arXiv subjects

Federica B. Bianco

Publications and source records attributed to Federica B. Bianco.

At least 19 recordsLinked to original sources

arXiv2026

Nearby Supernova Science in the Era of High Cadence, Deep Time Domain Surveys

We discuss science opportunities for nearby supernovae (SNe) in the era of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), emphasizing the youngest phases of SN evolution and the value of contemporaneous high-cadence shadowing surveys. LSST's Wide Fast Deep survey will discover enormous numbers of SNe, but its $\sim 3$ day cadence will miss signatures that evolve on timescales of hours to days, requiring cadence augmentation by other programs. We present simple depth and cadence metrics for early SN signatures and use empirical nearby SN discovery rates to estimate the resulting science opportunities. LSST will detect or set limits on precursor emission from hundreds of core-collapse SNe, including $\gtrsim 50$-100 Type IIn and normal Type II SNe annually, as well as $\gtrsim$5 Type Ibn. Within $\sim 200$ Mpc, roughly 250 Type Ia SNe are classified annually, providing the potential to identify hundreds of early light-curve excesses, and a robust measurement of the incidence of extreme high-velocity features ($>$25,000 km s$^{-1}$). A shadowing survey with a depth of $\sim 22.5$ mag could search for early light-curve features, akin to the light-curve excess seen in SN2023ixf, across dozens of nearby core-collapse SNe every year. Rapid spectroscopy of $\gtrsim$150 Type II SNe per year within $\sim$160 Mpc could constrain the incidence of short-lived flash features to $\lesssim$10% within a few years, while also tying this to the rate of precursor emission in normal Type II SNe. We also discuss the challenges posed by early classification ambiguity and faint nonterminal transients, and we request that the 80 hour embargo on LSST images be waived for nearby galaxy fields. Finally, we advocate for a Nearby Galaxy Transient Broker that integrates transient streams, archival data, forced photometry, and follow-up triggering in a single platform.

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arXiv2026

How Low Can We Go? Minimum Spectroscopic Requirements For Supernova Subtype Classification

Millions of supernovae will be discovered with the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). As a result, spectrographs around the world will have to make difficult decisions about which supernova candidates receive spectroscopic follow-ups. This work identifies the minimum spectral resolution, $R_λ = \fracλ{Δλ}$, as a function of signal-to-noise ratio (SNR) at which spectral classification of supernova subtypes becomes impossible. We include supernova types Ia, Ia-91T, Ia-91bg, Iax, Ib, Ic, broad-lined Ic, IIb, IIP, and Ibn in this work. We produce a definition of SNR based on specific lines for each SN subtype that allows us to generate homogeneous datasets at 16 different values of $R_λ$ and 14 different SNR's and we tested the classification performance of a recently developed deep-learning classifier, ABC-SN, on each $R_λ$ and SNR combination. We find that classification of supernova spectra into a refined taxonomy that separates, for example, between different subtypes of stripped envelope supernovae, is possible at low resolution and low SNR with no loss in model performance down to $R_λ = 50$ and $\text{SNR} = 5$. Classification performance is only minimally impacted even as low as $R_λ = 25$. We hope that astronomers using the LSST alert stream, as well as designers of future instruments and observatories, will benefit from knowing what spectral resolution is necessary to classify a supernova for arbitrary \SNR{}.

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arXiv2026

ABC-SN: Attention Based Classifier for Supernova Spectra

While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will discover, classifying SNe spectroscopically remains the best way to determine most subtypes of SNe. Traditional spectrum classification tools use template matching techniques (eg., SNID, SuperFit) and require significant human supervision. Two deep learning spectral classifiers, DASH and SNIascore, define the state of the art, but SNIascore is a binary classifier devoted to maximizing the purity of the SN Ia-norm sample, while DASH is no longer maintained and the original work suffers from contamination of multi-epoch spectra in the training and test sets. We have explored several neural network architectures in order to create a new automated method for classifying SN subtypes, settling on an attention-based model we call ABC-SN. We benchmark our results against an updated version of DASH, thus providing the community with an up-to-date general-purpose SN classifier. Our dataset is comprised of ten different SN subtypes, including subtypes of SN Ia, core collapse, and interacting SNe. We find that ABC-SN outperforms DASH for nearly all classes, including an improvement of $26\%$ in SN Ia completeness ($\sim88\%$) and $2.4\%$ in SN Ia purity ($\sim95\%$) when unthresholded (improvements for each class can further be obtained by tuned thresholds), and we discuss the limitations of current SN datasets for benchmarking performance.

