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<h1>FASTLab Publications</h1>
<p>Research papers published by FASTLab members</p>
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<p>The Federica's AstroSTatistics Lab (FASTLab) focuses on time-domain astronomy and data science methods, producing research on explosive transients, stellar variability, and novel astronomical detection techniques.</p>
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<article>
<a href="https://arxiv.org/pdf/2607.03532" class="image"><img src="images/Fortino27.png" alt="Placeholder Figure" /></a>
<h3>How Low Can We Go? Minimum Spectroscopic Requirements For Supernova Subtype Classification</h3>
<h4>
Willow Fox Fortino, Federica B. Bianco, Maryam Modjaz, Thomas Matheson, and Umer Zubair
</h4>
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<p>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, $R_λ = \fracλ{∆λ}$, 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{}.</p>
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<article>
<a href="https://doi.org/10.21203/rs.3.rs-8613311/v1" class="image"><img src="images/acero26.png" alt="Placeholder Figure" /></a>
<h3>Automated detection and segmentation of smallholder fish ponds in Nigeria</h3>
<h4>
Tatiana Acero-Cuellar, Rodiat Ayinde, John Uponi, Bhoktear Khan, Oluwaseun Adeluyi, Tunrayo Alabi, Olawale Olayide, Jessica A. Gephart, Federica B. Bianco, and Kyle Frankel Davis
</h4>
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<p>Fish and other aquatic foods are a vital source of nutrients and livelihoods around the world, and with the stagnation of capture fisheries, countries have increasingly turned to aquaculture. While the geographic expansion of aquaculture is evident from national statistics, much less is known about subnational patterns of growth. Notably, the spatial extent of smallholder aquaculture ponds is poorly understood, limiting our ability to ask basic questions about local risks and benefits stemming from aquaculture investments. To begin to address this, we develop an open-source automated segmentation pipeline to systematically delineate individual fish ponds. As Africa's most populous nation and third-largest fish producer, Nigeria plays a crucial role in regional food security. With its diversity of geographies and fish production systems, Nigeria offers the ideal case for developing automated approaches to fine-scale fish pond mapping. First, we use georeferenced point labels of fish pond locations to manually delineate fish pond boundaries and create a labeled dataset (N=14,021). Combining these labels with high-resolution satellite imagery, we then train a national segmentation model and perform predictions across the entire country (precision: 86.3%; recall: 87.9%). We then train a post-processing random forest classifier to distinguish fish ponds from false positives (precision: 99.1%; recall: 98.5%). Applying this approach, we identify a total of 250,936 fish ponds throughout Nigeria. Our scalable approach offers a flexible means for fine-scale tracking of smallholder aquaculture patterns potentially applicable across other smallholder-dominated geographies. Such data are central for food system monitoring and informing interventions to bolster smallholder productivity and market access.</p>
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<article>
<a href="https://arxiv.org/pdf/2606.05285" class="image"><img src="images/ouyang26.png" alt="Placeholder Figure" /></a>
<h3>An Information-Theoretic Metric for Transient Classification and Novelty Detection</h3>
<h4>
Yu-Qian Ouyang, Alex I. Malz, Ming Lian, Shar Daniels, Federica Bianco, and Mathilda Nilsson
</h4>
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<p>The development of the observing strategy for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) requires a broad optimization across science cases inside and outside of time-domain astronomy. We introduce a novel metric for transient science with LSST based on information-theoretic cross-entropy. We demonstrate its utility for distinguishing populations of objects and discuss applications for observing strategy / detection pipeline optimization as well as novelty detection and follow-up resource allocation.</p>
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<article>
<a href="https://arxiv.org/pdf/2605.27527" class="image"><img src="images/Chaini26b.png" alt="Placeholder Figure" /></a>
<h3>Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP</h3>
<h4>
Siddharth Chaini, Federica B. Bianco, and Ashish Mahabal
</h4>
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<p>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.</p>
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<h3>Toward decision-aware AI for LSST-scale time-domain astronomy</h3>
<h4>
C. R. Bom, A. Mahabal, F. Bianco, P. Darc, B. Fraga, R. Bonito, S. Chaini, M. W. Coughlin, S. Dillmann, F. Fontinele Nunes, A. Gomboc, N. Hernitschek, X. Li, F. Z. Majidi, A. I. Malz, A. Melandri, V. Petrecca, S. Piranomonte, M. Rabus, F. Ragosta, O. Razim, M. C. Romão, N. Sarin, A. Sasli, V. A. Srećković, A. Tramuto, V. Vujčić, M. J. Vyas, Rubin LSST Transients, and Variable Stars Science Collaboration
</h4>
