arXiv subjects

Nabeel Rehemtulla

Publications and source records attributed to Nabeel Rehemtulla.

At least 19 recordsLinked to original sources

arXiv2026

Follow-up of SN 2025wny II: Superluminous Supernova Physics at Cosmic Noon

SN 2025wny is a gravitationally lensed, hydrogen-poor superluminous supernova (SLSN-I) at z = 2.015. To date, it is the most extensively observed high-redshift core-collapse SN and has the most detailed rest-frame UV observations of any SLSN. We present densely sampled rest-frame UV-to-optical photometry and spectroscopy out to +80 d post-peak (rest frame) from several facilities, including JWST, Keck, VLT, Gemini, the Palomar 200-inch, the Fraunhofer Telescope at Wendelstein, and the Liverpool Telescope. Correcting for lensing magnification, SN 2025wny reaches a peak pseudo-bolometric luminosity of $L_{\rm peak}\gtrsim4\times10^{44}$ erg s$^{-1}$ over rest-frame 1500-4230 \r{A}, placing it within the luminosity range of typical SLSNe-I. SN 2025wny exhibits several unusual features, including a continuum excess and sharp spectral features in the FUV from +20-60 d that coincide with an FUV light-curve plateau and higher inferred blackbody temperatures. SN 2025wny's spectra also show little to no UV line blanketing, no obvious O II absorption despite high temperatures, and evidence for C II, H$\alpha$, and possible He I. Light-curve modeling suggests that SN 2025wny may require a hybrid or non-standard power source. This work provides some of the first detailed constraints on high-redshift SLSNe and establishes SN 2025wny as an essential spectral and photometric reference for identifying and interpreting high-redshift SLSNe discovered by Rubin and Roman.

astro-ph.CO↗

arXiv2026

Follow-up of SN 2025wny III: Spectroscopic Time-delay Measurements of a Strongly Gravitationally Lensed Superluminous Supernova

We present spatially resolved spectra and infer the time-delays between the multiple images of the strongly gravitationally lensed superluminous supernova (SLSN) 2025wny at z=2.015. SN 2025wny is the first known spatially resolved strongly lensed SLSN and provides a unique opportunity to measure lensing delays through the temporal evolution of supernova spectra. We present a spectroscopic dataset spanning several months, including spatially resolved spectra of images A, B, C, D, and E. We identify and measure the wavelength evolution of spectral features using Gaussian-process modeling. The time delays are inferred by jointly fitting the temporal evolution of the spectral features, yielding $\Delta t_{AB}=-10.3 \pm 2.3$, $\Delta t_{AC}=0.1 \pm 3.6$, $\Delta t_{AD}=-65.7 \pm 3.5$, and $\Delta t_{AE}=3.7 \pm 8.8$ days (68% confidence intervals). These are the among most precise time-delay measurements obtained for a lensed supernova to date, whether from spectroscopic or photometric methods. The longest delay ($\Delta t_{AD}$) is particularly well constrained, with a ~5% precision. Combined with the lens model presented by M\"ortsell et al. (2026), the spectroscopic time-delays give a Hubble constant $H_0 = 70.2^{+8.2}_{-6.1}$ km/s/Mpc. Our analysis demonstrates that spectroscopic evolution provides an independent and complementary route to time-delay measurements in lensed supernova systems, avoiding reliance on photometric light curves alone. As future surveys discover larger samples of lensed supernovae, spectroscopic time-delay measurements will provide an important avenue for precision cosmography.

astro-ph.CO↗

arXiv2026

JWST Spectroscopy of Type Ia Supernova 2025rbs from Maximum Light to the Nebular Phase

