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Javier Cenarro

Publications and source records attributed to Javier Cenarro.

At least 19 recordsLinked to original sources
arXiv2026

J-PAS: unprecedented precision in stellar populations of diffuse tidal features

Galaxies frequently interact with nearby systems, a process that can significantly alter their morphology and star formation activity. However, spectroscopic studies of their faint and diffuse remnants require very long exposure times and often exceed the limited field of view of integral field units (IFUs). On the other hand, broad-band imaging can have a much wider field of view, but lacks the spectral resolution to identify key spectral features, restricting accurate constraints on stellar population properties. With its 54 narrow-band filters in the optical and wide coverage (planned 8000 square degrees), J-PAS fills this gap. In this case study, we examine PGC 3087775, a massive galaxy at z = 0.046179 (~ 201 Mpc) in the later stages of a major merger in the J-PAS early data release. Photometry was validated with MaNGA IFU data (for the central part). Stellar population properties was derived using both J-PAS and SDSS photometry. SDSS indicates a metal-rich population with an extended star formation history (SFH) and elevated star formation rates. J-PAS instead points to a less metal-rich population with moderate extinction and a more rapid SFH, consistent with a quenched stellar population. The average Dn(4000) index of the tidal features is 1.24, suggesting that it was a non-dry merger and a fourfold improvement in the precision of stellar mass and Dn (4000) was found with J-PAS. We also assessed two heuristic methods for estimating the mass-to-light ratio from SDSS filters and found that they overestimate the stellar mass in this galaxy by 0.5 dex and 0.4 dex relative to SED fitting results from J-PAS and SDSS, respectively. Future work will extend this analysis to a larger sample of merging galaxies and evolution of the stellar populations of such structures across the nearby Universe to unprecedented detail. This project is fully reproducible, through Maneage (commit 0f0d7e2).

astro-ph.GA↗
arXiv2026

J-PAS: Semi-Supervised Sim-to-Obs Transfer for Robust Star--Galaxy--Quasar Classification

Modern studies in astrophysics and cosmology increasingly rely on simulations and cross-survey analyses, yet differences in data generation, instrumentation, calibration, and unmodeled physics introduce distribution mismatches between datasets (``domain shift''). In machine-learning pipelines, this occurs when the joint distribution of inputs and labels differs between the training (source) and application (target) domains, causing source-trained models to underperform on the target. Transfer learning and domain adaptation provide principled ways to mitigate this effect. We study a concrete simulation-to-observation case: semi-supervised domain adaptation (SSDA) to transfer a four-class spectral classifier -- high-redshift quasars, low-redshift quasars, galaxies, and stars -- from J-PAS mock catalogs based on DESI spectra to real J-PAS observations. Our pipeline pretrains on abundant labeled DESI$\rightarrow$J-PAS mocks and adapts to the target domain using a small labeled J-PAS subset. We benchmark SSDA against two baselines: a J-PAS--only supervised model trained with the same target-label budget, and a mocks-only model evaluated on held-out J-PAS data. On this held-out J-PAS data, SSDA achieves a macro-F1 score (balancing precision and recall) of $0.82$ and an overall true positive rate of $0.89$, compared to $0.79/0.85$ for the J-PAS--only baseline and $0.73/0.87$ for the mocks-only model. The gains are driven primarily by improved quasar classification, especially in the high-redshift subclass ($\mathrm{F1}=0.66$ vs.\ $0.55/0.37$), yielding better-calibrated candidate lists for spectroscopic targeting (e.g., WEAVE-QSO) and AGN searches. This study shows how modest target supervision enables robust, data-efficient simulation-to-observation transfer when simulations are plentiful but target labels are scarce.

astro-ph.IM↗
arXiv2025

The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and photometric catalogs, we construct a robust labeled dataset comprising 14594 sources classified into extended (galaxies) and point-like (stars and quasars) objects. We assess dataset representativeness using UMAP analysis, confirming broad and consistent coverage of feature space. An XGBoost classifier, with hyperparameters tuned using automated optimization, is trained using purely photometric data (60-band J-PAS magnitudes) and combined photometric and morphological features, with performance thoroughly evaluated via ROC and purity-completeness metrics. Incorporating morphology significantly improves classification, outperforming the baseline classifications available in the catalogs. Permutation importance analysis reveals morphological parameters, particularly concentration, normalized peak surface brightness, and PSF, alongside photometric features around 4000 and 6900 A, as crucial for accurate classifications. We release a value-added catalog with our models for star-galaxy classification, enhancing the utility of miniJPAS and J-NEP for subsequent cosmological and astrophysical analyses.

