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Context. Recent developments in time domain astronomy, such as Zwicky Transient Facility (ZTF), have made it possible to conduct daily scans of the entire visible sky, leading to the discovery of hundreds of new transients every night. Among these detections, 10 to 15 of these objects are supernovae (SNe), which have to be classified prior to cosmological use. The spectral energy distribution machine (SEDM) is a low-resolution ( ℛ ~ 100) integral field spectrograph designed, built, and operated with the aim of spectroscopically observing and classifying targets detected by the ZTF main camera. Aims. As the current pysedm pipeline can only handle isolated point sources, it is limited by contamination when the transient is too close to its host galaxy core. This can lead to an incorrect typing and ultimately bias the cosmological analyses, affecting the homogeneity of the SN sample in terms of local environment properties. We present a new scene modeler to extract the transient spectrum from its structured background, with the aim of improving the typing efficiency of the SEDM. Methods. H yper G al is a fully chromatic scene modeler that uses archival pre-transient photometric images of the SN environment to generate a hyperspectral model of the host galaxy. It is based on the cigale SED fitter used as a physically-motivated spectral interpolator. The galaxy model, complemented by a point source for the transient and a diffuse background component, is projected onto the SEDM spectro-spatial observation space and adjusted to observations, and the SN spectrum is ultimately extracted from this multi-component model. The full procedure, from scene modeling to transient spectrum extraction and typing, is validated on 5000 simulated cubes built from actual SEDM observations of isolated host galaxies, covering a broad range of observing conditions and scene parameters. Results. We introduce the contrast, c , as the transient-to-total flux ratio at the SN location, integrated over the ZTF r -band. From estimated contrast distribution of real SEDm observations, we show that H yper G al correctly classifies ~95% of SNe Ia, and up to 99% for contrast c ≳ 0.2, representing more than 90% of the observations. Compared to the standard point-source extraction method (without the hyperspectral galaxy modeling step), H yper G al correctly classifies 20% more SNe Ia between 0.1 < c < 0.6 (50% of the observation conditions), with less than 5% of SN Ia misidentifications. The false-positive rate is less than 2% for c > 0.1 (> 99% of the observations), which represents half as much as the standard extraction method. Assuming a similar contrast distribution for core-collapse SNe, H yper G al classifies 14% additional SNe II and 11% additional SNe Ibc. Conclusions. H yper G al has proven to be extremely effective in extracting and classifying SNe in the presence of strong contamination by the host galaxy, providing a significant improvement with respect to the single point-source extraction. 

Abstract We apply the color–magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric data set. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the linear region in the color–magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter,ω_XY, the size of the “bump” feature near maximum brightness for arbitrary filtersXandY. We find a significant correlation between the slope of the linear region,β_XY, in the CMAGIC diagram andω_XY. These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally,ω_XYis computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and “bump” size seen in our data. In 1D simulations, we show that the correlation between the size of the “bump” feature andβ_XYcan be understood as a result of chemical mixing due to large-scale Rayleigh–Taylor instabilities. 

Abstract We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope–based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300–9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys. 

Abstract We construct a physically parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of Type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an autoencoder that is interpreted probabilistically after training using a normalizing flow. We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multistage training setup alongside our physically parameterized network, we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including the automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an rms of 0.091 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova. 

This paper presents a search for high redshift galaxies from theEuclidEarly Release Observations program ‘Magnifying Lens.’ The 1.5 deg²area covered by the twin Abell lensing cluster fields is comparable in size to the few other deep near-infrared surveys such as COSMOS, and so provides an opportunity to significantly increase known samples of rare UV-bright galaxies atz≈ 6–8 (M_UV≲ −22). Beyond their still uncertain role in reionisation, these UV-bright galaxies are ideal laboratories from which to study galaxy formation and constrain the bright-end of the UV luminosity function. Of the 501 994 sources detected from a combinedY_E,J_E, andH_ENISP detection image, 168 do not have any appreciable VIS/I_Eflux. These objects span a range in spectral colours, separated into two classes: 139 extremely red sources; and 29 Lyman-break galaxy candidates. Best-fit redshifts and spectral templates suggest the former is composed of bothz≳ 5 dusty star-forming galaxies andz≈ 1–3 quiescent systems. The latter is composed of more homogeneous Lyman-break galaxies atz≈ 6–8. In both cases, contamination by L- and T-type dwarfs cannot be ruled out withEuclidimages alone. Additional contamination from instrumental persistence is investigated using a novel time series analysis. This work lays the foundation for future searches within the Euclid Deep Fields, where thousands morez≳ 6 Lyman-break systems and extremely red sources will be identified. 