We present JWST observations of the Type Ia supernova (SN Ia) 2025rbs ($D=$14.5 Mpc) at +1, +23, and +84 days after B-band maximum, spanning peak light through a wavelength-dependent transition toward the nebular phase. Combined with ground-based optical and near-infrared (NIR) data, our panchromatic spectra (0.4-14 $\mu$m) include the first maximum-light mid-infrared (MIR) spectrum and the earliest MIR spectroscopic sequence of an SN Ia to date. At peak light, the MIR spectrum exhibits a continuum with permitted and forbidden features, including Si II, Ni II, and early-emerging [Ni III-IV] and [Ar II-III]. By +23 days the MIR is dominated by forbidden lines with a weak continuum, and by +84 days it is fully nebular, whereas the optical/NIR spectra remain transitional. The nebular spectrum reveals strongly stratified ejecta, with stable Ni concentrated at the lowest velocities, radioactive Co at intermediate velocities but absent within ~2000 km s$^{-1}$, and Ar occupying an outer shell. We detect small-scale substructure in [Ca IV] 3.21 $\mu$m with fractional amplitudes of a few percent and a characteristic velocity scale of ~800 km s$^{-1}$, which may reflect compositional structure, ionization variations, or both. Radiative-transfer calculations substantially underpredict these MIR Mg II features despite approximately reproducing the NIR Mg II 1.0927 $\mu$m line, suggesting that the relative strengths of these transitions are sensitive to the treatment of Mg ionization and excitation. These observations demonstrate that MIR spectroscopy beginning near maximum light simultaneously probes the emerging inner ejecta and rapidly fading outer burning products, providing new constraints for explosion and radiative-transfer models.

astro-ph.HE↗

arXiv2026

Catching Disguised Transients with ASTRANet: Anomaly-Aware Spectroscopic Classification and Conformal Calibration

Time-domain surveys discover thousands of transients per year, but the spectroscopic identification of rare and physically peculiar objects remains rate-limited by closed-set classifiers that confidently assign every input to a known class -- including spectra that genuinely belong to no known class. We present the \texttt{ASTRANet} framework, a confidence-aware infrastructure for spectroscopic transient classification built around three coupled modules: a hierarchical spectral classifier that operates directly on observer-frame spectra without requiring host-galaxy redshift or spectral phase as inputs; an anomaly detection layer (\texttt{ASTRANet-Sentinel}) that non-linearly combines $16$ embedding-space anomaly scores spanning four physically motivated families; and a conformal uncertainty quantification layer (\texttt{ASTRANet-CP}). We validate the framework on a held-out evaluation set of $289$ rare and out-of-taxonomy transients spanning $11$ classes deliberately excluded from training, chosen to span the full physical diversity of the rare-anomaly population: AGN-related outliers, GRB-related events, gap transients, novae, and peculiar supernovae. Through five astrophysically distinct failure modes of closed-set classifiers, we show that classifier-internal uncertainty and embedding-based anomaly detection are structurally complementary axes of confidence rather than alternative implementations of the same estimator. We further introduce AD-stratified Mondrian conformal prediction (AD-MCP) within \texttt{ASTRANet-CP}, achieving uniform conditional coverage across anomaly-score strata where vanilla Mondrian under-covers in the operational regime. This establishes the methodological infrastructure for confidence-aware spectroscopic discovery in the Vera C.\ Rubin Observatory era.

astro-ph.IM↗

arXiv2026

Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility

Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a central challenge in time-domain astronomy. Robust early classification is crucial for making informed decisions, but is hindered by sparse light curves and degeneracies between classes. In this work, we leverage multimodality to substantially improve real-time classification and demonstrate the practicality of our approach by deploying our model on the ZTF alert stream. Building on the Online Ranked Astrophysical CLass Estimator (ORACLE), we introduce the ORACLE-2 models, which combine light curves, metadata, and images for real-time hierarchical classification. Using both real and simulated datasets, we show that incorporating additional modalities consistently improves classification performance. On observations from ZTF's Bright Transient Survey, our best-performing model, ORACLE-2 Omni, achieves a macro F1 score of 0.73 -- an improvement of up to 11% over models using light curves and metadata alone, and up to 40% over light-curve-only models, with the strongest gains realized at early times. To demonstrate applicability to the Legacy Survey of Space and Time, which will increase alert volume by more than an order of magnitude, we train a light curve + metadata variant on the simulated ELAsTiCC dataset. This model achieves a macro F1 score of 0.88, an improvement of up to 13% over the light-curve-only variant, matching the performance of other state-of-the-art models. Finally, we quantify the trade-offs between performance and throughput, identifying regimes where multimodal approaches offer the greatest benefit. These results show that combining multiple modalities improves early-time classification, enabling more effective triage of high-volume alert streams for current and future time-domain surveys.

