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1. SN 2023ehl: A Normal Type Ia Supernova with High-velocity Features

   https://doi.org/10.3847/1538-4357/adcf1b  

   Zeng, Xiangyun; Hu, Lei; Zheng, Sheng; Li, Xiaolong; Luo, Xiaoyu; Li, Sai; Howell, D Andrew; Dong, Yize; Valenti, Stefano; Fu, Liping; et al (June 2025, The Astrophysical Journal)

   Abstract SN 2023ehl, a normal Type Ia supernova with a typical decline rate, was discovered in the galaxy UGC 11555 and offers valuable insights into the explosion mechanisms of white dwarfs. We present a detailed analysis of SN 2023ehl, including spectroscopic and photometric observations. The supernova exhibits high-velocity features in its ejecta, which are crucial for understanding the physical processes during the explosion. We compared the light curves of SN 2023ehl with other well-observed Type Ia supernovae, finding similarities in their evolution. The line strength ratioR(Siii) was calculated to be 0.17 ± 0.04, indicating a higher photospheric temperature compared to other supernovae. The maximum quasi-bolometric luminosity was determined to be 1.52 × 10⁴³erg s⁻¹, and the synthesized⁵⁶Ni mass was estimated at 0.77 ± 0.05M_⊙. The photospheric velocity atB-band maximum light was measured as 10,150 ± 240 km s⁻¹, classifying SN 2023ehl as a normal velocity Type Ia supernova. Our analysis suggests that SN 2023ehl aligns more with both the gravitationally confined detonation, providing a comprehensive view of the diversity and complexity of Type Ia supernovae. 

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2. Improving the Determination of Supernova Cosmological Redshifts by Using Galaxy Groups

   https://doi.org/10.3847/1538-4357/ada285  

   Peterson, Erik R; Carreres, Bastien; Carr, Anthony; Scolnic, Daniel; Bailey, Ava; Davis, Tamara M; Brout, Dillon; Howlett, Cullan; Jones, David O; Riess, Adam G; et al (January 2025, The Astrophysical Journal)

   Abstract At the low-redshift end (z< 0.05) of the Hubble diagram with Type Ia Supernovae (SNe Ia), the contribution to Hubble residual scatter from peculiar velocities (PVs) is of similar size to that due to the limitations of the standardization of the SN Ia light curves. A way to improve the redshift measurement of the SN host galaxy is to utilize the average redshift of the galaxy group, effectively averaging over small-scale/intracluster PVs. One limiting factor is the fraction of SN host galaxies in galaxy groups, previously found to be 30% using (relatively incomplete) magnitude-limited galaxy catalogs. Here, we do the first analysis ofN-body simulations to predict this fraction, finding ∼73% should have associated groups and group averaging should improve redshift precision by ∼135 km s⁻¹(∼0.04 mag atz= 0.025). Furthermore, using spectroscopic data from the Anglo-Australian Telescope, we present results from the first pilot program to evaluate whether or not 23 previously unassociated SN Ia hosts belong in groups. We find that 91% of these candidates can be associated with groups, consistent with predictions from simulations given the sample size. Combining with previously assigned SN host galaxies in Pantheon+, we demonstrate improvement in Hubble residual scatter equivalent to 145 km s⁻¹, also consistent with simulations. For new and upcoming low-zsamples from, for example, the Zwicky Transient Facility and the Legacy Survey of Space and Time, a separate follow-up program identifying galaxy groups of SN hosts is a highly cost-effective way to enhance their constraining power. 

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3. A Uniform Type Ia Supernova Distance Ladder with the Zwicky Transient Facility: Absolute Calibration Based on the Tip of the Red Giant Branch Method

   https://doi.org/10.3847/1538-4357/ac7ceb  

   Dhawan, Suhail; Goobar, Ariel; Johansson, Joel; Jang, In Sung; Rigault, Mickael; Harvey, Luke; Maguire, Kate; Freedman, Wendy L.; Madore, Barry F.; Smith, Mathew; et al (August 2022, The Astrophysical Journal)

