arXiv subjects

Patrick D. Aleo

Publications and source records attributed to Patrick D. Aleo.

13 recordsLinked to original sources

arXiv2025

An Updated Detection Pipeline for Precursor Emission in Type II Supernova 2020tlf

We present a new photometric pipeline for the detection of pre-supernova (pre-SN) emission in the Young Supernova Experiment (YSE) sky survey. The method described is applied to SN 2020tlf, a type II SN (SN II) with precursor emission in the last ~100 days before first light. We re-analyze the YSE griz-band light curves of SN 2020tlf and provide revised pre-explosion photometry that includes a robust list of confident detection and limiting magnitudes. Compared to the results of Jacobson-Galan et al. 2022a, this new analysis yields fewer total r/i/z-band pre-SN detections at phases > -100 days. Furthermore, we discourage the use of the blackbody modeling of the pre-explosion spectral energy distribution, the pre-SN bolometric light curve and the blackbody model parameters presented in Jacobson-Galan et al. 2022a. Nevertheless, binned photometry of SN 2020tlf confirms a consistent progenitor luminosity of ~10$^{40}$ erg s$^{-1}$ before explosion.

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arXiv2024

Exploring the Universe with SNAD: Anomaly Detection in Astronomy

SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. The work carried out by SNAD not only contributes to the discovery and classification of various astronomical phenomena but also enhances our understanding and implementation of machine learning techniques within the field of astrophysics. This paper provides a review of the SNAD project and summarizes the advancements and achievements made by the team over several years.

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arXiv2023

First Impressions: Early-Time Classification of Supernovae using Host Galaxy Information and Shallow Learning

Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for triggering rapid follow-up of ongoing phenomena. In this work, we introduce a neural network with a single recurrent layer designed explicitly for early photometric classification of supernovae. Our algorithm leverages transfer learning to account for model misspecification, host galaxy photometry to solve the data scarcity problem soon after discovery, and a custom weighted loss to prioritize accurate early classification. We first train our algorithm using state-of-the-art transient and host galaxy simulations, then adapt its weights and validate it on the spectroscopically-confirmed SNe Ia, SNe II, and SNe Ib/c from the Zwicky Transient Facility Bright Transient Survey. On observed data, our method achieves an overall accuracy of $82 \pm 2$% within 3 days of an event's discovery, and an accuracy of $87 \pm 5$% within 30 days of discovery. At both early and late phases, our method achieves comparable or superior results to the leading classification algorithms with a simpler network architecture. These results help pave the way for rapid photometric and spectroscopic follow-up of scientifically-valuable transients discovered in massive synoptic surveys.

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arXiv2023

Supernova search with active learning in ZTF DR3

We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this work is to explore the potential of adaptive learning techniques in application to big data sets. Our SNAD team used Active Anomaly Discovery (AAD) as a tool to search for new supernova (SN) candidates in the photometric data from the first 9.4 months of the Zwicky Transient Facility (ZTF) survey, namely, between March 17 and December 31 2018 (58194 < MJD < 58483). We analysed 70 ZTF fields at a high galactic latitude and visually inspected 2100 outliers. This resulted in 104 SN-like objects being found, 57 of which were reported to the Transient Name Server for the first time and with 47 having previously been mentioned in other catalogues, either as SNe with known types or as SN candidates. We visually inspected the multi-colour light curves of the non-catalogued transients and performed fittings with different supernova models to assign it to a probable photometric class: Ia, Ib/c, IIP, IIL, or IIn. Moreover, we also identified unreported slow-evolving transients that are good superluminous SN candidates, along with a few other non-catalogued objects, such as red dwarf flares and active galactic nuclei. Beyond confirming the effectiveness of human-machine integration underlying the AAD strategy, our results shed light on potential leaks in currently available pipelines. These findings can help avoid similar losses in future large-scale astronomical surveys. Furthermore, the algorithm enables direct searches of any type of data and based on any definition of an anomaly set by the expert.