astro-ph.IM↗

arXiv2026

Multiwavelength Analysis of Six Luminous Fast Blue Optical Transients

We present multiwavelength observations and analysis of six luminous fast blue optical transients (LFBOTs) discovered in Zwicky Transient Facility (ZTF) survey data. We identified these LFBOTs from their fast light-curve evolution ($t_{1/2}\leq 12 $d), blue colors at peak brightness ($g-r\leq-0.5 $mag), a visible host galaxy, high optical luminosity ($M_g<-20$), and an X-ray or radio detection. With the exception of AT2024aehp (ZTF24abygbss), these transients exhibit peaks in their $10\,$GHz radio light curves at $t_{\text{rest}} \approx 50-100$ d, with peak radio luminosities ranging from $10^{38}-10^{40}$ erg s$^{-1}$. Modeling the radio emission as synchrotron radiation indicates a fast ($v=0.1-0.3c$) shock in a dense ($n_e\approx10^{3}-10^{4}$ cm$^{-3}$) medium. The X-ray emission varies by $\approx2$ orders of magnitude in luminosity ($10^{42}-10^{44}$ erg s$^{-1}$) at $t_{\text{rest}}\sim20 $d. Analysis of the host-galaxy photometry and spectroscopy for each transient shows that they are predominantly nonnuclear (a few kpc offset) with star-forming host galaxies of stellar masses $10^{9}-10^{11} ,M_\odot$. Unlike all other LFBOTs to date, AT2024aehp exhibited a luminous ($M<-19 $mag) plateau in the optical light curve; spectra during this plateau phase showed a featureless blue continuum. The $6-15$ GHz radio emission of AT2024aehp brightened by over an order of magnitude from $t_{\text{rest}} \approx70 $d to $t_{\mathrm{rest}} \approx130 $d. The mostly consistent radio behavior between optically selected LFBOTs implies a similar circumburst medium, leading us to prefer a progenitor scenario in which mass is lost in a consistent way shortly prior to the terminal event, such as a massive star merging with a compact object.

astro-ph.HE↗

arXiv2026

AT2024wpp: An Extremely Luminous Fast Ultraviolet Transient Powered by Accretion onto a Black Hole

We present the discovery of AT 2024wpp ("Whippet"), a fast and luminous 18cow-like transient. At a redshift of z=0.0868, revealed by Keck Cosmic Web Imager spectroscopy of its faint star-forming host, it is the fourth-nearest example of its class to date. Rapid identification of the source in the Zwicky Transient Facility data stream permitted ultraviolet-through-optical observations to be obtained prior to peak, allowing the first determination of the peak bolometric luminosity (2x10^45 erg/s), maximum photospheric radius (10^15 cm), and total radiated energy (10^51 erg) of an 18cow-like object. We present results from a comprehensive multiwavelength observing campaign, including a far-UV spectrum from the Cosmic Origins Spectrograph on the Hubble Space Telescope and deep imaging extending >100 days post-explosion from the Very Large Telescope, Hubble Space Telescope, Very Large Array, and Atacama Large Millimetre Array. We interpret the observations under a model in which a rapidly-accreting central engine blows a fast (~0.2c) wind into the surrounding medium and irradiates it with X-rays. The high Doppler velocities and intense ionization within this wind prevent identifiable spectroscopic features from appearing in the ejecta or in the surrounding circumstellar material. Weak H and He signatures do emerge in the spectra after 35 days in the form of double-peaked narrow lines. Each peak is individually narrow (full width ~3000 km/s) but the two components are separated by ~6600 km/s, indicating stable structures of denser material, possibly representing streams of tidal ejecta or an ablated companion star.

astro-ph.HE↗

arXiv2025

Pre-training vision models for the classification of alerts from wide-field time-domain surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

astro-ph.IM↗

arXiv2025

The automation of optical transient discovery and classification in Rubin-era time-domain astronomy

Robotic wide-field time-domain surveys, such as the Zwicky Transient Facility and the Asteroid Terrestrial-impact Last Alert System, capture dozens of transients each night. The workflows for discovering and classifying transients in survey data streams have become increasingly automated over decades of development. The recent integration of machine learning and artificial intelligence tools has produced major milestones, including the fully automated end-to-end discovery and classification of an optical transient, and has enabled automated rapid-response space-based follow-up. The now-operational Vera C. Rubin Observatory and its Legacy Survey of Space and Time are accelerating the rate of transient discovery and producing large volumes of data at incredible rates. Given the expected order-of-magnitude increase in transient discoveries, one promising path forwards for optical time-domain astronomy is heavily investing in accelerating the automation of our workflows. Here we review the current paradigm of real-time transient workflows, project their evolution during the Rubin era and present recommendations for accelerating transient astronomy with automation.