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arXiv2026

First Results from the LSST Shadow Survey: The Restless Luminous Blue Variable AT2017des in the Virgo-Cluster Galaxy, NGC4532

The Legacy Survey of Space and Time (LSST) will start in late-summer 2026, revolutionizing transient astronomy. Here, we present the Dark Energy Camera (DECam) Shadow Survey, which is designed to maximize the science potential of LSST by shadowing LSST observations of local galaxy-cluster fields, producing a nightly cadence of these fields. The Shadow Survey will discover extremely young supernovae (SNe), SN precursors, as well as other explosive transients and exotic phenomena, helping to characterize such transients at unprecedented cadence and depth when combined with LSST. We describe our workflow, pipeline, public data releases, and candidate vetting. As an early result of Shadow, we present the fitful luminous blue variable (LBV) eruptions of AT2017des in the Virgo-Cluster galaxy NGC4532. AT2017des has short-timescale variability (of order 10 days), peaking at around $M_r=-12.5$mag, brighter than normal LBVs, and similar to the more extreme flaring of hot LBVs/SN impostors such as SN2000ch, AT2016blu, and the precursor activity of SN2009ip. Our spectral time-series reveals features typical of these hot LBVs and SN impostors/precursors. Combining our data with long-baseline photometry from additional observatories, we find that the peaks of the outbursts of AT2017des are getting brighter over time, with 2026 peak fluxes being up to 5 times greater than in 2023 and an average brightening of $\sim0.05$ mag yr$^{-1}$. The peaks of AT2017des are more luminous than those of most other LBVs, only being fainter than bright precursors such as SN2009ip, and extreme SN impostors such as AT2016blu. AT2017des may therefore be ``ramping up'' to a terminal explosion.

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arXiv2026

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves. On the eve of the Vera C. Rubin Observatory Legacy Survey of Space and Time, its dataset offers unprecedented opportunities for transient science. Yet a key challenge remains its cadence, sparse and irregular across six bands, limiting inference. Interpolation helps mitigate this, with Gaussian Processes the standard, but they struggle with cross-band correlations, require a priori kernel specification, and must be fit to each light curve individually, hence scaling poorly. Here, we introduce the neural process family for light curve reconstruction, combining the probabilistic framework of Gaussian Processes with the scalability of deep learning. By meta-learning on diverse simulated transients, Attentive Neural Processes shift the bulk of computation to training, enabling rapid, amortized inference with a class-agnostic model. Evaluated on realistic Rubin cadences across 15 transient classes, we show that even an unoptimized, out-of-the-box Attentive Neural Process consistently outperforms all benchmarks -- a suite of Gaussian Processes and neural networks -- on every tested metric, spanning regression quality, astrophysical feature recovery, and probabilistic calibration. Our model interpolates all bands simultaneously in microseconds, over four orders of magnitude faster than the next-best neural benchmark and five faster than Gaussian Processes, demonstrating the potential of neural processes for the nightly Rubin alert stream. Attentive Neural Processes avoid the overconfidence of standard neural networks and the underconfidence of Gaussian Processes, delivering sharp, well-calibrated uncertainties. This work establishes the neural process family as a scalable, probabilistic foundation for real-time transient science in the Rubin era.

