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During their formation, galaxies are subject to tidal forces, which create correlations between their shapes and the large-scale structure of the Universe, known as intrinsic alignment. This alignment is a contamination for cosmic-shear measurements as one needs to disentangle correlations induced by external lensing effects from those intrinsically present in galaxies. We constrain the amplitude of intrinsic alignment and test models by making use of the overlap between the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) covering $3500 \, \mathrm{deg}^2$, and spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS/eBOSS). By comparing our results to measurements from other lensing surveys on the same spectroscopic tracers, we can test the reliability of these estimates and verify they are not survey dependent. We measure projected correlation functions between positions and ellipticities, which we model with perturbation theory to constrain the commonly used non-linear alignment model and its higher-order expansion. Using the non-linear alignment model, we obtain a $13\sigma$ detection with CMASS galaxies, a $3\sigma$ detection with LRGs, and a detection compatible with the null hypothesis for ELGs. We test the tidal alignment and tidal torque model, a higher-order alignment model, which we find to be in good agreement with the non-linear alignment prediction and for which we can constrain the second-order parameters. We show a strong scaling of our intrinsic alignment amplitude with luminosity. We demonstrate that the UNIONS sample is robust against systematic contributions, particularly concerning PSF biases. We reached a reasonable agreement when comparing our measurements to other lensing samples for the same spectroscopic samples. We take this agreement as an indication that direct measurements of intrinsic alignment are mature for stage IV priors., Comment: To be submitted to A&A, 18 pages, 17 figures

We report the detection of 239 trans-Neptunian Objects discovered through the on-going New Horizons survey for distant minor bodies being performed with the Hyper Suprime-Cam mosaic imager on the Subaru Telescope. These objects were discovered in images acquired with either the r2 or the recently commissioned EB-gri filter using shift and stack routines. Due to the extremely high stellar density of the search region down stream of the spacecraft, new machine learning techniques had to be developed to manage the extremely high false positive rate of bogus candidates produced from the shift and stack routines. We report discoveries as faint as r2$\sim26.5$. We highlight an overabundance of objects found at heliocentric distances $R\gtrsim70$~au compared to expectations from modelling of the known outer Solar System. If confirmed, these objects betray the presence of a heretofore unrecognized abundance of distant objects that can help explain a number of other observations that otherwise remain at odds with the known Kuiper Belt, including detections of serendipitous stellar occultations, and recent results from the Student Dust Counter on-board the New Horizons spacecraft., Comment: Accepted for publication in the Planetary Science Journal, 28 pages, 7 figures, 3 tables

Merging and interactions can radically transform galaxies. However, identifying these events based solely on structure is challenging as the status of observed mergers is not easily accessible. Fortunately, cosmological simulations are now able to produce more realistic galaxy morphologies, allowing us to directly trace galaxy transformation throughout the merger sequence. To advance the potential of observational analysis closer to what is possible in simulations, we introduce a supervised deep learning Convolutional Neural Network (CNN) and Vision Transformer (ViT) hybrid framework, Mummi (MUlti Model Merger Identifier). Mummi is trained on realism-added synthetic data from IllustrisTNG100-1, and is comprised of a multi-step ensemble of models to identify mergers and non-mergers, and to subsequently classify the mergers as interacting pairs or post-mergers. To train this ensemble of models, we generate a large imaging dataset of 6.4 million images targeting UNIONS with RealSimCFIS. We show that Mummi offers a significant improvement over many previous machine learning classifiers, achieving 95% pure classifications even at Gyr long timescales when using a jury-based decision making process, mitigating class imbalance issues that arise when identifying real galaxy mergers from $z=0$ to $0.3$. Additionally, we can divide the identified mergers into pairs and post-mergers at 96% success rate. We drastically decrease the false positive rate in galaxy merger samples by 75%. By applying Mummi to the UNIONS DR5-SDSS DR7 overlap, we report a catalog of 13,448 high confidence galaxy merger candidates. Finally, we demonstrate that Mummi produces powerful representations solely using supervised learning, which can be used to bridge galaxy morphologies in simulations and observations., Comment: 23 pages, 16 figures. Accepted for publication in MNRAS

We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure ($\sim$0.2s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image., Comment: Accepted for publication in MNRAS (advance copy available at https://doi.org/10.1093/mnras/stae1018). Project website available at https://dancecam.info/ . 20 pages, 17 figures