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arXiv2023

The SNAD Viewer: Everything You Want to Know about Your Favorite ZTF Object

We describe the SNAD Viewer, a web portal for astronomers which presents a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, including data gathered from multiple publicly available astronomical archives and data sources. Initially built to enable efficient expert feedback in the context of adaptive machine learning applications, it has evolved into a full-fledged community asset that centralizes public information and provides a multi-dimensional view of ZTF sources. For users, we provide detailed descriptions of the data sources and choices underlying the information displayed in the portal. For developers, we describe our architectural choices and their consequences such that our experience can help others engaged in similar endeavors or in adapting our publicly released code to their requirements. The infrastructure we describe here is scalable and flexible and can be personalized and used by other surveys and for other science goals. The Viewer has been instrumental in highlighting the crucial roles domain experts retain in the era of big data in astronomy. Given the arrival of the upcoming generation of large-scale surveys, we believe similar systems will be paramount in enabling an optimal exploitation of the scientific potential enclosed in current terabyte and future petabyte-scale data sets. The Viewer is publicly available online at https://ztf.snad.space

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arXiv2021

Final Moments I: Precursor Emission, Envelope Inflation, and Enhanced Mass loss Preceding the Luminous Type II Supernova 2020tlf

We present panchromatic observations and modeling of supernova (SN) 2020tlf, the first normal type II-P/L SN with confirmed precursor emission, as detected by the Young Supernova Experiment transient survey with the Pan-STARRS1 telescope. Pre-explosion emission was detected in $riz-$bands at 130 days prior to SN 2020tlf and persisted at relatively constant flux until first light. Soon after discovery, "flash" spectroscopy of SN 2020tlf revealed prominent narrow symmetric emission lines ($v_w < 300$ km s$^{-1}$) that resulted from the photo-ionization of unshocked circumstellar material (CSM) shedded in progenitor mass loss episodes in the final weeks to months before explosion. Surprisingly, this novel display of pre-SN emission and associated mass loss occurred in a RSG progenitor with ZAMS mass of only 10-12 M$_{\odot}$, as inferred from nebular spectra. Modeling of the light curve and multi-epoch spectra with the non-LTE radiative transfer code CMFGEN and radiation-hydrodynamical (RHD) code HERACLES suggests a dense CSM limited to $r \approx 10^{15}$ cm, and mass loss rate of $10^{-2}$ M$_{\odot}$ yr$^{-1}$. The subsequent luminous light-curve plateau and persistent blue excess indicates an extended progenitor, compatible with a RSG model with $R_{\star} = 1100$ R$_{\odot}$. Inferences from the limits on the shock-powered X-ray and radio luminosity are consistent with these conclusions and suggest a CSM density of $ρ< 2 \times 10^{-16}$ g cm$^{-3}$ for distances of $r \approx 5 \times 10^{15}$ cm, as well as a mass loss rate of $\dot M<1.3 \times 10^{-5}\,\rm{M_{\odot}\,yr^{-1}}$ at larger distances. A promising power source for the observed precursor emission is the ejection of stellar material following energy disposition into the stellar envelope as a result of gravity waves emitted during either neon/oxygen burning or a nuclear flash from silicon combustion.

