arXiv subjects

Emille E. O. Ishida

Publications and source records attributed to Emille E. O. Ishida.

At least 37 records · Page 2Linked to original sources

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.

astro-ph.HE↗

arXiv2023

Repeating Outbursts from the Young Stellar Object Gaia23bab (= SPICY 97589)

The light curve of Gaia23bab (= SPICY 97589) shows two significant ($ΔG>2$ mag) brightening events, one in 2017 and an ongoing event starting in 2022. The source's quiescent spectral energy distribution indicates an embedded ($A_V>5$ mag) pre-main-sequence star, with optical accretion emission and mid-infrared disk emission. This characterization is supported by the source's membership in an embedded cluster in the star-forming cloud DOBASHI 1604 at a distance of $900\pm45$~pc. Thus, the brightening events are probable accretion outbursts, likely of EX Lup-type.

astro-ph.SR↗

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

astro-ph.IM↗

arXiv2023

Sidestepping the inversion of the weak-lensing covariance matrix with Approximate Bayesian Computation

Weak gravitational lensing is one of the few direct methods to map the dark-matter distribution on large scales in the Universe, and to estimate cosmological parameters. We study a Bayesian inference problem where the data covariance $\mathbf{C}$, estimated from a number $n_{\textrm{s}}$ of numerical simulations, is singular. In a cosmological context of large-scale structure observations, the creation of a large number of such $N$-body simulations is often prohibitively expensive. Inference based on a likelihood function often includes a precision matrix, $Ψ= \mathbf{C}^{-1}$. The covariance matrix corresponding to a $p$-dimensional data vector is singular for $p \ge n_{\textrm{s}}$, in which case the precision matrix is unavailable. We propose the likelihood-free inference method Approximate Bayesian Computation (ABC) as a solution that circumvents the inversion of the singular covariance matrix. We present examples of increasing degree of complexity, culminating in a realistic cosmological scenario of the determination of the weak-gravitational lensing power spectrum for the upcoming European Space Agency satellite Euclid. While we found the ABC parameter estimate variances to be mildly larger compared to likelihood-based approaches, which are restricted to settings with $p < n_{\textrm{s}}$, we obtain unbiased parameter estimates with ABC even in extreme cases where $p / n_{\textrm{s}} \gg 1$. The code has been made publicly available to ensure the reproducibility of the results.

astro-ph.CO↗

arXiv2022

Finding active galactic nuclei through Fink

We present the Active Galactic Nuclei (AGN) classifier as currently implemented within the Fink broker. Features were built upon summary statistics of available photometric points, as well as color estimation enabled by symbolic regression. The learning stage includes an active learning loop, used to build an optimized training sample from labels reported in astronomical catalogs. Using this method to classify real alerts from the Zwicky Transient Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall. We also describe the modifications necessary to enable processing data from the upcoming Vera C. Rubin Observatory Large Survey of Space and Time (LSST), and apply them to the training sample of the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC). Results show that our designed feature space enables high performances of traditional machine learning algorithms in this binary classification task.

astro-ph.IM↗

arXiv2022

Ridges in the Dark Energy Survey for cosmic trough identification

Cosmic voids and their corresponding redshift-projected mass densities, known as troughs, play an important role in our attempt to model the large-scale structure of the Universe. Understanding these structures enables us to compare the standard model with alternative cosmologies, constrain the dark energy equation of state, and distinguish between different gravitational theories. In this paper, we extend the subspace-constrained mean shift algorithm, a recently introduced method to estimate density ridges, and apply it to 2D weak lensing mass density maps from the Dark Energy Survey Y1 data release to identify curvilinear filamentary structures. We compare the obtained ridges with previous approaches to extract trough structure in the same data, and apply curvelets as an alternative wavelet-based method to constrain densities. We then invoke the Wasserstein distance between noisy and noiseless simulations to validate the denoising capabilities of our method. Our results demonstrate the viability of ridge estimation as a precursor for denoising weak lensing observables to recover the large-scale structure, paving the way for a more versatile and effective search for troughs.

astro-ph.CO↗

arXiv2022

Spectroscopic Confirmation of a Population of Isolated, Intermediate-Mass YSOs

Wide-field searches for young stellar objects (YSOs) can place useful constraints on the prevalence of clustered versus distributed star formation. The Spitzer/IRAC Candidate YSO (SPICY) catalog is one of the largest compilations of such objects (~120,000 candidates in the Galactic midplane). Many SPICY candidates are spatially clustered, but, perhaps surprisingly, approximately half the candidates appear spatially distributed. To better characterize this unexpected population and confirm its nature, we obtained Palomar/DBSP spectroscopy for 26 of the optically-bright (G<15 mag) "isolated" YSO candidates. We confirm the YSO classifications of all 26 sources based on their positions on the Hertzsprung-Russell diagram, H and Ca II line-emission from over half the sample, and robust detection of infrared excesses. This implies a contamination rate of <10% for SPICY stars that meet our optical selection criteria. Spectral types range from B4 to K3, with A-type stars most common. Spectral energy distributions, diffuse interstellar bands, and Galactic extinction maps indicate moderate to high extinction. Stellar masses range from ~1 to 7 $M_\odot$, and the estimated accretion rates, ranging from $3\times10^{-8}$ to $3\times10^{-7}$ $M_\odot$ yr$^{-1}$, are typical for YSOs in this mass range. The 3D spatial distribution of these stars, based on Gaia astrometry, reveals that the "isolated" YSOs are not evenly distributed in the Solar neighborhood but are concentrated in kpc-scale dusty Galactic structures that also contain the majority of the SPICY YSO clusters. Thus, the processes that produce large Galactic star-forming structures may yield nearly as many distributed as clustered YSOs.

