ABSTRACT
Astrophysical surveys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband 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 a 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 Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) collaboration covering ∼1 deg2 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.
We present a synthetic galaxy lightcone specially designed for narrow-band optical photometric surveys. To reduce time-discreteness effects, unlike previous works, we directly include the lightcone ...construction in the L-Galaxies semi-analytic model applied to the subhalo merger trees of the Millennium simulation. Additionally, we add a model for the nebular emission in star-forming regions, which is crucial for correctly predicting the narrow- and medium-band photometry of galaxies. Specifically, we consider, individually for each galaxy, the contribution of 9 different lines: Lyα (1216 Å), Hβ (4861 Å), Hα (6563 Å), O II (3727 Å, 3729 Å), O III (4959 Å, 5007 Å), Ne III (3870 Å), O I (6300 Å), N II (6548 Å, 6583 Å), and S II (6717 Å, 6731 Å). We validate our lightcone by comparing galaxy number counts, angular clustering, and Hα, Hβ, O II, and O III5007 luminosity functions to a compilation of observations. As an application of our mock lightcones, we generated catalogues tailored for J-PLUS, a large optical galaxy survey featuring five broad-band and seven medium-band filters. We study the ability of the survey to correctly identify, with a simple three-filter method, a population of emission-line galaxies at various redshifts. We show that the 4000 Å break in the spectral energy distribution of galaxies can be misidentified as line emission. However, all significant excess (> 0.4 mag) can be correctly and unambiguously attributed to emission-line galaxies. Our catalogues are publicly released to facilitate their use in interpreting narrow-band surveys and in quantifying the impact of line emission in broad-band photometry.
Context.
The Javalambre Photometric Local Universe Survey (J-PLUS) has obtained precise photometry in 12 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,
T
eff
, 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 the first data release (DR1) of J-PLUS.
Methods.
By combining recalibrated photometric data from J-PLUS DR1,
Gaia
DR2, and spectroscopic labels from the Large sky Area Multi-Object fiber Spectroscopic Telescope, we designed and trained a set of cost-sensitive neural networks, the CSNet, to learn the nonlinear mapping from stellar colours to their labels. Special attention was paid to the poorly populated regions of the label space by giving different weights according to their density distribution.
Results.
We achieved precisions of
δ
T
eff
∼ 55 K,
δ
log
g
∼ 0.15 dex, and
δ
Fe/H ∼ 0.07 dex, respectively, over a wide range of temperatures, surface gravities, and metallicities. The uncertainties of the abundance estimates for
α
/Fe and the four individual elements are in the 0.04–0.08 dex range. We compare our parameter and abundance estimates with those from other spectroscopic catalogs such as the Apache Point Observatory for Galactic Evolution Experiment and the Galactic Archaeology with High Efficiency and Resolution Multi-Element Spectrograph 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 obtained the aforementioned parameters for about two million stars, providing an outstanding dataset for chemo-dynamic analyses of the Milky Way. The catalog of the estimated parameters is publicly accessible.
Context.
We explore the stellar content of the Javalambre Photometric Local Universe Survey (J-PLUS) Data Release 2 and show its potential for identifying low-metallicity stars using the Stellar ...Parameters Estimation based on Ensemble Methods (SPEEM) pipeline.
Aims.
SPEEM is a tool used to provide determinations of atmospheric parameters for stars and separate stellar sources from quasars based on the unique J-PLUS photometric system. The adoption of adequate selection criteria allows for the identification of metal-poor star candidates that are suitable for spectroscopic follow-up investigations.
Methods.
SPEEM consists of a series of machine-learning models that use a training sample observed by both J-PLUS and the SEGUE spectroscopic survey. The training sample has temperatures,
T
eff
, between 4800 K and 9000 K, values of log
g
between 1.0 and 4.5, as well as −3.1 < Fe/H < +0.5. The performance of the pipeline was tested with a sample of stars observed by the LAMOST survey within the same parameter range.
Results.
The average differences between the parameters of a sample of stars observed with SEGUE and J-PLUS, obtained with the SEGUE Stellar Parameter Pipeline and SPEEM, respectively, are Δ
T
eff
~ 41 K, Δlog
g
~ 0.11 dex, and ΔFe/H ~ 0.09 dex. We define a sample of 177 stars that have been identified as new candidates with Fe/H < −2.5, with 11 of them having 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.
Conclusions.
Using SPEEM in combination with the J-PLUS filter system has demonstrated their potential in estimating the stellar atmospheric parameters (
T
eff
, 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.
ABSTRACT
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 ≤ 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 modelling 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.
Abstract 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 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, log N – log S , down to 1.7 × 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 ( N H > 10 23 cm −2 ), and 18 − 8 + 20 % of the NuSTAR-detected sources are Compton-thick ( N H > 10 24 cm −2 ). These fractions are consistent with those measured in other NuSTAR surveys. Four sources presented >2 σ variability in the 3 yr 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 (0.5–10 keV) 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.
