arXiv subjects

Brad Tucker

Publications and source records attributed to Brad Tucker.

6 recordsLinked to original sources

arXiv2026

Supernovae Unite: Combining Pantheon+ and DES-SN5YR

We present the Hubble diagram and cosmological constraints resulting from the combination of the Pantheon+ and DES-SN5YR supernova (SN) samples, which we refer to as Unite. Unite updates Pantheon+, including methodology improvements that the DES sample enabled, and adds the new SN from the DES survey. This represents the most comprehensive and internally consistent SN Ia dataset available, consisting of 2884 likely type Ia supernovae. This work features consistent SN Ia modelling, sample selection, and bias corrections. We also redetermine host-galaxy stellar masses for over 98% of the sample using a consistent framework. Using Unite alone, for a flat universe with a cosmological constant (Flat-$Λ$CDM), we find $Ω_{\rm m} = 0.310^{+0.012}_{-0.011}$. We further present constraints using the Unite sample combined with Cosmic Microwave Background and both DES and DESI Baryon Acoustic Oscillation data. We find strong evidence of tension between CMB measurements and the combined BAO and supernova datasets when fitting a model with constant dark energy equation of state ($w$), which is alleviated when allowing $w$ to vary with time. For a flat universe with dark energy that has a time-evolving equation of state parametrized by $w_0$ and $w_a$ we find $(Ω_{\rm m},w_0,w_a) = (0.305\pm0.004,-0.861^{+0.044}_{-0.042},-0.60^{+0.17}_{-0.19})$ with a dark energy Figure of Merit of 315. This corresponds to a $\sim30\%$ reduction in the area of the $w_0-w_a$ confidence region relative to previous constraints from the combination of DES Y6, DESI-DR2, and CMB data (arXiv:2605.27221). The Bayesian evidence indicates only weak preference for time evolving dark energy, whereas frequentist analyses yield a preference over Flat-$Λ$CDM at a significance of $3.3σ$ ($3.1σ$) when using the maximum $\textit{a posteriori}$ probability (maximum likelihood).

astro-ph.CO↗

arXiv2026

JWST Nebular Spectroscopy of SN 2023qov: Circumstellar Dust Emission in a Normal Type Ia Supernova

We present panchromatic observations of the Type Ia supernova (SN Ia) 2023qov, ranging from $\sim$2 weeks before to $\sim$1 year after maximum light. \textit{JWST} near- and mid-infrared spectra at $+$276 and $+$363~days show $\sim$400 K dust emission that cools by $\sim$75 K between epochs, the first unambiguous spectroscopic detection of dust emission in a normal SN Ia. We find that the emission is well described by models of carbonaceous dust placed within $\sim$1 light year of the SN, with a dust mass of $\sim$$10^{-4}$ M$_{\odot}$. We do not see evidence of active dust creation, suggesting an infrared light echo by pre-existing circumstellar dust as the likely source of the emission. The \textit{JWST} nebular line profiles suggest asymmetric, stratified ejecta, similar to other normal SNe Ia, though a slight double-horn structure in the argon lines indicate a toroidal enhancement. SN 2023qov exhibits a slightly red, fast-declining early light curve ($Δm_{15}(B) = 1.47 \pm 0.05$ mag), from which we determine a $^{56}$Ni mass of $M_{56} = 0.21 \pm 0.04$ M$_{\odot}$, and a distance of $d = 36.0 \pm 1.8$ Mpc to the SN and its host, NGC 7029.

astro-ph.HE↗

arXiv2019

DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts

We present DASH (Deep Automated Supernova and Host classifier), a novel software package that automates the classification of the type, age, redshift, and host galaxy of supernova spectra. DASH makes use of a new approach that does not rely on iterative template matching techniques like all previous software, but instead classifies based on the learned features of each supernova's type and age. It has achieved this by employing a deep convolutional neural network to train a matching algorithm. This approach has enabled DASH to be orders of magnitude faster than previous tools, being able to accurately classify hundreds or thousands of objects within seconds. We have tested its performance on four years of data from the Australian Dark Energy Survey (OzDES). The deep learning models were developed using TensorFlow, and were trained using over 4000 supernova spectra taken from the CfA Supernova Program and the Berkeley SN Ia Program as used in SNID (Supernova Identification software, Blondin & Tonry 2007). Unlike template matching methods, the trained models are independent of the number of spectra in the training data, which allows for DASH's unprecedented speed. We have developed both a graphical interface for easy visual classification and analysis of supernovae, and a Python library for the autonomous and quick classification of several supernova spectra. The speed, accuracy, user-friendliness, and versatility of DASH presents an advancement to existing spectral classification tools. We have made the code publicly available on GitHub and PyPI (pip install astrodash) to allow for further contributions and development. The package documentation is available at https://astrodash.readthedocs.io.

