arXiv subjects

Simon Prunet

Publications and source records attributed to Simon Prunet.

At least 37 records · Page 2Linked to original sources

arXiv2024

Reconstructing Robust Background IFU spectra using Machine Learning

In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying physics of the emission mechanism. One crucial, and often overlooked, aspect of this model is the background emission, which contains foreground and background astrophysical sources, intervening atmospheric emission, and artifacts related to the instrument such as noise. This paper proposes an algorithmic approach to constructing a background model for SITELLE observations using statistical tools and supervised machine learning algorithms. We apply a segmentation algorithm implemented in photutils to divide the data cube into background and source spaxels. After applying a principal component analysis (PCA) on the background spaxels, we train an artificial neural network to interpolate from the background to the source spaxels in the PCA coefficient space, which allows us to generate a local background model over the entire data cube. We highlight the performance of this methodology by applying it to SITELLE observations obtained of a SIGNALS galaxy, \NGC4449, and the Perseus galaxy cluster of galaxies, NGC 1275. We discuss the physical interpretation of the principal components and noise reduction in the resulting PCA-based reconstructions. Additionally, we compare the fit results using our new background modeling approach to standard methods used in the literature and find that our method better captures the emission from HII regions in NGC 4449 and the faint emission regions in NGC 1275. These methods also demonstrate that the background does change as a function of the position of the datacube.

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arXiv2023

A Machine Learning Approach to Galactic Emission-Line Region Classification

Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study the applicability of supervised machine-learning techniques to systematically classify emission-line regions from the ratios of certain emission lines. Using the Million Mexican Model database, which contains information from grids of photoionization models using \texttt{cloudy}, and from shock models, we develop training and test sets of emission line fluxes for three key diagnostic ratios. The sets are created for three classifications: classic \hii{} regions, planetary nebulae, and supernova remnants. We train a neural network to classify a region as one of the three classes defined above given three key line ratios that are present both in the SITELLE and MUSE instruments' band-passes: [{\sc O\,iii}]$\lambda5007$/H$β$, [{\sc N\,ii}]$\lambda6583$/H$α$, ([{\sc S\,ii}]$\lambda6717$+[{\sc S\,ii}]$\lambda6731$)/H$α$. We also tested the impact of the addition of the [{\sc O\,ii}]$\lambda3726,3729$/[{\sc O\,iii}]$\lambda5007$ line ratio when available for the classification. A maximum luminosity limit is introduced to improve the classification of the planetary nebulae. Furthermore, the network is applied to SITELLE observations of a prominent field of M33. We discuss where the network succeeds and why it fails in certain cases. Our results provide a framework for the use of machine learning as a tool for the classification of extragalactic emission regions. Further work is needed to build more comprehensive training sets and adapt the method to additional observational constraints.

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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 low-rank approach to image defringing

In this work, we revisit the problem of interference fringe patterns in CCD chips occurring in near-infrared bands due to multiple light reflections within the chip. We briefly discuss the traditional approaches that were developed to remove these patterns from science images, and mention their limitations. We then introduce a new method to globally estimate the fringe patterns in a collection of science images without additional external data, allowing for some variation of the patterns between images. We demonstrate this new method on near-infrared images taken by the CFHT wide-field imager Megacam.

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arXiv2021

Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope

We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. Specifically, we develop accurate and interpretable models of the complex dependence between data features and observed IQ for CFHT's wide-field camera, MegaCam. Our contributions are several-fold. First, we collect, collate and reprocess several disparate data sets gathered by CFHT scientists. Second, we predict probability distribution functions (PDFs) of IQ and achieve a mean absolute error of $\sim0.07''$ for the predicted medians. Third, we explore the data-driven actuation of the 12 dome "vents" installed in 2013-14 to accelerate the flushing of hot air from the dome. We leverage epistemic and aleatoric uncertainties in conjunction with probabilistic generative modeling to identify candidate vent adjustments that are in-distribution (ID); for the optimal configuration for each ID sample, we predict the reduction in required observing time to achieve a fixed SNR. On average, the reduction is $\sim12\%$. Finally, we rank input features by their Shapley values to identify the most predictive variables for each observation. Our long-term goal is to construct reliable and real-time models that can forecast optimal observatory operating parameters to optimize IQ. We can then feed such forecasts into scheduling protocols and predictive maintenance routines. We anticipate that such approaches will become standard in automating observatory operations and maintenance by the time CFHT's successor, the Maunakea Spectroscopic Explorer, is installed in the next decade.

