Point Spread Function Deconvolution Using a Convolutional Autoencoder for Astronomical Applications

A major issue in optical astronomical image analysis is the combined effect of the instrument's point spread function (PSF) and the atmospheric seeing that blurs images and changes their shape in a way that is band and time-of-observation dependent. In this work we present a very simple neural network based approach to non-blind image deconvolution that relies on feeding a Convolutional Autoencoder (CAE) input images that have been preprocessed by convolution with the corresponding PSF and its regularized inverse. Compared to our previous work based on Deep Wiener Deconvolution, the new approach is conceptually simpler and computationally much less intensive while achieving only marginally worse results. In this work we also present a new approach for dealing with limited input dynamic range of neural networks compared to the dynamic range present in astronomical images.Comment: 10 pages, 5 figures, 2 table

Galaxy Deblending using Residual Dense Neural networks

We present a new neural network approach for deblending galaxy images in astronomical data using Residual Dense Neural network (RDN) architecture. We train the network on synthetic galaxy images similar to the typical arrangements of field galaxies with a finite point spread function (PSF) and realistic noise levels. The main novelty of our approach is the usage of two distinct neural networks: i) a deblending network which isolates a single galaxy postage stamp from the composite and, ii) a classifier network which counts the remaining number of galaxies. The deblending proceeds by iteratively peeling one galaxy at a time from the composite until the image contains no further objects as determined by the classifier, or by other stopping criteria. By looking at the consistency in the outputs of the two networks, we can assess the quality of the deblending. We characterize the flux and shape reconstructions in different quality bins and compare our deblender with the industry standard, SExtractor. We also discuss possible future extensions for the project with variable PSFs and noise levels.Comment: 15 pages, 13 figures, Accepted for publication in Physical Review

Neural Network Based Point Spread Function Deconvolution For Astronomical Applications

Optical astronomical images are strongly affected by the point spread function (PSF) of the optical system and the atmosphere (seeing) which blurs the observed image. The amount of blurring depends both on the observed band, and more crucially, on the atmospheric conditions during observation. A typical astronomical image will therefore have a unique PSF that is non-circular and different in different bands. Observations of known stars give us a determination of this PSF. Therefore, any serious candidate for production analysis of astronomical images must take the known PSF into account during the image analysis. So far the majority of applications of neural networks (NN) to astronomical image analysis have ignored this problem by assuming a fixed PSF in training and validation. We present a neural network based deconvolution algorithm based on Deep Wiener Deconvolution Network (DWDN) that takes the PSF shape into account when performing deconvolution as an example of one possible approach to enabling neural network to use the PSF information. We study the performance of several versions of this algorithm under realistic observational conditions in terms of recovery of most relevant astronomical quantities such as colors, ellipticities and orientations. We also investigate the performance of custom loss functions and find that they cause modest improvements in the recovery of astronomical quantities.Comment: 12 pages, 6 figure

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.Comment: 22 pages with appendix, 12 figures, 2 tables, accepted for publication in Astronomy and Astrophysic

Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

The authors would like to thank David Kirkby and Connor Sheere for insightful discussions. This work is part of the Recommendation System for Spectroscopic Followup (RESSPECT) project, governed by an inter-collaboration agreement signed between the Cosmostatistics Initiative (COIN) and the LSST Dark Energy Science Collaboration (DESC). This research is supported in part by the HPI Research Center in Machine Learning and Data Science at UC Irvine. EEOI and SS acknowledge financial support from CNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky Surveys. SGG and AKM acknowledge support by FCT under Project CRISP PTDC/FIS-AST-31546/2017. This work was partly supported by the Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of Houston. DOJ is supported by a Gordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa Cruz. Support for this work was provided by NASA through the NASA Hubble Fellowship grant HF2-51462.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. BQ is supported by the International Gemini Observatory, a program of NSF's NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini partnership of Argentina, Brazil, Canada, Chile, the Republic of Korea, and the United States of America. AIM acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. L.G. was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 839090. This work has been partially supported by the Spanish grant PGC2018-095317-B-C21 within the European Funds for Regional Development (FEDER).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 followup (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.HPI Research Center in Machine Learning and Data Science at UC IrvineCNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky SurveysFCT under Project CRISP PTDC/FIS-AST-31546/2017Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of HoustonGordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa CruzSpace Telescope Science InstituteNational Aeronautics & Space Administration (NASA) HF2-51462.001 NAS5-26555International Gemini Observatory, a program of NSF's NOIRLabNational Science Foundation (NSF)Max Planck SocietyFoundation CELLEXAlexander von Humboldt FoundationEuropean Commission 839090Spanish grant within the European Funds for Regional Development (FEDER) PGC2018-095317-B-C2

