Since these alerts are basically everything that changes in the sky, we should be able to find supernovae among all the alerts sent by the ZTF telescope. The problem is that other astronomical objects also produce alerts, such as stars that change their brightness (variable stars), active galactic nuclei (AGNs), asteroids and errors in the measurement (bogus alerts). Fortunately, there are some distinguishable features in the science, reference and difference images that could help us to identify which alert is supernovae, or other objects. We would like to effectively discriminate among these five classes of objects.
| ). In this work, we used the first alert only to quickly find supernovae. Our architecture provides rotational invariance by making 90° rotated copies of each image in the training set, to then apply average pooling to the dense representation of each rotated version of the image. Imposing rotational invariance in this problem is very helpful, since there is no particular orientation in which structures may appear in the images of the alert (). We also added part of the metadata contained in the alert, such as the position in sky coordinates, distance to other known objects, and atmospheric condition metrics. After training the model using cross-entropy, the probabilities were highly concentrated around values of 0 or 1, even in cases when the classifier was wrong in its predicted class. This is not so convenient when an expert further filters supernovae candidates after the model made a prediction. Saturated values of 0 or 1 do not give any insight about the chances of a wrong classification and second or third class guess made by the model. Therefore, in addition to the cross-entropy term in the loss function, we added an extra to maximize the entropy of the prediction, in order to spread the values of the output probabilities ( |
| Convolutional neural network with enhanced rotational invariance. Rotated copies for each input are created and fed to the same CNN architecture, to then apply average pooling in the dense layer before concatenating with the metadata. Finally, two other fully connected layers, and a softmax are applied to obtain the predictions. |
We found that the information within the images (science, reference and difference) is enough to obtain a good classification in the training set, but integrating the information from the metadata was critical to obtain the right spatial distribution of the predictions.
is a visualization tool that shows important information about the alert so the astronomer chooses which objects should report as supernovae. It also has a button to report wrong classifications made by our model, so we can add it to the training set to later improve the model using these examples labeled by hand.
Another key point of our effort is focused on finding rare objects using outlier detection techniques. This is a crucial task since these new telescopes will possibly reveal new kinds of astronomical objects due to the unprecedented sampling rate and the spatial depth of each observation. We think this new way of analyzing massive amounts of astronomical data will be not only helpful but necessary. The organization, classification and redistribution of the data for the scientific community is an important part of doing science with astronomical data. This task requires expertise from different fields, such as computer science, astronomy, engineering and mathematics. The construction of new modern telescopes such as The Vera C. Rubin Observatory will drastically change the way astronomers study celestial objects, and as the ALeRCE broker we will be ready to make this possible. For more information, please visit which describes the complete processing pipeline, the , which provides a more complex classification with a larger taxonomy of classes by using the a time series called light curve. Vollständiger Original-Bericht Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org. Wie bewertest du diesen Beitrag? 1 Klick Feedback Teilen mit Netzwerk & Team: Hat Ihnen dieser Tipp / Anleitung geholfen? Community-Analysen & Experten-Meinungen 0Verfasse deine eigene Analyse, teile Workarounds oder diskutiere diesen Vorfall im Blog. Noch keine Community-Analyse verfasst. Markiere einen Textabschnitt oder klicke oben auf „ Eigene Analyse verfassen“! Community Pulse: Relevanz-Einschätzung 1 Klick Experten-Votum 🔴 Akute Relevanz 0% 🟡 In Evaluierung 0% 🟢 Keine Auswirkung 0% Spannende Innovation 0% Verwandte Story-Cluster & Quellen (Vektor-KI) Tipp: Mit Pfeiltasten [ ← ] und [ → ] blättern 
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