astro-ph.GA↗

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.

astro-ph.IM↗

arXiv2022

How have astronomers cited other fields in the last decade?

We present a citation pattern analysis between astronomical papers and 13 other disciplines, based on the arXiv database over the past decade ($2010 - 2020$). We analyze 12,600 astronomical papers citing over 14,531 unique publications outside astronomy. Two striking patterns are unraveled. First, general relativity recently became the most cited field by astronomers, a trend highly correlated with the discovery of gravitational waves. Secondly, the fast growth of referenced papers in computer science and statistics, the first with a notable 15-fold increase since 2015. Such findings confirm the critical role of interdisciplinary efforts involving astronomy, statistics, and computer science in recent astronomical research.

astro-ph.IM↗

arXiv2022

Fink: early supernovae Ia classification using active learning

We describe how the Fink broker early supernova Ia classifier optimizes its ML classifications by employing an active learning (AL) strategy. We demonstrate the feasibility of implementation of such strategies in the current Zwicky Transient Facility (ZTF) public alert data stream. We compare the performance of two AL strategies: uncertainty sampling and random sampling. Our pipeline consists of 3 stages: feature extraction, classification and learning strategy. Starting from an initial sample of 10 alerts (5 SN Ia and 5 non-Ia), we let the algorithm identify which alert should be added to the training sample. The system is allowed to evolve through 300 iterations. Our data set consists of 23 840 alerts from the ZTF with confirmed classification via cross-match with SIMBAD database and the Transient name server (TNS), 1 600 of which were SNe Ia (1 021 unique objects). The data configuration, after the learning cycle was completed, consists of 310 alerts for training and 23 530 for testing. Averaging over 100 realizations, the classifier achieved 89% purity and 54% efficiency. From 01/November/2020 to 31/October/2021 Fink has applied its early supernova Ia module to the ZTF stream and communicated promising SN Ia candidates to the TNS. From the 535 spectroscopically classified Fink candidates, 459 (86%) were proven to be SNe Ia. Our results confirm the effectiveness of active learning strategies for guiding the construction of optimal training samples for astronomical classifiers. It demonstrates in real data that the performance of learning algorithms can be highly improved without the need of extra computational resources or overwhelmingly large training samples. This is, to our knowledge, the first application of AL to real alerts data.

astro-ph.IM↗

arXiv2021

Probabilistic modeling of asteroid diameters from Gaia DR2 errors

The Gaia Data Release 2 provides precise astrometry for nearly 1.5 billion sources across the entire sky, including several thousand asteroids. In this work, we provide evidence that reasonably large asteroids (diameter $>$ 20 km) have high correlations with Gaia relative flux uncertainties and systematic right ascension errors. We further capture these correlations using a logistic Bayesian additive regression tree model. We compile a small list of probable large asteroids that can be targeted for direct diameter measurements and shape reconstruction.

astro-ph.EP↗

arXiv2021

SPICY: The Spitzer/IRAC Candidate YSO Catalog for the Inner Galactic Midplane

We present ~120,000 Spitzer/IRAC candidate young stellar objects (YSOs) based on surveys of the Galactic midplane between l~255 deg and 110 deg, including the GLIMPSE I, II, and 3D, Vela-Carina, Cygnus X, and SMOG surveys (613 square degrees), augmented by near-infrared catalogs. We employed a classification scheme that uses the flexibility of a tailored statistical learning method and curated YSO datasets to take full advantage of IRAC's spatial resolution and sensitivity in the mid-infrared ~3-9 micron range. Multi-wavelength color/magnitude distributions provide intuition about how the classifier separates YSOs from other red IRAC sources and validate that the sample is consistent with expectations for disk/envelope-bearing pre-main-sequence stars. We also identify areas of IRAC color space associated with objects with strong silicate absorption or polycyclic aromatic hydrocarbon emission. Spatial distributions and variability properties help corroborate the youthful nature of our sample. Most of the candidates are in regions with mid-IR nebulosity, associated with star-forming clouds, but others appear distributed in the field. Using Gaia DR2 distance estimates, we find groups of YSO candidates associated with the Local Arm, the Sagittarius-Carina Arm, and the Scutum-Centaurus Arm. Candidate YSOs visible to the Zwicky Transient Facility tend to exhibit higher variability amplitudes than randomly selected field stars of the same magnitude, with many high-amplitude variables having light-curve morphologies characteristic of YSOs. Given that no current or planned instruments will significantly exceed IRAC's spatial resolution while possessing its wide-area mapping capabilities, Spitzer-based catalogs such as ours will remain the main resources for mid-infrared YSOs in the Galactic midplane for the near future.