   Abstract The current Cepheid-calibrated distance ladder measurement of H 0 is reported to be in tension with the values inferred from the cosmic microwave background (CMB), assuming standard cosmology. However, some tip of the red giant branch (TRGB) estimates report H 0 in better agreement with the CMB. Hence, it is critical to reduce systematic uncertainties in local measurements to understand the Hubble tension. In this paper, we propose a uniform distance ladder between the second and third rungs, combining Type Ia supernovae (SNe Ia) observed by the Zwicky Transient Facility (ZTF) with a TRGB calibration of their absolute luminosity. A large, volume-limited sample of both calibrator and Hubble flow SNe Ia from the same survey minimizes two of the largest sources of systematics: host-galaxy bias and nonuniform photometric calibration. We present results from a pilot study using the existing TRGB distance to the host galaxy of ZTF SN Ia SN 2021rhu (aka ZTF21abiuvdk) in NGC7814. Combining the ZTF calibrator with a volume-limited sample from the first data release of ZTF Hubble flow SNe Ia, we infer H 0 = 76.94 ± 6.4 km s −1 Mpc −1 , an 8.3% measurement. The error budget is dominated by the single object calibrating the SN Ia luminosity in this pilot study. However, the ZTF sample includes already five other SNe Ia within ∼20 Mpc for which TRGB distances can be obtained with the Hubble Space Telescope. Finally, we present the prospects of building this distance ladder out to 80 Mpc with James Webb Space Telescope observations of more than 100 ZTF SNe Ia. 

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4. Searching for Supernovae in HETDEX Data Release 3*

   https://doi.org/10.3847/1538-4357/acbfa8  

   Vinkó, József; Thomas, Benjamin P.; Wheeler, J. Craig; Ho, Anna Y. Q.; Cooper, Erin Mentuch; Gebhardt, Karl; Ciardullo, Robin; Farrow, Daniel J.; Hill, Gary J.; Jäger, Zoltán; et al (March 2023, The Astrophysical Journal)

   Abstract We have extracted 636 spectra taken at the positions of 583 transient sources from the third data release of the Hobby–Eberly Telescope Dark Energy eXperiment (HETDEX). The transients were discovered by the Zwicky Transient Facility (ZTF) during 2018–2022. The HETDEX spectra provide a potential means to obtain classifications for a large number of objects found by photometric surveys for free. We attempt to explore and classify the spectra by utilizing several template-matching techniques. We have identified two transient sources, ZTF20aatpoos = AT 2020fiz and ZTF19abdkelq, as supernova (SN) candidates. We classify AT 2020fiz as a Type IIP SN observed ∼10 days after explosion, and we propose ZTF19abdkelq as a likely Type Ia SN caught ∼40 days after maximum light. ZTF photometry of these two sources are consistent with their classifications as SNe. Beside these two objects, we have confirmed several ZTF transients as variable active galactic nuclei based on their spectral appearance, and determined the host galaxy types of several other ZTF transients. 

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5. SNIascore: Deep Learning Classification of Low-Resolution Supernova Spectra

   Fremling, Christoffer; Hall, Xander J; Coughlin, Michael W; Dahiwale, Aishwarya S; Duev, Dmitry A; Graham, Matthew J; Kasliwal, Mansi M; Kool, Erik C; Miller, Adam A; Neill, James D; et al (April 2021, ArXivorg)

   We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R ∼100) data. The goal of SNIascore is fully automated classification of SNe Ia with a very low false-positive rate (FPR) so that human intervention can be greatly reduced in large-scale SN classification efforts, such as that undertaken by the public Zwicky Transient Facility (ZTF) Bright Transient Survey (BTS). We utilize a recurrent neural network (RNN) architecture with a combination of bidirectional long short-term memory and gated recurrent unit layers. SNIascore achieves a <0.6% FPR while classifying up to 90% of the low-resolution SN Ia spectra obtained by the BTS. SNIascore simultaneously performs binary classification and predicts the redshifts of secure SNe Ia via regression (with a typical uncertainty of <0.005 in the range from z=0.01 to z=0.12). For the magnitude-limited ZTF BTS survey (≈70% SNe Ia), deploying SNIascore reduces the amount of spectra in need of human classification or confirmation by ≈60%. Furthermore, SNIascore allows SN Ia classifications to be automatically announced in real-time to the public immediately following a finished observation during the night. 

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