer a powerful way to constrain cosmological parameters using galaxy surveys.

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arXiv2022

Suppressing variance in 21-cm signal simulations during reionization

Current best limits on the 21-cm signal during reionization are provided at large scales ($\gtrsim$100 Mpc). To model these scales, enormous simulation volumes are required which are computationally expensive. We find that the primary source of uncertainty at these large scales is sample variance, which decides the minimum size of simulations required to analyse current and upcoming observations. In large-scale structure simulations, the method of `fixing' the initial conditions (ICs) to exactly follow the initial power spectrum and `pairing' two simulations with exactly out-of-phase ICs has been shown to significantly reduce sample variance. Here we apply this `fixing and pairing' (F\&P) approach to reionization simulations whose clustering signal originates from both density fluctuations and reionization bubbles. Using a semi-numerical code, we show that with the traditional method, simulation boxes of $L\simeq 500$ (300) Mpc are required to model the large-scale clustering signal at $k$=0.1 Mpc$^{-1}$ with a precision of 5 (10) per cent. Using F\&P, the simulation boxes can be reduced by a factor of 2 to obtain the same precision level. We conclude that the computing costs can be reduced by at least a factor of 4 when using the F\&P approach.

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arXiv2022

Statistics of biased tracers in variance-suppressed simulations

Cosmological simulations play an increasingly important role in analysing the observed large-scale structure of the Universe. Recently, they have been particularly important in building hybrid models that combine a perturbative bias expansion with displacement fields extracted from N-body simulations to describe the clustering of biased tracers. Here, we show that simulations that employ a technique referred to as "Fixing-and-pairing" (F&P) can dramatically improve the statistical precision of such hybrid models. Specifically, by numerical and analytic means, we show that F&P simulations provide unbiased estimates for all statistics employed by hybrid models while reducing, by up to two orders of magnitude, their uncertainty on large scales. This roughly implies that an EUCLID-like survey could be analysed using simulations of 2Gpc a side -- a 20% of the survey volume. Our work establishes the robustness of F&P for current hybrid theoretical models for galaxy clustering, an important step towards achieving an optimal exploitation of large-scale structure measurements.

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arXiv2022

The Bacco Simulation Project: Bacco Hybrid Lagrangian Bias Expansion Model in Redshift Space

We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a 2nd-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological $N$-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an 8-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to nonlinear scales ($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the Universe.

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arXiv2022

Shedding light on low-mass subhalo survival and annihilation luminosity with numerical simulations

In this work, we carry out a suite of specially-designed numerical simulations to shed further light on dark matter (DM) subhalo survival at mass scales relevant for gamma-ray DM searches, a topic subject to intense debate nowadays. Specifically, we have developed and employed an improved version of DASH, a GPU $N$-body code, to study the evolution of low-mass subhaloes inside a Milky Way-like halo with unprecedented accuracy, reaching solar-mass and sub-parsec resolution in our simulations. We simulate subhaloes with varying mass, concentration, and orbital properties, and consider the effect of the gravitational potential of the Milky Way galaxy itself. More specifically, we analyze the evolution of both the bound mass fraction and annihilation luminosity of subhaloes, finding that most subhaloes survive until present time, even though in some cases they lose more than 99% of their mass at accretion. Baryons in the host induce a much more severe mass loss, especially when the subhalo orbit is more parallel to the galactic disk. Many of these subhaloes cross the solar galactocentric radius, thus making it easier to detect their annihilation fluxes from Earth. We find subhaloes orbiting a DM-only halo with a pericentre in the solar vicinity to lose 70-90% of their initial annihilation luminosity at redshift zero, which increases up to 99% when baryons are also included in the host. We find a strong relation between subhalo's mass loss and the effective tidal field at pericentre. Indeed, much of the dependence on concentration, orbital parameters, host potential and baryonic components can be explained through this single parameter. In addition to shedding light on the survival of low-mass galactic subhaloes, our results can provide detailed predictions that will aid current and future quests for the nature of DM.

