arXiv subjects

Alex Gagliano

Publications and source records attributed to Alex Gagliano.

12 recordsLinked to original sources

arXiv2026

Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group

Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely available textbook at https://deeplearning4astro.com, curated from the NASA Cosmic Origins Artificial Intelligence and Machine Learning Science and Technology Interest Group (AI/ML STIG) lecture series. The book collects 23 chapters by 17 lecturers across six parts, moving from computational foundations and deep-learning architectures through generative modeling, simulation-based inference, reinforcement learning, and large-language-model agents to the practice of AI-laden science. Many include executable notebooks using astronomical data.

astro-ph.IM↗

arXiv2026

The Impact of Host Galaxy Properties on Supernova Classification with Hierarchical Labels

With the advent of the Vera C. Rubin Observatory, the discovery rate of supernovae (SNe) will surpass the rate of SNe with real time spectroscopic followup by three orders of magnitude. Accurate photometric classifiers are essential to both select interest events for followup in real time and for archival population-level studies. In this work, we investigate the impact of observable host galaxy information on the classification of SNe, both with and without additional light curve and redshift information. We find that host galaxy information alone can successfully isolate relatively pure (>90%) samples of Type Ia SNe with or without redshift information. With redshift information, we can additionally produce somewhat pure (>70%) samples of Type II SNe and superluminous supernovae. Additionally with redshift information, host galaxy properties do not significantly improve the accuracy of SN classification when paired with complete light curves. In the absence of redshift information, however, galaxy properties significantly increase the accuracy of photometric classification. As a part of this analysis, we present the first formal application of a new objective function, the weighted hierarchical cross-entropy, to the problem of supernova classification. This objective function more naturally accounts for the hierarchical nature of supernova classes and, more broadly, transients. Finally, we present a new set of SN classifications for the Pan-STARRS Medium Deep Survey of SNe that lack spectroscopic redshift, increasing the full photometric sample to >4400 events.

astro-ph.IM↗

arXiv2026

A Fully Photometric Approach to Type Ia Supernova Cosmology in the LSST Era: Host Galaxy Redshifts and Supernova Classification

The upcoming Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to discover nearly a million Type Ia supernovae (SNeIa), offering an unprecedented opportunity to constrain dark energy. The vast majority of these events will lack spectroscopic classification and redshifts, necessitating a fully photometric approach to maximize cosmology constraining power. We present detailed simulations based on the Extended LSST Astronomical Time Series Classification Challenge (ELAsTiCC), and a cosmological analysis using photometrically classified SNeIa with host galaxy photometric redshifts. This dataset features realistic multi-band light curves, non-SNIa contamination, host mis-associations, and transient-host correlations across the high-redshift Deep Drilling Fields (DDF) (~ 50 deg^2). We also include a spectroscopically confirmed low-redshift sample based on the Wide Fast Deep (WFD) fields. We employ a joint SN+host photometric redshift fit, a neural network based photometric classifier (SCONE), and BEAMS with Bias Corrections (BBC) methodology to construct a bias-corrected Hubble diagram. We produce statistical + systematic covariance matrices, and perform cosmology fitting with a prior using Cosmic Microwave Background constraints. We fit and present results for the wCDM dark energy model, and the more general Chevallier-Polarski-Linder (CPL) w0wa model. With a simulated sample of ~6000 events, we achieve a Figure of Merit (FoM) value of about 150, which is significantly larger than the DESVYR FoM of 54. Averaging analysis results over 25 independent samples, we find small but significant biases indicating a need for further analysis testing and development.

astro-ph.CO↗

arXiv2025

Characterizing Supernova Host Galaxies with FrankenBlast: A Scalable Tool for Transient Host Galaxy Association, Photometry, and Stellar Population Modeling