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arXiv2026

Searching for Ultracool Dwarfs in Early LSST Data Products

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) promises to drastically accelerate the discovery of ultracool dwarfs (UCDs) over the course of its 10-year survey of the Southern Hemisphere. With the official start of LSST imminent, we showcase LSST's capabilities for discovering and characterizing UCDs using early commissioning data (Data Preview 1). The LSST photometric system at this stage remains poorly understood for faint UCDs. Thus, we begin by cross-matching Data Preview 1 against known UCD catalogs. We recover 1 known UCD from the Ultracool Sheet, 17 UCDs from the Dark Energy Survey, and 17 low mass stars from the Gaia Catalog of Nearby Stars. Using these known UCDs alongside recent spectroscopically-confirmed Euclid objects, we select 89 ultracool dwarf candidates in LSST fields, 17 of which are unique to this work. We present our candidates, a photometric temperature estimate, and discuss lessons learned from using early LSST data products. Finally, we turn to the future and predict potential UCD counts in upcoming LSST commissioning data (Data Preview 2), which is expected to be available to the Rubin community in 2026. Using synthetic populations of brown dwarfs, we forecast over 17,000 objects may be discovered and characterized in Data Preview 2. We predict that several hundred known objects and thousands of as-of-yet undiscovered UCDs may be detected in Data Preview 2 fields.

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arXiv2025

Identification and photometric classification of extragalactic transients in the Vera C. Rubin Observatory's Data Preview 1

The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a LSSTComCam observing campaign between November and December 2024 with multi-band imaging of seven fields, covering roughly 0.4 square degrees each, providing a first glimpse into the data products that will become available once the Legacy Survey of Space and Time begins. In this work, we search three fields for extragalactic transients. We identify eight new likely supernovae, and three known ones from a sample of 369,644 difference image analysis objects. Photometric classification using Superphot+ assigns sub-classes with >95% confidence to only one SN Ia and one SN II in this sample. Our findings are in agreement with supernova detection rate predictions of $15\pm4$ supernovae from simulations using simsurvey. The supernova detection rate in the data is possibly affected by the lack of suitable templates. Nevertheless, this work demonstrates the quality of the data products delivered in DP1 and indicates that the Rubin Observatory's Legacy Survey of Space and Time (LSST) is well placed to fulfill its discovery potential in time domain astronomy.

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arXiv2025

In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys

Distance-based methods involve the computation of distance values between features and are a well-established paradigm in machine learning. In anomaly detection, anomalies are identified by their large distance from normal data points. However, the performance of these methods often hinges on a single, user-selected distance metric (e.g., Euclidean), which may not be optimal for the complex, high-dimensional feature spaces common in astronomy. Here, we introduce a novel anomaly detection method, Distance Multi-Metric Anomaly Detection (DiMMAD), which uses an ensemble of distance metrics to find novelties. Using multiple distance metrics is effectively equivalent to using different geometries in the feature space. By using a robust ensemble of diverse distance metrics, we overcome the metric-selection problem, creating an anomaly score that is not reliant on any single definition of distance. We demonstrate this multi-metric approach as a tool for simple, interpretable scientific discovery on astronomical time series -- (1) with simulated data for the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time, and (2) real data from the Zwicky Transient Facility. We find that DiMMAD excels at out-of-distribution anomaly detection -- anomalies in the data that might be new classes -- and beats other state-of-the-art methods in the goal of maximizing the diversity of new classes discovered. For rare in-distribution anomaly detection, DiMMAD performs similarly to other methods, but may allow for improved interpretability. All our code is open source: DiMMAD is implemented within DistClassiPy: https://github.com/sidchaini/distclassipy/, while all code to reproduce the results of this paper is available here: https://github.com/sidchaini/dimmad/.

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arXiv2024

Light Curve Classification with DistClassiPy: a new distance-based classifier

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and decrease computational costs. In particular, we applied DistClassiPy to classify light curves of variable stars, comparing the distances between objects of different classes. Using 18 distance metrics on a catalog of 6,000 variable stars across 10 classes, we demonstrate classification and dimensionality reduction. Our classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. Additionally, DistClassiPy can be tailored to specific objects by identifying the most effective distance metric for that classification. To facilitate broader applications within and beyond astronomy, we have made DistClassiPy open-source and available at https://pypi.org/project/distclassipy/.