Aims:Calibrating the point spread function (PSF) is a fundamental part of weak gravitational lensing analyses. Even with corrected galaxy images, imperfect calibrations can introduce biases. We propose an analytical framework for quantifying PSF-induced systematics as diagnostics for cross-correlation measurements of weak lensing with density tracers, e.g., galaxy-galaxy lensing. We show how those systematics propagate to physical parameters of the density tracers. Those diagnostics only require a shape catalogue of PSF stars and foreground galaxy positions. Methods:We consider the PSF-induced multiplicative bias, and introduce three second-order statistics as additive biases. We compute both biases for the weak-lensing derived halo mass of spectroscopic foreground galaxy samples, in particular, their effect on the tangential shear and fitted halo mass as a function of stellar mass. In addition, we assess their impact on the recently published black-hole - halo-mass relation for type I Active Galactic Nuclei (AGNs). Results:Using weak-lensing catalogues from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) and Dark Energy Survey (DES), we find the multiplicative biases in the tangential shear to be less than $0.5\%$. No correlations between additive bias and galaxy properties of the foreground sample are detected. The combined PSF systematics affect low-mass galaxies and small angular scales; halo mass estimates can be biased by up to 18$\%$ for a sample of central galaxies in the stellar mass range 9.0 $\leq$ log $M_*/\rm M_{\odot}$ < 9.5. Conclusions:The PSF-induced multiplicative bias is a subdominant contribution to current studies of weak-lensing - density cross-correlations, but might become significant for upcoming Stage-VI surveys. For samples with a low tangential shear, additive PSF systematics can induce a significant bias on derived properties such as halo mass., Comment: 11 pages, 5 figures

The ever-growing sample of observed supernovae enhances our capacity for comprehensive supernova population studies, providing a richer dataset for understanding the diverse characteristics of Type Ia supernovae and possibly that of their progenitors. Here, we present a data-driven analysis of observed Type Ia supernova photometric light curves collected in the Open Supernova Catalog. Where available, we add the environmental information from the host galaxy. We focus on identifying sub-classes of Type Ia supernovae without imposing the pre-defined sub-classes found in the literature to date. To do so, we employ an implicit-rank minimizing autoencoder neural network for developing low-dimensional data representations, providing a compact representation of the supernova light curve diversity. When we analyze light curves alone, we find that one of our resulting latent variables is strongly correlated with redshift, allowing us to approximately ``de-redshift'' the other latent variables describing each event. After doing so, we find that three of our latent variables account for $\sim$95\% of the variance in our sample, and provide a natural separation between 91T and 91bg thermonuclear supernovae. Of note, the 02cx subclass is not unambiguously delineated from the 91bg sample in our results, nor do either the over-luminous 91T or the under-luminous 91bg/02cx samples form a clearly distinct population from the broader sample of ``other'' SN Ia events. We identify the physical characteristics of supernova light curves which best distinguish SNe 91T from SNe 91bg \& 02cx, and discuss prospects for future refinements and applications to other classes of supernovae as well as other transients., Comment: Astrophysical Journal, accepetd

Context. Gaia DR3 has offered the scientific community a remarkable dataset of approximately one million spectra acquired with the Radial Velocity Spectrometer (RVS) in the Calcium II triplet region, that is well-suited to identify very metal-poor (VMP) stars. However, over 40% of these spectra have no released parameters by Gaia's GSP Spec pipeline in the domain of VMP stars, whereas VMP stars are key tracers of early Galactic evolution. Aims. We aim to provide spectroscopic metallicities for VMP stars using Gaia RVS spectra, thereby producing a catalogue of bright VMP stars distributed over the full sky that can serve as the basis to study early chemical evolution throughout the Galaxy. Methods. We select VMP stars using photometric metallicities from the literature and analyse the Gaia RVS spectra to infer spectroscopic metallicities for these stars. Results. The inferred metallicities agree very well with literature high-resolution metallicities with a median systematic offset of 0.1 dex and standard deviation of $\sim$0.15 dex. The purity of this sample in the VMP regime is $\sim$80% with outliers representing a mere $\sim$3%. Conclusions. We make available an all-sky catalogue of $\sim$1500 stars with reliable spectroscopic metallicities down to [Fe/H]$\sim$-4.0, of which $\sim$1000 are VMP stars. More than 75% of these stars have either no metallicity value in the literature to date or are flagged to be unreliable in their literature metallicity estimates. This catalogue of bright (G<13) VMP stars is three times larger than the current sample of well-studied VMP stars in the literature in this magnitude range, making it ideal for high-resolution spectroscopic follow-up and to study the properties of VMP stars in different parts of our Galaxy., Comment: Submitted to A&A. 11 pages, 4 pages appendix, 5 figures. Catalogue is made available public here: https://astroakshara.github.io/rvs-paper/Gaia-RVS-VMP-catalogue-AV23b-vSep23.csv Feel free to use it to do amazing science with the amazing data Gaia has given us