astro-ph.IM↗
arXiv2025

J-PAS: forecast on the primordial power spectrum reconstruction

We investigate the capability of the J-PAS survey to constrain the primordial power spectrum using a non-parametric Bayesian method. Specifically, we analyze simulated power spectra generated by a local oscillatory primordial feature template motivated by non-standard inflation. The feature is placed within the range of scales where the signal-to-noise ratio is maximized, and we restrict the analysis to $k \in [0.02,0.2] \text{ h} \text{ Mpc}^{-1}$, set by the expected J-PAS coverage and the onset of non-linear effects. Each primordial power spectrum is reconstructed by linearly interpolating $N$ knots in the $\{\log k, \log P_{\mathcal{R}}(k)\}$ plane, which are sampled jointly with the cosmological parameters $\{H_0,\Omega_b h^2, \Omega_c h^2\}$ using PolyChord. To test the primordial features, we apply two statistical tools: the Bayes factor and a hypothesis test that localizes the scales where features are detected. We assess the recovery under different J-PAS specifications, including redshift binning, tracer type, survey area, and filter strategy. Our results show that combining redshift bins and tracers allows the detection of oscillatory features as small as 2\%.

astro-ph.CO↗
arXiv2025

The miniJPAS survey quasar selection V: combined algorithm

Aims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or Baryon Acoustic Oscillations. Here, we present the final quasar catalogue, including redshift estimates, from the miniJPAS Data Release constructed using several flavours of machine-learning algorithms. Methods. In this work, we use a machine learning algorithm to classify quasars, optimally combining the output of 8 individual algorithms. We assess the relative importance of the different classifiers. We include results from 3 different redshift estimators to also provide improved photometric redshifts. We compare our final catalogue against both simulated data and real spectroscopic data. Our main comparison metric is the $f_1$ score, which balances the catalogue purity and completeness. Results. We evaluate the performance of the combined algorithm using synthetic data. In this scenario, the combined algorithm outperforms the rest of the codes, reaching $f_1=0.88$ and $f_1=0.79$ for high- and low-z quasars (with $z\geq2.1$ and $z<2.1$, respectively) down to magnitude $r=23.5$. We further evaluate its performance against real spectroscopic data, finding different performances. We conclude that our simulated data is not realistic enough and that a new version of the mocks would improve the performance. Our redshift estimates on mocks suggest a typical uncertainty of $\sigma_{\rm NMAD} =0.11$, which, according to our results with real data, could be significantly smaller (as low as $\sigma_{\rm NMAD}=0.02$). We note that the data sample is still not large enough for a full statistical consideration.

astro-ph.CO↗
arXiv2025

J-PAS: Forecasting constraints on Neutrino Masses

The large-scale structure survey J-PAS is taking data since October 2023. In this work, we present a forecast based on the Fisher matrix method to establish its sensitivity to the sum of the neutrino masses. We adapt the Fisher Galaxy Survey Code (FARO) to account for the neutrino mass under various configurations applied to galaxy clustering measurements. This approach allows us to test the sensitivity of J-PAS to the neutrino mass across different tracers, with and without non-linear corrections, and under varying sky coverage. We perform our forecast for two cosmological models: $\Lambda CDM + \sum m_\nu$ and $w_0w_a CDM + \sum m_\nu$. We combine our J-PAS forecast with Cosmic Microwave Background (CMB) data from the Planck Collaboration and Type Ia supernova (SN) data from Pantheon Plus. Our analysis shows that, for a sky coverage of 8,500 square degrees, J-PAS galaxy clustering data alone will constrain the sum of the neutrino masses to an upper limit at 95% C.L of $\sum m_\nu < 0.32$ eV for the $\Lambda CDM + \sum m_\nu$ model, and $\sum m_\nu < 0.36$ eV for the $w_0w_a CDM + \sum m_\nu$ model. When combined with Planck data, the upper limit improves significantly. For J-PAS+Planck at 95% C.L, we find $\sum m_\nu < 0.061$ eV for the $\Lambda CDM + \sum m_\nu$ model, and for J-PAS+Planck+Pantheon Plus, we obtain $\sum m_\nu < 0.12$ eV for the $w_0w_a CDM + \sum m_\nu$ model. These results demonstrate that J-PAS clustering measurements can play a crucial role in addressing challenges in the neutrino sector, including potential tensions between cosmological and terrestrial measurements of the neutrino mass, as well as in determining the mass ordering.

