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A. Lopes

Publications and source records attributed to A. Lopes.

4 recordsLinked to original sources

Estimating stellar population and emission line properties in S-PLUS galaxies

We present tests of a new method to simultaneously estimate stellar population and emission line (EL) properties of galaxies out of S-PLUS photometry. The technique uses the AlStar code, updated with an empirical prior which greatly improves its ability to estimate ELs using only the survey's 12 bands. The tests compare the output of (noise-perturbed) synthetic photometry of SDSS galaxies to properties derived from previous full spectral fitting and detailed EL analysis. For realistic signal-to-noise ratios, stellar population properties are recovered to better than 0.2 dex in masses, mean ages, metallicities and $\pm 0.2$ mag for the extinction. More importantly, ELs are recovered remarkably well for a photometric survey. We obtain input $-$ output dispersions of 0.05--0.2 dex for the equivalent widths of $[\mathrm{O}\,\rm{II}]$, $[\mathrm{O}\,\rm{III}]$, H$\beta$, H$\alpha$, $[\mathrm{N}\,\rm{II}]$, and $[\mathrm{S}\,\rm{II}]$, and even better for lines stronger than $\sim 5$ $\mathring{A}$. These excellent results are achieved by combining two empirical facts into a prior which restricts the EL space available for the fits: (1) Because, for the redshifts explored here, H$\alpha$ and $[\mathrm{N}\,\rm{II}]$ fall in a single narrow band (J0660), their combined equivalent width is always well recovered, even when $[\mathrm{N}\,\rm{II}]$/H$\alpha$ is not. (2) We know from SDSS that $W_{H\alpha+[\mathrm{N}\,\rm{II}]}$ correlates with $[\mathrm{N}\,\rm{II}]$/H$\alpha$, which can be used to tell if a galaxy belongs to the left or right wings in the classical BPT diagnostic diagram. Example applications to integrated light and spatially resolved data are also presented, including a comparison with independent results obtained with MUSE-based integral field spectroscopy.

astro-ph.GA

The S-PLUS: a star/galaxy classification based on a Machine Learning approach

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21, matched to SDSS/DR13, and morphological parameters. The metric of importance is defined as the relative decrease of the initial accuracy when all correlations related to a certain feature is vanished. In general, the broad photometric bands presented higher importance when compared to narrow ones. The influence of the morphological parameters has been evaluated training the RF with and without the inclusion of morphological parameters, presenting accuracy values of 95.0\% and 88.1\%, respectively. Particularly, the morphological parameter {\rm FWHM/PSF} performed the highest importance over all features to distinguish between stars and galaxies, indicating that it is crucial to classify objects into stars and galaxies. We investigate the misclassification of stars and galaxies in the broad-band colour-colour diagram $(g-r)$ versus $(r-i)$. The morphology can notably improve the classification of objects at regions in the diagram where the misclassification was relatively high. Consequently, it provides cleaner samples for statistical studies. The expected contamination rate of red galaxies as a function of the redshift is estimated, providing corrections for red galaxy samples. The classification of QSOs as extragalactic objects is slightly better using photometric-only case. An extragalactic point-source catalogue is provided using the classification without any morphology feature (only the SED information) with additional constraints on photometric redshifts and {\rm FWHM/PSF} values.

astro-ph.GA

C*-Algebras, Approximately Proper Equivalence Relations, and Thermodynamic Formalism

We introduce a non-commutative generalization of the notion of (approximately proper) equivalence relation and propose the construction of a "quotient space". We then consider certain one-parameter groups of automorphisms of the resulting C*-algebra and prove the existence of KMS states at every temperature. In a model originating from Thermodynamics we prove that these states are unique as well. We also show a relationship between maximizing measures (the analogue of the Aubry-Mather measures for expanding maps) and ground states. In the last section we explore an interesting example of phase transitions.

math.OA

MAXIMUM LIKELIHOOD AND MINIMUM ENTROPY IDENTIFICATION OF GRAMMARS

Using the Thermodynamic Formalism, we introduce a Gibbsian model for the identification of regular grammars based only on positive evidence. This model mimics the natural language acquisition procedure driven by prosody which is here represented by the thermodynamical potential. The statistical question we face is how to estimate the incidenc e matrix of a subshift of finite type from a sample produced by a Gibbs state whose potential is known. The model acquaints for both the robustness of t he language acquisition procedure and language changes. The probabilistic appr oach we use avoids invoking ad-hoc restrictions as Berwick's Subset Principle.

cmp-lg