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<p>The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will generate approximately (10^7) alerts per night, pushing time-domain astronomy beyond pipelines that treat discovery as a static labeling problem. We argue that LSST is better understood as a partially observed dynamical environment, in which scientific return depends on the quality of follow-up decisions made under uncertainty and finite observational resources. The central challenge is therefore to maintain evolving, uncertainty-aware representations of astrophysical sources and to select actions that maximize long-term scientific value. We propose that foundation models trained on heterogeneous time-domain data can learn survey-scale representations of source state, while decision-theoretic policies support principled, auditable allocation of follow-up resources. Embedded within human-supervised agentic systems, these components position AI as part of the operational inference loop rather than as a downstream predictive tool. The way such systems represent belief, optimize utility, and expose their reasoning will shape observational efficiency, the distribution of scientific agency, including who participates in discovery and the scientific questions that receive priority.</p>
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<article>
<a href="https://iopscience.iop.org/article/10.3847/1538-3881/ae521f/pdf" class="image"><img src="images/RTN11.png" alt="Placeholder Figure" /></a>
<h3>The Vera C. Rubin Observatory Data Preview 1</h3>
<h4>
Vera C. Rubin Observatory Team, Tatiana Acero-Cuellar, Emily Acosta, Christina L. Adair, et al.
</h4>
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<p>We present Rubin Data Preview 1 (DP1), the first data from the National Science Foundation─Department of Energy Vera C. Rubin Observatory, comprising raw and calibrated single-epoch images, coadds, difference images, detection catalogs, and ancillary data products. DP1 is based on 1792 optical─near-infrared exposures acquired over 48 distinct nights by the Rubin Commissioning Camera (LSSTComCam) on the Simonyi Survey Telescope at the Summit Facility on Cerro Pachón, Chile in late 2024. DP1 covers ∼15 deg<SUP>2</SUP> distributed across seven roughly equal-sized noncontiguous fields, each independently observed in six broad photometric bands, ugrizy. The median FWHM of the point-spread function across all bands is approximately 1.″14, with the sharpest images reaching about 0.″58. The 5σ point-source depths for coadded images in the deepest field, the Extended Chandra Deep Field South, are u = 24.55, g = 26.18, r = 25.96, i = 25.71, z = 25.07, and y = 23.1. Other fields are no more than 2.2 mag shallower in any band, where they have nonzero coverage. DP1 contains approximately 2.3 million distinct astrophysical objects, of which 1.6 million are extended in at least one band in coadds, and 431 solar system objects, of which 93 are new discoveries. DP1 is approximately 3.5 TB in size and is available to Vera C. Rubin Observatory data rights holders via the Rubin Science Platform, a cloud-based environment for the analysis of petascale astronomical data. While small compared to future LSST releases, its high quality and diversity of data support a broad range of early science investigations ahead of full operations in 2026.</p>
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<article>
<a href="https://iopscience.iop.org/article/10.3847/1538-4357/ae6ce9/pdf" class="image"><img src="images/honaker16.png" alt="Placeholder Figure" /></a>
<h3>Searching for Ultracool Dwarfs in Early LSST Data Products</h3>
<h4>
Easton J. Honaker, John E. Gizis, Christian Aganze, Siddharth Chaini, Federica B. Bianco, Maruša Žerjal, Eduardo L. Martín, Riley W. Clarke, Ashton Southwick, Harrison Petrie, and Tyler Blask
</h4>
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<p>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 yr 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 untested for faint UCDs. Thus, we begin by crossmatching Data Preview 1 against known UCD catalogs. We recover one known UCD from the UltracoolSheet, 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 UCD candidates in LSST fields using both LSST and Euclid photometry, 17 of which are unique to this work. We present our candidates, photometric temperature estimates, 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 L and T dwarfs may be detected and characterized in Data Preview 2, including several hundred known objects.</p>
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<article>
<a href="https://iopscience.iop.org/article/10.3847/2041-8213/ae0f13/pdf" class="image"><img src="images/Clarke25.png" alt="Placeholder Figure" /></a>
<h3>First Temperature Profile of a Stellar Flare Using Differential Chromatic Refraction</h3>
<h4>
Riley W. Clarke, Federica Bianco, James R. A. Davenport, Jeffery Cooke, Sara Webb, Igor Andreoni, Tyler Pritchard, and Aaron Roodman
</h4>