astro-ph.CO↗
arXiv2025

Unveiling the small-scale web around galaxies with miniJPAS and DESI

We present the first statistical observational study detecting filaments in the immediate surroundings of galaxies, i.e. the local web of galaxies. Simulations predict that cold gas, the fuel for star formation, is channeled through filamentary structures into galaxies. Yet, direct observational evidence for this process has been limited by the challenge of mapping the cosmic web at small scales. Using miniJPAS spectro-photometric data combined with spectroscopic DESI redshifts when available, we construct a high-density observational galaxy sample spanning 0.2 10^(10) Msun using all nearby galaxies as tracers, combined with a probabilistic adaptation of the DisPerSE algorithm designed to overcome limitations due to photometric redshift uncertainties. Our methodology is tested and validated using mock catalogues built with random forest models applied to a simulated lightcone. Besides recovering the expected increase in galaxy connectivity (defined as the number of filaments attached to a galaxy) with stellar mass, we show that our connectivity measurements agree with 3D reference estimates from the mock galaxies. Thanks to these filament reconstructions, we explore the relation between small-scale connectivity and galaxy star formation rate, finding a mild positive trend which needs to be confirmed by follow up studies with larger sample sizes. We propose galaxy connectivity to local filaments as a powerful and physically motivated metric of environment, offering new insights into the role of cosmic structure in galaxy evolution.

astro-ph.CO↗
arXiv2025

J-PAS and PFS surveys in the era of dark energy and neutrino mass measurements

Fisher-matrix forecasts are presented for the cosmological surveys of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) and the Subaru Prime Focus Spectrograph (PFS). The wide, low-redshift coverage of J-PAS and the high-density, high-redshift mapping of PFS are strongly complementary: combining the two reduces marginalized uncertainties on all primary parameters compared with either survey individually. Adding the joint J-PAS+PFS data to next-generation CMB measurements from the Simons Observatory (SO) and \textsc{LiteBird} yields an expected precision of $\sigma(\sum m_\nu)=0.017\,$eV in the $\Lambda$CDM$+\sum m_\nu+N_{\rm eff}$ framework, sufficient to disfavour the inverted neutrino hierarchy at $2.34\,\sigma$ if the true mass sum equals the normal-ordering minimum. Motivated by recent DESI results, we also forecast within a $w_0w_a$CDM$+\sum m_\nu+N_{\rm eff}$ cosmology, adopting the DESI\,DR2 best-fit values ($w_0=-0.758$, $w_a=-0.82$) as fiducial. The combination CMB+J-PAS+PFS then delivers $\sigma(w_0)=0.044$ and $\sigma(w_a)=0.18$, corresponding to a $5.1\,\sigma$ preference for a time-varying dark-energy equation of state. These findings show that J-PAS and PFS, especially when coupled with Stage-IV CMB observations, will provide competitive tests of neutrino physics and the dynamics of cosmic acceleration.

astro-ph.CO↗
arXiv2025

Photometric segregation of dwarf and giant FGK stars using the SVO Filter Profile Service and photometric tools

This paper is focused on the segregation of FGK dwarf and giant stars through narrow-band photometric data using the Spanish Virtual Observatory (SVO) Filter Profile Service and associated photometric tools. We selected spectra from the MILES, STELIB, and ELODIE stellar libraries, and used SVO photometric tools to derive the synthetic photometry in 15 J-PAS narrow filters, which were especially selected to cover spectral features sensitive to gravity changes. Using machine-learning techniques as the Gaussian mixture model and the support vector machine, we defined several criteria based on J-PAS colours to discriminate between dwarf and giant stars. We selected five colour-colour diagrams that presented the most promising separation between both samples. Our results show an overall accuracy in the studied sample of $\sim$0.97 for FGK stars, although a dependence on the luminosity type and the stellar effective temperature was found. We also defined a colour-temperature relation for dwarf stars with effective temperatures between 4\,000 and 7\,000\,K, which allows one to estimate the stellar effective temperature from four J-PAS filters ($J0450$, $J0510$, $J0550$, and $J0620$). Additionally, we extended the study to M-type giant and dwarf stars, achieving a similar accuracy to that for FGK stars.

astro-ph.SR↗
arXiv2024

J-PLUS: Beyond Spectroscopy III. Stellar Parameters and Elemental-abundance Ratios for Five Million Stars from DR3

