Galaxy Zoo Supernovae

This paper presents the first results from a new citizen science project: Galaxy Zoo Supernovae. This proof of concept project uses members of the public to identify supernova candidates from the latest generation of wide-field imaging transient surveys. We describe the Galaxy Zoo Supernovae operations and scoring model, and demonstrate the effectiveness of this novel method using imaging data and transients from the Palomar Transient Factory (PTF). We examine the results collected over the period April-July 2010, during which nearly 14,000 supernova candidates from PTF were classified by more than 2,500 individuals within a few hours of data collection. We compare the transients selected by the citizen scientists to those identified by experienced PTF scanners, and find the agreement to be remarkable - Galaxy Zoo Supernovae performs comparably to the PTF scanners, and identified as transients 93% of the ~130 spectroscopically confirmed SNe that PTF located during the trial period (with no false positive identifications). Further analysis shows that only a small fraction of the lowest signal-to-noise SN detections (r > 19.5) are given low scores: Galaxy Zoo Supernovae correctly identifies all SNe with > 8{\sigma} detections in the PTF imaging data. The Galaxy Zoo Supernovae project has direct applicability to future transient searches such as the Large Synoptic Survey Telescope, by both rapidly identifying candidate transient events, and via the training and improvement of existing machine classifier algorithms.Comment: 13 pages, 10 figures, accepted MNRA

A Deep Neural Network Based Reverse Radio Spectrogram Search Algorithm

Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The "needle in a haystack" nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a B-Variational Autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical Transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the B-Variational Autoencoder to extract features from small (~ 715,Hz, with a resolution of 2.79Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data. This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for "lookalike" signals in large astronomical datasets.Comment: 8 pages, 8 figure

Galaxy Zoo : Building the low-mass end of the red sequence with local post-starburst galaxies

We present a study of local post-starburst galaxies (PSGs) using the photometric and spectroscopic observations from the Sloan Digital Sky Survey and the results from the Galaxy Zoo project. We find that the majority of our local PSG population have neither early- nor late-type morphologies but occupy a well-defined space within the colour-stellar mass diagram, most notably, the low-mass end of the 'green valley' below the transition mass thought to be the mass division between low-mass star-forming galaxies and high-mass passively evolving bulge-dominated galaxies. Our analysis suggests that it is likely that local PSGs will quickly transform into 'red', low-mass early-type galaxies as the stellar morphologies of the 'green' PSGs largely resemble that of the early-type galaxies within the same mass range. We propose that the current population of PSGs represents a population of galaxies which is rapidly transitioning between the star-forming and the passively evolving phases. Subsequently, these PSGs will contribute towards the build-up of the low-mass end of the 'red sequence' once the current population of young stars fade and stars are no longer being formed. These results are consistent with the idea of 'downsizing' where the build-up of smaller galaxies occurs at later epochs.Peer reviewe

Black hole growth and host galaxy morphology

We use data from large surveys of the local Universe (SDSS+Galaxy Zoo) to show that the galaxy-black hole connection is linked to host morphology at a fundamental level. The fraction of early-type galaxies with actively growing black holes, and therefore the AGN duty cycle, declines significantly with increasing black hole mass. Late-type galaxies exhibit the opposite trend: the fraction of actively growing black holes increases with black hole mass.Comment: 4 pages, 2 figures. Proceedings of the IAU Symposium no. 267, "Co-Evolution of Central Black Holes and Galaxies: Feeding and Feedback", eds. B.M. Peterson, R.S. Somerville and T. Storchi-Bergman

Galaxy Zoo Green Peas: discovery of a class of compact extremely star-forming galaxies

