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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. 

Currently time-domain astronomy can scan the entire sky on a daily basis, discovering thousands of interesting transients every night. Classifying the ever-increasing number of new transients is one of the main challenges for the astronomical community. One solution that addresses this issue is the robotically controlled Spectral Energy Distribution Machine (SEDM) which supports the Zwicky Transient Facility (ZTF). SEDM with its pipelinepysedmdemonstrates that real-time robotic spectroscopic classification is feasible. In an effort to improve the quality of the current SEDM data, we present here two new modules,byecrandcontsep. The first removes contamination from cosmic rays, and the second removes contamination from non-target light. These new modules are part of the automatedpysedmpipeline and fully integrated with the whole process. Employingbyecrandcontsepmodules together automatically extracts more spectra than the currentpysedmpipeline. UsingSNIDclassification results, the new modules show an improvement in the classification rate and accuracy of 2.8% and 1.7%, respectively, while the strength of the cross-correlation remains the same. Improvements to the SEDM astrometry would further boost the improvement of thecontsepmodule. This kind of robotic follow-up with a fully automated pipeline has the potential to provide the spectroscopic classifications for the transients discovered by ZTF and also by the Rubin Observatory’s Legacy Survey of Space and Time.

 

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. 

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.

 

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.