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<p>We present the first derivation of a stellar flare temperature profile from single-band photometry. Stellar flare DWF 030225.574−545707.45129 was detected in 2015 by the Dark Energy Camera as part of the Deeper, Wider, Faster program. The brightness (∆m<SUB>g</SUB> = −6.12) of this flare, combined with the high air mass (1.45 ≲ X ≲ 1.75) and blue filter (DES g, 398─548 nm) in which it was observed, provided ideal conditions to measure the zenithward apparent motion of the source due to differential chromatic refraction (DCR) and, from that, infer the effective temperature of the event. We model the flare's spectral energy distribution as a blackbody to produce the constraints on flare temperature and geometric properties derived from single-band photometry. We additionally demonstrate how simplistic assumptions on the flaring spectrum, as well as on the evolution of flare geometry, can result in solutions that overestimate the effective temperature. Exploiting DCR enables studying chromatic phenomena with ground-based astrophysical surveys, and stellar flares on M dwarfs are a particularly enticing target for such studies due to their ubiquity across the sky and the heightened color contrast between their red quiescent photospheres and the blue flare emission. Our novel method will enable similar temperature constraints for a large sample of objects in upcoming photometric surveys like the Vera C. Rubin Legacy Survey of Space and Time.</p>
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<article>
<a href="https://arxiv.org/pdf/2510.23702" class="image"><img src="images/Chaini26a.png" alt="Placeholder Figure" /></a>
<h3>In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys</h3>
<h4>
Siddharth Chaini, Federica B. Bianco, and Ashish Mahabal
</h4>
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<p>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/.</p>
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<a href="https://iopscience.iop.org/article/10.3847/1538-4357/ae3b41/pdf" class="image"><img src="images/fortino26a.png" alt="Placeholder Figure" /></a>
<h3>ABC-SN: Attention-based Classifier for Supernova Spectra</h3>
<h4>
Willow Fox Fortino, Federica B. Bianco, Pavlos Protopapas, Daniel Muthukrishna, and Austin Brockmeier
</h4>
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<p>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 will discover, classifying SNe spectroscopically remains the best way to determine most subtypes of SNe. Traditional spectrum classification tools use template matching techniques 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 Type Ia SN (SN Ia)─norm sample. DASH is no longer maintained, and the original work suffers from contamination of multiepoch 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 comprises 10 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 (∼88%) and 2.4% in SN Ia purity (∼95%) when unthresholded (improvements for each class can further be obtained by tuned thresholds), and we discuss the limitation of current SN datasets for benchmarking performance.</p>
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<a href="https://iopscience.iop.org/article/10.3847/1538-4357/ada5ff/pdf" class="image"><img src="images/Khakpash25.png" alt="Placeholder Figure" /></a>
<h3>Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps</h3>
<h4>
Somayeh Khakpash, Federica B. Bianco, Georgios Vernardos, Gregory Dobler, and Charles Keeton
</h4>
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<p>Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extragalactic microlensing rely on computationally expensive magnification map generation. With large data sets expected from wide-field surveys like the Vera C. Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre-computed magnification maps covering the parameter space on a grid of κ, γ, and s. Our autoencoder creates a low-dimensional latent space representation of these maps, enabling efficient map generation. Quantifying the performance of magnification map generation from a low dimensional space is an essential step in the roadmap to develop neural network-based models that can replace traditional feed-forward simulation at much lower computational costs. We develop metrics to study various aspects of the autoencoder generated maps and show that the reconstruction is reliable. Even though we observe a mild loss of resolution in the generated maps, we find this effect to be smaller than the smoothing effect of convolving the original map with a source of a plausible size for its accretion disk in the red end of the optical spectrum and larger wavelengths and particularly one suitable for studying the broad-line region of quasars. Used to generate large samples of on-demand magnification maps, our model can enable fast modeling of microlensing variability in lensed quasars and supernovae.</p>
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<article>
<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ad7eaa/pdf" class="image"><img src="images/sesntemplates.png" alt="" /></a>
<h3>Multi-filter UV to NIR Data-driven Light curve Templates for Stripped Envelope Supernovae</h3>
<h4>
Somayeh Khakpash, Federica B. Bianco, Maryam Modjaz, Willow F. Fortino,
Alexander Gagliano, Conor Larison, and Tyler A. Pritchard</h4>
<button class="button small abstract-toggle">Show Abstract</button>
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<p>While the spectroscopic classification scheme for Stripped envelope supernovae (SESNe) is clear, and14
we know that they originate from massive stars that lost some or all their envelopes of Hydrogen and15