astro-ph.IM↗

arXiv2025

ZTF25abjmnps (AT2025ulz) and S250818k: A Candidate Superkilonova from a Sub-threshold Sub-Solar Gravitational Wave Trigger

On August 18, 2025, the LIGO-Virgo-KAGRA collaboration reported gravitational waves from a sub-threshold binary neutron star merger. If astrophysical, this event would have a surprisingly low chirp mass, suggesting that at least one neutron star was below a solar mass. The Zwicky Transient Facility mapped the coarse localization and discovered a transient, ZTF25abjmnps (AT2025ulz), that was spatially and temporally coincident with the gravitational wave trigger. The first week of follow-up suggested properties reminiscent of a GW170817-like kilonova. Subsequent follow-up suggests properties most similar to a young, stripped-envelope, Type IIb supernova. Although we cannot statistically rule out chance coincidence, we undertake due diligence analysis to explore the possible association between ZTF25abjmnps and S250818k. Theoretical models have been proposed wherein sub-solar neutron star(s) may form (and subsequently merge) via accretion disk fragmentation or core fission inside a core-collapse supernova i.e. a ``superkilonova". Here, we qualitatively discuss our multi-wavelength dataset in the context of the superkilonova picture. Future higher significance gravitational wave detections of sub-solar neutron star mergers with extensive electromagnetic follow-up would conclusively resolve this tantalizing multi-messenger association.

astro-ph.HE↗

arXiv2025

JWST Spectroscopy of SN Ia 2022aaiq and 2024gy: Evidence for Enhanced Central Stable Ni Abundance and a Deflagration-to-Detonation Transition

We present optical + near-infrared (NIR) + mid-infrared (MIR) observations of the normal Type Ia supernovae (SN Ia) 2022aaiq and 2024gy in the nebular phase, continuously spanning 0.35-28 microns. Medium-resolution JWST spectroscopy reveals novel narrow ($v_{\mathrm{FWHM}}<1500$ km s$^{-1}$) [Ni II] 1.94 and 6.64 micron cores in both events. The MIR [Ni II] 6.64 micron line exhibits a distinct narrow core atop a broader base, indicating a central enhancement of stable Ni. This structure points to high central densities consistent with a near-Chandrasekhar-mass ($M_{\text{Ch}}$) progenitor or a high-metallicity sub-$M_{\text{Ch}}$ progenitor. From detailed line-profile inversions of SN 2024gy, we derive emissivity profiles for stable iron-group elements (IGEs), radioactive material, and intermediate-mass elements (IMEs), revealing spatially distinct ejecta zones. The [Ni III] 7.35 micron line shows a shallow-to-steep slope transition - a "broken-slope" morphology - that matches predictions for delayed detonation explosions with separated deflagration and detonation ashes. We also reanalyze and compare to archival JWST spectra of SN 2021aefx and the subluminous SN 2022xkq. From the stable Ni luminosities, we infer that SN 2024gy produced ~5-10 times more stable Ni mass than SN 2022xkq, favoring a near-$M_{\text{Ch}}$ scenario for SN 2024gy and sub-$M_{\text{Ch}}$ scenario for SN 2022xkq. These results demonstrate that resolved line profiles, now accessible with JWST, provide powerful diagnostics of explosion geometry, central density, and progenitor mass in SN Ia.

astro-ph.HE↗

arXiv2025

AppleCiDEr II: SpectraNet -- A Deep Learning Network for Spectroscopic Data

Time-domain surveys such as the Zwicky Transient Facility (ZTF) have opened a new frontier in the discovery and characterization of transients. While photometric light curves provide broad temporal coverage, spectroscopic observations remain crucial for physical interpretation and source classification. However, existing spectral analysis methods -- often reliant on template fitting or parametric models -- are limited in their ability to capture the complex and evolving spectra characteristic of such sources, which are sometimes only available at low resolution. In this work, we introduce SpectraNet, a deep convolutional neural network designed to learn robust representations of optical spectra from transients. Our model combines multi-scale convolution kernels and multi-scale pooling to extract features from preprocessed spectra in a hierarchical and interpretable manner. We train and validate SpectraNet on low-resolution time-series spectra obtained from the Spectral Energy Distribution Machine (SEDM) and other instruments, demonstrating state-of-the-art performance in classification. Furthermore, in redshift prediction tasks, SpectraNet achieves a root mean squared relative redshift error of 0.02, highlighting its effectiveness in precise regression tasks as well.

