arXiv subjects

Sébastien Fabbro

Publications and source records attributed to Sébastien Fabbro.

27 records · Page 2Linked to original sources

arXiv2022

A stellar stream remnant of a globular cluster below the metallicity floor

Stellar ejecta gradually enrich the gas out of which subsequent stars form, making the least chemically enriched stellar systems direct fossils of structures formed in the early universe. Although a few hundred stars with metal content below one thousandth of the solar iron content are known in the Galaxy, none of them inhabit globular clusters, some of the oldest known stellar structures. These show metal content of at least ~0.2 percent of the solar metallicity ([Fe/H] > -2.7). This metallicity floor appears universal and it has been proposed that proto-galaxies that merge into the galaxies we observe today were simply not massive enough to form clusters that survived to the present day. Here, we report the discovery of a stellar stream, C-19, whose metallicity is less than 0.05 per cent the solar metallicity ([Fe/H]=-3.38 +/- 0.06 (stat.) +/- 0.20 (syst.)). The low metallicity dispersion and the chemical abundances of the C-19 stars show that this stream is the tidal remnant of the most metal-poor globular cluster ever discovered, and significantly below the purported metallicity floor: clusters with significantly lower metallicities than observed today existed in the past and contributed their stars to the Milky Way halo.

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arXiv2021

The Pristine survey -- XII: Gemini-GRACES chemo-dynamical study of newly discovered extremely metal-poor stars in the Galaxy

High-resolution optical spectra of 30 metal-poor stars selected from the \Pristine\ survey are presented, based on observations taken with the Gemini Observatory GRACES spectrograph. Stellar parameters \teff and \logg are determined using a Gaia DR2 colour-temperature calibration and surface gravity from the Stefan-Boltzmann equation. GRACES spectra are used to determine chemical abundances (or upper-limits) for 20 elements (Li, O, Na, Mg, K, Ca, Ti, Sc, Cr, Mn, Fe, Ni, Cu, Zn, Y, Zr, Ba, La, Nd, Eu). These stars are confirmed to be metal-poor ([Fe/H]$<-2.5$), with higher precision than from earlier medium-resolution analyses. The chemistry for most targets is similar to other extremely metal-poor stars in the Galactic halo. Three stars near [Fe/H]$=-3.0$ have unusually low Ca and high Mg, suggestive of contributions from few SN~II where alpha-element formation through hydrostatic nucleosynthesis was more efficient. Three new carbon-enhanced metal-poor stars are also identified (two CEMP-s and one potential CEMP-no star) when our chemical abundances are combined with carbon from previous medium-resolution analyses. The GRACES spectra also provide precision radial velocities ($σ_{\rm RV}\le0.2$km\,s$^{-1}$) for dynamical orbit calculations with the Gaia DR2 proper motions. Most of our targets are dynamically associated with the Galactic halo; however, five stars with [Fe/H]$<-3$ have planar-like orbits, including one retrograde star. Another five stars are dynamically consistent with the Gaia-Sequoia accretion event; three have typical halo [$α$/Fe] ratios for their metallicities, whereas two are [Mg/Fe]-deficient, and one is a new CEMP-s candidate. These results are discussed in terms of the formation and early chemical evolution of the Galaxy.

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arXiv2020

Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets

The advancements in stellar spectroscopy data acquisition have made it necessary to accomplish similar improvements in efficient data analysis techniques. Current automated methods for analyzing spectra are either (a) data-driven, which requires prior knowledge of stellar parameters and elemental abundances, or (b) based on theoretical synthetic models that are susceptible to the gap between theory and practice. In this study, we present a hybrid generative domain adaptation method that turns simulated stellar spectra into realistic spectra by applying unsupervised learning to large spectroscopic surveys. We apply our technique to the APOGEE H-band spectra at R=22,500 and the Kurucz synthetic models. As a proof of concept, two case studies are presented. The first of which is the calibration of synthetic data to become consistent with observations. To accomplish this, synthetic models are morphed into spectra that resemble observations, thereby reducing the gap between theory and observations. Fitting the observed spectra shows an improved average reduced $χ_R^2$ from 1.97 to 1.22, along with a reduced mean residual from 0.16 to -0.01 in normalized flux. The second case study is the identification of the elemental source of missing spectral lines in the synthetic modelling. A mock dataset is used to show that absorption lines can be recovered when they are absent in one of the domains. This method can be applied to other fields, which use large data sets and are currently limited by modelling accuracy. The code used in this study is made publicly available on github.

