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Laerte Sodré Jr.

Publications and source records attributed to Laerte Sodré Jr..

At least 19 recordsLinked to original sources

The role of filaments in the large-scale conformity signal

We investigate the large-scale conformity signal and its connection with the large-scale structure, focusing on low-mass central galaxies, after excluding galaxies that were satellites in the past. We measure the signal across different environments identified with the DisPerSE algorithm, including group outskirts, cluster outskirts, filaments, filament outskirts, and other environments (voids and walls) in order to assess how each population contributes to the large-scale conformity signal. We use the galaxy catalogs from the IllustrisTNG300 simulation and the semi-analytic model MDPL2-SAG. In SAG, galaxies embedded in filamentary regions emerge as the primary contributors to the large-scale conformity signal, closely reproducing its overall shape and amplitude. In TNG300, galaxies located in filamentary regions also exhibit a coherent, albeit weaker, conformity signal once galaxies that were satellites in the past are excluded. We also examine whether the signal depends on the adopted filament definition. We find no substantial differences when varying the filament thickness or considering galaxies in overlapping regions. Instead, the signal remains strong and largely insensitive to these differences. We then investigate the dependence of the conformity signal on filament linear density, length, and mass. The signal is enhanced in high-linear density, short, and high-mass filaments, but remains present in low-linear density, long, and low-mass filaments, although with a slightly lower amplitude. Finally, we find that low-mass central galaxies located in close proximity to interacting massive systems show an amplified conformity signal extending up to 4 Mpc/h. Overall, our results indicate that filamentary regions, and the tidal forces from neighboring massive systems play a fundamental role in shaping the large-scale conformity signal.

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The S-PLUS Fifth Data-Release: Over 4500 square degrees of the Southern Sky and a multicolor view of the Hydra and Antlia galaxy clusters

We present the 5th data release (DR5) of the Southern Photometric Local Universe Survey (S-PLUS), covering 4592 square degrees across 2491 fields. Observations were conducted with the T80-South, a Brazilian robotic telescope equipped with the Javalambre 12-filter system, containing five broad- and seven narrowband filters. Data products feature FITS images and extensive catalogs containing fluxes, magnitudes, and shape parameters for over 113 million detections. In addition, several value-added catalogs are provided, offering photometric redshifts (photo-zs), object classifications, masks, and extinction coefficients. For the first time, this release includes coverage of 110 square degrees along the Galactic disk, facilitating new research into Galactic structure and stellar populations. The release also provides full coverage of the Hydra Supercluster and numerous other nearby clusters with improved data reduction and calibration, enhancing photo-z accuracy, which is vital for large-scale structure studies. A preliminary analysis of the Hydra and Antlia galaxy clusters up to $5 \times R_{200}$ yields an updated catalog of 1706 cluster members based on both spectroscopic and high-quality photo-zs. Our photometric data shows that both clusters have a similar proportion of galaxies with an H$α$ excess relative to their clustercentric distance, though Hydra has a higher fraction near its center. Additionally, the spatial distribution of all objects in our sample highlights a bridge connecting both clusters. We verify that S-PLUS DR5 provides a solid foundation for future scientific investigations, ranging from Solar System studies to Cosmology.

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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).

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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 $\{H_0,Ω_b h^2, Ω_c h^2\}$ 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\%.

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The Journey to Dominance: How BCGs Evolve Differently from Other Massive Galaxies

We use the L-GALAXIES semi-analytic model to investigate the evolution of Brightest Cluster Galaxies (BCGs) found in clusters at $\rm z \sim 0$. BCGs are typically located in the central region of galaxy clusters, near the bottom of the potential well, exposing them to different environmental conditions compared to galaxies in the cluster outskirts or in the field. As a result, BCGs may follow a distinct evolutionary path and exhibit unique properties. We study the physical properties and merger histories of galaxies in 180 simulated clusters at $z \sim 0$, considering all cluster members with present-day stellar masses above $10^9 \ {\rm M_\odot}$ as the starting points for tracing their merger trees. We compare this sample of galaxies to a control sample of field galaxies and highlight their differences in evolution across cosmic time. We find that BCGs have distinct stellar mass formation histories compared to other massive galaxies from our control sample. Surprisingly, (proto)BCGs consistently become the most massive galaxy of their structure only at z $\sim$ 1.3. Despite this late dominance, (proto)BCGs are found to inhabit regions with higher galaxy and stellar mass density than the most massive galaxy in the structure throughout their entire history, indicating that their evolution is tightly linked to the environment from early times. These conditions shape a distinct evolutionary path for BCGs compared to other massive galaxies in clusters and in the field, underscoring the unique nature of BCGs.

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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.