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

© 2018 The Author(s). We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy And Mass Assembly survey to test the feasibility of using automated algorithms to classify galaxies. Using 10 features measured for each galaxy (sizes, colours, shape parameters, and stellar mass), we apply the techniques of Support Vector Machines, Classification Trees, Classification Trees with Random Forest (CTRF) and Neural Networks, and returning True Prediction Ratios (TPRs) of 75.8 per cent, 69.0 per cent, 76.2 per cent, and 76.0 per cent, respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification ('unanimous disagreement') serves as a potential indicator of human error in classification, occurring in ~ 9 per cent of ellipticals, ~ 9 per cent of little blue spheroids, ~ 14 per cent of early-type spirals, ~ 21 per cent of intermediate-type spirals, and ~ 4 per cent of late-type spirals and irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy data sets. Adopting the CTRF algorithm, the TPRs of the five galaxy types are: E, 70.1 per cent; LBS, 75.6 per cent; S0-Sa, 63.6 per cent; Sab-Scd, 56.4 per cent, and Sd-Irr, 88.9 per cent. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS, and S0-Sa) and disc-dominated (Sab-Scd and Sd-Irr), achieving an overall accuracy of 89.8 per cent. This translates into an accuracy of 84.9 per cent for spheroid-dominated systems and 92. 5 per cent for disc-dominated systems

