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Astronomical image time series classification using CONVolutional attENTION (ConvEntion)
Aims: The treatment of astronomical image time series has won increasing attention in recent years. Indeed, numerous surveys following up on transient objects are in progress or under construction, such as the Vera C. Rubin Observatory Legacy Survey for Space and Time (LSST), which is poised to produce huge amounts of these time series. The associated scientific topics are extensive, ranging from the study of objects in our galaxy to the observation of the most distant supernovae for measuring the expansion of the universe. With such a large amount of data available, the need for robust automatic tools to detect and classify celestial objects is growing steadily. Methods: This study is based on the assumption that astronomical images contain more information than light curves. In this paper, we propose a novel approach based on deep learning for classifying different types of space objects directly using images. We named our approach ConvEntion, which stands for CONVolutional attENTION. It is based on convolutions and transformers, which are new approaches for the treatment of astronomical image time series. Our solution integrates spatio-temporal features and can be applied to various types of image datasets with any number of bands. Results: In this work, we solved various problems the datasets tend to suffer from and we present new results for classifications using astronomical image time series with an increase in accuracy of 13%, compared to state-of-the-art approaches that use image time series, and a 12% increase, compared to approaches that use light curves.
PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem
In this paper, we present a new algorithm for estimating the Point Spread Function (PSF) in wide-field astronomical images with extreme source crowding. Robust and accurate PSF estimation in crowded astronomical images dramatically improves the fidelity of astrometric and photometric measurements extracted from wide-field sky monitoring imagery. Our radically new approach utilizes convolutional sparse representations to model the continuous functions involved in the image formation. This approach avoids the need to detect and precisely localize individual point sources that is shared by existing methods. In experiments involving simulated astronomical imagery, it significantly outperforms the recent alternative method with which it is compared.
ATAT: Astronomical Transformer for time series and Tabular data
Context. The advent of next-generation survey instruments, such as theVera C. RubinObservatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as theVera C. RubinObservatory, in real-world brokering scenarios.
SnowPac : a multiscale cubic B-spline wavelet compressor for astronomical images
ABSTRACT As more advanced and complex survey telescopes are developed, the size and scale of data being captured grows at increasing rates. Across various domains, data compression through wavelets has enabled the reduction of data size and increase in computation efficiency. In this paper, we provide qualitative and quantitative tests of a new wavelet-based image compression method compared against the current standard for astronomical images. The analysis is improved by making use of state-of-the-art object detection systems to accurately measure the impact of the compression. We find that a combination of lossy wavelet-based methods, efficient quantization, and lossless dictionary compressors can preserve up to 98 per cent of astronomical objects at a 10:1 compression ratio. This significant reduction in file size also preserves astronomical object properties better than existing methods. These methods help further reduce future workloads for image-heavy processing pipelines.
Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions
Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a novel NPE-based cataloging method that trains an inference network with semisynthetic astronomical images generated using PSFs and backgrounds sampled from the Sloan Digital Sky Survey. In experiments with semisynthetic images, we evaluate the method on key cataloging tasks: light source detection, star/galaxy separation, and flux measurement. A “generalist” inference network—trained with diverse PSFs and backgrounds—performs as well as a “specialist” network even when both are evaluated on the specialist’s particular PSF/background combination. This result suggests that a single NPE network can generalize across spatial variations, eliminating the need for retraining on each observational condition.
