arXiv subjects

Laurie Rousseau-Nepton

Publications and source records attributed to Laurie Rousseau-Nepton.

33 records · Page 2Linked to original sources

arXiv2022

The Astrophysical Distance Scale: V. A 2% Distance to the Local Group Spiral M33 via the JAGB Method, Tip of the Red Giant Branch, and Leavitt Law

The J-region asymptotic giant branch (JAGB) method is a new standard candle that is based on the stable intrinsic J-band magnitude of color-selected carbon stars, and has a precision comparable to other primary distance indicators such as Cepheids and the TRGB. We further test the accuracy of the JAGB method in the Local Group Galaxy M33. M33's moderate inclination, low metallicity, and nearby proximity make it an ideal laboratory for tests of systematics in local distance indicators. Using high-precision optical BVI and near-infrared JHK photometry, we explore the application of three independent distance indicators: the JAGB method, the Cepheid Leavitt Law, and the TRGB. We find: $μ_0$ (TRGB I) = 24.72 +/- 0.02 (stat) +/- 0.07 (sys) mag, $μ_0$ (TRGB NIR) = 24.72 +/- 0.04 (stat) +/- 0.10 (sys) mag, $μ_0$ (JAGB) = 24.67 +/- 0.03 (stat) +/- 0.04 (sys) mag, $μ_0$ (Cepheid) = 24.71 +/- 0.04 (stat) +/- 0.01 (sys) mag. For the first time, we also directly compare a JAGB distance using ground-based and space-based photometry. We measure: $μ_0$ (JAGB F110W) = 24.71 +/- 0.06 (stat) +/- 0.05 (sys) mag using the (F814-F110W) color combination to effectively isolate the JAGB stars. In this paper, we measure a distance to M33 accurate to 2% and provide further evidence that the JAGB method is a powerful extragalactic distance indicator that can effectively probe a local measurement of the Hubble constant using spaced-based observations. We expect to measure the Hubble constant via the JAGB method in the near future, using observations from JWST.

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arXiv2022

WISDOM project -- XI. Star Formation Efficiency in the Bulge of the AGN-host Galaxy NGC 3169 with SITELLE and ALMA

The star formation efficiency (SFE) has been shown to vary across different environments, particularly within galactic starbursts and deep within the bulges of galaxies. Various quenching mechanisms may be responsible, ranging from galactic dynamics to feedback from active galactic nuclei (AGN). Here, we use spatially-resolved observations of warm ionised gas emission lines from the imaging Fourier transform spectrograph SITELLE at the Canada-France-Hawaii Telescope (CFHT) and cold molecular gas (CO(2-1)) from the Atacama Large Millimeter/sub-millimeter Array (ALMA) to study the SFE in the bulge of the AGN-host galaxy NGC 3169. After distinguishing star-forming regions from AGN-ionised regions using emission-line ratio diagnostics, we measure spatially-resolved molecular gas depletion times (τ_dep = 1/SFE) with a spatial resolution of \approx 100 pc within a galactocentric radius of 1.8 kpc. We identify a star-forming ring located at radii 1.25 \pm 0.6 kpc with an average τ_dep of 0.3 Gyr. At radii < 0.9 kpc, however, the molecular gas surface densities and depletion times increase with decreasing radius, the latter reaching approximately 2.3 Gyr at a radius \approx 500 pc. Based on analyses of the gas kinematics and comparisons with simulations, we identify AGN feedback, bulge morphology and dynamics as the possible causes of the radial profile of SFE observed in the central region of NGC 3169.