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The Connection between Dusty Star-Forming Galaxies and the First Massive Quenched Galaxies

High-redshift (z > 2) massive quiescent (MQ) galaxies provide an opportunity to probe the key physical processes driving the fuelling and quenching of star formation in the early Universe. Observational evidence suggests a possible evolutionary link between MQs and dusty star-forming galaxies (DSFGs; or submillimetre galaxies), another extreme high-redshift population. However, galaxy formation models have historically struggled to reproduce these populations - especially simultaneously - limiting our understanding of their formation and connection, particularly in light of recent JWST findings. In previous work, we presented a re-calibrated version of the L-Galaxies semi-analytic model that provides an improved match to observationally-inferred number densities of both DSFG and MQ populations. In this work, we use this new model to investigate the progenitors of MQs at z > 2 and the physical mechanisms that lead to their quenching. We find that most MQs at z > 2 were sub-millimetre-bright ($S_{870}$ > 1 mJy) at some point in their cosmic past. The stellar mass of MQs is strongly correlated with the maximum submillimetre flux density attained over their history, and this relation appears to be independent of redshift. However, only a minority of high-redshift DSFGs evolve into MQs by z = 2. The key distinction between typical DSFGs and MQ progenitors lies in their merger histories: MQ progenitors experience an early major merger that triggers a brief, intense starburst and rapid black hole growth, depleting their cold gas reservoirs. In our model, AGN feedback subsequently prevents further gas cooling, resulting in quenching. In contrast, the broader DSFG population remains sub-millimetre-bright, with star formation proceeding primarily via secular processes, becoming quenched later.

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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: $ΛCDM + \sum m_ν$ and $w_0w_a CDM + \sum m_ν$. 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 $\sum m_ν< 0.32$ eV for the $ΛCDM + \sum m_ν$ model, and $\sum m_ν< 0.36$ eV for the $w_0w_a CDM + \sum m_ν$ model. When combined with Planck data, the upper limit improves significantly. For J-PAS+Planck at 95% C.L, we find $\sum m_ν< 0.061$ eV for the $ΛCDM + \sum m_ν$ model, and for J-PAS+Planck+Pantheon Plus, we obtain $\sum m_ν< 0.12$ eV for the $w_0w_a CDM + \sum m_ν$ 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.

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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.

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J-PLUS: Turning Off the Bright Stars

Photometric surveys require precise point spread function (PSF) characterization, as it varies across filters and is crucial for accurate photometry and low surface brightness (LSB) studies. However, the small PSF size provided by default pipelines suits only barely resolved objects, making it difficult to analyze regions near bright stars (rendering those regions unusable). These components are then combined to generate a final PSF for each exposure and filter, spanning 15 mag arcsec-2 in surface brightness and 4 arcmin in radius in the broad bands. In narrow-band filters, the J-PLUS PSF exhibits two rings, whereas in broad-band filters, only one ring is observed. Additionally, the position of the ring shifts with filter wavelength: as the filters become redder, the ring radius increases. We find that there is no significant variation in the extended PSF observed as a function of time (within 2.5h) or position in the field of view. The radial profile of NGC 4212 (which is close to a star) is also studied before/after PSF-subtraction. We developed a novel method to determine the central coordinates of saturated stars, and classify stars without using Gaia magnitudes. Additionally, mirror reflections are automatically detected and masked. Furthermore, in combining different stars and various components of the PSF, we avoided the use of a fixed radius by introducing a new method that does not depend on radial measurements. Accurate characterization of the extended PSF and its subtraction improves sky subtraction, increases the effective area of the survey by about 10%, and enables the study of extended large LSB features in wide area surveys like J-PLUS. Our pipeline is published as free software (GNU GPLv3) an can be customized to other surveys such as J-PAS, where its impact will be even greater due to its depth. This paper is fully reproducible and produced from Commit 4860c70.

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Selecting Clusters and Protoclusters via Stellar Mass Density: II. Application to HSC-SSP Observations

We present a selection of candidates of clusters and protoclusters of galaxies identified in the photometric data of the HSC-SSP Wide Public Data Release 3 (PDR3), spanning the redshift range $\rm 0.1 \leq z \leq 2$. The selection method, detailed in Vicentin et al. (2025), involves detecting massive galaxies located in high-density regions of matter, identified as potential central dominant galaxies, i.e., (proto)BCGs. Probabilistic criteria based on proximity to the candidate central galaxy and the expected stellar mass of member galaxies are applied to identify likely members of each structure. We produced updated photometric redshift estimates using deep learning methods trained on a dataset combining spectroscopic redshifts from the HSC-SSP Wide PDR3, high-accuracy photometric redshifts from the COSMOS2020 catalog, and mid-infrared data from the unWISE catalog for matched sources. Our method achieves a predicted purity of $\sim 90\%$ in detecting (proto)clusters, with $\gtrsim 65\%$ correctly identifying the (proto)BCG. A total of 16,007 candidate (proto)clusters were identified over an effective area of $\rm \sim 850 \ deg^{2}$ within the HSC-SSP Wide footprint. Comparisons with other existing catalogs reveal a good level of consistency, while also highlighting that different methods yield complementary discoveries. We further compare richness and halo masses from our optical catalog with those from recent X-ray cluster catalogs (eROSITA and MCXC-II), finding a moderate positive correlation and a scatter of $\rm \sim 0.4$ dex. This catalog provides a valuable new set of targets for the Prime Focus Spectrograph (PFS) instrument.