Photonic ring resonator notch filters for astronomical OH line suppression
Photonic ring resonator arrays used as notch filters are a promising novel solution to improve the signal-to-noise ratio of ground-based astronomical observations by suppressing OH emission lines in the near-infrared (NIR) wavelength range (0.9-2.5 μm). We aim to fabricate a series of ring resonators connected by a waveguide, each with its resonance wavelength and full-width-half-maximum (FWHM) matched with one of the OH emission lines. The fundamental structure of ring resonator-based notch filters is a ring resonator evanescently coupled to a waveguide. Due to the optical loss of the ring resonator, the transmission of light through the waveguide will be suppressed at the resonance wavelength of the ring resonator. The degree of suppression can be controlled by varying the coupling strength between the waveguide and the ring resonator, while the resonance wavelength can be controlled by the geometry and size of the ring. However, since a single ring resonator has multiple modes each with a different wavelength separated by its free spectral range (FSR), the additional suppression at other wavelengths may undermine the signal. Therefore, the ideal FSR off the ring resonators needs to be larger than the measurement window. For this application, we aim the FSR to be 1 larger than 30 nm . Our initial measurement results revealed two major challenges for this application. First, the FSR of the ring resonators is limited by the bending loss of ring resonators. In a standard 220 nm silicon-on-insulator material platform, the FSR of ring resonators cannot exceed 30 nm; Second, due to the wafer thickness variation and fabrication inaccuracy, the achievable accuracy in the resonance wavelength of a fabricated single ring resonator is only ~1.5 nm. In this work, we report our novel solutions to the above two challenges. For the first one, we have developed a novel structure based on the Vernier effect to greatly raise the FSR of ring resonators. Serially coupled Vernier ring structures is a typical approach to increase the FSR without encountering bending loss, but it requires restrictive matching of the coupling and optical loss of the rings and any imperfect matching can result in even noisier signals. On the contrary, our structure, as shown in shown in Fig. 1, does not require the restrictive matching. The signals of two output ports in our structure marked in Fig. 1 will be added together to form the final output signal, and Fig. 2 shows the finite-difference time-domain (FDTD) simulation results of the final output signal. As can be seen, the FSR of the entire structure is well-beyond 60 nm with little noise at other wavelengths. For the second one, we have developed an easy-to-implement method to permanently tune the resonance wavelengths of ring resonators within 0.1 nm accuracy. The tuning mechanism is based on the property of hydrogen silsesquioxane (HSQ) resist. As a negative tone electron beam resist, HSQ upon exposure will cross-link and eventually form SiO2. The refractive 2 index of HSQ resist and formed SiO2 are slightly different , enabling the tuning. By having a thin layer of HSQ resist on top of ring resonator structures followed by SiO2 cladding, the resonance wavelengths of ring resonators can be post-tuned Advances in Optical Astronomical Instrumentation 2019, edited by Simon Ellis, Céline d'Orgeville, Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 23 Sep 2020 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use through electron beam exposure. We have found that, within 5 nm tuning range, the resonance wavelength shift and dosage applied obeys a linear relationship. So a simple calibration beforehand can result in resonance wavelength tuning within 0.1 nm accuracy. The transmission spectra of a 3-ring device before and after tuning is shown in Fig. 3.
Astronomical context of Solar System formation from molybdenum isotopes in meteorite inclusions
Calcium-aluminum–rich inclusions (CAIs) in meteorites are the first solids to have formed in the Solar System, defining the epoch of its birth on an absolute time scale. This provides a link between astronomical observations of star formation and cosmochemical studies of Solar System formation. We show that the distinct molybdenum isotopic compositions of CAIs cover almost the entire compositional range of material that formed in the protoplanetary disk. We propose that CAIs formed while the Sun was in transition from the protostellar to pre–main sequence (T Tauri) phase of star formation, placing Solar System formation within an astronomical context. Our results imply that the bulk of the material that formed the Sun and Solar System accreted within the CAI-forming epoch, which lasted less than 200,000 years.
Detecting Anomalous Images in Astronomical Datasets
Abstract Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large-scale galaxy surveys, in which image qualities are crucial for the success of galaxy shape measurements. In this work we present two automatic methods for detecting anomalous images in astronomical data sets. The anomalous features can be divided into two types: one is associated with the source images, and the other appears on the background. Our first method, called the entropy method, utilizes the randomness of the orientation distribution of the source shapes and the background gradients to quantify the likelihood of an exposure being anomalous. Our second method involves training a neural network (autoencoder) to detect anomalies. We evaluate the effectiveness of the entropy method on the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) and Dark Energy Camera Legacy Survey (DECaLS DR3) data. In CFHTLenS, with 1171 exposures, the entropy method outperforms human inspection by detecting 12 of the 13 anomalous exposures found during human inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the entropy method detects a significant number of anomalous exposures while keeping a low false-positive rate. We find that although the neural network performs relatively well in detecting source anomalies, its current performance is not as good as the entropy method.
