Aims. Our goal is to morphologically classify the sources identified in the images of the J-PLUS early data release (EDR) as compact (stars) or extended (galaxies) using a dedicated Bayesian ...classifier. Methods. J-PLUS sources exhibit two distinct populations in the r-band magnitude versus concentration plane, corresponding to compact and extended sources. We modelled the two-population distribution with a skewed Gaussian for compact objects and a log-normal function for the extended objects. The derived model and the number density prior based on J-PLUS EDR data were used to estimate the Bayesian probability that a source is a star or a galaxy. This procedure was applied pointing-by-pointing to account for varying observing conditions and sky positions. Finally, we combined the morphological information from the g, r, and i broad bands in order to improve the classification of low signal-to-noise sources. Results. The derived probabilities are used to compute the pointing-by-pointing number counts of stars and galaxies. The former increases as we approach the Milky Way disk, and the latter are similar across the probed area. The comparison with SDSS in the common regions is satisfactory up to r ~ 21, with consistent numbers of stars and galaxies, and consistent distributions in concentration and (g−i) colour spaces. Conclusions. We implement a morphological star/galaxy classifier based on probability distribution function analysis, providing meaningful probabilities for J-PLUS sources to one magnitude deeper (r ~ 21) than a classical Boolean classification. These probabilities are suited for the statistical study of 150 thousand stars and 101 thousand galaxies with 15 < r ≤ 21 present in the 31.7 deg2 of the J-PLUS EDR. In a future version of the classifier, we will include J-PLUS colour information from 12 photometric bands.
Context.
Future astrophysical surveys such as J-PAS will produce very large datasets, the so-called “big data”, which will require the deployment of accurate and efficient machine-learning (ML) ...methods. In this work, we analyze the miniJPAS survey, which observed about ∼1 deg
2
of the AEGIS field with 56 narrow-band filters and 4
u
g
r
i
broad-band filters. The miniJPAS primary catalog contains approximately 64 000 objects in the
r
detection band (mag
A
B
≲ 24), with forced-photometry in all other filters.
Aims.
We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g., stars) objects, which is a step required for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools that are based on explicit modeling. In particular, our goal is to release a value-added catalog with our best classification.
Methods.
In order to train and test our classifiers, we cross-matched the miniJPAS dataset with SDSS and HSC-SSP data, whose classification is trustworthy within the intervals 15 ≤
r
≤ 20 and 18.5 ≤
r
≤ 23.5, respectively. We trained and tested six different ML algorithms on the two cross-matched catalogs: K-nearest neighbors, decision trees, random forest (RF), artificial neural networks, extremely randomized trees (ERT), and an ensemble classifier. This last is a hybrid algorithm that combines artificial neural networks and RF with the J-PAS stellar and galactic loci classifier. As input for the ML algorithms we used the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also used the mean point spread function in the
r
detection band for each pointing.
Results.
We find that the RF and ERT algorithms perform best in all scenarios. When the full magnitude range of 15 ≤
r
≤ 23.5 is analyzed, we find an area under the curve AUC = 0.957 with RF when photometric information alone is used, and AUC = 0.986 with ERT when photometric and morphological information is used together. When morphological parameters are used, the full width at half maximum is the most important feature. When photometric information is used alone, we observe that broad bands are not necessarily more important than narrow bands, and errors (the width of the distribution) are as important as the measurements (central value of the distribution). In other words, it is apparently important to fully characterize the measurement.
Conclusions.
ML algorithms can compete with traditional star and galaxy classifiers; they outperform the latter at fainter magnitudes (
r
≳ 21). We use our best classifiers, with and without morphology, in order to produce a value-added catalog.