The miniJPAS survey quasar selection Pérez-Ràfols, Ignasi; Abramo, Luis Raul; Martínez-Solaeche, Ginés ...
Astronomy and astrophysics (Berlin),
10/2023, Letnik:
678
Journal Article
Recenzirano
Odprti dostop
Aims
. Quasar catalogues from photometric data are used in a variety of applications including those targeting spectroscopic follow-up, measurements of supermassive black hole masses, Baryon Acoustic ...Oscillations, or non-Gaussianities. Here, we present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. miniJPAS is a small proof-of-concept survey covering 1 deg
2
with the full J-PAS filter system, consisting of 54 narrow filters and 2 broader filters covering the entire optical wavelength range.
Methods
. This work is based on the 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 the use of user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, we have adapted it for multi-band photometric data; that is we treat multiple narrow-band filters as very low-resolution spectra. We trained our models using specialised mocks. We estimated our redshift precision using the normalised median absolute deviation,
σ
NMAD
, applied to our test sample.
Results
. Our test sample returns an
f
1
score (effectively the purity and completeness) of 0.49 for high-
z
quasars (with
z
≥ 2.1) down a to magnitude of
r
= 24.3 and 0.24 for low-
z
quasars (with
z
< 2.1), also down to a magnitude of
r
= 24.3. For high-
z
quasars, this goes up to 0.9 for magnitudes of
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.
Supernova environments in J-PLUS González-Díaz, Raúl; Galbany, Lluís; Kangas, Tuomas ...
Astronomy and astrophysics (Berlin),
04/2024, Letnik:
684
Journal Article
Recenzirano
Odprti dostop
We investigated the local environmental properties of 418 supernovae (SNe) of all types using data from the Javalambre Photometric Local Universe Survey (J-PLUS), which includes five broad-band and ...seven narrow-band imaging filters. Our study involves two independent analyses: (1) the normalized cumulative-rank (NCR) method, which utilizes 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 1 kpc2 SN environment using the 12 broad- and narrow-band filters. Improvements on previous works include: (i) the extension of the NCR technique to other filters (broad and narrow) and the use a set of homogeneous data (same telescope and instruments); (ii) a correction for extinction to all bands based on the relation between the g − i color and the color excess E(B − V); and (iii) a correction for the contamination of the N II λ6583 line that falls within the Hα filter. All NCR distributions in the broad-band filters, tracing the overall light distribution in each galaxy, are similar to each other. The main difference is that 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, whereas other types of SNe occur throughout the galaxies without a distinct preference for a specific location. For the Hα filter we recover the sequence from SNe Ic, which has the highest NCR, to SNe Ia, which has the lowest; this is interpreted as a sequence in progenitor mass and age. All core-collapse SN types are strongly correlated to the O II emission, which also traces star formation rate (SFR), following the same sequence as in Hα. 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 seven J-PLUS narrow filters in the fitting process has a more significant effect on the core-collapse SN environmental parameters than for SNe Ia, shifting their values toward more extincted, younger, and more star-forming environments, due to the presence of strong emission lines and stellar absorptions in those narrow bands.
ABSTRACT
With a unique set of 54 overlapping narrow-band and two broader filters covering the entire optical range, the incoming Javalambre-Physics of the Accelerating Universe Astrophysical Survey ...(J-PAS) will provide a great opportunity for stellar physics and near-field cosmology. In this work, we use the miniJPAS data in 56 J-PAS filters and 4 complementary SDSS-like filters to explore and prove the potential of the J-PAS filter system in characterizing stars and deriving their atmospheric parameters. We obtain estimates for the effective temperature with a good precision (<150 K) from spectral energy distribution fitting. We have constructed the metallicity-dependent stellar loci in 59 colours for the miniJPAS FGK dwarf stars, after correcting certain systematic errors in flat-fielding. The very blue colours, including uJAVA − r, J0378 − r, J0390 − r, uJPAS − r, show the strongest metallicity dependence, around 0.25 mag dex−1. The sensitivities decrease to about 0.1 mag dex−1 for the J0400 − r, J0410 − r, and J0420 − r colours. The locus fitting residuals show peaks at the J0390, J0430, J0510, and J0520 filters, suggesting that individual elemental abundances such as Ca/Fe, C/Fe, and Mg/Fe can also be determined from the J-PAS photometry. Via stellar loci, we have achieved a typical metallicity precision of 0.1 dex. The miniJPAS filters also demonstrate strong potential in discriminating dwarfs and giants, particularly the J0520 and J0510 filters. Our results demonstrate the power of the J-PAS filter system in stellar parameter determinations and the huge potential of the coming J-PAS survey in stellar and Galactic studies.
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