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arXiv2024

Multi-filter UV to NIR Data-driven Light Curve Templates for Stripped Envelope Supernovae

While the spectroscopic classification scheme for Stripped envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all their envelopes of Hydrogen and Helium, the photometric evolution of classes within this family is not fully characterized. Photometric surveys, like the Vera C. Rubin Legacy Survey of Space and Time, will discover tens of thousands of transients each night and spectroscopic follow-up will be limited, prompting the need for photometric classification and inference based solely on photometry. We have generated 54 data-driven photometric templates for SESNe of subtypes IIb, Ib, Ic, Ic-bl, and Ibn in U/u, B, g, V, R/r, I/i, J, H, Ks, and Swift w2, m2, w1 bands using Gaussian Processes and a multi-survey dataset composed of all well-sampled open-access light curves (165 SESNe, 29531 data points) from the Open Supernova Catalog. We use our new templates to assess the photometric diversity of SESNe by comparing final per-band subtype templates with each other and with individual, unusual and prototypical SESNe. We find that SNe Ibns and Ic-bl exhibit a distinctly faster rise and decline compared to other subtypes. We also evaluate the behavior of SESNe in the PLAsTiCC and ELAsTiCC simulations of LSST light curves highlighting differences that can bias photometric classification models trained on the simulated light curves. Finally, we investigate in detail the behavior of fast-evolving SESNe (including SNe Ibn) and the implications of the frequently observed presence of two peaks in their light curves.

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arXiv2024

Maximizing the scientific return of Roman and Rubin with a joint wide-sky observing strategy

This work presents the case for a single-band LSST-matched depth Roman Community Survey over the footprint of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Wide-Fast-Deep to enhance the key science programs of both missions. We propose to observe the ~18K sq deg LSST Wide-Fast-Deep footprint in the F146 filter to mAB~25; this will take approximately 5 months of Roman observing time. The combination of the multiwavelength nature of LSST and angular resolution of Roman would lead to enhanced scientific returns for both the Roman and LSST surveys. Galaxy deblending and crowded field photometry will be significantly improved. The extension of Rubin LSST six-band optical photometry to IR wavelengths would improve photometric redshift (photo-z) estimation, leading to improved cosmological parameter estimation, penetrate interstellar dust in the Galactic plane, improve differential chromatic refraction derived Spectral Energy Distributions, maximize galaxy-star separation and minimize crowding confusion through improved angular resolution. Conversely, the LSST survey will provide a time-domain extension of the Roman survey on the shared footprint and 6-band optical photometry with sensitivity extending all the way to ultraviolet wavelengths.

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arXiv2023

SpectAcLE: An Improved Method for Modeling Light Echo Spectra

Light echoes give us a unique perspective on the nature of supernovae and non-terminal stellar explosions. Spectroscopy of light echoes can reveal details on the kinematics of the ejecta, probe asymmetry, and reveal details on its interaction with circumstellar matter, thus expanding our understanding of these transient events. However, the spectral features arise from a complex interplay between the source photons, the reflecting dust geometry, and the instrumental setup and observing conditions. In this work we present an improved method for modeling these effects in light echo spectra, one that relaxes the simplifying assumption of a light curve weighted sum, and instead estimates the true relative contribution of each phase. We discuss our logic, the gains we obtain over light echo analysis method(s) used in the past, and prospects for further improvements. Lastly, we show how the new method improves our analysis of echoes from Tycho's supernova (SN 1572) as an example.