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arXiv2022

Stellar feedback in M83 as observed with MUSE -- I. Overview, an unprecedented view of the stellar and gas kinematics and evidence of outflowing gas

We present a large VLT/MUSE mosaic (3.8 x 3.8 kpc) of the nearby spiral galaxy M83, with a spatial resolution ~20 pc. We obtained the kinematics of the stars and ionised gas, and compared them with molecular gas kinematics from ALMA CO(2-1). We separated the ionised gas into HII regions and diffuse ionised gas (DIG) and determined the fraction of Ha luminosity originating from the DIG (f_DIG). We observe that both stars and gas trace the galactic disk rotation, as well as a fast-rotating nuclear component, likely connected to secular processes driven by the galactic bar. In the gas kinematics, we observe a stream east of the nucleus, redshifted with respect to the disk. The stream is surrounded by an extended ionised gas region with enhanced velocity dispersion and a high ionisation state, which is largely consistent with being ionised by slow shocks. We interpret this feature as either the superposition of the disk and an extraplanar layer of DIG, or as a bar-driven inflow of shocked gas. A double Gaussian component fit to the Ha line also reveals the presence of a nuclear biconic structure whose axis of symmetry is perpendicular to the bar. The two cones appear blue- and redshifted along the line of sight and stand out for having an Ha emission separated by up to 200 km s-1 from that of the disk, and a high velocity dispersion ~80-200 km s-1. At the far end of the cones, we observe that the gas is consistent with being ionised by shocks. These features had never been observed before in M83; we postulate that they are tracing a starburst-driven outflow shocking into the surrounding ISM. Finally, we obtain f_DIG ~ 13% in our field of view. We inspect the emission of the HII regions and DIG in `BPT' diagrams, finding that in HII regions photoionisation accounts for 99.8% of the Ha flux, whereas the DIG has a mixed contribution from photoionisation (94.9%) and shocks (5.1%). [abridged]

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arXiv2021

Updates to LUCI: A New Fitting Paradigm Using Mixture Density Networks

LUCI is an general-purpose spectral line-fitting pipeline which natively integrates machine learning algorithms to initialize fit functions. LUCI currently uses point-estimates obtained from a convolutional neural network (CNN) to inform optimization algorithms; this methodology has shown great promise by reducing computation time and reducing the chance of falling into a local minimum using convex optimization methods. In this update to LUCI, we expand upon the CNN developed in Rhea et al. 2020 so that it outputs Gaussian posterior distributions of the fit parameters of interest (the velocity and broadening) rather than simple point-estimates. Moreover, these posteriors are then used to inform the priors in a Bayesian inference scheme, either emcee or dynesty. The code is publicly available at https://github.com/crhea93/LUCI.

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arXiv2021

A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: III. Disentangling Multiple Components in Hii regions

In the first two papers of this series (Rhea et al. 2020; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line ratios directly from the spectra observed by the SITELLE instrument located at the Canada-France-Hawai'i Telescope. In this third installment, we develop a framework using a convolutional neural network trained on synthetic spectra to determine the number of line-of-sight components present in the SN3 filter (656--683nm) spectral range of SITELLE. We compare this methodology to standard practice using Bayesian Inference. Our results demonstrate that a neural network approach returns more accurate results and uses less computational resources over a range of spectral resolutions. Furthermore, we apply the network to SITELLE observations of the merging galaxy system NGC2207/IC2163. We find that the closest interacting sector and the central regions of the galaxies are best characterized by two line-of-sight components while the outskirts and spiral arms are well-constrained by a single component. Determining the number of resolvable components is crucial in disentangling different galactic components in merging systems and properly extracting their respective kinematics.

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arXiv2021

LUCI: A Python package for SITELLE spectral analysis

High-resolution optical integral field units (IFUs) are rapidly expanding our knowledge of extragalactic emission nebulae in galaxies and galaxy clusters. By studying the spectra of these objects -- which include classic HII regions, supernova remnants, planetary nebulae, and cluster filaments -- we are able to constrain their kinematics (velocity and velocity dispersion). In conjunction with additional tools, such as the BPT diagram, we can further classify emission regions based on strong emission-line flux ratios. LUCI is a simple-to-use python module intended to facilitate the rapid analysis of IFU spectra. LUCI does this by integrating well-developed pre-existing python tools such as astropy and scipy with new machine learning tools for spectral analysis (Rhea et al. 2020). Furthermore, LUCI provides several easy-to-use tools to access and fit SITELLE data cubes.