We used the spectro-photometric information of ~219 million stars from Gaia's DR3 to calculate synthetic, narrow-band, metallicity-sensitive CaHK magnitudes that mimic the observations of the Pristine survey, a survey of photometric metallicities of Milky Way (MW) stars that has been mapping >6,500 deg^2 of the northern sky with CFHT since 2015. These synthetic magnitudes were used for an absolute recalibration of the deeper Pristine photometry and, combined with broadband Gaia information, synthetic and Pristine CaHK magnitudes were used to estimate photometric metallicities over the whole sky. The resulting metallicity catalogue is accurate down to [Fe/H]~-3.5 and is particularly suited for the exploration of the metal-poor MW ([Fe/H]<-1.0). We make available here the catalogue of synthetic CaHK_syn magnitudes for all stars with BP/RP information in Gaia DR3, as well as an associated catalogue of more than ~30 million photometric metallicities for high S/N FGK stars. This paper further provides the first public data release of the Pristine catalogue in the form of higher quality recalibrated Pristine CaHK magnitudes and photometric metallicities for all stars in common with the BP/RP spectro-photometric information in Gaia DR3. When available, the much deeper Pristine data greatly enhance the quality of the derived metallicities, in particular at the faint end of the catalogue (G_BP > 16). Combined, both photometric metallicity catalogues include more than two million metal-poor star candidates ([Fe/H]_phot<-1.0) as well as more than 200,000 and ~8,000 very and extremely metal-poor candidates ([Fe/H]_phot<-2.0 and <-3.0, respectively). Finally, we show that these metallicity catalogues can be used efficiently, among other applications to hunt for the most metal-poor stars, and to study how the structure of the MW varies with metallicity., Comment: 31 pages, 25 figures, accepted for publication in A&A. Minor changes in v2, including ~2.5% more coverage for Pristine DR1 from new observations. First two authors are co-first author. The CaHK photometry catalogue and the two photometric metallicity catalogues are available, before acceptance, as large compressed csv files at: https://seafile.unistra.fr/d/ee0c0f05719d4368bcbb/

A relatively new concern for the forthcoming massive spectroscopic sky surveys is the impact of contamination from low earth orbit satellites. Several hundred thousand of these satellites are licensed for launch in the next few years and it has been estimated that, in some cases, up to a few percent of spectra could be contaminated when using wide field, multi-fiber spectrographs. In this paper, a multi-staged approach is used to assess the practicality and limitations of identifying and minimizing the impact of satellite contamination in a WEAVE-like stellar spectral survey. We develop a series of convolutional-network based architectures to attempt identification, stellar parameter and chemical abundances recovery, and source separation of stellar spectra that we artificially contaminate with satellite (i.e. solar-like) spectra. Our results show that we are able to flag 67% of all contaminated sources at a precision level of 80% for low-resolution spectra and 96% for high-resolution spectra. Additionally, we are able to remove the contamination from the spectra and recover the clean spectra with a $<$1% reconstruction error. The errors in stellar parameter predictions reduce by up to a factor of 2-3 when either including contamination as an augmentation to a training set or by removing the contamination from the spectra, with overall better performance in the former case. The presented methods illustrate several machine learning mitigation strategies that can be implemented to improve stellar parameters for contaminated spectra in the WEAVE stellar spectroscopic survey and others like it., Comment: 15 pages. To be published in MNRAS

For natural guide start adaptive optics (AO) systems, pyramid wavefront sensors (PWFSs) can provide significant increase in sensitivity over the traditional Shack-Hartmann, but at the cost of a reduced linear range. When using a linear reconstructor, non-linearities result in wavefront estimation errors, which can have a significant impact on the image quality delivered by the AO system. Here we simulate a wavefront passing through a PWFS under varying observing conditions to explore the possibility of using a non-linear machine learning model to estimate wavefront errors better than a linear reconstruction. We find significant improvement even with light-weight models, underscoring the need for further investigation of this approach.

