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\(\alpha\) 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.
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, \(\sigma_{\rm NMAD}\) 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 \(\sigma_{NMAD}=0.92\%\) 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.
Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, ...but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a Machine Learning-based method that employs Convolutional Neural Networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering \(\sim\) 1 deg\(^2\) of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established Machine Learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars and unresolved galaxies. Our results are a proof-of-concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.
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.
A&A 614, A116 (2018) Context: Recent near-infrared data have contributed to unveil massive and
obscured stellar populations in both new and previously known clusters in our
Galaxy. These discoveries ...lead us to view the Milky Way as an active
star-forming machine. Aims: We look for young massive cluster candidates as
over-densities of OB-type stars. The first search, focused on the Galactic
direction $l=38^{\circ}$, resulted in the detection of two objects with a
remarkable population of OB-type star candidates. Methods: With a modified
version of the friends-of-friends algorithm AUTOPOP and using 2MASS and
UKIDSS-GPS near-infrared ($J$, $H$, and $K$) photometry for one of our cluster
candidates (named Masgomas-6) we selected 30 stars for multi-object and
long-slit $H$- and $K$-spectroscopy. With the spectral classification and the
near-infrared photometric data, we derive individual distance, extinction and
radial velocity. Results: Of the 30 spectroscopically observed stars, 20 are
classified as massive stars, including OB-types (dwarfs, giants and
supergiants), two red supergiants, two Wolf-Rayet (WR122-11 and the new
WR122-16), and one transitional object (the LBV candidate IRAS 18576+0341). The
individual distances and radial velocities do not agree with a single cluster,
indicating that we are observing two populations of massive stars in the same
line-of-sight: Masgomas-6a and Masgomas-6b. The first group of massive stars,
located at 3.9$^{+0.4}_{-0.3}$ kpc, contains both Wolf-Rayets and most of the
OB-dwarfs, and Masgomas-6b, at $9.6\pm0.4$ kpc, hosts the LBV candidate and an
evolved population of supergiants. We are able to identify massive stars at two
Galactic arms, but we can not clearly identify whether these massive stars form
clusters or associations.
Precise measurements of black hole masses are essential to understanding the coevolution of these sources and their host galaxies. We develop a novel approach for computing black hole virial masses ...using measurements of continuum luminosities and emission line widths from partially overlapping, narrow-band observations of quasars; we refer to this technique as single-epoch photometry. This novel method relies on forward-modelling quasar observations for estimating emission line widths, which enables unbiased measurements even for lines coarsely resolved by narrow-band data. We assess the performance of this technique using quasars from the Sloan Digital Sky Survey (SDSS) observed by the miniJPAS survey, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering \(\simeq1\,\mathrm{deg}^2\) of the northern sky using the 56 J-PAS narrow-band filters. We find remarkable agreement between black hole masses from single-epoch SDSS spectra and single-epoch miniJPAS photometry, with no systematic difference between these and a scatter ranging from 0.4 to 0.07 dex for masses from \(\log(M_\mathrm{BH})\simeq8\) to 9.75, respectively. Reverberation mapping studies show that single-epoch masses present approximately 0.4 dex precision, letting us conclude that our novel technique delivers black hole masses with only mildly lower precision than single-epoch spectroscopy. The J-PAS survey will soon start observing thousands of square degrees without any source preselection other than the photometric depth in the detection band, and thus single-epoch photometry has the potential to provide details on the physical properties of quasar populations that do not satisfy the preselection criteria of previous spectroscopic surveys.
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 {\alpha}/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 {\delta}Teff {\sim}55K, {\delta}logg{\sim}0.15dex, and {\delta}Fe/H{\sim}0.07dex, respectively, over a wide range of temperature, surface gravity, and metallicity. The uncertainties of the abundance estimates for {\alpha}/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.
In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative ...sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to compute synthetic photometry of quasars, galaxies and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range \(17.5\leq r<24\), we augment our sample of available spectra by shifting the original \(r\)-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.
We consider a cosmological model where dark matter and dark energy feature a coupling that only affects their momentum transfer in the corresponding Euler equations. We perform a fit to cosmological ...observables and confirm previous findings within these scenarios that favour the presence of a coupling at more than \(3\sigma\). This improvement is driven by the Sunyaev-Zeldovich data. We subsequently perform a forecast for future J-PAS data and find that clustering measurements will permit to clearly discern the presence of an interaction within a few percent level with the uncoupled case at more than \(10\sigma\) when the complete survey, covering \(8500\) sq. deg., is considered. We found that the inclusion of weak lensing measurements will not help to further constrain the coupling parameter. For completeness, we compare to forecasts for DESI and Euclid, which provide similar discriminating power.
We present a near-IR and optical spectrophotometric characterization of the stellar population of Sh2-152, as part of our MASGOMAS project. Using new broad band photometry (J, H and KS) for the ...cluster and a control field, we have constructed CMD in order to select OB-candidates for H and K spectroscopy. Also, we have obtained the cluster mass function, with the disc population subtracted using the control field mass function. From the 13 spectroscopically observed stars, 6 were classified as B-dwarfs and with individual distance and extinction estimations. With these values we have obtained estimations for the distance (3.01 ± 0.11) kpc, mass (1.86 ± 0.83)·103 Mo and age < 8.1 Myr for Sh2-152. We also present a new optical spectrum for the central ionizing star of Sh2-152, showing some peculiarities associated to this central object and shed some light over the interesting star deeply embedded into the bright KS nebulosity close to the IRAS source IRAS 22566+5828.