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arXiv2021

An Early-Time Optical and Ultraviolet Excess in the type-Ic SN 2020oi

We present photometric and spectroscopic observations of Supernova 2020oi (SN 2020oi), a nearby ($\sim$17 Mpc) type-Ic supernova (SN Ic) within the grand-design spiral M100. We undertake a comprehensive analysis to characterize the evolution of SN 2020oi and constrain its progenitor system. We detect flux in excess of the fireball rise model $δt \approx 2.5$ days from the date of explosion in multi-band optical and UV photometry from the Las Cumbres Observatory and the Neil Gehrels Swift Observatory, respectively. The derived SN bolometric luminosity is consistent with an explosion with $M_{\rm ej} = 0.81 \pm 0.03 M_{\odot}$, $E_{k}= 0.79 \pm 0.09 \times 10^{51} \rm{erg} \rm{s}^{-1}$, and $M_{\rm Ni56} = 0.08 \pm 0.02 M_{\odot}$. Inspection of the event's decline reveals the highest $Δm_{15,\rm{bol}}$ reported for a stripped-envelope event to date. Modeling of optical spectra near event peak indicates a partially mixed ejecta comparable in composition to the ejecta observed in SN 1994I, while the earliest spectrum shows signatures of a possible interaction with material of a distinct composition surrounding the SN progenitor. Further, Hubble Space Telescope (HST) pre-explosion imaging reveals a stellar cluster coincident with the event. From the cluster photometry, we derive the mass and age of the SN progenitor using stellar evolution models implemented in the BPASS library. Our results indicate that SN 2020oi occurred in a binary system from a progenitor of mass $M_{\rm ZAMS} \approx 9.5 \pm 1.0 M_{\odot}$, corresponding to an age of $27 \pm 7$ Myr. SN 2020oi is the dimmest SN Ic event to date for which an early-time flux excess has been observed, and the first in which an early excess is unlikely to be associated with shock-cooling.

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arXiv2021

El-CID: A filter for Gravitational-wave Electromagnetic Counterpart Identification

As gravitational-wave (GW) interferometers become more sensitive and probe ever more distant reaches, the number of detected binary neutron star mergers will increase. However, detecting more events farther away with GWs does not guarantee corresponding increase in the number of electromagnetic counterparts of these events. Current and upcoming wide-field surveys that participate in GW follow-up operations will have to contend with distinguishing the kilonova from the ever increasing number of transients they detect, many of which will be consistent with the GW sky-localization. We have developed a novel tool based on a temporal convolutional neural network architecture, trained on sparse early-time photometry and contextual information for Electromagnetic Counterpart Identification (El-CID). The overarching goal for El-CID is to slice through list of new transient candidates that are consistent with the GW sky localization, and determine which sources are consistent with kilonovae, allowing limited target-of-opportunity resources to be used judiciously. In addition to verifying the performance of our algorithm on an extensive testing sample, we validate it on AT2017gfo - the only EM counterpart of a binary neutron star merger discovered to date - and AT2019npv - a supernova that was initially suspected as a counterpart of the gravitational-wave event, GW190814, but was later ruled out after further analysis.

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arXiv2021

The ANTARES Astronomical Time-Domain Event Broker

We describe the Arizona-NOIRLab Temporal Analysis and Response to Events System (ANTARES), a software instrument designed to process large-scale streams of astronomical time-domain alerts. With the advent of large-format CCDs on wide-field imaging telescopes, time-domain surveys now routinely discover tens of thousands of new events each night, more than can be evaluated by astronomers alone. The ANTARES event broker will process alerts, annotating them with catalog associations and filtering them to distinguish customizable subsets of events. We describe the data model of the system, the overall architecture, annotation, implementation of filters, system outputs, provenance tracking, system performance, and the user interface.

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arXiv2020

Clustering-informed Cinematic Astrophysical Data Visualization with Application to the Moon-forming Terrestrial Synestia

Scientific visualization tools are currently not optimized to create cinematic, production-quality representations of numerical data for the purpose of science communication. In our pipeline \texttt{Estra}, we outline a step-by-step process from a raw simulation into a finished render as a way to teach non-experts in the field of visualization how to achieve production-quality outputs on their own. We demonstrate feasibility of using the visual effects software Houdini for cinematic astrophysical data visualization, informed by machine learning clustering algorithms. To demonstrate the capabilities of this pipeline, we used a post-impact, thermally-equilibrated Moon-forming synestia from \cite{Lock18}. Our approach aims to identify "physically interpretable" clusters, where clusters identified in an appropriate phase space (e.g. here we use a temperature-entropy phase-space) correspond to physically meaningful structures within the simulation data. Clustering results can then be used to highlight these structures by informing the color-mapping process in a simplified Houdini software shading network, where dissimilar phase-space clusters are mapped to different color values for easier visual identification. Cluster information can also be used in 3D position space, via Houdini's Scene View, to aid in physical cluster finding, simulation prototyping, and data exploration. Our clustering-based renders are compared to those created by the Advanced Visualization Lab (AVL) team for the full dome show "Imagine the Moon" as proof of concept. With \texttt{Estra}, scientists have a tool to create their own production-quality, data-driven visualizations.