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arXiv2022

J-PLUS: A catalogue of globular cluster candidates around the M81/M82/NGC3077 triplet of galaxies

Globular clusters (GCs) are proxies of the formation assemblies of their host galaxies. However, few studies exist targeting GC systems of spiral galaxies up to several effective radii. Through 12-band Javalambre Photometric Local Universe Survey (J-PLUS) imaging, we study the point sources around the M81/M82/NGC3077 triplet in search of new GC candidates. We develop a tailored classification scheme to search for GC candidates based on their similarity to known GCs via a principal components analysis (PCA) projection. Our method accounts for missing data and photometric errors. We report 642 new GC candidates in a region of 3.5 deg$^2$ around the triplet, ranked according to their Gaia astrometric proper motions when available. We find tantalising evidence for an overdensity of GC candidate sources forming a bridge connecting M81 and M82. Finally, the spatial distribution of the GC candidates $(g-i)$ colours is consistent with halo/intra-cluster GCs, i.e. it gets bluer as they get further from the closest galaxy in the field. We further employ a regression-tree based model to estimate the metallicity distribution of the GC candidates based on their J-PLUS bands. The metallicity distribution of the sample candidates is broad and displays a bump towards the metal-rich end. Our list increases the population of GC candidates around the triplet by 3-fold, stresses the usefulness of multi-band surveys in finding these objects, and provides a testbed for further studies analysing their spatial distribution around nearby (spirals) galaxies.

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arXiv2022

Priors on Lagrangian bias parameters from galaxy formation modelling

We study the relations among the parameters of the hybrid Lagrangian bias expansion model, fitting biased auto and cross power spectra up to $k_{\rm max} = 0.7 \, h \, \mathrm{Mpc}^{-1}$. We consider $\sim 8000$ halo and galaxy samples, with different halo masses, redshifts, galaxy number densities, and varying the parameters of the galaxy formation model. Galaxy samples are obtained through state-of-the-art extended subhalo abundance matching techniques and include both stellar-mass and star-formation-rate selected galaxies. All of these synthetic galaxies samples are publicly available at https://bacco.dipc.org/galpk.html. We find that the hybrid Lagrangian bias model provides accurate fits to all of our halo and galaxy samples. The coevolution relations between galaxy bias parameters, although roughly compatible with those obtained for haloes, show systematic shifts and larger scatter. We explore possible sources of this difference in terms of dependence on halo occupation and assembly bias of each sample. The bias parameter relations displayed in this work can be used as a prior for future Bayesian analyses employing the hybrid Lagrangian bias expansion model.

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arXiv2022

Accelerating Large-Scale-Structure data analyses by emulating Boltzmann solvers and Lagrangian Perturbation Theory

The linear matter power spectrum is an essential ingredient in all theoretical models for interpreting large-scale-structure observables. Although Boltzmann codes such as CLASS or CAMB are very efficient at computing the linear spectrum, the analysis of data usually requires $10^4$-$10^6$ evaluations, which means this task can be the most computationally expensive aspect of data analysis. Here, we address this problem by building a neural network emulator that provides the linear theory (total and cold) matter power spectrum in about one millisecond with 0.2% (0.5%) accuracy over redshifts $z \le 3$ ($z \le 9$), and scales $10^{-4} \le k \, [h {\rm Mpc^{-1}}] < 50$. We train this emulator with more than 200,000 measurements, spanning a broad cosmological parameter space that includes massive neutrinos and dynamical dark energy. We show that the parameter range and accuracy of our emulator is enough to get unbiased cosmological constraints in the analysis of a Euclid-like weak lensing survey. Complementing this emulator, we train 15 other emulators for the cross-spectra of various linear fields in Eulerian space, as predicted by 2nd-order Lagrangian Perturbation theory, which can be used to accelerate perturbative bias descriptions of galaxy clustering. Our emulators are specially designed to be used in combination with emulators for the nonlinear matter power spectrum and for baryonic effects, all of which are publicly available at http://www.dipc.org/bacco.

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arXiv2021

J-PLUS: Stellar Parameters, C, N, Mg, Ca and [α/Fe] Abundances for Two Million Stars from DR1

Context. The Javalambre Photometric Local Universe Survey (J-PLUS) has obtained precise photometry in twelve specially designed filters for large numbers of Galactic stars. Deriving their precise stellar atmospheric parameters and individual elemental abundances is crucial for studies of Galactic structure, and the assembly history and chemical evolution of our Galaxy. Aims. Our goal is to estimate not only stellar parameters (effective temperature, Teff, surface gravity, log g, and metallicity, [Fe/H]), but also [α/Fe] and four elemental abundances ([C/Fe], [N/Fe], [Mg/Fe], and [Ca/Fe]) using data from J-PLUS DR1. Methods. By combining recalibrated photometric data from J-PLUS DR1, Gaia DR2, and spectroscopic labels from LAMOST, we design and train a set of cost-sensitive neural networks, the CSNet, to learn the non-linear mapping from stellar colors to their labels. Results. We have achieved precisions of δTeff {\sim}55K, δlogg{\sim}0.15dex, and δ[Fe/H]{\sim}0.07dex, respectively, over a wide range of temperature, surface gravity, and metallicity. The uncertainties of the abundance estimates for [α/Fe] and the four individual elements are in the range 0.04-0.08 dex. We compare our parameter and abundance estimates with those from other spectroscopic catalogs such as APOGEE and GALAH, and find an overall good agreement. Conclusions. Our results demonstrate the potential of well-designed, high-quality photometric data for determinations of stellar parameters as well as individual elemental abundances. Applying the method to J-PLUS DR1, we have obtained the aforementioned parameters for about two million stars, providing an outstanding data set for chemo-dynamic analyses of the Milky Way. The catalog of the estimated parameters is publicly accessible.

