Automatic Classification of Microlensing Candidates

It is both exciting and important to look for life beyond our planet. To find signs of life on distant planets, there is a need to search across the vast space that surrounds us and find planets outside our solar system, called exoplanets. Among the many search techniques which have been developed to detect exoplanets, ‘microlensing’ holds the advantage of finding Earth-like planets. In order to detect a microlensing event, there is a need to scan millions of stars simultaneously for the case of perfect alignment of two stars. This chance alignment typically lasts for weeks or days, until the two stars move out of alignment. Hence, there is a need to follow up on all detected events in real-time, to capture information about the properties of the star system. Large scale astronomical surveys like the Global Astrometric Interferometer for Astrophysics (Gaia) mission and Large Synoptic Survey Telescope (LSST) will capture terabytes of data every night. Hence, building an automatic classifier, using tools from machine learning in order to sift through this data and detect microlensing events is crucial. The scope of work includes identification and development of three appropriate methods to establish an automatic classifier. The first method makes classification decisions based on five characteristics of microlensing translated into statistical features. The second and third methods detect microlensing events without relying on any specific characteristics of microlensing, but differ in the way they handle data. These methods are applied to datasets from three different astronomical surveys and the results thus obtained are evaluated to make sure that all the occurrences of this rare event, microlensing, are detected. The third method uses an RNN to detect all the events in the training set. It is concluded that, this method can be easily extended to exoplanet detection

Automatic Classification of Microlensing Candidates

It is both exciting and important to look for life beyond our planet. To find signs of life on distant planets, there is a need to search across the vast space that surrounds us and find planets outside our solar system, called exoplanets. Among the many search techniques which have been developed to detect exoplanets, ‘microlensing’ holds the advantage of finding Earth-like planets. In order to detect a microlensing event, there is a need to scan millions of stars simultaneously for the case of perfect alignment of two stars. This chance alignment typically lasts for weeks or days, until the two stars move out of alignment. Hence, there is a need to follow up on all detected events in real-time, to capture information about the properties of the star system. Large scale astronomical surveys like the Global Astrometric Interferometer for Astrophysics (Gaia) mission and Large Synoptic Survey Telescope (LSST) will capture terabytes of data every night. Hence, building an automatic classifier, using tools from machine learning in order to sift through this data and detect microlensing events is crucial. The scope of work includes identification and development of three appropriate methods to establish an automatic classifier. The first method makes classification decisions based on five characteristics of microlensing translated into statistical features. The second and third methods detect microlensing events without relying on any specific characteristics of microlensing, but differ in the way they handle data. These methods are applied to datasets from three different astronomical surveys and the results thus obtained are evaluated to make sure that all the occurrences of this rare event, microlensing, are detected. The third method uses an RNN to detect all the events in the training set. It is concluded that, this method can be easily extended to exoplanet detection

Deep-learning based measurement of planetary radial velocities in the presence of stellar variability

We present a deep-learning based approach for measuring small planetary radial velocities in the presence of stellar variability. We use neural networks to reduce stellar RV jitter in three years of HARPS-N sun-as-a-star spectra. We develop and compare dimensionality-reduction and data splitting methods, as well as various neural network architectures including single line CNNs, an ensemble of single line CNNs, and a multi-line CNN. We inject planet-like RVs into the spectra and use the network to recover them. We find that the multi-line CNN is able to recover planets with 0.2 m/s semi-amplitude, 50 day period, with 8.8% error in the amplitude and 0.7% in the period. This approach shows promise for mitigating stellar RV variability and enabling the detection of small planetary RVs with unprecedented precision.Comment: Draft, unsubmitted, 10 pages, 8 figure

Automatic Classification of Microlensing Candidates

It is both exciting and important to look for life beyond our planet. To find signs of life on distant planets, there is a need to search across the vast space that surrounds us and find planets outside our solar system, called exoplanets. Among the many search techniques which have been developed to detect exoplanets, ‘microlensing’ holds the advantage of finding Earth-like planets. In order to detect a microlensing event, there is a need to scan millions of stars simultaneously for the case of perfect alignment of two stars. This chance alignment typically lasts for weeks or days, until the two stars move out of alignment. Hence, there is a need to follow up on all detected events in real-time, to capture information about the properties of the star system. Large scale astronomical surveys like the Global Astrometric Interferometer for Astrophysics (Gaia) mission and Large Synoptic Survey Telescope (LSST) will capture terabytes of data every night. Hence, building an automatic classifier, using tools from machine learning in order to sift through this data and detect microlensing events is crucial. The scope of work includes identification and development of three appropriate methods to establish an automatic classifier. The first method makes classification decisions based on five characteristics of microlensing translated into statistical features. The second and third methods detect microlensing events without relying on any specific characteristics of microlensing, but differ in the way they handle data. These methods are applied to datasets from three different astronomical surveys and the results thus obtained are evaluated to make sure that all the occurrences of this rare event, microlensing, are detected. The third method uses an RNN to detect all the events in the training set. It is concluded that, this method can be easily extended to exoplanet detection