Disk Evolution Study through Imaging of Nearby Young Stars (DESTINYS): A Panchromatic View of DO Tau’s Complex Kilo-astronomical-unit Environment
While protoplanetary disks are often treated as isolated systems in planet formation models, observations increasingly suggest that vigorous interactions between Class II disks and their environments are not rare. DO Tau is a T Tauri star that has previously been hypothesized to have undergone a close encounter with the HV Tau system. As part of the DESTINYS ESO Large Programme, we present new Very Large Telescope (VLT)/SPHERE polarimetric observations of DO Tau and combine them with archival Hubble Space Telescope (HST) scattered-light images and Atacama Large Millimeter/submillimeter Array (ALMA) observations of CO isotopologues and CS to map a network of complex structures. The SPHERE and ALMA observations show that the circumstellar disk is connected to arms extending out to several hundred astronomical units. HST and ALMA also reveal stream-like structures northeast of DO Tau, some of which are at least several thousand astronomical units long. These streams appear not to be gravitationally bound to DO Tau, and comparisons with previous Herschel far-IR observations suggest that the streams are part of a bridge-like structure connecting DO Tau and HV Tau. We also detect a fainter redshifted counterpart to a previously known blueshifted CO outflow. While some of DO Tau's complex structures could be attributed to a recent disk–disk encounter, they might be explained alternatively by interactions with remnant material from the star formation process. These panchromatic observations of DO Tau highlight the need to contextualize the evolution of Class II disks by examining processes occurring over a wide range of size scales.
On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse data sets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), long-short term memory NNs, gated recurrent units and temporal convolutional NNs (tCNNs). Additionally, we also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time series per source). Performance—applied to a sample of 17,604 variable stars (VSs) from the MAssive Compact Halo Objects (MACHO) survey across 10 imbalanced classes—is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with recurrent NNs generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers perform better. We conclude by discussing the advantages and limitations of deep architectures for VS classification, with a particular eye toward next-generation surveys such as the Legacy Survey of Space and Time, the Roman Space Telescope, and Zwicky Transient Facility.
Using Mask R-CNN to detect and mask ghosting and scattered-light artifacts in astronomical images
Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as “ghosting artifacts” or “ghosts”) and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered- light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms that model the physical processes that lead to such artifacts, thus providing a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.
Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning
Modern astronomical experiments are designed to achieve multiple scientific goals, from studies of galaxy evolution to cosmic acceleration. These goals require data of many different classes of night-sky objects, each of which has a particular set of observational needs. These observational needs are typically in strong competition with one another. This poses a challenging multi-objective optimization problem that remains unsolved. The effectiveness of Reinforcement Learning (RL) as a valuable paradigm for training autonomous systems has been well-demonstrated, and it may provide the basis for self-driving telescopes capable of optimizing the scheduling for astronomy campaigns. Simulated datasets containing examples of interactions between a telescope and a discrete set of sky locations on the celestial sphere can be used to train an RL model to sequentially gather data from these several locations to maximize a cumulative reward as a measure of the quality of the data gathered. We use simulated data to test and compare multiple implementations of a Deep Q-Network (DQN) for the task of optimizing the schedule of observations from the Stone Edge Observatory (SEO). We combine multiple improvements on the DQN and adjustments to the dataset, showing that DQNs can achieve an average reward of 87%+-6% of the maximum achievable reward in each state on the test set. This is the first comparison of offline RL algorithms for a particular astronomical challenge and the first open-source framework for performing such a comparison and assessment task.
The Spectroscopic Classification of Astronomical Transients (SCAT) Survey: Overview, Pipeline Description, Initial Results, and Future Plans
Here, we present the Spectroscopic Classification of Astronomical Transients (SCAT) survey, which is dedicated to spectrophotometric observations of transient objects such as supernovae and tidal disruption events. SCAT uses the SuperNova Integral-Field Spectrograph (SNIFS) on the University of Hawai’i 2.2 m (UH2.2m) telescope. SNIFS was designed specifically for accurate transient spectrophotometry, including absolute flux calibration and host-galaxy removal. We describe the data reduction and calibration pipeline including spectral extraction, telluric correction, atmospheric characterization, nightly photometricity, and spectrophotometric precision. We achieve ≲5% spectrophotometry across the full optical wavelength range (3500–9000 Å) under photometric conditions. The inclusion of photometry from the SNIFS multi-filter mosaic imager allows for decent spectrophotometric calibration (10%–20%) even under unfavorable weather/atmospheric conditions. SCAT obtained ≈640 spectra of transients over the first 3 yr of operations, including supernovae of all types, active galactic nuclei, cataclysmic variables, and rare transients such as superluminous supernovae and tidal disruption events. These observations will provide the community with benchmark spectrophotometry to constrain the next generation of hydrodynamic and radiative transfer models.
DeepSurveySim: Simulation Software and Benchmark Challenges for Astronomical Observation Scheduling
Modern astronomical surveys have multiple competing scientific goals. Optimizing the observation schedule for these goals presents significant computational and theoretical challenges, and state-of-the-art methods rely on expensive human inspection of simulated telescope schedules. Automated methods, such as Reinforcement Learning (RL), have recently been explored to accelerate scheduling. DeepSurveySim provides methods for tracking and approximating sky conditions for a set of observations from a user-supplied telescope configuration.