Although diffuse interstellar bands (DIBs) were discovered over 100 years ago, for most of them, their origins are still unknown. Investigation on the correlations between different DIBs is an important way to study the behavior and distributions of their carriers. Based on stacking thousands of spectra from the Pristine Inner Galaxy Survey, we study the correlations between two DIBs at 442.8 nm ($\lambda$442.8) and 862.1 nm ($\lambda$862.1), as well as the dust grains, in a range of latitude spanning ${\sim}22^{\circ}$ ($4^{\circ}\,{<}\,|b|\,{<}\,15^{\circ}$) toward the Galactic center ($|\ell|\,{<}\,11^{\circ}$). Tight linear intensity correlations can be found between $\lambda$442.8, $\lambda$862.1, and dust grains for $|b|\,{<}\,12^{\circ}$ or $E(B-V)\,{>}\,0.3$ mag. For $|b|\,{>}\,12^{\circ}$, $\lambda$442.8 and $\lambda$862.1 present larger relative strength with respect to the dust grains. A systematic variation of the relative strength between $\lambda$442.8 and $\lambda$862.1 with $|b|$ and $E(B-V)$ concludes that the two DIBs do not share a common carrier. Furthermore, the carrier of $\lambda$862.1 is more abundant at high latitudes than that of $\lambda$442.8. This work can be treated as an example showing the significance and potentials to the DIB research covering a large latitude range., Comment: 13 pages, 14 figures, accepted for publication in MNRAS

Cold dark matter haloes are expected to be triaxial, and so appear elliptical in projection. We use weak gravitational lensing from the Canada-France Imaging Survey (CFIS) component of the Ultraviolet-Near Infrared Optical Northern Survey (UNIONS) to measure the ellipticity of the dark matter haloes around Luminous Red Galaxies (LRGs) from the Sloan Digital Sky Survey Data Release 7 (DR7) and from the CMASS and LOWZ samples of the Baryon Oscillation Spectroscopic Survey (BOSS), assuming their major axes are aligned with the stellar light. We find that DR7 LRGs with masses $M \sim 2.7\times10^{13} \mathrm{M}_{\odot}/h$ have halo ellipticities $e=0.46\pm0.10$. Expressed as a fraction of the galaxy ellipticity, we find $f_h = 2.2\pm0.6$. For BOSS LRGs, the detection is of marginal significance: $e = 0.20\pm0.10$ and $f_h=0.7\pm0.7$. These results are in agreement with other measurements of halo ellipticity from weak lensing and, taken together with previous results, suggest an increase of halo ellipticity of $0.10\pm0.06$ per decade in halo mass. This trend agrees with the predictions from hydrodynamical simulations, which find that at higher halo masses, not only do dark matter haloes become more elliptical, but that the misalignment between major axis of the stellar light in the central galaxy and that of the dark matter decreases., Comment: MNRAS, accepted

UNIONS is an ongoing collaboration that will provide the largest deep photometric survey of the Northern sky in four optical bands to date. As part of this collaboration, CFIS is taking $r$-band data with an average seeing of 0.65 arcsec, which is complete to magnitude 24.5 and thus ideal for weak-lensing studies. We perform the first weak-lensing analysis of CFIS $r$-band data over an area spanning 1700 deg$^2$ of the sky. We create a catalogue with measured shapes for 40 million galaxies, corresponding to an effective density of 6.8 galaxies per square arcminute, and demonstrate a low level of systematic biases. This work serves as the basis for further cosmological studies using the full UNIONS survey of 4800 deg$^2$ when completed. Here we present ShapePipe, a newly developed weak-lensing pipeline. This pipeline makes use of state-of-the-art methods such as Ngmix for accurate galaxy shape measurement. Shear calibration is performed with metacalibration. We carry out extensive validation tests on the Point Spread Function (PSF), and on the galaxy shapes. In addition, we create realistic image simulations to validate the estimated shear. We quantify the PSF model accuracy and show that the level of systematics is low as measured by the PSF residuals. Their effect on the shear two-point correlation function is sub-dominant compared to the cosmological contribution on angular scales <100 arcmin. The additive shear bias is below 5x$10^{-4}$, and the residual multiplicative shear bias is at most $10^{-3}$ as measured on image simulations. Using COSEBIs we show that there are no significant B-modes present in second-order shear statistics. We present convergence maps and see clear correlations of the E-mode with known cluster positions. We measure the stacked tangential shear profile around Planck clusters at a significance higher than $4\sigma$.