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arXiv2020

Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework

In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we compare the stellar parameters determined using five popular synthetic spectral grids (INTRIGOSS, FERRE, AMBRE, PHOENIX, and MPIA/1DNLTE) with our convolutional neural network (CNN, $\texttt{StarNet}$). The stellar parameters are determined for six physical properties (effective temperature, surface gravity, metallicity, [$α$/Fe], radial velocity, and rotational velocity) given the spectral resolution, signal-to-noise, and wavelength range of optical FLAMES-UVES spectra from the Gaia-ESO Survey. Both CNN modelling and epistemic uncertainties are incorporated through training an ensemble of networks. $\texttt{StarNet}$ training was also adapted to mitigate differences between the synthetic grids and observed spectra by augmenting with realistic observational signatures (i.e. resolution matching, wavelength sampling, Gaussian noise, zeroing flux values, rotational and radial velocities, continuum removal, and masking telluric regions). Using the FLAMES-UVES spectra for FGK type dwarfs and giants as a test set, we quantify the accuracy and precision of the stellar label predictions from $\texttt{StarNet}$. We find excellent results over a wide range of parameters when $\texttt{StarNet}$ is trained on the MPIA/1DNLTE synthetic grid, and acceptable results over smaller parameter ranges when trained on the 1DLTE grids. These tests also show that our CNN pipeline is highly adaptable to multiple simulation grids.

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arXiv2020

Interpreting Stellar Spectra with Unsupervised Domain Adaptation

We discuss how to achieve mapping from large sets of imperfect simulations and observational data with unsupervised domain adaptation. Under the hypothesis that simulated and observed data distributions share a common underlying representation, we show how it is possible to transfer between simulated and observed domains. Driven by an application to interpret stellar spectroscopic sky surveys, we construct the domain transfer pipeline from two adversarial autoencoders on each domains with a disentangling latent space, and a cycle-consistency constraint. We then construct a differentiable pipeline from physical stellar parameters to realistic observed spectra, aided by a supplementary generative surrogate physics emulator network. We further exemplify the potential of the method on the reconstructed spectra quality and to discover new spectral features associated to elemental abundances.

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arXiv2020

Photometric Redshifts with the LSST II: The Impact of Near-Infrared and Near-Ultraviolet Photometry

Accurate photometric redshift (photo-$z$) estimates are essential to the cosmological science goals of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this work we use simulated photometry for mock galaxy catalogs to explore how LSST photo-$z$ estimates can be improved by the addition of near-infrared (NIR) and/or ultraviolet (UV) photometry from the Euclid, WFIRST, and/or CASTOR space telescopes. Generally, we find that deeper optical photometry can reduce the standard deviation of the photo-$z$ estimates more than adding NIR or UV filters, but that additional filters are the only way to significantly lower the fraction of galaxies with catastrophically under- or over-estimated photo-$z$. For Euclid, we find that the addition of ${JH}$ $5σ$ photometric detections can reduce the standard deviation for galaxies with $z>1$ ($z>0.3$) by ${\sim}20\%$ (${\sim}10\%$), and the fraction of outliers by ${\sim}40\%$ (${\sim}25\%$). For WFIRST, we show how the addition of deep ${YJHK}$ photometry could reduce the standard deviation by ${\gtrsim}50\%$ at $z>1.5$ and drastically reduce the fraction of outliers to just ${\sim}2\%$ overall. For CASTOR, we find that the addition of its ${UV}$ and $u$-band photometry could reduce the standard deviation by ${\sim}30\%$ and the fraction of outliers by ${\sim}50\%$ for galaxies with $z<0.5$. We also evaluate the photo-$z$ results within sky areas that overlap with both the NIR and UV surveys, and when spectroscopic training sets built from the surveys' small-area deep fields are used.