We report the discovery of a complete Einstein ring around the elliptical galaxy NGC 6505, atz = 0.042. This is the first strong gravitational lens discovered inEuclidand the first in an NGC object from any survey. The combination of the low redshift of the lens galaxy, the brightness of the source galaxy (I_E = 18.1 lensed,I_E = 21.3 unlensed), and the completeness of the ring make this an exceptionally rare strong lens, unidentified until its observation byEuclid. We present deep imaging data of the lens from theEuclidVisible Camera (VIS) and Near-Infrared Spectrometer and Photometer (NISP) instruments, as well as resolved spectroscopy from theKeckCosmic Web Imager (KCWI). TheEuclidimaging in particular presents one of the highest signal-to-noise ratio optical/near-infrared observations of a strong gravitational lens to date. From the KCWI data we measure a source redshift ofz = 0.406. Using data from the Dark Energy Spectroscopic Instrument (DESI) we measure a velocity dispersion for the lens galaxy ofσ_⋆ = 303 ± 15 km s⁻¹. We model the lens galaxy light in detail, revealing angular structure that varies inside the Einstein ring. After subtracting this light model from the VIS observation, we model the strongly lensed images, finding an Einstein radius of 2.″5, corresponding to 2.1 kpc at the redshift of the lens. This is small compared to the effective radius of the galaxy,R_eff ∼ 12.″3. Combining the strong lensing measurements with analysis of the spectroscopic data we estimate a dark matter fraction inside the Einstein radius off_DM = (11.1_−3.5^+5.4)% and a stellar initial mass-function (IMF) mismatch parameter ofα_IMF = 1.26_−0.08^+0.05, indicating a heavier-than-Chabrier IMF in the centre of the galaxy. 

The Cosmic Dawn Survey (DAWN survey) provides multiwavelength (UV/optical to mid-IR) data across the combined 59 deg²of the Euclid Deep and Auxiliary fields (EDFs and EAFs). In this work, the first public data release from the DAWN survey is presented. The catalogues made available herein consist of a subset of the full DAWN survey that includes two EDFs: EDF North (EDF-N) and EDF Fornax (EDF-F). Each field has been covered by the ongoing Hawaii Twenty Square Degree Survey (H20), which includes imaging from the CFHT MegaCam in theufilter and from the Subaru Hyper Suprime-Cam (HSC) in thegrizfilters. Each field has been further covered bySpitzer/IRAC 3.6–4.5µm imaging spanning 10 deg²and reaching ~25 mag AB (5σ). All present H20 imaging and all publicly available imaging from the aforementioned facilities were combined with the deepSpitzer/IRAC data to create source catalogues spanning a total area of 16.87 deg²in EDF-N and 2.85 deg²in EDF-F for this first release. These catalogues are referred to as the ‘pre-launch’ (PL), asEucliddata is not yet public for these fields and therefore it is not included. Photometry was measured from these multiwavelength data usingThe Farmer, a novel and well validated model-based photometry code. Photometric redshifts and stellar masses were computed using two independent codes for modelling spectral energy distributions:EAZYandLePhare. Photometric redshifts show good agreement with spectroscopic redshifts (σ_NMAD~ 0.5,η <8% ati< 25). Number counts, photometric redshifts and stellar masses were further validated in comparison to the COSMOS2020 catalogue. The DAWN survey PL catalogues are designed to be of immediate use in these two EDFs and will be continuously updated and made available as both new ground-based data and spaced-based data fromEuclidare acquired and made public. Future data releases will provide catalogues of all EDFs and EAFs and includeEucliddata. 

Euclidwill provide deep near-infrared (NIR) imaging to ∼26.5 AB magnitude over ∼59 deg²in its deep and auxiliary fields. The Cosmic DAWN survey combines dedicated and archival UV–NIR observations to provide matched depth multiwavelength imaging of theEucliddeep and auxiliary fields. The DAWN survey will provide consistently measuredEuclidNIR-selected photometric catalogues, accurate photometric redshifts, and measurements of galaxy properties to a redshift ofz ∼ 10. The DAWN catalogues includeSpitzerIRAC data that are critical for stellar mass measurements atz ≳ 2.5 and high-zscience. These catalogues complement the standardEuclidcatalogues, which will not includeSpitzerIRAC data. In this paper, we present an overview of the survey, including the footprints of the survey fields, the existing and planned observations, and the primary science goals for the combined data set. 

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown.Euclidis a medium-class mission in the Cosmic Vision 2015–2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14 000 deg²of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance.