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This study examines the ways in which public participation in the production of scientific knowledge, influences the practices and expertise of the scientists in Galaxy Zoo, an online Big Data citizen science initiative. The need for citizen science in the field of Astronomy arose in response to the challenges of rapid advances in data gathering technologies, which demanded pattern recognition capabilities that were too advanced for existing computer algorithms. To address these challenges, Galaxy Zoo scientists recruited volunteers through their online website, a strategy which proved to be remarkably reliable and efficient. In doing so, they opened up the boundaries of scientific processes to the public. This shift has led to important outcomes in terms of the scientific discovery of new Astronomical objects; the creation and refining of scientific practices; and the development of new forms of expertise among key actors while they continue to pursue their scientific goals. This thesis attempts to answer the over-arching research question: How is citizen science shaping the practices and expertise of Galaxy Zoo scientists? The emergence of new practices and development of the expertise in the domain of managing citizen science projects were observed through following the work of the Galaxy Zoo scientists and in particular the Principal Investigator and the project's Technical Lead, from February 2010 to April 2013. A broadly ethnographic approach was taken, which allowed the study to be sensitive to the uncertainty and unprecedented events that characterised the development of Galaxy Zoo as a pioneering project in the field of data-intensive citizen science. Unstructured interviewing was the major source of data on the work of the PI and TL; while the communication between these participants, the broader Science Team and their inter-institutional collaborators was captured through analyses of the team emailing list, their official blog and their social media posts. The process of data analysis was informed by an initial conceptualisation of Galaxy Zoo as a knowledge production system and the concept of knowledge object (Knorr-Cetina,1999), as an unfolding epistemic entity, became a primary analytical tool. Since the direction and future of Galaxy Zoo involved addressing new challenges, the study demanded periodic recursive analysis of the conceptual framework and the knowledge objects of both Galaxy Zoo and the present examination of its development. The key findings were as follows. The involvement of public volunteers shaped the practices of the Science Team, while they pursued robust scientific outcomes. Changes included: negotiating collaborations; designing the classification tasks for the volunteers; re-examining data reduction methods and data release policies; disseminating results; creating new epistemic communities; and science communication. In addition, new kinds of expertise involved in running Galaxy Zoo were identified. The relational and adaptive aspects of expertise were seen as important. It was therefore proposed that the development of the expertise in running citizen science projects should be recognised as a domain-expertise in its own right. In Galaxy Zoo, the development of the expertise could be attributed to a combined understanding of: the design principles of doing good science; innovation in methods; and creating a dialogic space for scientists and volunteers. The empirical and theoretical implications of this study therefore lie in (i) identifying emergent practices in citizen science while prioritising scientific knowledge production and (ii) a re-examination of expertise for science in the emerging context of data-intensive science.

Study of the time variable sources and phenomena in Astrophysics provides us with important insights into the stellar evolution, galactic evolution, stellar population studies and cosmological studies such as number density of dark massive objects. Study of these sources and phenomena forms the basis of Time Domain surveys, where the telescopes while scanning the sky regularly for a period of time provides us with positional and temporal data of various Astrophysical sources and phenomena happening in the Universe. Our vantage point within the Milky Way galaxy greatly limits studying our galaxy in its entirety. In such a scenario our nearest neighbour The Andromeda galaxy (M31) proves to be an excellent choice as its proximity and inclination allows us to resolve millions of stars using space based telescopes. Zwicky Transient Facility (ZTF) is a new optical time domain survey at Palomar Observatory, which has collected data in the direction of M31 for over 6 months using multiple filters. This Thesis involves exploitation of this rich data set. Stars in M31 are not resolved in ZTF as it is a ground based facility. This requires us to use the large public catalogue of stars observed with Hubble Space Telescope (HST): The Panchromatic Hubble Andromeda Treasury (PHAT). The PHAT catalogue provides us with stellar coordinates and observed brightness for millions of resolved stars in the direction of the M31 in multiple filters. Processing of the large volumes of data generated by the time domain surveys, requires us to develop new data processing pipelines and utilize statistical techniques for determining various statistical features of the data and using machine learning algorithms to classify the data into different categories. End result of such processing of the data is the astronomical catalogues of various astrophysical sources and phenomena and their light curves. In this thesis we have developed a data processing and analysis pipeline based on Forced Aperture Photometry Technique. Since the stars are not resolved in ZTF, we performed photometry at pixel level. Only small portion of the ZTF dataset has been analyzed and photometric light curves have been generated for few interesting sources. In our preliminary investigations we have used a Machine Learning Algorithm to classify the resulting time series data into different categories. We also performed cross comparison with data from other studies in the region of the Andromeda galaxy.