astro-ph.GA↗

arXiv2020

Fink, a new generation of broker for the LSST community

Fink is a broker designed to enable science with large time-domain alert streams such as the one from the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). It exhibits traditional astronomy broker features such as automatised ingestion, annotation, selection and redistribution of promising alerts for transient science. It is also designed to go beyond traditional broker features by providing real-time transient classification which is continuously improved by using state-of-the-art Deep Learning and Adaptive Learning techniques. These evolving added values will enable more accurate scientific output from LSST photometric data for diverse science cases while also leading to a higher incidence of new discoveries which shall accompany the evolution of the survey. In this paper we introduce Fink, its science motivation, architecture and current status including first science verification cases using the Zwicky Transient Facility alert stream.

astro-ph.IM↗

arXiv2020

Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic follow-up (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms.

astro-ph.IM↗

arXiv2020

Active Anomaly Detection for time-domain discoveries

We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active learning strategy where the learning algorithm chooses objects which can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional Isolation Forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery (AAD) algorithm to 2 data sets: simulated light curves from the PLAsTiCC challenge and real light curves from the Open Supernova Catalog. We compare the AAD results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ~2% highest anomaly scores. We show that, in the real data scenario, AAD was able to identify ~80\% more true anomalies than the IF. This result is the first evidence that AAD algorithms can play a central role in the search for new physics in the era of large scale sky surveys.

astro-ph.IM↗

arXiv2020

Periodic Astrometric Signal Recovery through Convolutional Autoencoders

Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope[39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In particular the mission will be optimised to search for habitable-zone planets around Alpha Centauri AB. If the separation between these two stars can be monitored with sufficient precision, tiny perturbations due to the gravitational tug from an unseen planet can be witnessed and, given the configuration of the optical system, the scale of the shifts in the image plane are about one millionth of a pixel. Image registration at this level of precision has never been demonstrated (to our knowledge) in any setting within science. In this paper we demonstrate that a Deep Convolutional Auto-Encoder is able to retrieve such a signal from simplified simulations of the TOLIMAN data and we present the full experimental pipeline to recreate out experiments from the simulations to the signal analysis. In future works, all the more realistic sources of noise and systematic effects present in the real-world system will be injected into the simulations.

astro-ph.IM↗

arXiv2020

21st Century Statistical and Computational Challenges in Astrophysics

Modern astronomy has been rapidly increasing our ability to see deeper into the universe, acquiring enormous samples of cosmic populations. Gaining astrophysical insights from these datasets requires a wide range of sophisticated statistical and machine learning methods. Long-standing problems in cosmology include characterization of galaxy clustering and estimation of galaxy distances from photometric colors. Bayesian inference, central to linking astronomical data to nonlinear astrophysical models, addresses problems in solar physics, properties of star clusters, and exoplanet systems. Likelihood-free methods are growing in importance. Detection of faint signals in complicated noise is needed to find periodic behaviors in stars and detect explosive gravitational wave events. Open issues concern treatment of heteroscedastic measurement errors and understanding probability distributions characterizing astrophysical systems. The field of astrostatistics needs increased collaboration with statisticians in the design and analysis stages of research projects, and to jointly develop new statistical methodologies. Together, they will draw more astrophysical insights into astronomical populations and the cosmos itself.

astro-ph.IM↗

arXiv2019

Anomaly Detection in the Open Supernova Catalog

In the upcoming decade large astronomical surveys will discover millions of transients raising unprecedented data challenges in the process. Only the use of the machine learning algorithms can process such large data volumes. Most of the discovered transients will belong to the known classes of astronomical objects. However, it is expected that some transients will be rare or completely new events of unknown physical nature. The task of finding them can be framed as an anomaly detection problem. In this work, we perform for the first time an automated anomaly detection analysis in the photometric data of the Open Supernova Catalog (OSC), which serves as a proof of concept for the applicability of these methods to future large scale surveys. The analysis consists of the following steps: 1) data selection from the OSC and approximation of the pre-processed data with Gaussian processes, 2) dimensionality reduction, 3) searching for outliers with the use of the isolation forest algorithm, 4) expert analysis of the identified outliers. The pipeline returned 81 candidate anomalies, 27 (33%) of which were confirmed to be from astrophysically peculiar objects. Found anomalies correspond to a selected sample of 1.4% of the initial automatically identified data sample of ~2000 objects. Among the identified outliers we recognised superluminous supernovae, non-classical Type Ia supernovae, unusual Type II supernovae, one active galactic nucleus and one binary microlensing event. We also found that 16 anomalies classified as supernovae in the literature are likely to be quasars or stars. Our proposed pipeline represents an effective strategy to guarantee we shall not overlook exciting new science hidden in the data we fought so hard to acquire. All code and products of this investigation are made publicly available.

astro-ph.HE↗