arXiv subjects

L. Nakazono

Publications and source records attributed to L. Nakazono.

11 recordsLinked to original sources

arXiv2026

CLAS+: A large catalog of Changing-Look AGN candidates selected through S-PLUS narrow-band photometry

Changing-look active galactic nuclei (CLAGN) exhibit rapid spectral transitions on timescales of months to years, challenging standard AGN unification models and providing unique insight into accretion-disk variability and obscuration processes. Despite their scientific importance, systematic searches for CLAGN remain limited by the scarcity of multi-epoch spectroscopy and the difficulty of constructing large samples efficiently. We introduce CLAS+, a catalog constructed by comparing synthetic photometry derived from DESI DR1 and SDSS DR17 spectra with contemporaneous narrow-band imaging from the S-PLUS survey. Applying our pipeline to a parent sample of 98,139 quasars, we select high-confidence CLAGN candidates above a threshold of reduced chi-squared $\chi_r^2 > 15$. We identify 3,554 strong CLAGN candidates, corresponding to 3.6\% of the parent sample. This selected fraction is conditional on the adopted criterion and should not be interpreted as an intrinsic CLAGN occurrence rate. Cross-matching with existing CLAGN compilations shows that CLAS+ identifies a large population of previously unreported high-priority candidates (3,481). Among the spectroscopic--photometric comparisons with reliable temporal classification, 40\% are classified as photometric turn-on, 41\% as intermediate, and 19\% as turn-off. We also find an apparent redshift dependence in the relative fractions, with photometric turn-on candidates becoming more common at higher redshifts within the CLAS+ selected sample. Therefore, narrow-band photometry provides a powerful and efficient alternative to purely spectroscopic searches for identifying CLAGN candidates at scale. CLAS+ substantially expands the known CLAGN candidate population and provides a valuable target list for future spectroscopic follow-up and time-domain studies with facilities such as Rubin/LSST.

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arXiv2026

S-PLUS Clusters And Large-scale Environments (SCALE): I. A catalog of known clusters and groups in DR5 and a pilot study of Abell 4038

Within the framework of the Southern Photometric Local Universe Survey (S-PLUS), we introduce ${\bf S}$-PLUS ${\bf C}$lusters ${\bf A}$nd ${\bf L}$arge-scale ${\bf E}$nvironments (SCALE), a project dedicated to the study of galaxy clusters, groups, and their environments using 12-band photometry of S-PLUS combined with spectroscopic and photometric data from the literature. In this first paper, we present a catalog of 83 previously known systems in the redshift range $0.008 \leq z_{\rm spec} \leq 0.1$, for which we derive $R_{200}$, $M_{200}$, and velocity dispersions. Spectroscopic members are selected and matched with S-PLUS photometric redshifts (photo-$z$s). We find very good agreement between literature spectroscopic redshifts (spec-$z$s) and S-PLUS photometric redshifts (photo-$z$s), demonstrating the potential of the latter for cluster and group membership determination. As a proof of concept, we obtain photometric memberships for Abell 4038 using the Reliable Photometric Membership technique. A two- and three-dimensional analysis of the region within $10 h^{-1}$ Mpc ($10\times R_{200}$) from the center of Abell 4038 reveals about a dozen substructures including two additional clusters within $1.3\times R_{200}$ (Abell 4038B and Abell 4049). A color-luminosity segregation analysis shows that more luminous (less luminous) galaxies are redder (bluer), as expected. Low-concentration galaxies ($C \leq 2.5$) exhibit a weaker color-luminosity dependence, compared to higher-concentration ones, indicating mass-dependent evolutionary pathways that challenge a simple morphology-color dichotomy, with low-luminosity galaxies presenting bluer colors largely independent of concentration. The SCALE catalog provides a valuable basis for future studies of large-scale structures and their connection to galaxy evolution.