We extend the spectral range of our stellar population synthesis models based on the MILES and CaT empirical stellar spectral libraries. For this purpose, we combine these two libraries with the ...Indo-U.S. to construct composite stellar spectra to feed our models. The spectral energy distributions (SEDs) computed with these models and the originally published models are combined to construct composite SEDs for single-age, single-metallicity stellar populations (SSPs) covering the range λλ3465-9469 Å at moderately high and uniform resolution (full width at half-maximum = 2.51 Å). The colours derived from these SSP SEDs provide good fits to Galactic globular cluster data. We find that the colours involving redder filters are very sensitive to the initial mass function (IMF), as well as a number of features and molecular bands throughout the spectra. To illustrate the potential use of these models, we focus on the Na i doublet at 8200 Å and with the aid of the newly synthesized SSP model SEDs, we define a new IMF-sensitive index that is based on this feature, which overcomes various limitations from previous index definitions for low-velocity dispersion stellar systems. We propose an index-index diagram based on this feature and the neighbouring Ca ii triplet at 8600 Å, to constrain the IMF if the age and Na/Fe abundance are known. Finally we also show a survey-oriented spectrophotometric application which evidences the accurate flux calibration of these models for carrying out reliable spectral fitting techniques. These models are available through our user-friendly website.
In the years to come, the Javalambre-Physics of the Accelerated Universe Astrophysical Survey (J-PAS) will observe 8000 deg
2
of the northern sky with 56 photometric bands. J-PAS is ideal for the ...detection of nebular emission objects. This paper presents a new method based on artificial neural networks (ANNs) that is aimed at measuring and detecting emission lines in galaxies up to
z
= 0.35. These lines are essential diagnostics for understanding the evolution of galaxies through cosmic time. We trained and tested ANNs with synthetic J-PAS photometry from CALIFA, MaNGA, and SDSS spectra. To this aim, we carried out two tasks. First, we clustered galaxies in two groups according to the values of the equivalent width (EW) of H
α
, H
β
, N
II
, and O
III
lines measured in the spectra. Then we trained an ANN to assign a group to each galaxy. We were able to classify them with the uncertainties typical of the photometric redshift measurable in J-PAS. Second, we utilized another ANN to determine the values of those EWs. Subsequently, we obtained the N
II
/H
α
, O
III
/H
β
, and O 3N 2 ratios, recovering the BPT diagram (O
III
/H
β
versus N
II
/H
α
). We studied the performance of the ANN in two training samples: one is only composed of synthetic J-PAS photo-spectra (J-spectra) from MaNGA and CALIFA (CALMa set) and the other one is composed of SDSS galaxies. We were able to fully reproduce the main sequence of star-forming galaxies from the determination of the EWs. With the CALMa training set, we reached a precision of 0.092 and 0.078 dex for the N
II
/H
α
and O
III
/H
β
ratios in the SDSS testing sample. Nevertheless, we find an underestimation of those ratios at high values in galaxies hosting an active galactic nuclei. We also show the importance of the dataset used for both training and testing the model. Such ANNs are extremely useful for overcoming the limitations previously expected concerning the detection and measurements of the emission lines in such surveys as J-PAS. Furthermore, we show the capability of the method to measure a EW of 10 Å in H
α
, H
β
, N
II
and O
III
lines with a signal-to-noise ratio (S/N) of 5, 1.5, 3.5, and 10, respectively, in the photometry. Finally, we compare the properties of emission lines in galaxies observed with miniJPAS and SDSS. Despite the limitation of such a comparison, we find a remarkable correlation in their EWs.
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.
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•The specimens of UHMWPE retained their mirror finish after plasma treatment.•Contact angle values were lowest the day after atmospheric plasma treatment.•Initial increase of αx ...fraction in UHMWPE was related to the increase in wettability.•Contact angle values are recovered as time without reaching the initial values.•Contact angle values stabilize 30 days after plasma treatment.