Astronomical Spectroscopy with Skipper CCDs: First Results from a Skipper CCD Focal Plane Prototype at SIFS
We present the first on-sky results from an ultra-low-readout-noise Skipper CCD focal plane prototype for the SOAR Integral Field Spectrograph (SIFS). The Skipper CCD focal plane consists of four 6k $\times$ 1k, 15 $\mu$m pixel, fully-depleted, $p$-channel devices that have been thinned to $\sim$250 $\mu$m, backside processed, and treated with an anti-reflective coating. These Skipper CCDs were configured for astronomical spectroscopy, i.e., a single-sample readout noise $< 4.3$\,e$^-$\,rms/pixel, the ability to achieve multi-sample readout noise $\ll 1\,$e$^-$\,rms/pix, full-well capacities $\sim$40,000--65,000 e$^-$, low dark current and charge transfer inefficiency ($\sim 2$ $\times$ $10^{-4}$ e$^-$/pixel/s and 3.44 $\times$ $10^{-7}$, respectively), and an absolute quantum efficiency of $\gtrsim 80\%$ between 450\,nm and 980\,nm ($\gtrsim 90\%$ between 600\,nm and 900\,nm). We optimize the readout sequence timing to achieve sub-electron noise ($\sim 0.5$\,e$^-$\,rms/pix) and photon-counting ($\sim 0.22$\,e$^-$\,rms/pix) readout noise over an area of 2k $\times$ 4k and 110 $\times$ 4k pixels, respectively in a readout time of $\lesssim 17$min. We observed two Lyman-$\alpha$ emitting quasars (HB89\,1159$+$123 and QSO\,J1621$–$0042) at redshift $z \sim 3.5$, two moderate redshift galaxy clusters (CL\,J1001$+$0220 and SPT-CL\,J2040$-$4451), an emission line galaxy at $z = 0.3239$, a candidate member star of the Bo\"{o}tes~II ultra-faint dwarf galaxy, and five CALSPEC spectrophotometric standard stars (HD074000, HD60753, HD106252, HD101452, HD200654). We present charge-quantized, photon-counting observation of the quasar HB89\,1159$+$123 and show the detector sensitivity increase for faint spectral features. We demonstrate signal-to-noise performance improvement for SIFS for observations in the low-background, readout-noise-dominated regime. We outline preliminary scientific studies that will leverage the SIFS-Skipper CCD data to derive new cosmological measurements in the context of dark matter and galaxy evolution.
Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories
High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear – correlated distortions of images of distant galaxies due to weak gravitational lensing caused by the large-scale mass distribution in the Universe. For unbiased measurements of cosmic shear, all other sources of correlated image distortions must be modeled or removed. One such source is the correlated blurring of images due to optical turbulence in the atmosphere, which dominates the point-spread function (PSF) for ground-based instruments. In this work, we leverage data from weather forecasting models to produce wind speeds and directions, and turbulence parameters, that are realistically correlated with altitude. To study the resulting correlations in the size and shape of the PSF, we generate simulated images of the PSF across a ~10 square-degree field of view – the size of the camera focal plane for the Vera C. Rubin Observatory in Chile – using weather data and historical seeing for a geographic location near the Observatory. We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. We observe a strong anisotropy in the two-dimensional 2PCF, which is expected based on observations in real images, and study the dependence of the orientation of the anisotropy on dominant wind directions near the ground and at higher altitudes.
A VIPA Spectrograph with Ultra-high Resolution and Wavelength Calibration for Astronomical Applications
A prototype spectrograph using a Virtually Imaged Phased Array (VIPA) as the main dispersion element is presented, and its performance is fully examined in our laboratory. The single-mode, fiber-fed spectrograph with simultaneous wavelength calibration possesses a spectral resolution well in excess of R≈1.12×10{sup 6} while the size of the VIPA is several orders of magnitude smaller than that of a conventional échelle with comparable resolution. In laboratory tests, the VIPA-based instrument with a homemade Yb:fiber ring laser frequency comb demonstrates a mode-to-mode tracking stability of 41 cm s{sup −1} over a period of 6 hr. The VIPA spectrograph has promising applications in various astronomical observations in which ultra-high resolution and calibration precision are imperative, such as solar physics research, exoplanet searching with the radial velocity method, and O{sub 2} detection in the atmosphere of Earth-like planets. Ultimately, feasible optimizations for night-sky observations under seeing limited conditions are discussed.