J-PLUS: analysis of the intracluster light in the Coma cluster

The intracluster light (ICL) is a luminous component of galaxy clusters composed of stars that are gravitationally bound to the cluster potential but do not belong to the individual galaxies. Previous studies of the ICL have shown that its formation and evolution are intimately linked to the evolutionary stage of the cluster. Thus, the analysis of the ICL in the Coma cluster will give insights into the main processes driving the dynamics in this highly complex system. Using a recently developed technique, we measure the ICL fraction in Coma at several wavelengths, using the J-PLUS unique filter system. The combination of narrow- and broadband filters provides valuable information on the dynamical state of the cluster, the ICL stellar types, and the morphology of the diffuse light. We use the Chebyshev-Fourier Intracluster Light Estimator (CICLE) to disentangle the ICL from the light of the galaxies, and to robustly measure the ICL fraction in seven J-PLUS filters. We obtain the ICL fraction distribution of the Coma cluster at different optical wavelengths, which varies from $\sim 7\%-21\%$, showing the highest values in the narrowband filters J0395, J0410, and J0430. This ICL fraction excess is distinctive pattern recently observed in dynamically active clusters (mergers), indicating a higher amount of bluer stars in the ICL compared to the cluster galaxies. Both the high ICL fractions and the excess in the bluer filters are indicative of a merging state. The presence of younger/lower-metallicity stars the ICL suggests that the main mechanism of ICL formation for the Coma cluster is the stripping of the stars in the outskirts of infalling galaxies and, possibly, the disruption of dwarf galaxies during past/ongoing mergers.Comment: 10 pages, 3 figures, 1 table. Accepted for publication in A&

J-PLUS: Identification of low-metallicity stars with artificial neural networks using SPHINX

We present a new methodology for the estimation of stellar atmospheric parameters from narrow- and intermediate-band photometry of the Javalambre Photometric Local Universe Survey (J-PLUS), and propose a method for target pre-selection of low-metallicity stars for follow-up spectroscopic studies. Photometric metallicity estimates for stars in the globular cluster M15 are determined using this method. By development of a neural-network-based photometry pipeline, we aim to produce estimates of effective temperature, $T_{\rm eff}$, and metallicity, [Fe/H], for a large subset of stars in the J-PLUS footprint. The Stellar Photometric Index Network Explorer, SPHINX, is developed to produce estimates of $T_{\rm eff}$ and [Fe/H], after training on a combination of J-PLUS photometric inputs and synthetic magnitudes computed for medium-resolution (R ~ 2000) spectra of the Sloan Digital Sky Survey. This methodology is applied to J-PLUS photometry of the globular cluster M15. Effective temperature estimates made with J-PLUS Early Data Release photometry exhibit low scatter, \sigma($T_{\rm eff}$) = 91 K, over the temperature range 4500 < $T_{\rm eff}$ (K) < 8500. For stars from the J-PLUS First Data Release with 4500 < $T_{\rm eff}$ (K) < 6200, 85 $\pm$ 3% of stars known to have [Fe/H] <-2.0 are recovered by SPHINX. A mean metallicity of [Fe/H]=-2.32 $\pm$ 0.01, with a residual spread of 0.3 dex, is determined for M15 using J-PLUS photometry of 664 likely cluster members. We confirm the performance of SPHINX within the ranges specified, and verify its utility as a stand-alone tool for photometric estimation of effective temperature and metallicity, and for pre-selection of metal-poor spectroscopic targets.Comment: 18 pages, 12 figure

J-PAS: forecasts on interacting vacuum energy models

The next generation of galaxy surveys will allow us to test some fundamental aspects of the standard cosmological model, including the assumption of a minimal coupling between the components of the dark sector. In this paper, we present the Javalambre Physics of the Accelerated Universe Astrophysical Survey (J-PAS) forecasts on a class of unified models where cold dark matter interacts with a vacuum energy, considering future observations of baryon acoustic oscillations, redshift-space distortions, and the matter power spectrum. After providing a general framework to study the background and linear perturbations, we focus on a concrete interacting model without momentum exchange by taking into account the contribution of baryons. We compare the J-PAS results with those expected for DESI and Euclid surveys and show that J-PAS is competitive to them, especially at low redshifts. Indeed, the predicted errors for the interaction parameter, which measures the departure from a $\Lambda$CDM model, can be comparable to the actual errors derived from the current data of cosmic microwave background temperature anisotropies.Comment: 34 pages, 9 figures, 14 table