We present a catalog of stellar parameters (effective temperature $T_{\rm eff}$, surface gravity $\log g$, age, and metallicity [Fe/H]) and elemental-abundance ratios ([C/Fe], [Mg/Fe], and [$α$/Fe]) for some five million stars (4.5 million dwarfs and 0.5 million giants stars) in the Milky Way, based on stellar colors from the Javalambre Photometric Local Universe Survey (J-PLUS) DR3 and \textit{Gaia} EDR3. These estimates are obtained through the construction of a large spectroscopic training set with parameters and abundances adjusted to uniform scales, and trained with a Kernel Principal Component Analysis. Owing to the seven narrow/medium-band filters employed by J-PLUS, we obtain precisions in the abundance estimates that are as good or better than derived from medium-resolution spectroscopy for stars covering a wide range of the parameter space: 0.10-0.20 dex for [Fe/H] and [C/Fe], and 0.05 dex for [Mg/Fe] and [$α$/Fe]. Moreover, systematic errors due to the influence of molecular carbon bands on previous photometric-metallicity estimates (which only included two narrow/medium-band blue filters) have now been removed, resulting in photometric-metallicity estimates down to [Fe/H] $\sim -4.0$, with typical uncertainties of 0.25 dex and 0.40 dex for dwarfs and giants, respectively. This large photometric sample should prove useful for the exploration of the assembly and chemical-evolution history of our Galaxy.

astro-ph.SR↗
arXiv2024

The miniJPAS Survey: The radial distribution of star formation rates in faint X-ray active galactic nuclei

We study the impact of black hole nuclear activity on both the global and radial star formation rate (SFR) profiles in X-ray-selected active galactic nuclei (AGN) in the field of miniJPAS, the precursor of the much wider J-PAS project. Our sample includes 32 AGN with z < 0.3 detected via the XMM-Newton and Chandra surveys. For comparison, we assembled a control sample of 71 star-forming (SF) galaxies with similar magnitudes, sizes, and redshifts. To derive the global properties of both the AGN and the control SF sample, we used CIGALE to fit the spectral energy distributions derived from the 56 narrowband and 4 broadband filters from miniJPAS. We find that AGN tend to reside in more massive galaxies than their SF counterparts. After matching samples based on stellar mass and comparing their SFRs and specific SFRs (sSFRs), no significant differences appear. This suggests that the presence of AGN does not strongly influence overall star formation. However, when we used miniJPAS as an integral field unit (IFU) to dissect galaxies along their position angle, a different picture emerges. We find that AGN tend to be more centrally concentrated in mass with respect to SF galaxies. Moreover, we find a suppression of the sSFR up to 1Re and then an enhancement beyond 1Re , strongly contrasting with the decreasing radial profile of sSFRs in SF galaxies. This could point to an inside-out quenching of AGN host galaxies. These findings suggest that the reason we do not see differences on a global scale is because star formation is suppressed in the central regions and enhanced in the outer regions of AGN host galaxies. While limited in terms of sample size, this work highlights the potential of the upcoming J-PAS as a wide-field low-resolution IFU for thousands of nearby galaxies and AGN.

astro-ph.GA↗
arXiv2024

PEARLS: NuSTAR and XMM-Newton Extragalactic Survey of the JWST North Ecliptic Pole Time-Domain Field II

We present the second NuSTAR and XMM-Newton extragalactic survey of the JWST North Ecliptic Pole (NEP) Time-Domain Field (TDF). The first NuSTAR NEP-TDF survey (Zhao et al. 2021) had 681 ks total exposure time executed in NuSTAR cycle 5, in 2019 and 2020. This second survey, acquired from 2020 to 2022 in cycle 6, adds 880 ks of NuSTAR exposure time. The overall NuSTAR NEP-TDF survey is the most sensitive NuSTAR extragalactic survey to date, and a total of 60 sources were detected above the 95% reliability threshold. We constrain the hard X-ray number counts, logN-log S, down to 1.7 x 10$^{-14}$ erg cm$^{-2}$ s$^{-1}$ at 8-24 keV and detect an excess of hard X-ray sources at the faint end. About 47% of the NuSTAR-detected sources are heavily obscured (NH > 10$^{23}$ cm$^{-2}$), and 18+20% of the NuSTAR-detected sources are Compton-thick (N>10$^{24}$ cm$^{-2}$). These fractions are consistent with those measured in other NuSTAR surveys. Four sources presented >2$σ$ variability in the 3-year survey. In addition to NuSTAR, a total of 62 ks of XMM-Newton observations were taken during NuSTAR cycle 6. The XMM-Newton observations provide soft X-ray (0.5-10keV) coverage in the same field and enable more robust identification of the visible and infrared counterparts of the NuSTAR-detected sources. A total of 286 soft X-ray sources were detected, out of which 214 XMM-Newton sources have secure counterparts from multiwavelength catalogs.