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arXiv2021

A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios

In the first paper of this series (Rhea et al. 2020), we demonstrated that neural networks can robustly and efficiently estimate kinematic parameters for optical emission-line spectra taken by SITELLE at the Canada-France-Hawaii Telescope. This paper expands upon this notion by developing an artificial neural network to estimate the line ratios of strong emission-lines present in the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000 synthetic spectra using line ratios taken from the Mexican Million Model database replicating Hii regions. Residual analysis of the network on the test set reveals the network's ability to apply tight constraints to the line ratios. We verified the network's efficacy by constructing an activation map, checking the [N ii] doublet fixed ratio, and applying a standard k-fold cross-correlation. Additionally, we apply the network to SITELLE observation of M33; the residuals between the algorithm's estimates and values calculated using standard fitting methods show general agreement. Moreover, the neural network reduces the computational costs by two orders of magnitude. Although standard fitting routines do consistently well depending on the signal-to-noise ratio of the spectral features, the neural network can also excel at predictions in the low signal-to-noise regime within the controlled environment of the training set as well as on observed data when the source spectral properties are well constrained by models. These results reinforce the power of machine learning in spectral analysis.

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arXiv2020

Carbon stars as standard candles: II. The median J magnitude as a distance indicator

We introduce a new distance determination method using carbon-rich asymptotic giant branch stars (CS) as standard candles and the Large and Small Magellanic Clouds (LMC and SMC) as the fundamental calibrators. We select the samples of CS from the ($(J-K_{s})_0$, $J_0$) colour-magnitude diagrams, as, in this combination of filters, CS are bright and easy to identify. We fit the CS $J$-band luminosity functions using a Lorentzian distribution modified to allow the distribution to be asymmetric. We use the parameters of the best-fit distribution to determine if the CS luminosity function of a given galaxy resembles that of the LMC or SMC. Based on this resemblance, we use either the LMC or SMC as the calibrator and estimate the distance to the given galaxy using the median $J$ magnitude ($\overline{J}$) of the CS samples. We apply this new method to the two Local Group galaxies NGC 6822 and IC 1613. We find that NGC 6822 has an "LMC-like" CS luminosity function while IC 1613 is more "SMC-like". Using the values for the median absolute $J$ magnitude for the LMC and SMC found in Paper I we find a distance modulus of $μ_{0}=23.54\pm0.03$ (stat) for NGC 6822 and $μ_{0}=24.34\pm0.05$ (stat) for IC 1613.

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arXiv2020

Machine Learning Approach to Integral Field Unit Spectroscopy Observations: I. HII Region Kinematics

SITELLE is a novel integral field unit spectroscopy instrument that has an impressive spatial (11 by 11 arcmin), spectral coverage, and spectral resolution (R=1-20000). SIGNALS is anticipated to obtain deep observations (down to 3.6x10-17ergs s-1cm-2) of 40 galaxies, each needing complex and substantial time to extract spectral information. We present a method that uses Convolution Neural Networks (CNN) for estimating emission line parameters in optical spectra obtained with SITELLE as part of the SIGNALS large program. Our algorithm is trained and tested on synthetic data representing typical emission spectra for HII regions based on Mexican Million Models database(3MdB) BOND simulations. The network's activation map demonstrates its ability to extract the dynamical (broadening and velocity) parameters from a set of 5 emission lines (e.g. Hα, N[II] doublet, and S[II] doublet) in the SN3 (651-685 nm) filter of SITELLE. Once trained, the algorithm was tested on real SITELLE observations in the SIGNALS program of one of the South West fields of M33. The CNN recovers the dynamical parameters with an accuracy better than 5 km s-1 in regions with a signal-to-noise ratio greater than 15 over the Hαline. More importantly, our CNN method reduces calculation time by over an order of magnitude on the spectral cube with native spatial resolution when compared with standard fitting procedures. These results clearly illustrate the power of machine learning algorithms for the use in future IFU-based missions. Subsequent work will explore the applicability of the methodology to other spectral parameters such as the flux of key emission lines.