We present FrankenBlast, a customized and improved version of the Blast web application. FrankenBlast associates transients to their host galaxies, performs host photometry, and runs a innovative SED fitting code to constrain host stellar population properties--all within minutes per object. We test FrankenBlast on 14,432 supernovae (SNe), ~half of which are spectroscopically-classified, and are able to constrain host properties for 9262 events. When contrasting the host stellar masses ($M_*$), specific star formation rates (sSFR), and host dust extinction ($A_V$) between spectroscopically and photometrically-classified SNe Ia, Ib/c, II, and IIn, we determine that deviations in these distributions are primarily due to misclassified events contaminating the photometrically-classified sample. We further show that the higher redshifts of the photometrically-classified sample also force their $M_*$ and sSFR distributions to deviate from those of the spectroscopically-classified sample, as these properties are redshift-dependent. We compare host properties between spectroscopically-classified SN populations and determine if they primarily trace $M_*$ or SFR. We find that all SN populations seem to both depend on $M_*$ and SFR, with SNe II and IIn somewhat more SFR-dependent than SNe Ia and Ib/c, and SNe Ia more $M_*$-dependent than all other classes. We find the difference in the SNe Ib/c and II hosts the most intriguing and speculate that SNe Ib/c must be more dependent on higher $M_*$ and more evolved environments for the right conditions for progenitor formation. All data products and FrankenBlast are publicly available, along with a developing FrankenBlast version intended for Rubin Observatory science products.

astro-ph.HE↗

arXiv2025

ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST

We present ORACLE, the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable astrophysical phenomena. ORACLE is a recurrent neural network with Gated Recurrent Units (GRUs), and has been trained using a custom hierarchical cross-entropy loss function to provide high-confidence classifications along an observationally-driven taxonomy with as little as a single photometric observation. Contextual information for each object, including host galaxy photometric redshift, offset, ellipticity and brightness, is concatenated to the light curve embedding and used to make a final prediction. Training on $\sim$0.5M events from the Extended LSST Astronomical Time-Series Classification Challenge, we achieve a top-level (Transient vs Variable) macro-averaged precision of 0.96 using only 1 day of photometric observations after the first detection in addition to contextual information, for each event; this increases to $>$0.99 once 64 days of the light curve has been obtained, and 0.83 at 1024 days after first detection for 19-way classification (including supernova sub-types, active galactic nuclei, variable stars, microlensing events, and kilonovae). We also compare ORACLE with other state-of-the-art classifiers and report comparable performance for the 19-way classification task, in addition to delivering accurate top-level classifications much earlier. The code and model weights used in this work are publicly available at our associated GitHub repository (https://github.com/uiucsn/ELAsTiCC-Classification).

astro-ph.IM↗

arXiv2025

Enabling Early Transient Discovery in LSST via Difference Imaging with DECam

We present SLIDE, a pipeline that enables transient discovery in data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), using archival images from the Dark Energy Camera (DECam) as templates for difference imaging. We apply this pipeline to the recently released Data Preview 1 (DP1; the first public release of Rubin commissioning data) and search for transients in the resulting difference images. The image subtraction, photometry extraction, and transient detection are all performed on the Rubin Science Platform. We demonstrate that SLIDE effectively extracts clean photometry by circumventing poor or missing LSST templates. We identified 29 previously unreported transients, 12 of which would not have been detected based on the DP1 DiaObject catalog. SLIDE will be especially useful for transient analysis in the early years of LSST, when template coverage will be largely incomplete or when templates may be contaminated by transients present at the time of acquisition. We present multiband light curves for a sample of known transients, along with new transient candidates identified through our search. Finally, we discuss the prospects of applying this pipeline during the main LSST survey. Our pipeline is broadly applicable and will support studies of all transients with slowly evolving phases.

astro-ph.HE↗

arXiv2025

Spectral Diversity in Type Ibn Supernovae and the Large Host Offset of SN2024acyl