astro-ph.HE↗
arXiv2024

Supernova environments in J-PLUS. Normalized Cumulative Rank distributions and stellar population synthesis, combining narrow- and broad-band filters

We study the local environmental properties of 418 supernovae (SNe) of all types using data from the Javalambre Photometric Local Universe Survey (J-PLUS), which includes 5 broad- and 7 narrow-band imaging filters, using two independent analyses: 1) the Normalized Cumulative Rank (NCR) method, utilizing all 12 single bands along with five continuum-subtracted narrow-band emission and absorption bands, and 2) simple stellar population (SSP) synthesis, where we build spectral energy distributions (SED) of the surrounding SN environment using the 12 filters. Improvements over previous works include: (i) the extension of the NCR technique to other filters using a set of homogeneous data; (ii) a correction for extinction to all bands based on the relation between the g-i color and the color excess; and (iii) a correction for the [NII] line contamination that falls within the H$α$ filter. All NCR distributions in the broad-band filters, tracing the overall light distribution in each galaxy, are similar to each other, being type Ia, II and IIb SNe are preferably located in redder environments than the other SN types. The radial distribution of the SNe shows that type IIb SNe seem to have a preference for occurring in the inner regions of galaxies. All core-collapse SN (CC) types are strongly correlated to the [OII] emission, which traces SFR. The NCR distributions of the Ca II triplet show a clear division between II/IIb/Ia and Ib/Ic/IIn subtypes, which is interpreted as a difference in the environmental metallicity. Regarding the SSP synthesis, we found that including the 7 J-PLUS narrow filters in the fitting process has a more significant effect for the CC SN environmental parameters than for SNe Ia, shifting their values towards more extinct, younger, and more star-forming environments, due to the presence of strong emission-lines and stellar absorptions in those narrow-bands.

astro-ph.GA↗
arXiv2023

J-PLUS: Detecting and studying extragalactic globular clusters -- the case of NGC 1023

Extragalactic globular clusters (GCs) are key objects for studying the history of galaxies. The arrival of wide-field surveys such as the Javalambre Photometric Local Universe Survey (J-PLUS) offers new possibilities for the study of these systems. We perform the first study of GCs in J-PLUS to recover information about the history of NGC 1023 taking advantage of wide-field images and 12 filters. We develop the semiautomatic pipeline GCFinder that detects GC candidates in J-PLUS images and can also be adapted to similar surveys. We study the stellar population properties of a sub-sample of GC candidates using spectral energy distribution (SED) fitting. We find 523 GC candidates in NGC 1023, of which $\sim$300 are new. We identify subpopulations of GC candidates, where age and metallicity distributions have multiple peaks. By comparing our results with simulations, we report a possible broad age-metallicity relation, evidence that NGC 1023 experienced accretion events in the past. The dominating age peak is at $10^{10}$ yr. We report a correlation between masses and ages that suggests that massive GC candidates are more likely to survive the turbulent history of the host galaxy. Modeling the light of NGC 1023, we find two spiral-like arms and detect a displacement of the galaxy's photometric center with respect to the outer isophotes and center of GC distribution ($\sim$700 pc and $\sim$1600 pc, respectively), which could be the result of ongoing interaction between NGC 1023 and NGC 1023A. By studying the GC system of NGC 1023 with J-PLUS we showcase the power of multi-band surveys for this kind of study and find evidence of a complex accretion history of the host galaxy.

astro-ph.GA↗
arXiv2023

The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE

We present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. This work is based on machine-learning classification of photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a `black box' when compared with other classifiers. Another key advantage is that using user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, here we adapt it for multi-band photometric data, i.e. we treat multiple narrow-band filters as very low-resolution spectra. We train our models using specialized mocks from Queiroz et al. (2022). We estimate our redshift precision using the normalized median absolute deviation, $σ_{\rm NMAD}$ applied to our test sample. Our test sample returns an $f_1$ score (effectively the purity and completeness) of 0.49 for quasars down to magnitude $r=24.3$ with $z\geq2.1$ and 0.24 for quasars with $z<2.1$. For high-z quasars, this goes up to 0.9 for $r<21.0$. We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a $σ_{NMAD}=0.92\%$ for the entire sample, reduced to 0.81\% for $r<22.5$ and 0.74\% for $r<21.3$. Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings.

astro-ph.IM↗
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.

astro-ph.SR↗
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↗