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arXiv2021

Large-scale dark matter simulations

We review the field of collisionless numerical simulations for the large-scale structure of the Universe. We start by providing the main set of equations solved by these simulations and their connection with General Relativity. We then recap the relevant numerical approaches: discretization of the phase-space distribution (focusing on N-body but including alternatives, e.g., Lagrangian submanifold and Schrödinger-Poisson) and the respective techniques for their time evolution and force calculation (Direct summation, mesh techniques, and hierarchical tree methods). We pay attention to the creation of initial conditions and the connection with Lagrangian Perturbation Theory. We then discuss the possible alternatives in terms of the micro-physical properties of dark matter (e.g., neutralinos, warm dark matter, QCD axions, Bose-Einstein condensates, and primordial black holes), and extensions to account for multiple fluids (baryons and neutrinos), primordial non-Gaussianity and modified gravity. We continue by discussing challenges involved in achieving highly accurate predictions. A key aspect of cosmological simulations is the connection to cosmological observables, we discuss various techniques in this regard: structure finding, galaxy formation and baryonic modelling, the creation of emulators and light-cones, and the role of machine learning. We finalise with a recount of state-of-the-art large-scale simulations and conclude with an outlook for the next decade.

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arXiv2021

Simulating the complexity of the dark matter sheet II: halo and subhalo mass functions for non-cold dark matter models

We present "sheet+release" simulations that reliably follow the evolution of dark matter structure at and below the dark matter free-streaming scale, where instabilities in traditional N-body simulations create a large population of spurious artificial haloes. Our simulations sample a large range of power-spectrum cutoff functions, parameterized through the half-mode scale $k_{\rm{hm}}$ and a slope parameter $β$. This parameter space can represent many non-cold dark matter models, including thermal relic warm dark matter, sterile-neutrinos, fuzzy dark matter, and a significant fraction of ETHOS models. Combining these simulations with additional N-body simulations, we find the following results. (1) Even after eliminating spurious haloes, the halo mass function in the strongly suppressed regime ($n_{\rm{X}}/n_{\rm{CDM}} < 5\%$) remains uncertain because it depends strongly on the definition of a halo. At these mass scales traditional halo finders primarily identify overdensities that are unbound, highly elongated, dominated by tidal fields, or far from virialized. (2) The regime where the suppression is smaller than a factor of 20 is quite robust to these uncertainties, however, and can be inferred reliably from suitable N-body simulations. (3) Parameterizing the suppression in the halo- and subhalo mass functions through the scales where the suppression reaches $20\%$, $50\%$ and $80\%$, we provide simple formulae which enable predictions for many non-cold dark matter models. (4) The halo mass-concentration relations in our sheet+release simulations agree well with previous results based on N-body simulations. (5) In general, we confirm the validity of previous N-body studies of warm dark matter models, largely eliminating concerns about the effects of artificial haloes.

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arXiv2021

The Boosted Potential

The global gravitational potential, $ϕ$, is not commonly employed in the analysis of cosmological simulations, since its level sets do not show any clear correspondence to the underlying density field and its persistent structures. Here, we show that the potential becomes a locally meaningful quantity when considered from a boosted frame of reference, defined by subtracting a uniform gradient term $ϕ_{\rm{boost}}(\boldsymbol{x}) = ϕ(\boldsymbol{x}) + \boldsymbol{x} \cdot \boldsymbol{a}_0$ with acceleration $\boldsymbol{a}_0$. We study this "boosted potential" in a variety of scenarios and propose several applications: (1) The boosted potential can be used to define a binding criterion that naturally incorporates the effect of tidal fields. This solves several problems of commonly-used self-potential binding checks: i) it defines a tidal boundary for each halo, ii) it is much less likely to misidentify caustics as haloes (specially in the context of warm dark matter cosmologies), and iii) performs better at identifying virialized regions of haloes -- yielding to the expected value of 2 for the virial ratio. (2) This binding check can be generalized to filaments and other cosmic structures. (3) The boosted potential facilitates the understanding of the disruption of satellite subhaloes. We propose a picture where most mass loss is explained through a lowering of the escape energy through the tidal field. (4) We discuss the possibility of understanding the topology of the potential field in a way that is independent of constant offsets in the first derivative $\boldsymbol{a}_0$. We foresee that this novel perspective on the potential can help to develop more accurate models and improve our understanding of structure formation.