In this paper, we first present observations of SN~2024acyl, a normal Type Ibn supernova with a large projected offset ($\sim$35~kpc) from its host galaxy. The low star-formation rate measured at the explosion site raises the possibility that the progenitor of SN~2024acyl may not have been a massive star. We then examine, more broadly, the spectral diversity of Type Ibn supernovae around 20--35 days after peak brightness and identify two distinct groups: Group I, which shows bluer rest-frame optical color and narrower He~I emission lines; and Group II, which shows redder rest-frame optical color and broader He~I lines. Group~I also tends to show higher peak luminosities. The diversity we identify appears to be closely connected to the diversity observed around peak and to persist into late phases ($>80$ days after peak). Given its redder color and broader He~I lines, we classify SN~2024acyl as belonging to Group II. Based on the current dataset, we find no clear connection between this spectral diversity and either the host environments of Type Ibn SNe or their pre-explosion activity. The observed diversity in Type Ibn SNe likely reflects differences in circumstellar material properties and/or explosion energetics. These differences could result from a range of progenitor properties, such as different helium star mass, orbital period and companion type if they are in binary systems, and may indicate fundamentally diverse progenitors. Whether a continuous distribution exists between the two groups remains to be determined and will require further data to explore.

astro-ph.HE↗

arXiv2025

A Detection of Helium in the Bright Superluminous Supernova SN 2024rmj

We present extensive ultraviolet (UV), optical, and near-infrared (NIR) photometric and spectroscopic observations of the nearby hydrogen-poor superluminous supernova (SLSN-I) SN2024rmj at z = 0.1189. SN 2024rmj reached a peak absolute magnitude of Mg $\approx$ -21.9, placing it at the luminous end of the SLSN-I distribution. The light curve exhibits a pronounced pre-peak bump ($\approx$ 60 d before the main peak) and a post-peak bump ($\approx$ 55 d after the main peak). The bulk of the light curve is otherwise well fit by a magnetar spin-down model, with typical values (spin: $\approx$ 2.1 ms; magnetic field: $\approx$ 6 $\times$ 10$^{13}$ G; ejecta mass: $\approx$ 12 M$_\odot$). The optical spectra exhibit characteristic SLSN-I features and evolution, but with a relatively high velocity of $\approx$ 8,000 km s$^{-1}$ post-peak. Most significantly, we find a clear detection of helium in the NIR spectra at He I $λ$1.083 $μ$m and $λ$2.058 $μ$m, blueshifted by $\approx$ 15,000 km s$^{-1}$ (13 d before peak) and $\approx$ 13,000 km s$^{-1}$ (40 d after peak), indicating that helium is confined to the outermost ejecta; based on these NIR detections, we also identify likely contribution from He I $λ$5876 Å in the optical spectra on a similar range of timescales. This represents the most definitive detection of helium in a bright SLSN-I to date, and indicates that progenitors with a thin helium layer can still explode as SLSNe.

astro-ph.HE↗

arXiv2024

Find the haystacks, then look for needles: The rate of strongly lensed transients in galaxy-galaxy strong gravitational lenses

The time delay between appearances of multiple images of a gravitationally lensed supernova (glSN) is sensitive to the Hubble constant, $H_0$. As well as time delays, a lensed host galaxy is needed to enable precise inference of $H_0$. In this work we investigate the connection between discoverable lensed transients and their host galaxies. We find that LSST will discover 88 glSNe per year, of which $54\%$ will also have a strongly lensed host. The rates can change by approximately 30 percent uncertainty depending primarily on the choice of unlensed SN population and uncertainties in the redshift evolution of the deflector population, but the fraction of glSNe with a lensed host is consistently around a half. LSST will discover 20 glSNe per year in systems that could plausibly have been identified by Euclid as galaxy-galaxy lenses before the discovery of the glSN. Such systems have preferentially longer time delays and therefore are well suited for cosmography. We define a golden sample of glSNe Ia with time delays over 10 days, image separations greater than 0.8 arcseconds, and a multiply imaged host. For this golden sample, we find $91\%$ occur in systems that should already be discoverable as galaxy-galaxy lenses in Euclid. For cosmology with glSNe, monitoring Euclid lenses is a plausible alternative to searching the entire LSST alert stream.