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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

Linear response theory and damped modes of stellar clusters

Because all stars contribute to its gravitational potential, stellar clusters amplify perturbations collectively. In the limit of small fluctuations, this is described through linear response theory, via the so-called response matrix. While the evaluation of this matrix is somewhat straightforward for unstable modes (i.e. with a positive growth rate), it requires a careful analytic continuation for damped modes (i.e. with a negative growth rate). We present a generic method to perform such a calculation in spherically symmetric stellar clusters. When applied to an isotropic isochrone cluster, we recover the presence of a low-frequency weakly damped $\ell = 1$ mode. We finally use a set of direct $N$-body simulations to test explicitly this prediction through the statistics of the correlated random walk undergone by a cluster's density centre.

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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

Astronomical Image Quality Prediction based on Environmental and Telescope Operating Conditions

Intelligent scheduling of the sequence of scientific exposures taken at ground-based astronomical observatories is massively challenging. Observing time is over-subscribed and atmospheric conditions are constantly changing. We propose to guide observatory scheduling using machine learning. Leveraging a 15-year archive of exposures, environmental, and operating conditions logged by the Canada-France-Hawaii Telescope, we construct a probabilistic data-driven model that accurately predicts image quality. We demonstrate that, by optimizing the opening and closing of twelve vents placed on the dome of the telescope, we can reduce dome-induced turbulence and improve telescope image quality by (0.05-0.2 arc-seconds). This translates to a reduction in exposure time (and hence cost) of $\sim 10-15\%$. Our study is the first step toward data-based optimization of the multi-million dollar operations of current and next-generation telescopes.

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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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arXiv2020

When do cosmic peaks, filaments or walls merge? A theory of critical events in a multi-scale landscape

The merging rate of cosmic structures is computed, relying on the Ansatz that they can be predicted in the initial linear density field from the coalescence of critical points with increasing smoothing scale, used here as a proxy for cosmic time. Beyond the mergers of peaks with saddle points (a proxy for halo mergers), we consider the coalescence and nucleation of all sets of critical points, including wall-saddle to filament-saddle and wall-saddle to minima (a proxy for filament and void mergers respectively), as they impact the geometry of galactic infall, and in particular filament disconnection. Analytical predictions of the one-point statistics are validated against multiscale measurements in 2D and 3D realisations of Gaussian random fields (the corresponding code being available upon request) and compared qualitatively to cosmological $N$-body simulations at early times ($z\geq 10$) and large scales ($\geq 5\, \mathrm{Mpc}/h$). The rate of filament coalescence is compared to the merger rate of haloes and the two-point clustering of these events is computed, along with their cross-correlations with critical points. These correlations are qualitatively consistent with the preservation of the connectivity of dark matter haloes, and the impact of the large scale structures on assembly bias. The destruction rate of haloes and voids as a function of mass and redshift is quantified down to $z=0$ for a $Λ$CDM cosmology. The one-point statistics in higher dimensions are also presented, together with consistency relations between critical point and critical event counts.

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arXiv2020

Wide field-of-view study of the Eagle Nebula with the Fourier transform imaging spectrograph SITELLE at CFHT

We present the very first wide-field, 11 by 11 arcmin, optical spectral mapping of M16, one of the most famous star-forming regions in the Galaxy. The data were acquired with the new imaging Fourier transform spectrograph SITELLE mounted on the Canada-France-Hawaii Telescope (CFHT). We obtained three spectral cubes with R=10'000 (SN1 filter), 1500 (SN2 filter) and 600 (SN3 filter), centered on the Pillars of Creation and the HH216 flow, covering the main optical nebular emission lines: [O II]3726,29 (SN1), Hb, [O III]4959,5007 (SN2), [N II]6548,84, Ha, and [S II]6717,31 (SN3). We validate the performance, calibration, and data reduction of SITELLE, and analyze the structures in the large field-of-view in terms of their kinematics and nebular emission. We compared the SITELLE data to MUSE integral field observations and other spectroscopic and narrow-band imaging data to validate the performance of SITELLE. We computed gas-phase metallicities via the strong-line method, performed a pixel-by-pixel fit to the main emission lines to derive kinematics of the ionized gas, computed the mass-loss rate of the Eastern pillar (the Spire), and combined the SITELLE data with near-infrared narrow-band imaging to characterize the HH216 flow. The comparison with previously published fluxes demonstrates very good agreement. We disentangle the dependence of the gas-phase metallicities (derived via abundance-tracing line ratios) on the degree of ionization and obtain metallicities that are in excellent agreement with the literature. We confirm the bipolar structure of HH216, find evidence for episodic accretion from the source of the flow, and identify its likely driving source. We compute the mass-loss rate of the Spire pillar on the East side of the H II region and find excellent agreement with the correlation between the mass-loss rate and the ionizing photon flux from the nearby cluster NGC6611.