astro-ph.CO↗

arXiv2021

J-PLUS: Searching for very metal-poor star candidates using the SPEEM pipeline

We explore the stellar content of the Javalambre Photometric Local Universe Survey (J-PLUS) Data Release 2 and show its potential to identify low-metallicity stars using the Stellar Parameters Estimation based on Ensemble Methods (SPEEM) pipeline. SPEEM is a tool to provide determinations of atmospheric parameters for stars and separate stellar sources from quasars, using the unique J-PLUS photometric system. The adoption of adequate selection criteria allows the identification of metal-poor star candidates suitable for spectroscopic follow-up. SPEEM consists of a series of machine learning models which uses a training sample observed by both J-PLUS and the SEGUE spectroscopic survey. The training sample has temperatures Teff between 4\,800 K and 9\,000 K; $\log g$ between 1.0 and 4.5, and $-3.1<[Fe/H]<+0.5$. The performance of the pipeline has been tested with a sample of stars observed by the LAMOST survey within the same parameter range. The average differences between the parameters of a sample of stars observed with SEGUE and J-PLUS, which were obtained with the SEGUE Stellar Parameter Pipeline and SPEEM, respectively, are $ΔTeff\sim 41$ K, $Δ\log g\sim 0.11$ dex, and $Δ[Fe/H]\sim 0.09$ dex. A sample of 177 stars have been identified as new candidates with $[Fe/H]<-2.5$ and 11 of them have been observed with the ISIS spectrograph at the William Herschel Telescope. The spectroscopic analysis confirms that $64\%$ of stars have $[Fe/H]<-2.5$, including one new star with $[Fe/H]<-3.0$. SPEEM in combination with the J-PLUS filter system has shown the potential to estimate the stellar atmospheric parameters (Teff, $\log g$, and [Fe/H]). The spectroscopic validation of the candidates shows that SPEEM yields a success rate of $64\%$ on the identification of very metal-poor star candidates with $[Fe/H]<-2.5$.

astro-ph.SR↗

arXiv2021

Modelling galaxy clustering in redshift space with a Lagrangian bias formalism and $N$-body simulations

Improving the theoretical description of galaxy clustering on small scales is an important challenge in cosmology, as it can considerably increase the scientific return of forthcoming galaxy surveys -- e.g. tightening the bounds on neutrino masses and deviations from general relativity. In this paper, we propose and test a new model for the clustering of galaxies that is able to accurately describe redshift-space distortions even down to small scales. This model corresponds to a second-order perturbative Lagrangian bias expansion which is advected to Eulerian space employing a displacement field extracted from $N$-body simulations. Eulerian coordinates are then transformed into redshift space by directly employing simulated velocity fields augmented with nuisance parameters capturing various possible satellite fractions and intra-halo small-scale velocities. We quantify the accuracy of our approach against samples of physically-motivated mock galaxies selected according to either Stellar Mass (SM) or Star Formation Rate (SFR) at multiple abundances and at $z=0$ and $1$. We find our model describes the monopole, quadrupole, and hexadecapole of the galaxy-power spectra down to scales of $k\approx 0.6 [h/$Mpc] within the accuracy of our simulations. This approach could pave the way to significantly increase the amount of cosmological information to be extracted from future galaxy surveys.