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arXiv2019

The Canada-France Imaging Survey: Reconstructing the Milky Way Star Formation History from its White Dwarf Population

As the remnants of stars with initial masses $\lesssim$ 8 M$_{\odot}$, white dwarfs contain valuable information on the formation histories of stellar populations. In this paper, we use deep, high-quality, u-band photometry from the Canada France Imaging Survey (CFIS), griz photometry from Pan-STARRS 1 (PS1), as well as proper motions from Gaia DR2, to select 25,156 white dwarf candidates over $\sim$4500 deg$^2$ using a reduced proper motion diagram. We develop a new white dwarf population synthesis code that returns mock observations of the Galactic field white dwarf population for a given star formation history, while simultaneously taking into account the geometry of the Milky Way, survey parameters, and selection effects. We use this model to derive the star formation histories of the thin disk, thick disk, and stellar halo. Our results show that the Milky Way disk began forming stars (11.3 $\pm$ 0.5) Gyr ago, with a peak rate of (8.8 $\pm$ 1.4) M$_{\odot}$yr$^{-1}$ at (9.8 $\pm$ 0.4) Gyr, before a slow decline to a constant rate until the present day --- consistent with recent results suggesting a merging event with a satellite galaxy. Studying the residuals between the data and best-fit model shows evidence for a slight increase in star formation over the past 3 Gyr. We fit the local fraction of helium-atmosphere white dwarfs to be (21 $\pm$ 3) %. Incorporating this methodology with data from future wide-field surveys such as LSST, Euclid, CASTOR, and WFIRST should provide an unprecedented view into the formation of the Milky Way at its earliest epoch through its white dwarfs.

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arXiv2018

A-type stars in the Canada-France Imaging Survey I. The stellar halo of the Milky Way traced to large radius by blue horizontal branch stars

We present the stellar density profile of the outer halo of the Galaxy traced over a range of Galactocentric radii from $15< R_{GC} < 220$ kpc by blue horizontal branch (BHB) stars. These stars are identified photometrically using deep $u-$band imaging from the new Canada-France-Imaging-Survey (CFIS) that reaches 24.5 mag. This is combined with $griz$ bands from Pan-STARRS 1 and covers a total of $\sim4000$ deg$^2$ of the northern sky. We present a new method to select BHB stars that has low contamination from blue stragglers and high completeness. We use this sample to measure and parameterize the three dimensional density profile of the outer stellar halo. We fit the profile using (i) a simple power-law with a constant flattening (ii) a flattening that varies as a function of Galactocentric radius (iii) a broken power law profile. We find that outer stellar halo traced by the BHB is well modelled by a broken power law with a constant flattening of $q=0.86 \pm 0.02$, with an inner slope of $γ=4.24 \pm 0.08$. This is much steeper than the preferred outer profile that has a slope of $β=3.21\pm 0.07$ after a break radius of $r_b=41.4^{+2.5}_{-2.4}$ kpc. The outer profile of the stellar halo trace by BHB stars is shallower than that recently measured using RR Lyrae, a surprising result given the broad similarity of the ages of these stellar populations.

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arXiv2018

Dependence of Type Ia supernova luminosities on their local environment

We present a fully consistent catalog of local and global properties of host galaxies of 882 Type Ia supernovæ (SNIa) that were selected based on their light-curve properties, spanning the redshift range $0.01 < z < 1.\text{}$ This catalog corresponds to a preliminary version of the compilation sample and includes Supernova Legacy Survey (SNLS) 5-year data, Sloan Digital Sky Survey (SDSS), and low-redshift surveys. We measured low- and moderate-redshift host galaxy photometry in SNLS and SDSS images and used spectral energy distribution (SED) fitting techniques to derive host properties such as stellar mass and $U-V$ rest-frame colors; the latter are an indicator of the luminosity-weighted age of the stellar population in a galaxy. We also estimated the local observed fluxes at the supernova location within a proper distance radius of 3 kpc, and transposed them into local $U-V$ rest-frame colors. Selecting SNIa based on host photometry quality, we then performed cosmological fits using local color as a third standardization variable, for which we split the sample at the median value. We find a local color step as significant as the maximum mass step effect. Correcting for the maximum mass step correction, we still find a significant local color effect, which shows that additional information is provided by the close environment of SNIa. Departures from the initial choices were investigated, and we discuss the possible implications for cosmology. This will be of tremendous importance for the forthcoming SNIa surveys, and in particular for the Large Synoptic Survey Telescope (LSST), for which uncertainties on the dark energy equation of state will be comparable to the effects reported here.

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