Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding

Galaxy-scale strong gravitational lensing is not only a valuable probe of the dark matter distribution of massive galaxies, but can also provide valuable cosmological constraints, either by studying the population of strong lenses or by measuring time delays in lensed quasars. Due to the rarity of galaxy-scale strongly lensed systems, fast and reliable automated lens finding methods will be essential in the era of large surveys such as LSST, Euclid, and WFIRST. To tackle this challenge, we introduce CMU DeepLens, a new fully automated galaxy-galaxy lens finding method based on Deep Learning. This supervised machine learning approach does not require any tuning after the training step which only requires realistic image simulations of strongly lensed systems. We train and validate our model on a set of 20,000 LSST-like mock observations including a range of lensed systems of various sizes and signal-to-noise ratios (S/N). We find on our simulated data set that for a rejection rate of non-lenses of 99%, a completeness of 90% can be achieved for lenses with Einstein radii larger than 1.4" and S/N larger than 20 on individual $g$-band LSST exposures. Finally, we emphasize the importance of realistically complex simulations for training such machine learning methods by demonstrating that the performance of models of significantly different complexities cannot be distinguished on simpler simulations. We make our code publicly available at https://github.com/McWilliamsCenter/CMUDeepLens .Comment: 12 pages, 9 figures, submitted to MNRA

K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4

We are entering an era of unprecedented quantities of data from current and planned survey telescopes. To maximise the potential of such surveys, automated data analysis techniques are required. Here we implement a new methodology for variable star classification, through the combination of Kohonen Self Organising Maps (SOM, an unsupervised machine learning algorithm) and the more common Random Forest (RF) supervised machine learning technique. We apply this method to data from the K2 mission fields 0-4, finding 154 ab-type RR Lyraes (10 newly discovered), 377 Delta Scuti pulsators, 133 Gamma Doradus pulsators, 183 detached eclipsing binaries, 290 semi-detached or contact eclipsing binaries and 9399 other periodic (mostly spot-modulated) sources, once class significance cuts are taken into account. We present lightcurve features for all K2 stellar targets, including their three strongest detected frequencies, which can be used to study stellar rotation periods where the observed variability arises from spot modulation. The resulting catalogue of variable stars, classes, and associated data features are made available online. We publish our SOM code in Python as part of the open source PyMVPA package, which in combination with already available RF modules can be easily used to recreate the method.Comment: Accepted for publication in MNRAS, 16 pages, 13 figures. Updated with proof corrections. Full catalogue tables available at https://www2.warwick.ac.uk/fac/sci/physics/research/astro/people/armstrong/ or at the CD

Multinomial latent logistic regression

University of Technology Sydney. Faculty of Engineering and Information Technology.We are arriving at the era of big data. The booming of data gives birth to more complicated research objectives, for which it is important to utilize the superior discriminative power brought by explicitly designed feature representations. However, training models based on these features usually requires detailed human annotations, which is being intractable due to the exponential growth of data scale. A possible solution for this problem is to employ a restricted form of training data, while regarding the others as latent variables and performing latent variable inference during the training process. This solution is termed weakly supervised learning, which usually relies on the development of latent variable models. In this dissertation, we propose a novel latent variable model - multinomial latent logistic regression (MLLR), and present a set of applications on utilizing the proposed model on weakly supervised scenarios, which, at the same time, cover multiple practical issues in real-world applications. We first derive the proposed MLLR in Chapter 3, together with theoretical analysis including the concave and convex property, optimization methods, and the comparison with existing latent variable models on structured outputs. Our key discovery is that by performing “maximization” over latent variables and “averaging” over output labels, MLLR is particularly effective when the latent variables have a large set of possible values or no well-defined graphical structure is existed, and when probabilistic analysis is preferred on the output predictions. Based on it, the following three sections will discuss the application of MLLR in a variety of tasks on weakly supervised learning. In Chapter 4, we study the application of MLLR on a novel task of architectural style classification. Due to a unique property of this task that rich inter-class relationships between the recognizing classes make it difficult to describe a building using “hard” assignments of styles, MLLR is believed to be particularly effective due to its ability to produce probabilistic analysis on output predictions in weakly supervised scenarios. Experiments are conducted on a new self-collected dataset, where several interesting discoveries on architectural styles are presented together with the traditional classification task. In Chapter 5, we study the application of MLLR on an extreme case of weakly supervised learning for fine-grained visual categorization. The core challenge here is that the inter-class variance between subordinate categories is very limited, sometimes even lower than the intra-class variance. On the other hand, due to the non-convex objective function, latent variable models including MLLR are usually very sensitive to the initialization. To conquer these problems, we propose a novel multi-task co-localization strategy to perform warm start for MLLR, which in turn takes advantage of the small inter-class variance between subordinate categories by regarding them as related tasks. Experimental results on several benchmarks demonstrate the effectiveness of the proposed method, achieving comparable results with latest methods with stronger supervision. In Chapter 6, we aim to further facilitate and scale weakly supervised learning via a novel knowledge transferring strategy, which introduces detailed domain knowledge from sophisticated methods trained on strongly supervised datasets. The proposed strategy is proved to be applicable in a much larger web scale, especially accounting for the ability of performing noise removal with the help of the transferred domain knowledge. A generalized MLLR is proposed to solve this problem using a combination of strongly and weakly supervised training data

A new method for unveiling Open Clusters in Gaia: new nearby Open Clusters confirmed by DR2

Context. The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in astronomy. It includes precise astrometric data (positions, proper motions, and parallaxes) for more than 1.3 billion sources, mostly stars. To analyse such a vast amount of new data, the use of data-mining techniques and machine-learning algorithms is mandatory. Aims. A great example of the application of such techniques and algorithms is the search for open clusters (OCs), groups of stars that were born and move together, located in the disc. Our aim is to develop a method to automatically explore the data space, requiring minimal manual intervention. Methods. We explore the performance of a density-based clustering algorithm, DBSCAN, to find clusters in the data together with a supervised learning method such as an artificial neural network (ANN) to automatically distinguish between real OCs and statistical clusters. Results. The development and implementation of this method in a five-dimensional space (l, b, ϖ, μα*, μδ) with the Tycho-Gaia Astrometric Solution (TGAS) data, and a posterior validation using Gaia DR2 data, lead to the proposal of a set of new nearby OCs. Conclusions. We have developed a method to find OCs in astrometric data, designed to be applied to the full Gaia DR2 archive

Promising Practices for Supervisors of Teacher Candidates Enrolled in Yearlong, Co-taught Clinical Experiences

Promising Practices for Supervisors of Teacher Candidates Enrolled in Yearlong, Co-taught Clinical Experiences Abstract This self-study examined the pedagogical practices of university supervisors who supervised teacher candidates, enrolled in yearlong, co-taught, P-12, clinical experiences. Supervisory practices were situated in a collegial, reflective and developmental model of supervision. The participant sample included 41 teacher candidates, along with 41 of their collaborating teachers and 15 field supervisors who supervised four to six candidates throughout the yearlong experience. Our findings indicate that we, along with our collegial supervisors, used collaborative and non-directive approaches to structure the dialogue with our teacher candidates and the collaborating teachers as well as goal-setting techniques to promote self-directed and self-regulated learning in the candidate. Implications of the study include the following needs for colleges of education: (a) to prepare supervisors specifically to support their candidates in theorizing practice and justifying their instructional decisions with research; (b) to develop a shared language that defines and describes various forms and notions of assessment; and (c) to provide additional support and professional development for supervisors responsible for supervision of candidates in P-12 programs in this new era of national assessment and accountability for the outcomes of their candidates