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arXiv2021

The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology

We present the BACCO project, a simulation framework specially designed to provide highly-accurate predictions for the distribution of mass, galaxies, and gas as a function of cosmological parameters. In this paper, we describe our main suite of simulations (L $\sim2$ Gpc and $4320^3$ particles) and present various validation tests. Using a cosmology-rescaling technique, we predict the nonlinear mass power spectrum over the redshift range $0<z<1.5$ and over scales $10^{-2} < k/(h Mpc^{-1} ) < 5$ for 800 points in an 8-dimensional cosmological parameter space. For an efficient interpolation of the results, we build an emulator and compare its predictions against several widely-used methods. Over the whole range of scales considered, we expect our predictions to be accurate at the 2\% level for parameters in the minimal $Λ$ CDM model and to 3\% when extended to dynamical dark energy and massive neutrinos. We make our emulator publicly available under http://www.dipc.org/bacco

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arXiv2021

The galaxy size - halo mass scaling relations and clustering properties of central and satellite galaxies

In this work, we combine size and stellar mass measurements from the Sloan Digital Sky Server (SDSS) with the group finder algorithm of Rodriguez \& Merchán in order to determine the stellar and halo mass -- size relations of central and satellite galaxies separately. We show that, while central and satellite galaxies display similar stellar mass -- size relations, their halo mass -- size relations differ significantly. As expected, more massive haloes tend to host larger central galaxies. However, the size of satellite galaxies depends only slightly on halo virial mass. We show that these results are compatible with a remarkably simple model in which the size of central and satellite galaxies scales as the cubic root of their host halo mass, with the normalization for satellites being $\sim$ 30 \% smaller than that for central galaxies, which can be attributed to tidal stripping. We further check that our measurements are in excellent agreement with predictions from the IllustrisTNG hydrodynamical simulation. In the second part of this paper, we analyse how the clustering properties of central and satellite galaxies depend on their size. We demonstrate that, independently of the stellar mass threshold adopted, smaller galaxies are more tightly clustered than larger galaxies when either the entire sample or only satellites are considered. The opposite trend is observed on large scales when the size split is performed for the central galaxies alone. Our results place significant constraints for halo-galaxy connection models that link galaxy size with the properties of their hosting haloes.

astro-ph.GA↗

arXiv2021

J-PLUS: A first glimpse at spectrophotometry of asteroids -- The MOOJa catalog

Context: The Javalambre Photometric Local Universe Survey (J-PLUS) is an observational campaign that aims to obtain photometry in 12 ultraviolet-visible filters (0.3-1 μm) of approximately 8 500 deg{^2} of the sky observable from Javalambre (Teruel, Spain). Due to its characteristics and strategy of observation, this survey will let us analyze a great number of Solar System small bodies, with improved spectrophotometric resolution with respect to previous large-area photometric surveys in optical wavelengths. Aims: The main goal of this work is to present here the first catalog of magnitudes and colors of minor bodies of the Solar System compiled using the first data release (DR1) of the J-PLUS observational campaign: the Moving Objects Observed from Javalambre (MOOJa) catalog. Methods: Using the compiled photometric data we obtained very-low-resolution reflectance (photospectra) spectra of the asteroids. We first used a σ-clipping algorithm in order to remove outliers and clean the data. We then devised a method to select the optimal solar colors in the J-PLUS photometric system. These solar colors were computed using two different approaches: on one hand, we used different spectra of the Sun, convolved with the filter transmissions of the J-PLUS system, and on the other, we selected a group of solar-type stars in the J-PLUS DR1, according to their computed stellar parameters. Finally, we used the solar colors to obtain the reflectance spectra of the asteroids. Results: We present photometric data in the J-PLUS filters for a total of 3 122 minor bodies (3 666 before outlier removal), and we discuss the main issues of the data, as well as some guidelines to solve them

astro-ph.EP↗

arXiv2021

The cosmological dependence of halo and galaxy assembly bias

One of the main predictions of excursion set theory is that the clustering of dark matter haloes only depends on halo mass. However, it has been long established that the clustering of haloes also depends on other properties, including formation time, concentration, and spin; this effect is commonly known as halo assembly bias. We use a suite of gravity-only simulations to study the dependence of halo assembly bias on cosmology; these simulations cover cosmological parameters spanning 10$σ$ around state-of-the-art best-fitting values, including standard extensions of the $Λ$CDM paradigm such as neutrino mass and dynamical dark energy. We find that the strength of halo assembly bias presents variations smaller than 0.05 dex across all cosmologies studied for concentration and spin selected haloes, letting us conclude that the dependence of halo assembly bias upon cosmology is negligible. We then study the dependence of galaxy assembly bias (i.e. the manifestation of halo assembly bias in galaxy clustering) on cosmology using subhalo abundance matching. We find that galaxy assembly bias also presents very small dependence upon cosmology ($\sim$ 2$\%$-4$\%$ of the total clustering); on the other hand, we find that the dependence of this signal on the galaxy formation parameters of our galaxy model is much stronger. Taken together, these results let us conclude that the dependence of halo and galaxy assembly bias on cosmology is practically negligible.

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