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., Comment: 14 pages, 6 figures, 5 tables. Nature, accepted

We use telemetry data from the Gemini North ALTAIR adaptive optics system to investigate how well the commands for wavefront correction (both Tip/Tilt and high-order turbulence) can be forecasted in order to reduce lag error (due to wavefront sensor averaging and computational delays) and improve delivered image quality. We show that a high level of reduction ($\sim$ 5 for Tip-Tilt and $\sim$ 2 for high-order modes) in RMS wavefront error can be achieved by using a "forecasting filter" based on a linear auto-regressive model with only a few coefficients ($\sim$ 30 for Tip-Tilt and $\sim$ 5 for high-order modes) to complement the existing integral servo-controller. Updating this filter to adapt to evolving observing conditions is computationally inexpensive and requires less than 10 seconds worth of telemetry data. We also use several machine learning models (Long-Short Term Memory and dilated convolutional models) to evaluate whether further improvements could be achieved with a more sophisticated non-linear model. Our attempts showed no perceptible improvements over linear auto-regressive predictions, even for large lags where residuals from the linear models are high, suggesting that non-linear wavefront distortions for ALTAIR at the Gemini North telescope may not be forecasted with the current setup

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., Comment: 25 pages and 12 figures (main) + 7 pages and 8 figures (2 appendices). Accepted for publication in MNRAS

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 ($\sigma_{\rm RV}\le0.2$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 [$\alpha$/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., Comment: 18 pages, 15 figures plus additional figures and tables in the appendix. MNRAS accepted

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., Comment: 4 pages, 3 figures. Accepted to Machine Learning and the Physical Sciences Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS)

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. H{\alpha}, 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 H{\alpha}line. 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., Comment: 14 pages, 11 figures Accepted in ApJ

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., Comment: 4 pages, 4 figure, accepted to the ICML 2020 Machine Learning Interpretability for Scientific Discovery workshop. A full 20-page version is submitted to ApJ. The code used in this study is made publicly available on github: https://github.com/teaghan/Cycle_SN

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 $\chi_R^2$ 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., Comment: 23 pages, 15 figures, 2 tables, accepted for publication on Nov 12, 2020, Nov 12. A companion 4-page preview is accepted to the ICML 2020 Machine Learning Interpretability for Scientific Discovery workshop. The code used in this study is made publicly available on github: https://github.com/teaghan/Cycle_SN

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 ${JH}$ $5{\sigma}$ 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., Comment: 21 pages, 16 figures, 4 tables, accepted to AJ

Typically, binary classification lens-finding schemes are used to discriminate between lens candidates and non-lenses. However, these models often suffer from substantial false-positive classifications. Such false positives frequently occur due to images containing objects such as crowded sources, galaxies with arms, and also images with a central source and smaller surrounding sources. Therefore, a model might confuse the stated circumstances with an Einstein ring. It has been proposed that by allowing such commonly misclassified image types to constitute their own classes, machine learning models will more easily be able to learn the difference between images that contain real lenses, and images that contain lens imposters. Using Hubble Space Telescope (HST) images, in the F814W filter, we compare the usage of binary and multi-class classification models applied to the lens finding task. From our findings, we conclude there is not a significant benefit to using the multi-class model over a binary model. We will also present the results of a simple lens search using a multi-class machine learning model, and potential new lens candidates., Comment: PASP accepted, 14 pages, 10 figures, 4 tables

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, [$\alpha$/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., Comment: 20 pages, 17 figures, published by MNRAS. Code for StarNet available at https://github.com/Spiffical/StarNet

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.