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arXiv2026

S-PLUS Clusters And Large-scale Environments (SCALE): II. PZWav versus redMaPPer identification of eRosita groups

We present the construction and characterization of a multi-wavelength catalog of galaxy groups and clusters by matching optical detections from the Southern Photometric Local Universe Survey (S-PLUS) with extended X-ray emission from the first eROSITA all-sky survey data release (eRASS1). We employ a probabilistic matching framework, based on the modified Hausdorff distance, to associate galaxy systems identified by the PZWav cluster finder and characterized by the AME membership estimator with X-ray surface brightness contours. This method explicitly accounts for the photometric redshift probability distribution of galaxies and allows us to explore the critical trade-off between catalog completeness and purity. We investigate how the matched sample changes with different optical selection depths, defined by absolute magnitude cuts of $M_r$ < -18.5, -19, -19.5, and -20 sampling redshifts within 0.08 < z < 0.25, and across purity levels of 80%, 90%, and 95%. We find that fainter optical cuts enhance the recovery of low-mass, low-luminosity groups, while brighter cuts favor more massive clusters and increase the effective survey volume at higher redshifts. Stricter purity requirements reduce contamination but systematically lower completeness, particularly for low-luminosity systems. The derived X-ray luminosity functions agree well with previous determinations, and the logN-logS distributions confirm the high recovery rate of luminous clusters. Comparisons with the redMaPPer cluster catalog validate our approach, showing consistent trends and significant overlap, while our method offers improved completeness at the group scale. This work demonstrates a robust, flexible methodology for creating reliable multi-wavelength cluster catalogs, essential for cosmological studies and investigations of galaxy evolution in dense environments.

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arXiv2026

OJALÁ: Optimizing J-PAS Astronomy for Large-scale Analysis. A foundation model for the SED of galaxies, QSOs and stars

The advent of large-scale surveys requires efficient ML techniques to exploit the information of massive datasets. We present OJALA, a transformer-based autoregressive foundation model designed to simultaneously classify astronomical objects and infer their physical parameters using 54 narrow bands from J-PAS, combined with broad bands from the DESI Legacy Imaging Surveys and WISE. The model is trained on $\sim20$ million synthetic SEDs generated from DESI DR1 spectra. We validate OJALA using a cross-matched sample of $\sim121,000$ objects between J-PAS and DESI. The model achieves a weighted F1-score of approximately 0.9 for spectral classification (stars, galaxies, and QSOs) at $i < 21$. For galaxies, we recover photo-z with a precision of $σ_{\rm NMAD} < 0.01$, while for QSOs, the precision improves significantly at $z > 1.5$, reaching $σ_{\rm NMAD} \approx 0.006$ at $z \approx 3.5$. We demonstrate robust estimation of physical properties for galaxies, recovering stellar masses and SFR with a scatter of approximately 0.11 dex and 0.22 dex, respectively. Furthermore, the model accurately predicts EWs for major optical emission lines, allowing for the derivation of extinction-corrected H$α$ luminosities with a scatter of 0.29 dex. OJALA successfully reproduces the BPT and WHAN diagnostic diagrams, classifying SF, AGN, and passive galaxies with F1-scores typically ranging from 70% to 90% depending on the diagnostic class. For stars, the model reliably infers effective temperature and metallicity, though surface gravity remains challenging. Finally, we show the modularity of the architecture by fine-tuning the pre-trained embeddings to predict BH masses, a property not included in the primary training, recovering spectroscopic virial estimates with a precision of approximately 0.5 dex. We release the code, model weights, and a comprehensive VAC for the J-PAS EDR.

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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.