astro-ph.IM↗

arXiv2018

DES Science Portal: Computing Photometric Redshifts

A significant challenge facing photometric surveys for cosmological purposes is the need to produce reliable redshift estimates. The estimation of photometric redshifts (photo-zs) has been consolidated as the standard strategy to bypass the high production costs and incompleteness of spectroscopic redshift samples. Training-based photo-z methods require the preparation of a high-quality list of spectroscopic redshifts, which needs to be constantly updated. The photo-z training, validation, and estimation must be performed in a consistent and reproducible way in order to accomplish the scientific requirements. To meet this purpose, we developed an integrated web-based data interface that not only provides the framework to carry out the above steps in a systematic way, enabling the ease testing and comparison of different algorithms, but also addresses the processing requirements by parallelizing the calculation in a transparent way for the user. This framework called the Science Portal (hereafter Portal) was developed in the context the Dark Energy Survey (DES) to facilitate scientific analysis. In this paper, we show how the Portal can provide a reliable environment to access vast data sets, provide validation algorithms and metrics, even in the case of multiple photo-zs methods. It is possible to maintain the provenance between the steps of a chain of workflows while ensuring reproducibility of the results. We illustrate how the Portal can be used to provide photo-z estimates using the DES first year (Y1A1) data. While the DES collaboration is still developing techniques to obtain more precise photo-zs, having a structured framework like the one presented here is critical for the systematic vetting of DES algorithmic improvements and the consistent production of photo-zs in the future DES releases.

astro-ph.GA↗

arXiv2016

Discovery of a z=0.65 Post-Starburst BAL Quasar in the DES Supernova Fields

We present the discovery of a z=0.65 low-ionization broad absorption line (LoBAL) quasar in a post-starburst galaxy in data from the Dark Energy Survey (DES) and spectroscopy from the Australian Dark Energy Survey (OzDES). LoBAL quasars are a minority of all BALs, and rarer still is that this object also exhibits broad FeII (an FeLoBAL) and Balmer absorption. This is the first BAL quasar that has signatures of recently truncated star formation, which we estimate ended about 40 Myr ago. The characteristic signatures of an FeLoBAL require high column densities, which could be explained by the emergence of a young quasar from an early, dust-enshrouded phase, or by clouds compressed by a blast wave. The age of the starburst component is comparable to estimates of the lifetime of quasars, so if we assume the quasar activity is related to the truncation of the star formation, this object is better explained by the blast wave scenario.

astro-ph.GA↗

arXiv2015

Measuring nickel masses in Type Ia supernovae using cobalt emission in nebular phase spectra

The light curves of Type Ia supernovae (SNe Ia) are powered by the radioactive decay of $^{56}$Ni to $^{56}$Co at early times, and the decay of $^{56}$Co to $^{56}$Fe from ~60 days after explosion. We examine the evolution of the [Co III] 5892 A emission complex during the nebular phase for SNe Ia with multiple nebular spectra and show that the line flux follows the square of the mass of $^{56}$Co as a function of time. This result indicates both efficient local energy deposition from positrons produced in $^{56}$Co decay, and long-term stability of the ionization state of the nebula. We compile 77 nebular spectra of 25 SN Ia from the literature and present 17 new nebular spectra of 7 SNe Ia, including SN2014J. From these we measure the flux in the [Co III] 5892 A line and remove its well-behaved time dependence to infer the initial mass of $^{56}$Ni ($M_{Ni}$) produced in the explosion. We then examine $^{56}$Ni yields for different SN Ia ejected masses ($M_{ej}$ - calculated using the relation between light curve width and ejected mass) and find the $^{56}$Ni masses of SNe Ia fall into two regimes: for narrow light curves (low stretch s~0.7-0.9), $M_{Ni}$ is clustered near $M_{Ni}$ ~ 0.4$M_\odot$ and shows a shallow increase as $M_{ej}$ increases from ~1-1.4$M_\odot$; at high stretch, $M_{ej}$ clusters at the Chandrasekhar mass (1.4$M_\odot$) while $M_{Ni}$ spans a broad range from 0.6-1.2$M_\odot$. This could constitute evidence for two distinct SN Ia explosion mechanisms.

astro-ph.HE↗