The wettability of ultra-high molecular weight polyethylene surfaces was modified by atmospheric plasma treatment controlling exposure time and distance. The two-factor experimental design included treating samples for 0.5 and 1.5 s, placed at 2.0 and 4.0 cm from the plasma. The behavior of wettability was monitored for 90 days via the sessile drop method. Right after treatment, contact angle decreased from 85 to nearly 40 degrees. These changes coincided with an increase in the proportion of the orthorhombic crystalline phase and a reduction in the amorphous phase of the material. Later on, these structural changes were reversed. After 90 days of the plasma treatment, the contact angle was 66 and 73 degrees in samples located 2 and 4 cm away from the plasma, respectively. Although the plasma treatment increased the roughness of the polyethylene surface by 6 nm, it basically retained its mirror finish, except for a few blemishes here and there.
BACKGROUNDPassive immunotherapy with convalescent plasma (CP) is a potential treatment for COVID-19. Evidence from controlled clinical trials is inconclusive.METHODSWe conducted a randomized, ...open-label, controlled clinical trial at 27 hospitals in Spain. Patients had to be admitted for COVID-19 pneumonia within 7 days from symptom onset and not on mechanical ventilation or high-flow oxygen devices. Patients were randomized 1:1 to treatment with CP in addition to standard of care (SOC) or to the control arm receiving only SOC. The primary endpoint was the proportion of patients in categories 5 (noninvasive ventilation or high-flow oxygen), 6 (invasive mechanical ventilation or extracorporeal membrane oxygenation ECMO), or 7 (death) at 14 days. Primary analysis was performed in the intention-to-treat population.RESULTSBetween April 4, 2020, and February 5, 2021, 350 patients were randomly assigned to either CP (n = 179) or SOC (n = 171). At 14 days, proportion of patients in categories 5, 6, or 7 was 11.7% in the CP group versus 16.4% in the control group (P = 0.205). The difference was greater at 28 days, with 8.4% of patients in categories 5-7 in the CP group versus 17.0% in the control group (P = 0.021). The difference in overall survival did not reach statistical significance (HR 0.46, 95% CI 0.19-1.14, log-rank P = 0.087).CONCLUSIONCP showed a significant benefit in preventing progression to noninvasive ventilation or high-flow oxygen, invasive mechanical ventilation or ECMO, or death at 28 days. The effect on the predefined primary endpoint at 14 days and the effect on overall survival were not statistically significant.TRIAL REGISTRATIONClinicaltrials.gov, NCT04345523.FUNDINGGovernment of Spain, Instituto de Salud Carlos III.
In this paper we aim to validate a methodology designed to obtain Hα emission line fluxes from J-PLUS photometric data. J-PLUS is a multi narrow-band filter survey carried out with the 2 deg2 field ...of view T80Cam camera, mounted on the JAST/T80 telescope in the OAJ, Teruel, Spain. The information of the twelve J-PLUS bands, including the J0660 narrow-band filter located at rest-frame Hα, is used over the first 42 deg2 observed to retrieve de-reddened and NII decontaminated Hα emission line fluxes of 46 star-forming regions with previous SDSS and/or CALIFA spectroscopic information. The agreement between the J-PLUS Hα fluxes and those obtained with spectroscopic data is remarkable, finding a median comparison ratio with a scatter of R = F H α J − PLUS / F H α spec = 1.05 ± 0.25 $ \mathcal{R}\,{=}\,F^{\mathrm{J-PLUS}}_{\mathrm{H\alpha}}/F^{\mathrm{spec}}_{\mathrm{H\alpha}}\,{=}\,1.05\,{\pm}\,0.25 $ . This demonstrates that it is possible to retrieve reliable Hα emission line fluxes from J-PLUS photometric data. With an expected area of thousands of square degrees upon completion, the J-PLUS dataset will allow the study of several star formation science cases in the nearby universe, as the spatially resolved star formation rate of nearby galaxies at z ≤ 0.015, and how it is influenced by the environment, morphology, stellar mass, and nuclear activity. As an illustrative example, the close pair of interacting galaxies NGC 3994 and NGC 3995 is analysed, finding an enhancement of the star formation rate not only in the centre, but also in outer parts of the disk of NGC 3994.
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.
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