Helium, the photometric evolution of classes within this family is not fully characterized. Photometric16
surveys, like the Vera C. Rubin Legacy Survey of Space and Time, will discover tens of thousands of17
transients each night and spectroscopic follow-up will be limited, prompting the need for photometric18
classification and inference based solely on photometry. We have generated 54 data-driven photometric19
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, and20
Swift w2, m2, w1 bands using Gaussian Processes and a multi-survey dataset composed of all well-21
sampled open-access light curves (165 SESNe, 29531 data points) from the Open Supernova Catalog.22
We use our new templates to assess the photometric diversity of SESNe by comparing final per-band23
subtype templates with each other and with individual, unusual and prototypical SESNe. We find24
that SNe Ibn and Ic-bl exhibit a distinctly faster rise and decline compared to other subtypes. We also25
evaluate the behavior of SESNe in the PLAsTiCC and ELAsTiCC simulations of LSST light curves26
highlighting differences that can bias photometric classification models trained on the simulated light27
curves. Finally, we investigate in detail the behavior of fast-evolving SESNe (including SNe Ibn) and28
the implications of the frequently observed presence of two peaks in their light curves</p>
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<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ad4110/meta" class="image"><img src="images/flares1.png" alt="" /></a>
<h3>Every Datapoint Counts: Stellar Flares as a Case Study of Atmosphere Aided Studies of Transients in the LSST Era</h3>
<h4>
Riley W. Clarke,James R. A. Davenport, John Gizis, Melissa L. Graham, Xiaolong Li, Willow Fortino, Easton J. Honaker, Ian Sullivan, Yusra Alsayyad, James Bosch, Robert A. Knop, and Federica Bianco</h4>
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<p>Due to their short timescale, stellar flares are a challenging target for the most modern synoptic sky surveys. The upcoming Vera C. Rubin Legacy Survey of Space and Time (LSST), a project designed to collect more data than any precursor survey, is unlikely to detect flares with more than one data point in its main survey. We developed a methodology to enable LSST studies of stellar flares, with a focus on flare temperature and temperature evolution, which remain poorly constrained compared to flare morphology. By leveraging the sensitivity expected from the Rubin system, Differential Chromatic Refraction can be used to constrain flare temperature from a single-epoch detection, which will enable statistical studies of flare temperatures and constrain models of the physical processes behind flare emission using the unprecedentedly high volume of data produced by Rubin over the 10-year LSST. We model the refraction effect as a function of the atmospheric column density, photometric filter, and temperature of the flare, and show that flare temperatures at or above $\\sim$4,000$K$ can be constrained by a single $g$-band observation at airmass $X\\gtrsim1.2$, given the minimum specified requirement on single-visit relative astrometric accuracy of LSST, and that a surprisingly large number of LSST observations is in fact likely be conducted at $X\\gtrsim1.2$, in spite of image quality requirements pushing the survey to preferentially low $X$. Having failed to measure flare DCR in LSST precursor surveys, we make recommendations on survey design and data products that enable these studies in LSST and other future surveys.</p>
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<a href="#" class="image"><img src="images/distclassipy.png" alt="" /></a>
<h3>Light Curve Classification with DistClassiPy: a new distance-based classifier </h3>
<h4>Chaini, Siddharth ; Mahabal, Ashish ; Kembhavi, Ajit ; Bianco, Federica B.Bianco</h4>
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<p>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. Tree-based (e.g. Random Forests) and deep learning models represent the current standard in the field. We explore the use of different distance metrics to aid in the classification of objects. For this, we developed a new distance metric based classifier called DistClassiPy. The direct use of distance metrics is an approach that has not been explored in time-domain astronomy, but distance-based methods can aid in increasing the interpretability of the classification result and decrease the computational costs. In particular, we classify light curves of variable stars by comparing the distances between objects of different classes. Using 18 distance metrics applied to a catalog of 6,000 variable stars in 10 classes, we demonstrate classification and dimensionality reduction. We show that this classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. We have made DistClassiPy open-source and accessible at https://pypi.org/project/distclassipy/ with the goal of broadening its applications to other classification scenarios within and beyond astronomy..</p>
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<a href="https://iopscience.iop.org/article/10.1088/1538-3873/acdb9a/pdf" class="image"><img src="images/TVSroadmap.png" alt="" /></a>
<h3>Rubin Observatory LSST Transients and Variable Stars Roadmap</h3><h4>