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arXiv2025

Targeting cluster galaxies for the 4MOST CHANCES Low-z sub-survey with photometric redshifts

The evolution of galaxies is shaped by both internal processes and their external environments. Galaxy clusters and their surroundings provide ideal laboratories to study these effects, particularly mechanisms such as quenching and morphological transformation. The Chilean Cluster galaxy Evolution Survey (CHANCES) Low-z sub-survey is part of the CHileAN Cluster galaxy Evolution Survey, a 4MOST community survey designed to uncover the relationship between the formation and evolution of galaxies and hierarchical structure formation as it happens, through deep and wide multi-object spectroscopy. We present the target selection strategy followed to select galaxy cluster candidate members for the CHANCES low-z sub-survey, in and around 50 clusters and two superclusters at z<0.07, out to (5XR200) and down to mr= 20.4. Combining public photometric redshift estimates from the DESI Legacy Imaging Survey and T80S/S-PLUS iDR5, with custom photometric redshifts, we identify likely galaxy cluster candidate members whose photometric redshifts are consistent with being at the known redshift of the cluster and measure the average deviations of their photometric redshifts with respect to the spectroscopic redshift measurements σNMAD. We have successfully compiled our CHANCES-low-redshift catalogues, split into three different sub-surveys: low-z bright (mr<18.5), low-z faint (18.5<=mr<20.4) and low-z faint supplementary, by selecting>= 500,000 galaxy cluster candidate members and including confirmed spectroscopic galaxy cluster members, from which we expect to obtain 4MOST low-resolution (R~6500) spectra for ~320,000 galaxies. The CHANCES Low-z target catalogues form a statistically robust sample for spectroscopic follow-up, allowing studies of galaxy evolution and environmental effects in nearby cluster and supercluster environments.

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arXiv2024

The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts

The advent of massive broad-band photometric surveys enabled photometric redshift estimates for unprecedented numbers of galaxies and quasars. These estimates can be improved using better algorithms or by obtaining complementary data such as narrow-band photometry, and broad-band photometry over an extended wavelength range. We investigate the impact of both approaches on photometric redshifts for quasars using data from Southern Photometric Local Universe Survey (S-PLUS) DR4, Galaxy Evolution Explorer (GALEX) DR6/7, and the unWISE catalog for the Wide-field Infrared Survey Explorer (WISE) in three machine learning methods: Random Forest, Flexible Conditional Density Estimation (FlexCoDE), and Bayesian Mixture Density Network (BMDN). Including narrow-band photometry improves the root-mean-square error by 11% in comparison to a model trained with only broad-band photometry. Narrow-band information only provided an improvement of 3.8% when GALEX and WISE colours were included. Thus narrow bands play a more important role for objects that do not have GALEX or WISE counterparts, which respectively makes 92% and 25% of S-PLUS data considered here. Nevertheless, the inclusion of narrow-band information provided better estimates of the probability density functions obtained with FlexCoDE and BMDN. We publicly release a value-added catalogue of photometrically selected quasars with the photo-z predictions from all methods studied here. The catalogue provided with this work covers the S-PLUS DR4 area (~3000deg$^2$), containing 645 980, 244 912, 144 991 sources with the probability of being a quasar higher than, 80%, 90%, 95% up to r < 21.3 and good photometry quality in the detection image. More quasar candidates can be retrieved from the S-PLUS data base by considering less restrictive selection criteria.

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arXiv2024

ELEPHANT: ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients

Context. Transient astronomical events that exhibit no discernible association with a host galaxy are commonly referred to as hostless. These rare phenomena are associated with extremely energetic events, and they can offer unique insights into the properties and evolution of stars and galaxies. However, the sheer number of transients captured by contemporary high-cadence astronomical surveys renders the manual identification of all potential hostless transients impractical. Therefore, creating a systematic identification tool is crucial for studying these elusive events. Aims. We present the ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients (ELEPHANT), a framework for filtering hostless transients in astronomical data streams. Methods. We used Fink to access all the ZTF alerts produced between January/2022 and December/2023, selecting only those associated with extragalactic transients. We then processed the associated stamps using a sequence of image analysis techniques to retrieve hostless candidates. Results. We find that less than 2% of all analyzed transients are potentially hostless. Among them, approximately 10% have a spectroscopic class reported on TNS, with Type Ia supernova being the most common class, followed by SLSN. Among the hostless candidates retrieved by our pipeline, there was SN 2018ibb, which has been proposed to be a PISN candidate; and SN 2022ann, one of only five known SNe Icn. When no class is reported on TNS, the dominant classes are QSO and SN candidates, the former obtained from SIMBAD and the latter inferred using the Fink ML classifier. Conclusions. ELEPHANT represents an effective strategy to filter extragalactic events within large and complex astronomical alert streams. There are many applications for which this pipeline will be useful, ranging from transient selection for follow-up to studies of transient environments.