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arXiv2019

Indigenizing the next decade of astronomy in Canada

(Abridged) The Truth and Reconciliation Commission of Canada published its calls to action in 2015 with 94 recommendations. Many of these 94 recommendations are directly related to education, language, and culture, some of which the Canadian Astronomy community can address and contribute to as part of reconciliation. The Canadian Astronomy community has an additional obligation since it benefits from facilities on Indigenous territories across Canada and the world. Furthermore, Indigenous people are still underrepresented at all levels in Canadian astronomy. The purpose of this Community Paper is to develop recommendations for the Canadian astronomy community to support Indigenous inclusion in the science community, support Indigenous learning by developing Indigenous-based learning materials and facilitate access to professionals and science activities, and to recognize and acknowledge the great contributions of Indigenous communities to our science activities. As part of this work we propose the ten following recommendations for CASCA as an organization and throughout this Community Paper we will include additional recommendations for individuals: astronomers, students and academics.

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arXiv2019

WARPFIELD-EMP: The Self-Consistent Prediction of Emission Lines from Evolving HII Regions in Dense Molecular Clouds

We present the {\sc warpfield} emission predictor, {\sc warpfield-emp}, which couples the 1D stellar feedback code {\sc warpfield} with the {\sc cloudy} \hii region/PDR code and the {\sc polaris} radiative transfer code, in order to make detailed predictions for the time-dependent line and continuum emission arising from the H{\sc ii} region and PDR surrounding an evolving star cluster. {\sc warpfield-emp} accounts for a wide range of physical processes (stellar winds, supernovae, radiation pressure, gravity, thermal conduction, radiative cooling, dust extinction etc.) and yet runs quickly enough to allow us to explore broad ranges of different cloud parameters. We compare the results of an extensive set of models with SITELLE observations of a large sample of \hii regions in NGC~628 and find very good agreement, particularly for the highest signal-to-noise observations. We show that our approach of modeling individual clouds from first principles (instead of in terms of dimensionless quantities such as the ionization parameter) allows us to avoid long-standing degeneracies in the interpretation of \hii region diagnostics and enables us to relate these diagnostics to important physical parameters such as cloud mass or cluster age. Finally, we explore the implications of our models regarding the reliability of simple metallicity diagnostics, the properties of long-lived embedded clusters, and the role played by winds and supernovae in regulating \hii region and PDR line emission.

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arXiv2019

NGC628 with SITELLE : I. Imaging Spectroscopy of 4285 HII region candidates

This is the first paper of a series dedicated to nebular physics and the chemical evolution of nearby galaxies by investigating large samples of HII regions with the CFHT imaging spectrograph SITELLE. We present a technique adapted to imaging spectroscopy to identify and extract parameters from 4285 HII region candidates found in the disc of NGC628. Using both the spatial and spectral capabilities of SITELLE, our technique enables the extraction of the position, dust extinction, velocity, Halpha profile, diffuse ionised gas (DIG) background, luminosity, size, morphological type, and the emission line fluxes for individual spaxels and the integrated spectrum for each region. We have produced a well-sampled HII region luminosity function and studied its variation with galactocentric radius and level of the DIG background. We found a slope of 1.12+/-0.03 with no evidence of a break at high luminosity. Based on the width of the region profile, bright regions are rather compact, while faint regions are seen over a wide range of sizes. The radius function reveals a slope of 1.81+/-0.02. BPT diagrams of the individual spaxels and integrated line ratios confirm that most detections are HII regions. Also, maps of the line ratios show complex variations of the ionisation conditions within HII regions. All this information is compiled in a new catalogue for HII regions. The objective of this database is to provide a complete sample which will be used to study the whole parameter space covered by the physical conditions in active star-forming regions.