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arXiv2019

Deblending and Classifying Astronomical Sources with Mask R-CNN Deep Learning

We apply a new deep learning technique to detect, classify, and deblend sources in multi-band astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask R-CNN image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92% at 80% recall for stars and a precision of 98% at 80% recall for galaxies in a typical field with $\sim30$ galaxies/arcmin$^2$. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as LSST and WFIRST. Our code, Astro R-CNN, is publicly available at https://github.com/burke86/astro_rcnn.

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arXiv2018

Blind nucleosynthetic source discovery in astronomical elemental abundance data

We demonstrate physics-blind and purely data-based nucleosynthetic archetype discovery from heterogeneous stellar elemental abundance measurements. Archetype extraction is performed via non-negative matrix factorization (NMF). Because stellar elemental abundance measurements can be incomplete and sparsely sampled, we perform combined low-rank NMF and matrix completion on the Hypatia Catalog and JINAbase to extract archetypal elemental abundance patterns, the so-called endmembers, and impute the missing measurements. We qualitatively compare the discovered nucleosynthetic archetypes to patterns anticipated from direct astrophysical nucleosynthetic calculations. Our tentative results indicate that the physics-blind analysis relying entirely on the incomplete chemical mixing in star formation can discern nucleosynthetic archetypes associated with asymptotic giant branch (AGB) stellar sources of the s-process, thermonuclear supernovae, and non-thermonuclear supernovae with an r-process.

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arXiv2017

On the Iron Abundance Anomaly in K-dwarf and Hyades Stars

Using standard 1D-LTE model atmosphere analysis, we provide an in-depth investigation of iron abundance as derived from neutral and singly ionization iron lines (Fe {\scriptsize{I, II}}) in nearby star clusters. Specifically, we replicate the discrepancy regarding $Δ$[Fe/H], wherein the difference of Fe {\scriptsize{II}} - Fe {\scriptsize{I}} increases for stars of the same cluster with decreasing $T_\mathrm{eff}$, reaching an astonishing 1.0 dex at $T_\mathrm{eff}$ $\thicksim$ 4000 K. Previous studies have investigated this anomaly in the Pleiades and Hyades clusters with no concrete solution. In this analysis, we probe two samples: 63 wide binary field stars where the primary star is of sun-like temperatures and the secondary is a K-dwarf, ranging from 4231 K $\leq$ $T_\mathrm{eff}$ $\leq$ 6453 K, and 33 Hyades stars of temperatures 4268 K $\leq$ $T_\mathrm{eff}$ $\leq$ 6072 K. Previous studies have found discrepancies on the order of 1.0 dex. However, we find that these studies have neglected line-blending effects of certain Fe {\scriptsize{II}} lines, namely $λ$ = \{4508.29 Å, 4993.34 Å, 5197.58 Å, 5325.55 Å, 5425.26 Å, 6456.38 Å\}. When these lines are removed from the line-list, we find $Δ$[Fe/H] decreases to $\thicksim$ 0.6 dex in the field binaries and $\thicksim$ 0.3 dex in the Hyades. The reason for this remaining trend is investigated by probing NLTE effects, as well as age and activity considerations using Ca {\scriptsize{II}} H+K emission and Li absorption, but these results appear to be small to negligible.

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