Quantifying galaxy structure

Die vorliegende Arbeit beschäftigt sich mit der Erforschung und Quantifizierung von Galaxienmorphologien. Weiters handelt es sich um eine Studie, welche die Entstehung und Evolution von Galaxien als Funktion der Galaxienmorphologie untersucht. Im Hauptprojekt dieser Arbeit wird die Morphologie von Galaxien mit maschinellen Lerntechniken analysiert. Die folgenden Teilprojekte konzentrieren sich auf die astronomische Bildanalyse, die Quantifizierung von Merger-Resten, die Signaturen eines hierarchischen Strukturbildungsszenarios sind. Der letzte Punkt führt im Weiteren zur Untersuchung des CDM-Modells durch die Erforschung der Umgebung von Feldzwerggalaxien. Im ersten Teil der Arbeit wird die Zuverlässigkeit automatischer maschineller Lernalgorithmen zur Klassifizierung von Galaxienmorphologien an einer Stichprobe von 7941 Galaxien aus dem Galaxy And Mass Assembly (GAMA) Survey untersucht. Unter Verwendung von 10 gemessenen Funktionen für jede Galaxie wenden wir die Methoden der Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with random Forest (CTRF) und Neural Networks (NN) auf unsere Stichprobe an. Als Ergebniss erhalten wir die wahren Vorhersageverhältnisse (TPRs) von 75.8%, 69.0%, 76.2% bzw. 76.0% zurück. Angesichts der Einfachheit in der Formulierung und Implementierung schließen wir, dass die CTRF-Methode die optimale Methode zur Einschätzung des Galaxientyps laut Hubble-Klassifikation ist, wenn diese auf aktuelle Galaxien-Surveys angewendet wird. Zudem trainieren wir einen binären Klassifikator, der Galaxien in bulge- und scheibendominierte Typen aufteilt und eine Gesamtklassifikationsgenauigkeit von 89.8% erreicht. Dies ergibt eine Genauigkeit von 84.9% für blugedominierte Systeme und 92.5% für scheibendominierte Systeme. Im zweiten Teil der Arbeit stellen wir die Ergebnisse des Teilprojekts ‘Coherence enhancing diffusion filtering in image processing vor. Die Anwendung eines Coherence Enhancing Anisotropic Diffusion Filtering (CED) -Algorithmus untersucht in astronomischen Bildern die Struktur von Galaxien. Wir stellen fest, dass es die Sichtbarkeit von Strukturen wie Spiralarmen verbessert und möglicherweise dazu verwendet werden kann, schwache Strukturen wie Fusionsreste sichtbar machen. Im zweiten Teilprojekt mit dem Titel ‘Unveiling hidden structure around and within early-type galaxies messen wir die nicht-ebenmäßige Struktur (Merger-Reste) in zwei Schalen-Galaxien (NGC 3656 & NGC 7600) mit zwei Methoden: unscharfer Maskierung und Subtraktion des Galaxienlichtprofils mit IRAF. Wir stellen fest, dass für beide Objekte die in den Schalen vorhandene Helligkeit mit den Werte aus der Literatur übereinstimmt (2.7% bzw. 2.3%). Wir beabsichtigen, die zweite Methode, welche die robustere ist, auf eine Stichprobe von 111 elliptischen Galaxien anzuwenden. Das dritte und letzte Teilprojekt trägt den Titel ‘Detection and analysis of dwarf galaxy environments from the MATLAS survey und untersucht Zwerggalaxien in Umgebungen mit geringer Dichte. Dies geschieht mit tiefer, hochauflösender Bildgebung, um die Umgebung, innere Struktur, Form, Wechselwirkungen, Anzahl von Satelliten pro Galaxie, Sternentstehungsrate etc. dieser Objekte als eine Funktion der Morphologie untersuchen zu können. Nach der Datenverarbeitung und Selektion enthält die zu untersuchende letzte Stichprobe 2210 -Objekte aus 150 MATLAS-Feldern.This thesis is an effort to explore and quantify galaxy morphology and study processes involved in galaxy formation and evolution as a function of that morphology. In the main project of this work, the morphology of galaxies is analysed using machine learning techniques. The subsequent sub-projects focus on astronomical image analysis, quantifying merger remnants which are signatures of hierarchical structure formation scenario and further studying the CDM model by exploring the environment around field dwarf galaxies. In the first part of the thesis, the viability of using automatic machine learning algorithms to classify galaxy morphologies is tested on a sample of 7941 galaxies from the Galaxy and Mass Assembly (GAMA) survey. Using 10 measured features for each galaxy, we apply the methods of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forests (CTRF) and Neural Networks (NN) to our sample, returning True Prediction Ratios (TPRs) of 75.8%, 69.0%, 76.2% and 76.0%, respectively. Considering the simplicity in its formulation and implementation, we find the CTRF method to be the optimal estimator of galaxy Hubble type when applied to contemporary galaxy datasets. Further, we train a binary classifier that divides galaxies into spheroid-dominated and disk-dominated types, achieving an overall classification accuracy of 89.8%. This translates into an accuracy of 84.9% for spheroid-dominated systems and 92.5% for disk-dominated systems. In the second part of the thesis, we present the results from the sub-project titled ‘Coherence enhancing diffusion filtering in image processing, which explores the application of a Coherence Enhancing Anisotropic Diffusion Filtering (CED) algorithm to astronomical images in order to delineate the structure of galaxies. We find that it is effective in boosting the visibility of structures like spiral arms and could be potentially used to trace faint structures such as merger remnants. In the second sub-project, titled ‘Unveiling hidden structure around and within early-type galaxies, we measure the non-smooth structure (merger remnants) in 2 shell galaxies, NGC 3656 & NGC 7600 using two methods, unsharp masking and subtraction of the galaxy light profile using IRAF. We find that for both these objects the light fraction present in the shells is consistent with those in literature, 2.7% and 2.3% respectively. We intend to apply the second method (which is the more robust one) to a sample of 111 early-type galaxies. The third and final sub-project is titled ‘Detection and analysis of dwarf galaxy environments from the MATLAS survey and looks at dwarf galaxies in low density field environments with deep high resolution imaging to study the environment, internal structure, shape, interactions, number of satellites per galaxy, star formation rate etc. of these objects as a function of morphology. After data processing and cleaning, the final sample to be studied contains 2210 objects from 150 MATLAS fields.author: Sreevarsha SreejithZusammenfassung in deutscher SpracheUniversität Innsbruck, Dissertation, 2018OeBB(VLID)276296

The Most Interesting Anomalies Discovered in ZTF DR3 from the SNAD-III Workshop

International audienceThe search for objects with unusual astronomical properties, or anomalies, is one of the most anticipated results to be delivered by the next generation of large scale astronomical surveys. Moreover, given the volume and complexity of current data sets, machine learning algorithms will undoubtedly play an important role in this endeavor. The SNAD team is specialized in the development, adaptation and improvement of such techniques with the goal of constructing optimal anomaly detection strategies for astronomy. We present here the preliminary results from the third annual SNAD workshop (https://snad.space/2020/) that was held on-line in 2020 July

Could SNAD160 be a Pair-instability Supernova?

International audienceThe SNAD team reports the discovery of SNAD160 (AT2018lzi) within the Zwicky Transient Facility third data release. The transient has been found using the active anomaly detection algorithm, an adaptive learning strategy aimed at incorporating expert knowledge into machine learning models. Our preliminary analysis shows that SNAD160 could be a superluminous supernova powered by a pair-instability mechanism—its light curve behavior is consistent with the observed slow rise and slow decay expected from these events