astro-ph.CO↗

arXiv2023

Hierarchical Cross-entropy Loss for Classification of Astrophysical Transients

Astrophysical transient phenomena are traditionally classified spectroscopically in a hierarchical taxonomy; however, this graph structure is currently not utilized in neural net-based photometric classifiers for time-domain astrophysics. Instead, independent classifiers are trained for different tiers of classified data, and events are excluded if they fall outside of these well-defined but flat classification schemes. Here, we introduce a weighted hierarchical cross-entropy objective function for classification of astrophysical transients. Our method allows users to directly build and use physics- or observationally-motivated tree-based taxonomies. Our weighted hierarchical cross-entropy loss directly uses this graph to accurately classify all targets into any node of the tree, re-weighting imbalanced classes. We test our novel loss on a set of variable stars and extragalactic transients from the Zwicky Transient Facility, showing that we can achieve similar performance to fine-tuned classifiers with the advantage of notably more flexibility in downstream classification tasks.

astro-ph.IM↗

arXiv2023

Relative intrinsic scatter in hierarchical Type Ia supernova siblings analyses: Application to SNe 2021hpr, 1997bq & 2008fv in NGC 3147

We present Young Supernova Experiment $grizy$ photometry of SN 2021hpr, the third Type Ia supernova sibling to explode in the Cepheid calibrator galaxy, NGC 3147. Siblings are useful for improving SN-host distance estimates, and investigating the contributions towards the SN Ia intrinsic scatter (post-standardisation residual scatter in distance estimates). We thus develop a principled Bayesian framework for analyzing SN Ia siblings. At its core is the cosmology-independent relative intrinsic scatter parameter, $σ_{Rel}$: the dispersion of siblings distance estimates relative to one another within a galaxy. It quantifies the contribution towards the total intrinsic scatter, $σ_0$, from within-galaxy variations about the siblings' common properties. It also affects the combined-distance uncertainty. We present analytic formulae for computing a $σ_{Rel}$-posterior from individual siblings distances (estimated using any SN-model). Applying a newly trained BayeSN model, we fit the light curves of each sibling in NGC 3147 individually, to yield consistent distance estimates. However, the wide $σ_{Rel}$-posterior means $σ_{Rel}\approxσ_0$ is not ruled out. We thus combine the distances by marginalizing over $σ_{Rel}$ with an informative prior: $σ_{Rel}\sim U(0,σ_0)$. Simultaneously fitting the trio's light curves improves constraints on distance, and each sibling's individual dust parameters, compared to individual fits. Higher correlation also tightens dust parameter constraints. Therefore, $σ_{Rel}$-marginalization yields robust estimates of siblings distances for cosmology, and dust parameters for siblings-host correlation studies. Incorporating NGC 3147's Cepheid-distance yields $H_0=78.4\pm 6.5\,$km/s/Mpc. Our work motivates analyses of homogeneous siblings samples, to constrain $σ_{Rel}$, and its SN-model dependence.

astro-ph.CO↗

arXiv2021

GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae

We present GHOST, a database of 16,175 spectroscopically classified supernovae and the properties of their host galaxies. We have developed a host galaxy association method using image gradients that achieves fewer misassociations for low-z hosts and higher completeness for high-z hosts than previous methods. We use dimensionality reduction to identify the host galaxy properties that distinguish supernova classes. Our results suggest that the hosts of SLSNe, SNe Ia, and core collapse supernovae can be separated using host brightness information and extendedness measures derived from the host's light profile. Next, we train a random forest model with data from GHOST to predict supernova class using exclusively host galaxy information and the radial offset of the supernova. We can distinguish SNe Ia and core collapse supernovae with ~70% accuracy without any photometric data from the event itself. Vera C. Rubin Observatory will usher in a new era of transient population studies, demanding improved photometric tools for rapid identification and classification of transient events. By identifying the host features with high discriminatory power, we will maintain SN sample purities and continue to identify scientifically relevant events as data volumes increase. The GHOST database and our corresponding software for associating transients with host galaxies are both publicly available.

astro-ph.GA↗