J-PLUS DR3: Galaxy-Star-Quasar classification

The Javalambre Photometric Local Universe Survey (J-PLUS) is a 12-band photometric survey using the 83-cm JAST telescope. Data Release 3 includes 47.4 million sources (29.8 million with $r \le 21$) on 3192 deg$^2$ (2881 deg$^2$ after masking). J-PLUS DR3 only provides star-galaxy classification so that quasars are not identified from the other sources. Given the size of the dataset, machine learning methods could provide a valid alternative classification and a solution to the classification of quasars. Our objective is to classify J-PLUS DR3 sources into galaxies, stars and quasars, outperforming the available classifiers in each class. We use an automated machine learning tool called {\tt TPOT} to find an optimized pipeline to perform the classification. The supervised machine learning algorithms are trained on the crossmatch with SDSS DR12, LAMOST DR7 and \textit{Gaia} DR3. We checked that the training set of about 570 thousand galaxies, one million stars and 220 thousand quasars is both representative and pure to a good degree. We considered 37 features: besides the twelve photometric bands with their errors, six colors, four morphological parameters, galactic extinction with its error and the PSF relative to the corresponding pointing. After exploring numerous pipeline possibilities through the TPOT genetic algorithm, we found that XGBoost provides the best performance: the AUC for galaxies, stars and quasars is above 0.99 and the average precision is above 0.99 for galaxies and stars and 0.94 for quasars. XGBoost outperforms the star-galaxy classifiers already provided in J-PLUS DR3 and also efficiently classifies quasars. We also found that photometry was very important in the classification of quasars, showing the relevance of narrow-band photometry.Comment: 14 pages, 17 figure

Production of medium-chain fatty acids and higher alcohols by a synthetic co-culture grown on carbon monoxide or syngas

Synthesis gas, a mixture of CO, H2, and CO2, is a promising renewable feedstock for bio-based production of organic chemicals. Production of medium-chain fatty acids can be performed via chain elongation, utilizing acetate and ethanol as main substrates. Acetate and ethanol are main products of syngas fermentation by acetogens. Therefore, syngas can be indirectly used as a substrate for the chain elongation process.ERC Grant (Project 323009) and the Gravitation Grant (Project 024.002.002) of the Netherlands Ministry of Education, Culture and Science, and the Netherlands Science Foundation (NWO

The miniJPAS survey:star-galaxy classification using machine learning

Future astrophysical surveys such as J-PAS will produce very large datasets, 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 deg2 of the AEGIS field with 56 narrow-band filters and 4 ugri broad-band filters. We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g. stars) objects, a necessary step for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools based on explicit modeling. In order to train and test our classifiers, we crossmatched the miniJPAS dataset with SDSS and HSC-SSP data. We trained and tested 6 different ML algorithms on the two crossmatched catalogs. As input for the ML algorithms we use the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also use the mean PSF in the r detection band for each pointing. We find that the RF and ERT algorithms perform best in all scenarios. When analyzing the full magnitude range of 1521). We use our best classifiers, with and without morphology, in order to produce a value added catalog available at https://j-pas.org/datareleases

Adherence to Interferon β-1b Treatment in Patients with Multiple Sclerosis in Spain

Adherence to interferon β-1b (INFβ-1b) therapy is essential to maximize the beneficial effects of treatment in multiple sclerosis (MS). For that reason, the main objectives of this study are to assess adherence to INFβ-1b in patients suffering from MS in Spain, and to identify the factors responsible for adherence in routine clinical practice.This was an observational, retrospective, cross-sectional study including 120 Spanish patients with MS under INFβ-1b treatment. Therapeutic adherence was assessed with Morisky-Green test and with the percentage of doses received. The proportion of adherent patients assessed by Morisky-Green test was 68.3%, being indicative of poor adherence. Nevertheless, the percentage of doses received, which was based on the number of injected medication, was 94.3%. The main reason for missing INFβ-1b injections was forgetting some of the administrations (64%). Therefore, interventions that diminish forgetfulness might have a positive effect in the proportion of adherent patients and in the percentage of doses received. In addition, age and comorbidities had a significant effect in the number of doses injected per month, and should be considered in the management of adherence in MS patients.Among all the available methods for assessing adherence, the overall consumption of the intended dose has to be considered when addressing adherence

The miniJPAS survey: Identification and characterization of the emission line galaxies down to z<0.35z < 0.35 in the AEGIS field