astro-ph.IM↗

arXiv2025

StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars

Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling. Astronomical time series of stellar fluxes (light curves) are available in immense quantities and exhibit irregular sampling, multiple variates, and heteroskedasticity. We introduce StarEmbed, the first public benchmark for light curves comprised of real observations of ~40,000 stars expert-labeled across seven classes and evaluations in clustering, classification, and out-of-distribution (OOD) source detection. We benchmark TSFMs with differing architecture and training strategies as well as domain-specific transformers. Our results demonstrate that the Chronos family, despite being pre-trained on regularly sampled non-astronomical data, yields state-of-the-art (SOTA) performance in light curve clustering and OOD detection. While no TSFM strictly surpasses the classification performance of the long-established domain baseline, they do demonstrate excellent generalization abilities. StarEmbed marks a step toward universal light curve embeddings and improved TSFM performance on challenging data.

astro-ph.SR↗

arXiv2025

The mass of the Milky Way from outer halo stars measured by DESI DR1

As a benchmark for galaxy evolution and dark matter studies, the total mass of the Milky Way is a parameter of cosmological significance, and its value at large radii from the Galactic center remains highly uncertain. Following a hierarchical Bayesian inference approach, we measure the cumulative mass of the Milky Way using full 6D phase-space information of stars from the first data release of the Dark Energy Spectroscopic Instrument (DESI). We employ 330 blue horizontal-branch stars (BHBs) and 110 RR Lyrae stars (RRLs) in DESI covering Galactocentric distances in the range $\sim$50--100 kpc. Within 100 kpc from the Galactic center, we report an enclosed mass of $M(<100\ {\rm kpc}) = 0.57^{+0.08}_{-0.07}\times10^{12}$ M$_\odot$ and $M(<100\ {\rm kpc}) = 0.55^{+0.12}_{-0.10}\times10^{12}$ M$_\odot$ when using BHBs and RRLs, respectively. Extrapolating our mass profiles beyond the extent of our data, we find the virial mass of the Galaxy to be $M_{200}=0.85^{+0.16}_{-0.14}\times10^{12}$ M$_\odot$ and $M_{200}=0.78^{+0.19}_{-0.15}\times10^{12}$ M$_\odot$, respectively. We validate the effectiveness and limitations of our method using mock BHBs and RRLs from two AuriDESI halos. These tests show that the code recovers the enclosed mass of the mock galaxy with high precision and accuracy between 50 and 200 kpc, independent of the stellar tracer used and their spatial distribution. The tests also suggest an underestimation of the galaxy's cumulative mass at a level of up to $\sim20$\% if stars close to the Galactic center are used in the models. Our mass estimates lay the groundwork for future inference of the Galactic mass with upcoming DESI data releases and spectroscopic surveys mapping the halo.

astro-ph.GA↗

arXiv2025

Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure

Modern time-domain surveys like the Zwicky Transient Facility (ZTF) and the Legacy Survey of Space and Time (LSST) generate hundreds of thousands to millions of alerts, demanding automatic, unified classification of transients and variable stars for efficient follow-up. We present AppleCiDEr (Applying Multimodal Learning to Classify Transient Detections Early), a novel framework that integrates four key data modalities (photometry, image cutouts, metadata, and spectra) to overcome limitations of single-modality classification approaches. Our architecture introduces (i) two transformer encoders for photometry, (ii) a multimodal convolutional neural network (CNN) with domain-specialized metadata towers and Mixture-of-Experts fusion for combining metadata and images, and (iii) a CNN for spectra classification. Training on ~ 30,000 real ZTF alerts, AppleCiDEr achieves high accuracy, allowing early identification and suggesting follow-up for rare transient spectra. The system provides the first unified framework for both transient and variable star classification using real observational data, with seamless integration into brokering pipelines, demonstrating readiness for the LSST era.

astro-ph.IM↗

arXiv2025

Mid-Infrared Dust Evolution and Late-time Circumstellar Medium Interaction in SN 2017eaw