We present a new fully data-driven algorithm that uses photometric data from the Canada-France-Imaging-Survey (CFIS; $u$), Pan-STARRS 1 (PS1; $griz$), and Gaia ($G$) to discriminate between dwarf and giant stars and to estimate their distances and metallicities. The algorithm is trained and tested using the SDSS/SEGUE spectroscopic dataset and Gaia photometric/astrometric dataset. At [Fe/H]$<-1.2$, the algorithm succeeds in identifying more than 70% of the giants in the training/test set, with a dwarf contamination fraction below 30% (with respect to the SDSS/SEGUE dataset). The photometric metallicity estimates have uncertainties better than 0.2 dex when compared with the spectroscopic measurements. The distances estimated by the algorithm are valid out to a distance of at least $\sim 80$ kpc without requiring any prior on the stellar distribution, and have fully independent uncertainities that take into account both random and systematic errors. These advances allow us to estimate these stellar parameters for approximately 12 million stars in the photometric dataset. This will enable studies involving the chemical mapping of the distant outer disc and the stellar halo, including their kinematics using the Gaia proper motions. This type of algorithm can be applied in the Southern hemisphere to the first release of LSST data, thus providing an almost complete view of the external components of our Galaxy out to at least $\sim 80$ kpc. Critical to the success of these efforts will be ensuring well-defined spectroscopic training sets that sample a broad range of stellar parameters with minimal biases. A catalogue containing the training/test set and all relevant parameters within the public footprint of CFIS is available online., Comment: 27 pages. 21 Figures. Accepted for publication in AAS. Training/testing sample available at: http://www.canfar.net/storage/list/gthomas/dwarfOrGiants.fits

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 $\gamma=4.24 \pm 0.08$. This is much steeper than the preferred outer profile that has a slope of $\beta=3.21\pm 0.07$ 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., Comment: 15 pages, 17 figures, accepted in MNRAS

Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here we apply a deep neural network architecture to analyze both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our convolutional neural network model (StarNet) is trained on APOGEE spectra, we show that the stellar parameters (temperature, gravity, and metallicity) are determined with similar precision and accuracy as the APOGEE pipeline. StarNet can also predict stellar parameters when trained on synthetic data, with excellent precision and accuracy for both APOGEE data and synthetic data, over a wide range of signal-to-noise ratios. In addition, the statistical uncertainties in the stellar parameter determinations are comparable to the differences between the APOGEE pipeline results and those determined independently from optical spectra. We compare StarNet to other data-driven methods; for example, StarNet and the Cannon 2 show similar behaviour when trained with the same datasets, however StarNet performs poorly on small training sets like those used by the original Cannon. The influence of the spectral features on the stellar parameters is examined via partial derivatives of the StarNet model results with respect to the input spectra. While StarNet was developed using the APOGEE observed spectra and corresponding ASSET synthetic data, we suggest that this technique is applicable to other wavelength ranges and other spectral surveys., Comment: Accepted 19 December 2017 to MNRAS. 16 pages, 14 figures, 2 appendices

We present a fully consistent catalog of local and global properties of host galaxies of 882 Type Ia supernov\ae\ (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., Comment: 26 pages, 19 figures

We present a ground-based near-infrared search for lensed supernovae behind the massive cluster Abell 1689 at z=0.18, one of the most powerful gravitational telescopes that nature provides. Our survey was based on multi-epoch $J$-band observations with the HAWK-I instrument on VLT, with supporting optical data from the Nordic Optical Telescope. Our search resulted in the discovery of five high-redshift, $0.671

We present the first high-redshift Hubble diagram for Type II-P supernovae (SNe II-P) based upon five events at redshift up to z~0.3. This diagram was constructed using photometry from the Canada-France-Hawaii Telescope Supernova Legacy Survey and absorption line spectroscopy from the Keck observatory. The method used to measure distances to these supernovae is based on recent work by Hamuy & Pinto (2002) and exploits a correlation between the absolute brightness of SNe II-P and the expansion velocities derived from the minimum of the Fe II 516.9 nm P-Cygni feature observed during the plateau phases. We present three refinements to this method which significantly improve the practicality of measuring the distances of SNe II-P at cosmologically interesting redshifts. These are an extinction correction measurement based on the V-I colors at day 50, a cross-correlation measurement for the expansion velocity and the ability to extrapolate such velocities accurately over almost the entire plateau phase. We apply this revised method to our dataset of high-redshift SNe II-P and find that the resulting Hubble diagram has a scatter of only 0.26 magnitudes, thus demonstrating the feasibility of measuring the expansion history, with present facilities, using a method independent of that based upon supernovae of Type Ia., Comment: 36 pages, 16 figures, accepted for publication in ApJ