The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) is expected to map thousands of square degrees of the northern sky with 56 narrowband filters in the upcoming years. This will make J-PAS a very competitive and unbiased emission line survey compared to spectroscopic or narrowband surveys with fewer filters. The miniJPAS survey covered 1 deg$^2$, and it used the same photometric system as J-PAS, but the observations were carried out with the pathfinder J-PAS camera. In this work, we identify and characterize the sample of emission line galaxies (ELGs) from miniJPAS with a redshift lower than $0.35$. Using a method based on artificial neural networks, we detect the ELG population and measure the equivalent width and flux of the $H\alpha$, $H\beta$, [OIII], and [NII] emission lines. We explore the ionization mechanism using the diagrams [OIII]/H$\beta$ versus [NII]/H$\alpha$ (BPT) and EW(H$\alpha$) versus [NII]/H$\alpha$ (WHAN). We identify 1787 ELGs ($83$%) from the parent sample (2154 galaxies) in the AEGIS field. For the galaxies with reliable EW values that can be placed in the WHAN diagram (2000 galaxies in total), we obtained that $72.8 \pm 0.4$%, $17.7 \pm 0.4$% , and $9.4 \pm 0.2$% are star-forming (SF), active galactic nucleus (Seyfert), and quiescent galaxies, respectively. Based on the flux of $H\alpha$ we find that the star formation main sequence is described as $\log$ SFR $[M_\mathrm{\odot} \mathrm{yr}^{-1}] = 0.90^{+ 0.02}_{-0.02} \log M_{\star} [M_\mathrm{\odot}] -8.85^{+ 0.19}_{-0.20}$ and has an intrinsic scatter of $0.20^{+ 0.01}_{-0.01}$. The cosmic evolution of the SFR density ($\rho_{\text{SFR}}$) is derived at three redshift bins: $0 < z \leq 0.15$, $0.15 < z \leq 0.25$, and $0.25 < z \leq 0.35$, which agrees with previous results that were based on measurements of the $H\alpha$ emission line.Comment: 22 pages, 19 figure

TOPz: Photometric redshifts for J-PAS

The importance of photometric galaxy redshift estimation is rapidly increasing with the development of specialised powerful observational facilities. We develop a new photometric redshift estimation workflow TOPz to provide reliable and efficient redshift estimations for the upcoming large-scale survey J-PAS which will observe 8500 deg2 of the northern sky through 54 narrow-band filters. TOPz relies on template-based photo-z estimation with some added J-PAS specific features and possibilities. We present TOPz performance on data from the miniJPAS survey, a precursor to the J-PAS survey with an identical filter system. First, we generated spectral templates based on the miniJPAS sources using the synthetic galaxy spectrum generation software CIGALE. Then we applied corrections to the input photometry by minimising systematic offsets from the template flux in each filter. To assess the accuracy of the redshift estimation, we used spectroscopic redshifts from the DEEP2, DEEP3, and SDSS surveys, available for 1989 miniJPAS galaxies with r < 22 magAB. We also tested how the choice and number of input templates, photo-z priors, and photometric corrections affect the TOPz redshift accuracy. The general performance of the combination of miniJPAS data and the TOPz workflow fulfills the expectations for J-PAS redshift accuracy. Similarly to previous estimates, we find that 38.6% of galaxies with r < 22 mag reach the J-PAS redshift accuracy goal of dz/(1 + z) < 0.003. Limiting the number of spectra in the template set improves the redshift accuracy up to 5%, especially for fainter, noise-dominated sources. Further improvements will be possible once the actual J-PAS data become available.Comment: 20 pages, 24 figure

The miniJPAS survey: clusters and galaxy groups detection with AMICO

Samples of galaxy clusters allow us to better understand the physics at play in galaxy formation and to constrain cosmological models once their mass, position (for clustering studies) and redshift are known. In this context, large optical data sets play a crucial role. We investigate the capabilities of the Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) in detecting and characterizing galaxy groups and clusters. We analyze the data of the miniJPAS survey, obtained with the JPAS-Pathfinder camera and covering $1$ deg$^2$ centered on the AEGIS field to the same depths and with the same 54 narrow band plus 2 broader band near-UV and near-IR filters anticipated for the full J-PAS survey. We use the Adaptive Matched Identifier of Clustered Objects (AMICO) to detect and characterize groups and clusters of galaxies down to $S/N=2.5$ in the redshift range $0.05<z<0.8$. We detect 80, 30 and 11 systems with signal-to-noise ratio larger than 2.5, 3.0 and 3.5, respectively, down to $\sim 10^{13}\,M_{\odot}/h$. We derive mass-proxy scaling relations based on Chandra and XMM-Newton X-ray data for the signal amplitude returned by AMICO, the intrinsic richness and a new proxy that incorporates the galaxies' stellar masses. The latter proxy is made possible thanks to the J-PAS filters and shows a smaller scatter with respect to the richness. We fully characterize the sample and use AMICO to derive a probabilistic membership association of galaxies to the detected groups that we test against spectroscopy. We further show how the narrow band filters of J-PAS provide a gain of up to 100% in signal-to-noise ratio in detection and an uncertainty on the redshift of clusters of only $\sigma_z=0.0037(1+z)$ placing J-PAS in between broadband photometric and spectroscopic surveys. The performances of AMICO and J-PAS with respect to mass sensitivity, mass-proxies qualityComment: 15 pages, 12 figures, 3 tables, submitted to A&