We present JWST/MIRI and complementary ground-based near-infrared observations of the Type II SN 2017eaw taken 6 years post-explosion. SN 2017eaw is still detected out to 25 $\mu$m and there is minimal evolution in the mid-infrared spectral energy distribution (SED) between the newly acquired JWST/MIRI observations and those taken a year earlier. Modeling of the mid-infrared SED reveals a cool $\sim$160 K dust component of $5.5\times10^{-4}\ \mathrm{M}_\odot$ and a hot $\sim$1700 K component of $5.4\times10^{-8}\ \mathrm{M}_\odot$ both composed of silicate dust. Notably there is no evidence of temperature or mass evolution in the cool dust component in the year between JWST observations. We also present new and archival HST and ground-based ultraviolet (UV) and optical observations which reveal reduced but continued circumstellar medium (CSM)-ejecta interaction at $>$2000 days post-explosion. The UV and mid-infrared emission show similar decline rates, suggesting both probe the interface between the ejecta and CSM. Given this, the continued existence of boxy H$\alpha$ emission in the nebular spectra, the low inferred optical depth of the dust, and the lack of temperature and mass evolution, we suggest that the cool dust component in SN 2017eaw may be primarily due to pre-existing dust rather than newly-formed dust in the ejecta or cold dense shell.

astro-ph.HE↗

arXiv2025

Photometry and Spectroscopy of SN 2024pxl: A Luminosity Link Among Type Iax Supernovae

We present extensive ultraviolet to optical photometric and optical to near-infrared (NIR) spectroscopic follow-up observations of the nearby intermediate-luminosity ($M_V = -16.81\pm0.19$~mag) Type Iax supernova (SN) 2024pxl in NGC 6384. SN~2024pxl exhibits a faster light curve than the high-luminosity members of this class, and slower than low-luminosity events. The observationally well-constrained rise time of $\sim$11 days and an estimated synthesized $^{56}$Ni mass of 0.03\, M$_\odot$, based on analytical modeling of the integrated spectral energy distribution light curve, are consistent with models of the weak deflagration of a carbon-oxygen white dwarf. Our optical spectral sequence of SN~2024pxl shows weak \ion{Si}{2} lines and spectral evolution similar to other high-luminosity Type Iax SNe, but also a prominent early-time \ion{C}{2} line, like lower-luminosity Type Iax SNe. The late-time optical spectrum of SN~2024pxl closely matches that of SN~2014dt, and its NIR spectral evolution aligns with that of other well-studied, high-luminosity Type Iax SNe. The spectral-line expansion velocities of SN~2024pxl are at the lower end of the Type Iax SN velocity distribution, and the velocity distribution of iron-group elements compared to intermediate-mass elements suggests that the ejecta are mixed on large scales, as expected in pure deflagration models. SN~2024pxl exhibits characteristics intermediate between those of high-luminosity and low-luminosity Type~Iax SNe, further establishing a link across this diverse class.

astro-ph.HE↗

arXiv2025

JWST and Ground-based Observations of the Type Iax Supernovae SN 2024pxl and SN 2024vjm: Evidence for Weak Deflagration Explosions

We present panchromatic optical $+$ near-infrared (NIR) $+$ mid-infrared (MIR) observations of the intermediate-luminosity Type Iax supernova (SN Iax) 2024pxl and the extremely low-luminosity SN Iax 2024vjm. JWST observations provide unprecedented MIR spectroscopy of SN Iax, spanning from $+$11 to $+$42 days past maximum light. We detect forbidden emission lines in the MIR at these early times while the optical and NIR are dominated by permitted lines with an absorption component. Panchromatic spectra at early times can thus simultaneously show nebular and photospheric lines, probing both inner and outer layers of the ejecta. We identify spectral lines not seen before in SN Iax, including [Mg II] 4.76 $\mu$m, [Mg II] 9.71 $\mu$m, [Ne II] 12.81 $\mu$m, and isolated O I 2.76 $\mu$m that traces unburned material. Forbidden emission lines of all species are centrally peaked with similar kinematic distributions, indicating that the ejecta are well mixed in both SN 2024pxl and SN 2024vjm, a hallmark of pure deflagration explosion models. Radiative transfer modeling of SN 2024pxl shows good agreement with a weak deflagration of a near-Chandrasekhar-mass white dwarf, but additional IR flux is needed to match the observations, potentially attributable to a surviving remnant. Similarly, we find SN 2024vjm is also best explained by a weak deflagration model, despite the large difference in luminosity between the two supernovae. Future modeling should push to even weaker explosions and include the contribution of a bound remnant. Our observations demonstrate the diagnostic power of panchromatic spectroscopy for unveiling explosion physics in thermonuclear supernovae.

astro-ph.HE↗