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arXiv2019

3D Optical Spectroscopic Study of NGC3344 with SITELLE: I. Identification and Confirmation of Supernova Remnants

We present the first optical identification and confirmation of a sample of supernova remnants (SNRs) in the nearby galaxy NGC3344. Using high spectral and spatial resolution data, obtained with the CFHT imaging Fourier transform spectrograph SITELLE, we identified about 2200 emission line regions, many of which are HII regions, diffuse ionized gas regions, and also SNRs. Considering the stellar population and diffuse ionized gas background, that are quite important in NGC3344, we have selected 129 SNR candidates based on four criteria for regions where the emission lines flux ratio [S II]/H$α$$\ge$0.4. Emission lines of [O II]$λ$3727, H$α$, [O III]$λλ$4959,5007, H$α$, [N II]$λλ$6548,6583, and [S II]$λλ$6716,6731 have been measured to study the ionized gas properties of the SNR candidates. We adopted a self-consistent spectroscopic analysis, based on Sabbadin plots and BPT diagrams, to confirm the shock-heated nature of the ionization mechanism in the candidates sample. With this analysis, we end up with 42 Confirmed SNRs, 45 Probable SNRs, and 42 Less likely SNRs. Using shock models, the Confirmed SNRs seems to have a metallicity ranging between LMC and 2$\times$solar. We looked for correlations between the size of the Confirmed SNRs and their emission lines ratios, their galaxy environment, and their galactocentric distance: we see a trend for a metallicity gradient among the SNR population, along with some evolutionary effects.

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arXiv2019

Mapping Gas Phase Abundances and Enrichment Patterns Across Galaxy Disks

The distribution of gas-phase abundances in galaxy disks encodes the history of nucleosynthesis and transport through the interstellar medium (ISM) over cosmic time. Multi-object and high resolution integral-field spectroscopy have started to measure these distributions across hundreds of HII regions individually resolved at $\lesssim 100$ pc scales in a handful of objects, but in the coming decade these studies will expand to larger samples of galaxies. This will allow us to understand the role of feedback and turbulence in driving the mixing and diffusion of metals in the ISM, and statistically assess the role of galaxy environment and disk dynamics in modifying how mixing proceeds. Detailed searches for over- and under-enriched regions can address to what extent star formation is triggered by previous generations of star formation and by pristine and recycled gas flows. Local galaxies, for which these detailed measurements will be possible within the next decade, will inform the interpretation of integrated measurements at high-z, where very different dynamical gas-rich environments are found in early disk galaxies. Currently, progress in the field is severely hampered by the 0.2-0.3 dex level systematic uncertainties plaguing nebular abundance diagnostics. Improving our detailed understanding of ionized nebulae at $<$20 pc scales will help us find a solution to this problem, which will prove key to the study of metal enrichment and mixing across the galaxy population in the next decade.

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arXiv2018

SITELLE: An Imaging Fourier Transform Spectrometer for the Canada-France-Hawaii Telescope

We present an overview of SITELLE, an Imaging Fourier Transform Spectrometer (iFTS) available at the 3.6-meter Canada-France-Hawaii Telescope. SITELLE is a Michelson-type interferometer able to reconstruct the spectrum of every light source within its 11' field of view in filter-selected bands of the visible (350 to 900 nm). The spectral resolution can be adjusted up to R = 10 000 and the spatial resolution is seeing-limited and sampled at 0.32 arcsec per pixel. We describe the design of the instrument as well as the data reduction and analysis process. To illustrate SITELLE's capabilities, we present some of the data obtained during and since the August 2015 commissioning run. In particular, we demonstrate its ability to separate the components of the [OII] $λλ$ 3726,29 doublet in Orion and to reach R = 9500 around H-alpha; to detect diffuse emission at a level of 4 x 10e-17 erg/cm2/s/arcsec2; to obtain integrated spectra of stellar absorption lines in galaxies despite the well-known multiplex disadvantage of the iFTS; and to detect emission-line galaxies at different redshifts.

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