Engineering topics

Angulo, R. E.

Publications and source records attributed to Angulo, R. E..

3 records

DOE OSTI2022

J-PLUS: Uncovering a large population of extreme [OIII] emitters in the local Universe

Over the past decades, several studies have discovered a population of galaxies that undergo very strong star formation events. They are called extreme emission line galaxies (EELGs). We exploit the capabilities of the Javalambre Photometric Local Universe Survey (J-PLUS), a wide-field multifilter survey, with which 2000 square degrees of the northern sky are already observed. We use it to identify EELGs at low redshift by their [OIII]5007 emission line. We intend to provide a more complete, deep, and less biased sample of local EELGs. We selected objects with an excess of flux in the J-PLUS medium-band J0515 filter, which covers the [OIII] line at z < 0.06. We removed contaminants (stars and higher-redshift systems) using J-PLUS and WISE infrared photometry, with SDSS spectra as a benchmark. We performed spectral energy distribution fitting to estimate the physical properties of the galaxies: line fluxes, equivalent widths (EWs), masses, stellar population ages, and so on. We identify 466 EELGs at z < 0.06 with [OIII] EW over 300 Å and an r-band magnitude below 20, of which 411 were previously unknown. Most show compact morphologies, low stellar masses (log(M • /M ⊙ ) ~ 8.13 –0.58 +0.61 ), low dust extinction (E( B–V ) ~ 0.1 –0.1 +0.2 ), and very young bursts of star formation (3.0 –2.0 +2.7 Myr). Our method is up to ~20 times more efficient in detecting EELGs per Mpc 3 than broadband surveys, and it is as complete as magnitude-limited spectroscopic surveys (but reaches fainter objects). The sample is not directly biased against strong Hα emitters, in contrast with works using broadband surveys. We demonstrate that J-PLUS can identify a large sample of previously unknown EELGs showing unique properties following a clear selection process. A fraction of the EELGs are probably similar to the first galaxies in the Universe, but they are at a much lower redshift, which makes them ideal targets for follow-up studies.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Constraining the baryonic feedback with cosmic shear using the DES Year-3 small-scale measurements

ABSTRACT We use the small scales of the Dark Energy Survey (DES) Year-3 cosmic shear measurements, which are excluded from the DES Year-3 cosmological analysis, to constrain the baryonic feedback. To model the baryonic feedback, we adopt a baryonic correction model and use the numerical package baccoemu to accelerate the evaluation of the baryonic non-linear matter power spectrum. We design our analysis pipeline to focus on the constraints of the baryonic suppression effects, utilizing the implication given by a principal component analysis on the Fisher forecasts. Our constraint on the baryonic effects can then be used to better model and ameliorate the effects of baryons in producing cosmological constraints from the next-generation large-scale structure surveys. We detect the baryonic suppression on the cosmic shear measurements with a ∼2σ significance. The characteristic halo mass for which half of the gas is ejected by baryonic feedback is constrained to be $M_c \gt 10^{13.2} \, h^{-1} \, \mathrm{M}_{\odot }$ (95 per cent C.L.). The best-fitting baryonic suppression is $\sim 5{{\ \rm per\ cent}}$ at $k=1.0 \, {\rm Mpc}\ h^{-1}$ and $\sim 15{{\ \rm per\ cent}}$ at $k=5.0 \, {\rm Mpc} \ h^{-1}$. Our findings are robust with respect to the assumptions about the cosmological parameters, specifics of the baryonic model, and intrinsic alignments.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

J-PLUS: Support vector regression to measure stellar parameters

Stellar parameters are among the most important characteristics in studies of stars which, in traditional methods, are based on atmosphere models. However, time, cost, and brightness limits restrain the efficiency of spectral observations. The Javalambre Photometric Local Universe Survey (J-PLUS) is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools for efficiently analyzing large data sets, such as the one from J-PLUS, and enables us to expand the research domain to stellar parameters. The main goal of this study is to construct a support vector regression (SVR) algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. The training data for the parameter's regressions are featured with 12-waveband photometry from J-PLUS and are crossidentified with spectrum-based catalogs. These catalogs are from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Apache Point Observatory Galactic Evolution Experiment, and the Sloan Extension for Galactic Understanding and Exploration. We then label them with the stellar effective temperature, the surface gravity, and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach, in order to fully take into account, the uncertainties of both the magnitudes and the stellar parameters. The method utilizes more than 200 models to apply the uncertainty analysis. We present a catalog of 2 493 424 stars with the root mean square error of 160 K in the effective temperature regression, 0.35 in the surface gravity regression, and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.

79 ASTRONOMY AND ASTROPHYSICS↗