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arXiv2023

An Evenly-Spaced LSST Cadence for Rapidly Variable Stars

Stars exhibit a bewildering variety of rapidly variable behaviors ranging from explosive magnetic flares to stochastically changing accretion to periodic pulsations or rotation. The principal Rubin Observatory Legacy Survey of Space and Time (LSST) surveys will have cadences too sparse and irregular to capture many of these phenomena. We propose here a LSST micro-survey to observe a single Galactic field, rich in unobscured stars, in a continuous sequence of 30 second exposures for one long winter night in a single photometric band. The result will be a unique dataset of $\sim 1$ million regularly spaced stellar light curves (LCs). The LCs will constitute a comprehensive collection of late-type stellar flaring, but also other classes like short-period binary systems and cataclysmic variables, young stellar objects and ultra-short period exoplanets. An unknown variety of anomalous Solar System, Galactic and extragalactic variables and transients may also be present. A powerful array of statistical procedures can be applied to individual LCs from the long-standing fields of time series analysis, signal processing and econometrics. Dozens of `features' describing the variability can be extracted and the ensemble of light curves can be subject to advanced machine learning clustering procedures. This will give a unique, authoritative, objective taxonomy of the rapidly variable sky derived from identically cadenced LCs. This micro-survey is best performed early in the Rubin Observatory program, and the results can inform the wider community on the best approaches to variable star identification and classification from the sparse, irregular cadences that dominate the planned surveys.

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arXiv2022

Rubin Observatory LSST Transients and Variable Stars Roadmap

The Vera C. Rubin Legacy Survey of Space and Time holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document represents a snapshot of our plans and preparatory work in the final years and months leading up to the survey's first light.

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arXiv2022

Toward automated detection of light echoes in synoptic surveys: considerations on the application of the Deep Convolutional Neural Networks

Light Echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time, LSST, will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point-sources and will entirely miss LEs. Over the past several years, Artificial Intelligence (AI) object detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we prepare a dataset from the ATLAS telescope and test a popular AI object detection framework, You Only Look Once, or YOLO, developed in the computer vision community, to demonstrate the potential of AI in the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited dataset. We explore and highlight challenges, including class imbalance and label incompleteness, and roadmap the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys.

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arXiv2022

From Data to Software to Science with the Rubin Observatory LSST

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.

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arXiv2022

Target of Opportunity Observations of Gravitational Wave Events with Vera C. Rubin Observatory

The discovery of the electromagnetic counterpart to the binary neutron star merger GW170817 has opened the era of gravitational-wave multi-messenger astronomy. Rapid identification of the optical/infrared kilonova enabled a precise localization of the source, which paved the way to deep multi-wavelength follow-up and its myriad of related science results. Fully exploiting this new territory of exploration requires the acquisition of electromagnetic data from samples of neutron star mergers and other gravitational wave sources. After GW170817, the frontier is now to map the diversity of kilonova properties and provide more stringent constraints on the Hubble constant, and enable new tests of fundamental physics. The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) can play a key role in this field in the 2020s, when an improved network of gravitational-wave detectors is expected to reach a sensitivity that will enable the discovery of a high rate of merger events involving neutron stars (about tens per year) out to distances of several hundred Mpc. We design comprehensive target-of-opportunity observing strategies for follow-up of gravitational-wave triggers that will make the Rubin Observatory the premier instrument for discovery and early characterization of neutron star and other compact object mergers, and yet unknown classes of gravitational wave events.

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arXiv2021

Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: a pioneering process of community-focused experimental design

Vera C. Rubin Observatory is a ground-based astronomical facility under construction, a joint project of the National Science Foundation and the U.S. Department of Energy, designed to conduct a multi-purpose 10-year optical survey of the southern hemisphere sky: the Legacy Survey of Space and Time. Significant flexibility in survey strategy remains within the constraints imposed by the core science goals of probing dark energy and dark matter, cataloging the Solar System, exploring the transient optical sky, and mapping the Milky Way. The survey's massive data throughput will be transformational for many other astrophysics domains and Rubin's data access policy sets the stage for a huge potential users' community. To ensure that the survey science potential is maximized while serving as broad a community as possible, Rubin Observatory has involved the scientific community at large in the process of setting and refining the details of the observing strategy. The motivation, history, and decision-making process of this strategy optimization are detailed in this paper, giving context to the science-driven proposals and recommendations for the survey strategy included in this Focus Issue.

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