‘The definitive version is available at www3.interscience.wiley.com '. Copyright Royal Astronomical Society. DOI: 10.1111/j.1365-2966.2009.15383.xWe investigate a class of rapidly growing emission line galaxies, known as 'Green Peas', first noted by volunteers in the Galaxy Zoo project because of their peculiar bright green colour and small size, unresolved in Sloan Digital Sky Survey imaging. Their appearance is due to very strong optical emission lines, namely [O iii]λ5007 Å, with an unusually large equivalent width of up to ∼1000 Å. We discuss a well-defined sample of 251 colour-selected objects, most of which are strongly star forming, although there are some active galactic nuclei interlopers including eight newly discovered narrow-line Seyfert 1 galaxies. The star-forming Peas are low-mass galaxies (M∼ 108.5–1010 M⊙) with high star formation rates (∼10 M⊙ yr−1) , low metallicities (log[O/H]+ 12 ∼ 8.7) and low reddening [ E(B−V) ≤ 0.25 ] and they reside in low-density environments. They have some of the highest specific star formation rates (up to ∼10−8 yr−1 ) seen in the local Universe, yielding doubling times for their stellar mass of hundreds of Myr. The few star-forming Peas with Hubble Space Telescope imaging appear to have several clumps of bright star-forming regions and low surface density features that may indicate recent or ongoing mergers. The Peas are similar in size, mass, luminosity and metallicity to luminous blue compact galaxies. They are also similar to high-redshift ultraviolet-luminous galaxies, e.g. Lyman-break galaxies and Lyα emitters, and therefore provide a local laboratory with which to study the extreme star formation processes that occur in high-redshift galaxies. Studying starbursting galaxies as a function of redshift is essential to understanding the build up of stellar mass in the Universe.Peer reviewe

Galaxy Zoo: Disentangling the Environmental Dependence of Morphology and Colour

We analyze the environmental dependence of galaxy morphology and colour with two-point clustering statistics, using data from the Galaxy Zoo, the largest sample of visually classified morphologies yet compiled, extracted from the Sloan Digital Sky Survey. We present two-point correlation functions of spiral and early-type galaxies, and we quantify the correlation between morphology and environment with marked correlation functions. These yield clear and precise environmental trends across a wide range of scales, analogous to similar measurements with galaxy colours, indicating that the Galaxy Zoo classifications themselves are very precise. We measure morphology marked correlation functions at fixed colour and find that they are relatively weak, with the only residual correlation being that of red galaxies at small scales, indicating a morphology gradient within haloes for red galaxies. At fixed morphology, we find that the environmental dependence of colour remains strong, and these correlations remain for fixed morphology \textit{and} luminosity. An implication of this is that much of the morphology--density relation is due to the relation between colour and density. Our results also have implications for galaxy evolution: the morphological transformation of galaxies is usually accompanied by a colour transformation, but not necessarily vice versa. A spiral galaxy may move onto the red sequence of the colour-magnitude diagram without quickly becoming an early-type. We analyze the significant population of red spiral galaxies, and present evidence that they tend to be located in moderately dense environments and are often satellite galaxies in the outskirts of haloes. Finally, we combine our results to argue that central and satellite galaxies tend to follow different evolutionary paths.Comment: 19 pages, 18 figures. Accepted for publication in MNRA

Galaxy Zoo: Reproducing Galaxy Morphologies Via Machine Learning

We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural network is trained on a subset of objects classified by the human eye and we test whether the machine learning algorithm can reproduce the human classifications for the rest of the sample. We find that the success of the neural network in matching the human classifications depends crucially on the set of input parameters chosen for the machine-learning algorithm. The colours and parameters associated with profile-fitting are reasonable in separating the objects into three classes. However, these results are considerably improved when adding adaptive shape parameters as well as concentration and texture. The adaptive moments, concentration and texture parameters alone cannot distinguish between early type galaxies and the point sources/artifacts. Using a set of twelve parameters, the neural network is able to reproduce the human classifications to better than 90% for all three morphological classes. We find that using a training set that is incomplete in magnitude does not degrade our results given our particular choice of the input parameters to the network. We conclude that it is promising to use machine- learning algorithms to perform morphological classification for the next generation of wide-field imaging surveys and that the Galaxy Zoo catalogue provides an invaluable training set for such purposes.Comment: 13 Pages, 5 figures, 10 tables. Accepted for publication in MNRAS. Revised to match accepted version

Selection of radio pulsar candidates using artificial neural networks

Radio pulsar surveys are producing many more pulsar candidates than can be inspected by human experts in a practical length of time. Here we present a technique to automatically identify credible pulsar candidates from pulsar surveys using an artificial neural network. The technique has been applied to candidates from a recent re-analysis of the Parkes multi-beam pulsar survey resulting in the discovery of a previously unidentified pulsar.Comment: Accepted for publication in Monthly Notices of the Royal Astronomical Society. 9 pages, 7 figures, and 1 tabl