Automatic Classification of Microlensing Candidates

It is both exciting and important to look for life beyond our planet. To find signs of life on distant planets, there is a need to search across the vast space that surrounds us and find planets outside our solar system, called exoplanets. Among the many search techniques which have been developed to detect exoplanets, ‘microlensing’ holds the advantage of finding Earth-like planets. In order to detect a microlensing event, there is a need to scan millions of stars simultaneously for the case of perfect alignment of two stars. This chance alignment typically lasts for weeks or days, until the two stars move out of alignment. Hence, there is a need to follow up on all detected events in real-time, to capture information about the properties of the star system. Large scale astronomical surveys like the Global Astrometric Interferometer for Astrophysics (Gaia) mission and Large Synoptic Survey Telescope (LSST) will capture terabytes of data every night. Hence, building an automatic classifier, using tools from machine learning in order to sift through this data and detect microlensing events is crucial. The scope of work includes identification and development of three appropriate methods to establish an automatic classifier. The first method makes classification decisions based on five characteristics of microlensing translated into statistical features. The second and third methods detect microlensing events without relying on any specific characteristics of microlensing, but differ in the way they handle data. These methods are applied to datasets from three different astronomical surveys and the results thus obtained are evaluated to make sure that all the occurrences of this rare event, microlensing, are detected. The third method uses an RNN to detect all the events in the training set. It is concluded that, this method can be easily extended to exoplanet detection

Screen Space Ambient Occlusion Using Partial Scene Representation

Screen space ambient occlusion (SSAO) is a technique in real-time rendering forapproximating amount by which a point on a surface is occluded by surrounding geometry, whichhelps in adding soft shadows to diffuse objects. Most of the current methods use the depth bufferas an approximation to scene geometry to sample the occlusion factor. We introduce a noveltechnique which uses a partial representation of the scene (here triangle information in screenspace) using compact triangle storage and a ray-marching approach to find a betterapproximation of the occlusion factor.Computer Scienc

Simulating the early detection and intervention of vascular disease in the Caerphilly cohort

Introduction: The purpose of the project is to simulate the effect of hypothetical intervention on risk of vascular disease in the Caerphilly cohort. The cohort comprises a total population sample of 2959 men aged 45--59 years at the recruitment who has been followed up for 20 years. During that time there has been particular emphasis on assessing exposure to vascular risk factors and assessing vascular related outcomes. Aim: The aim of the thesis is to estimate the effects at population level of public health interventions to change the levels of modifiable risk factors for the vascular disease. Methods: Various statistical techniques such as logistic, fractional polynomial and Cox's proportional hazards models along with various parametric models were used to analyse the data. New risk prediction models were estimated and compared with the existing models in the literature. Various standard simulation techniques were used to simulate hypothetical data using Caerphilly data parameters. Hypothetical interventions were carried out on these generated samples to assess the public health impact. Results: Multivariate analysis suggested that the combined effect of psychological variables measured in the study were significantly associated with the increased risk of MI. New risk prediction models constructed using the Caerphilly study data showed that they were significantly different from the standard available models from the literature. Simulation results suggested that there could be a reduction MI events by 25--30% and stroke events by 50--55% using plausible intervention scenarios available from the literature review. Conclusion: A hypothetical intervention to modify psychological factors showed a higher reduction in MI events. Therefore, plausible interventions to modify psychological factors should be commissioned along with the standard biological and behavioural interventions

Influence of Tick and Mammalian Physiological Temperatures on Borrelia Burgdorferi Biofilms

The spirochaete bacterium Borrelia burgdorferi sensu lato is the aetiologic agent of Lyme disease. Borrelia is transmitted to mammals through tick bite and is adapted to survive at tick and mammalian physiological temperatures. We have previously shown that B. burgdorferi can exist in different morphological forms, including the antibiotic-resistant biofilm form, in vitro and in vivo. B. burgdorferi forms aggregates in ticks as well as in humans, indicating potential of biofilm formation at both 23 and 37 °C. However, the role of various environmental factors that influence Borrelia biofilm formation remains unknown. In this study, we investigated the effect of tick (23 °C), mammalian physiological (37 °C) and standard in vitro culture (33 °C) temperatures with the objective of elucidating the effect of temperature on Borrelia biofilm phenotypes invitro using two B. burgdorferi sensu stricto strains (B31 and 297). Our findings show increased biofilm quantity, biofilm size, exopolysaccharide content and enhanced adherence as well as reduced free spirochaetes at 37 °C for both strains, when compared to growth at 23 and 33 °C. There were no significant variations in the biofilm nano-topography and the type of extracellular polymeric substance in Borrelia biofilms formed at all three temperatures. Significant variations in extracellular DNA content were observed in the biofilms of both strains cultured at the three temperatures. Our results indicate that temperature is an important regulator of Borrelia biofilm development, and that the mammalian physiological temperature favours increased biofilm formation in vitro compared to tick physiological temperature and in vitro culture temperature

Biofilm Formation by Borrelia Burgdorferi Sensu Lato

Bacterial biofilms are microbial communities held together by an extracellular polymeric substance matrix predominantly composed of polysaccharides, proteins and nucleic acids. We had previously shown that Borrelia burgdorferi sensu stricto, the causative organism of Lyme disease in the United States is capable of forming biofilms in vitro. Here, we investigated biofilm formation by B. afzelii and B. garinii, which cause Lyme disease in Europe. Using various histochemistry and microscopy techniques, we show that B. afzelii and B. garinii form biofilms, which resemble biofilms formed by B. burgdorferisensu stricto. High-resolution atomic force microscopy revealed similarities in the ultrastructural organization of the biofilms form by three Borrelia species. Histochemical experiments revealed a heterogeneous organization of exopolysaccharides among the three Borrelia species. These results suggest that biofilm formation might be a common trait of Borrelia genera physiology