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arXiv2022

S-PLUS DR1 galaxy clusters and groups catalogue using PzWav

We present a catalogue of 4499 groups and clusters of galaxies from the first data release of the multi-filter (5 broad, 7 narrow) Southern Photometric Local Universe Survey (S-PLUS). These groups and clusters are distributed over 273 deg$^2$ in the Stripe 82 region. They are found using the PzWav algorithm, which identifies peaks in galaxy density maps that have been smoothed by a cluster scale difference-of-Gaussians kernel to isolate clusters and groups. Using a simulation-based mock catalogue, we estimate the purity and completeness of cluster detections: at S/N>3.3 we define a catalogue that is 80% pure and complete in the redshift range 0.1 10^{14}$ M$_\odot$. We also assessed the accuracy of the catalogue in terms of central positions and redshifts, finding scatter of $σ_R=12$ kpc and $σ_z=8.8 \times 10^{-3}$, respectively. Moreover, less than 1% of the sample suffers from fragmentation or overmerging. The S-PLUS cluster catalogue recovers ~80% of all known X-ray and Sunyaev-Zel'dovich selected clusters in this field. This fraction is very close to the estimated completeness, thus validating the mock data analysis and paving an efficient way to find new groups and clusters of galaxies using data from the ongoing S-PLUS project. When complete, S-PLUS will have surveyed 9300 deg$^{2}$ of the sky, representing the widest uninterrupted areas with narrow-through-broad multi-band photometry for cluster follow-up studies.

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arXiv2022

Photometric redshifts for the S-PLUS Survey: is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. It achieves single-point photometric redshifts with scatter ($σ_\text{NMAD}$) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r-auto magnitudes between 16 and 21.

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arXiv2021

On the discovery of stars, quasars, and galaxies in the Southern Hemisphere with S-PLUS DR2

This paper provides a catalogue of stars, quasars, and galaxies for the Southern Photometric Local Universe Survey Data Release 2 (S-PLUS DR2) in the Stripe 82 region. We show that a 12-band filter system (5 Sloan-like and 7 narrow bands) allows better performance for object classification than the usual analysis based solely on broad bands (regardless of infrared information). Moreover, we show that our classification is robust against missing values. Using spectroscopically confirmed sources retrieved from the Sloan Digital Sky Survey DR16 and DR14Q, we train a random forest classifier with the 12 S-PLUS magnitudes + 4 morphological features. A second random forest classifier is trained with the addition of the W1 (3.4 $μ$m) and W2 (4.6 $μ$m) magnitudes from the Wide-field Infrared Survey Explorer (WISE). Forty-four percent of our catalogue have WISE counterparts and are provided with classification from both models. We achieve 95.76% (52.47%) of quasar purity, 95.88% (92.24%) of quasar completeness, 99.44% (98.17%) of star purity, 98.22% (78.56%) of star completeness, 98.04% (81.39%) of galaxy purity, and 98.8% (85.37%) of galaxy completeness for the first (second) classifier, for which the metrics were calculated on objects with (without) WISE counterpart. A total of 2,926,787 objects that are not in our spectroscopic sample were labelled, obtaining 335,956 quasars, 1,347,340 stars, and 1,243,391 galaxies. From those, 7.4%, 76.0%, and 58.4% were classified with probabilities above 80%. The catalogue with classification and probabilities for Stripe 82 S-PLUS DR2 is available for download.

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