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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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arXiv2017

Cylinders out of a top hat: counts-in-cells for projected densities

Large deviation statistics is implemented to predict the statistics of cosmic densities in cylinders applicable to photometric surveys. It yields few percent accurate analytical predictions for the one-point probability distribution function (PDF) of densities in concentric or compensated cylinders; and also captures the density-dependence of their angular clustering (cylinder bias). All predictions are found to be in excellent agreement with the cosmological simulation Horizon Run 4 in the quasi-linear regime where standard perturbation theory normally breaks down. These results are combined with a simple local bias model that relates dark matter and tracer densities in cylinders and validated on simulated halo catalogues. This formalism can be used to probe cosmology with existing and upcoming photometric surveys like DES, Euclid or WFIRST containing billions of galaxies.

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arXiv2016

Optimal fitting of gaussian-apodized or under-resolved emission lines in Fourier Transform spectra providing new insights on the velocity structure of NGC 6720

An analysis of the kinematics of NGC 6720 is performed on the commissioning data obtained with SITELLE, the Canada-France-Hawaii Telescope's new imaging Fourier transform spectrometer. In order to measure carefully the small broadening effect of a shell expansion on an unresolved emission line, we have determined a computationally robust implementation of the convolution of a Gaussian with a sinc instrumental line shape which avoids arithmetic overflows. This model can be used to measure line broadening of typically a few km/s even at low spectral resolution (R less than 5000). We have also designed the corresponding set of Gaussian apodizing functions that are now used by ORBS, the SITELLE's reduction pipeline. We have implemented this model in ORCS, a fitting engine for SITELLE's data, and used it to derive the [SII] density map of the central part of the nebula. The study of the broadening of the [NII] lines shows that the Main Ring and the Central Lobe are two different shells with different expansion velocities. We have also derived deep and spatially resolved velocity maps of the Halo in [NII] and Halpha and found that the brightest bubbles are originating from two bipolar structures with a velocity difference of more than 35 km/s lying at the poles of a possibly unique Halo shell expanding at a velocity of more than 15 km/s.

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arXiv2015

Topology beyond the horizon: how far can it be probed?

The standard cosmological model does not determine the spatial topology of the universe. This article revisits the signature of a non-trivial topology on the properties of the cosmic microwave background anisotropies. We show that the correlation function of the coefficients of the expansion of the temperature and polarization anisotropies in spherical harmonics, encodes a topological signature that can be used to distinguish a multi-connected space from an infinite space on sizes larger than the last scattering surface. The effect of the instrumental noise and of a galactic cut are estimated. We thus establish boundaries for the size of the biggest torus dintinguisable with temperature and polarization CMB data. We also describe the imprint of the spatial topology on the 3-point function and on non-Gaussianity.

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arXiv2015

Secular resonant dressed orbital diffusion I : method and WKB limit for tepid discs

The equation describing the secular diffusion of a self-gravitating collisionless system induced by an exterior perturbation is derived while assuming that the timescale corresponding to secular evolution is much larger than that corresponding to the natural frequencies of the system. Its two dimensional formulation for a tepid galactic disc is also derived using the epicyclic approximation. Its WKB limit is found while assuming that only tightly wound transient spirals are sustained by the disc. It yields a simple quadrature for the diffusion coefficients which provides a straightforward understanding of the loci of maximal diffusion within the disc.

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arXiv2015

Overview of Complementarity and Synergy with Other Wavelengths in Cosmology in the SKA era

We give an overview of complementarity and synergy in cosmology between the Square Kilometre Array and future survey projects in other wavelengths. In the SKA era, precision cosmology will be limited by systematic errors and cosmic variance, rather than statistical errors. However, combining and/or cross-correlating multi-wavelength data, from the SKA to the cosmic microwave background, optical/infrared and X-ray, substantially reduce these limiting factors. In this chapter, we summarize future survey projects and show highlights of complementarity and synergy, which can be very powerful to probe major cosmological problems such as dark energy, modified gravity and primordial non-Gaussianity.

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