Hambleton, Kelly M.; Bianco, Federica B. ; Street, Rachel ; et al. </h4>
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<p>The Vera C. Rubin Legacy Survey of Space and Time (LSST) 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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<a href="https://iopscience.iop.org/article/10.1088/1538-3873/acdb9a/pdf" class="image"><img src="images/whatsthedifference.jpeg" alt="" /></a>
<h3>What's the Difference? The Potential for Convolutional Neural Networks for Transient Detection without Template Subtraction</h3><h4>
Acero-Cuellar, Tatiana ; Bianco, Federica ; Dobler, Gregory ; Sako, Masao ; Qu, Helen ; LSST Dark Energy Science Collaboration</h4>
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<p>We present a study of the potential for convolutional neural networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as "real-bogus" classification, without requiring a template-subtracted (or difference) image, which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the real-bogus classification and (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses "image triplets" (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input, finding that the testing accuracy is reduced from ~96% to ~91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for real-bogus classification that rely exclusively on the imaging data and require no feature engineering task and (2) demonstrates that high-accuracy (>90%) models can be built without the need to construct difference images, but some accuracy is lost. Because, once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory\'s Legacy Survey of Space and Time by bypassing the difference image analysis entirely.
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<a href="https://ui.adsabs.harvard.edu/link_gateway/2022AJ....164..250L/PUB_PDF" class="image"><img src="images/AILE1.jpeg" alt="" /></a>
<h3>Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks
</h3><h4>Li, Xiaolong ; Bianco, Federica B. ; Dobler, Gregory ; Partoush, Roee ; Rest, Armin ; Acero-Cuellar, Tatiana ; Clarke, Riley ; Fortino, Willow Fox ; Khakpash, Somayeh ; Lian, Ming </h4>
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<p>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 leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for 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 data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map 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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<a href="https://ui.adsabs.harvard.edu/link_gateway/2022ApJS..258....2L/PUB_PDF" class="image"><img src="images/unk2.jpeg" alt="" /></a>
<h3>Preparing to Discover the Unknown with Rubin LSST: Time Domain</h3><h4>Li, Xiaolong ; Ragosta, Fabio ; Clarkson, William I. ; Bianco, Federica B. </h4>
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<p>Perhaps the most exciting promise of the Rubin Observatory Legacy Survey of Space and Time (LSST) is its capability to discover phenomena never before seen or predicted: true astrophysical novelties; but the ability of LSST to make these discoveries will depend on the survey strategy. Evaluating candidate strategies for true novelties is a challenge both practically and conceptually. Unlike traditional astrophysical tracers like supernovae or exoplanets, for anomalous objects, the template signal is by definition unknown. We approach this problem by assessing survey completeness in a phase space defined by object color and flux (and their evolution), and considering the volume explored by integrating metrics within this space with the observation depth, survey footprint, and stellar density. With these metrics, we explore recent simulations of the Rubin LSST observing strategy across the entire observed spatial footprint and in specific Local Volume regions: the Galactic Plane and Magellanic Clouds. Under our metrics, observing strategies with greater diversity of exposures and time gaps tend to be more sensitive to genuinely new transients, particularly over time-gap ranges left relatively unexplored by previous surveys. To assist the community, we have made all of the tools developed publicly available. While here we focus on transients, an extension of the scheme to include proper motions and the detection of associations or populations of interest will be communicated in Paper II of this series. This paper was written with the support of the Vera C. Rubin LSST Transients and Variable Stars and Stars, Milky Way, Local Volume Science Collaborations.
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<a href="https://iopscience.iop.org/article/10.3847/1538-4365/ac3e72" class="image"><img src="images/rubinobs.jpeg" alt="" /></a>
<h3>Optimization of the Observing Cadence for the Rubin Observatory Legacy Survey of Space and Time: A Pioneering Process of Community-focused Experimental Design</h3><h4>
Bianco, Federica B. et al. ; ...</h4>
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<p>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 multipurpose 10 yr 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 community of potential users. 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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