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Selecting Clusters and Protoclusters via Stellar Mass Density: I. Method and tests on Mock HSC-SSP catalogs

We present an algorithm designed to identify galaxy (proto)clusters in wide-area photometric surveys by first selecting their dominant galaxy-i.e., the Brightest Cluster Galaxy (BCG) or protoBCG-through the local stellar mass density traced by massive galaxies. We focus on its application to the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Wide Survey to detect candidates up to $\rm z \sim 2$. In this work, we apply the method to mock galaxy catalogs that replicate the observational constraints of the HSC-SSP Wide Survey. We derive functions that describe the probability of a massive galaxy being the dominant galaxy in a structure as a function of its stellar mass density contrast within a given redshift interval. We show that galaxies with probabilities greater than 50\% yield a sample of BCGs/protoBCGs with $\gtrsim 65\%$ purity, where most of the contamination arises from galaxies in massive groups below our cluster threshold. Using the same threshold, the resulting (proto)cluster sample achieves 80\% purity and 50\% completeness for halos with $M_{\rm{halo}} \geq 10^{14} \ M_{\odot}$, reaching nearly 100\% completeness for $M_{\rm{halo}} \geq 10^{14.5} \ M_{\odot}$. We also assign probabilistic membership to surrounding galaxies based on stellar mass and distance to the dominant galaxy, from which we define the cluster richness as the number of galaxies more likely to be true members than contaminants. This allows us to derive a halo mass-richness relation. In a companion paper, we apply the algorithm to the HSC-SSP data and compare our catalog with others based on different cluster-finding techniques and X-ray detections.

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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 $σ_{\rm NMAD} =0.11$, which, according to our results with real data, could be significantly smaller (as low as $σ_{\rm NMAD}=0.02$). We note that the data sample is still not large enough for a full statistical consideration.

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Simultaneously Modelling Dusty Star Forming Galaxies and Massive Quiescents: A Calibration Framework for Galaxy Formation Models

Galaxy formation models, particularly semi-analytic models (SAMs), rely on differential equations with free parameters to describe the physical mechanisms governing galaxy formation and evolution. Traditionally, most SAMs calibrate these parameters manually to match observational data. However, this approach fails to fully explore the multidimensional parameter space, resulting in limited robustness and inconsistency with some observations. In contrast, the L-Galaxies SAM features a unique Markov Chain Monte Carlo (MCMC) mode, enabling robust model calibration. Using this functionality, we address a long-standing tension in galaxy formation models: simultaneously reproducing the number densities of dusty star-forming galaxies (DSFGs) and high-redshift massive quiescent galaxies (MQs). We test nine combinations of observational constraints - including stellar mass functions, quiescent fractions, neutral hydrogen mass functions, and DSFG number densities - across different redshifts. We then analyze the resulting galaxy property predictions and discuss the underlying physical mechanisms. Our results identify a model that reasonably matches the number density of DSFGs while remaining consistent with observationally-derived lower limits on the number density of high-redshift MQs. This model requires high star formation efficiencies in mergers and a null dependency of supermassive black hole (SMBH) cold gas accretion on halo mass, facilitating rapid stellar mass and SMBH growth. Additionally, our findings highlight the importance of robust calibration procedures to address the significant degeneracies inherent to multidimensional galaxy formation models.

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Modelling the multi-wavelength detection of protoclusters. I: An excess of submillimetre galaxies in protocluster cores

Studies of galaxy protoclusters yield insights into galaxy cluster formation complementary to those obtained via `archaeological' studies of present-day galaxy clusters. Submillimetre-selected galaxies (SMGs) are one class of sources used to find high-redshift protoclusters. However, due to the rarity of protoclusters (and thus the large simulation volume required) and the complexity of modeling dust emission from galaxies, the relationship between SMGs and protoclusters has not been adequately addressed in the theoretical literature. In this work, we apply the L-GALAXIES semi-analytic model (SAM) to the Millennium N-body simulation. We assign submillimetre (submm) flux densities to the model galaxies using a scaling relation from previous work, in which dust radiative transfer was performed on high-resolution galaxy zoom simulations. We find that the fraction of model galaxies that are submm-bright is higher in protocluster cores than in both protocluster `outskirts' and the field; the fractions for the latter two are similar. This excess is not driven by an enhanced starburst frequency. Instead, the primary reason is that overdense environments have a relative overdensity of high-mass halos and thus `oversample' the high-mass end of the star formation main sequence relative to less-dense environments. The fraction of SMGs that are optically bright is dependent on stellar mass and redshift but independent of environment. The fraction of galaxies for which the majority of star formation is dust-obscured is higher in protocluster cores, primarily due to the dust-obscured fraction being correlated with stellar mass. Our results can be used to guide and interpret multi-wavelength studies of galaxy populations in protoclusters.

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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.

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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.

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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.

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