The POINT–AGAPE (Pixel‐lensing Observations with the Isaac Newton Telescope–Andromeda Galaxy Amplified Pixels Experiment) survey is an optical search for gravitational microlensing events towards the Andromeda galaxy (M31). As well as microlensing, the survey is sensitive to many different classes of variable stars and transients. In our first paper of this series, we reported the detection of 20 classical novae (CNe) observed in Sloan r′ and i′ passbands. An analysis of the maximum magnitude versus rate of decline (MMRD) relationship in M31 is performed using the resulting POINT–AGAPE CN catalogue. Within the limits of the uncertainties of extinction internal to M31, good fits are produced to the MMRD in two filters. The MMRD calibration is the first to be performed for Sloan r′ and i′ filters. However, we are unable to verify that novae have the same absolute magnitude 15 d after peak (the t15 relationship), nor any similar relationship for either Sloan filter. The subsequent analysis of the automated pipeline has provided us with the most thorough knowledge of the completeness of a CN survey to date. In addition, the large field of view of the survey has permitted us to probe the outburst rate well into the galactic disc, unlike previous CCD imaging surveys. Using this analysis, we are able to probe the CN distribution of M31 and evaluate the global nova rate. Using models of the galactic surface brightness of M31, we show that the observed CN distribution consists of a separate bulge and disc population. We also show that the M31 bulge CN eruption rate per unit r′ flux is more than five times greater than that of the disc. Through a combination of the completeness, M31 surface brightness model and our M31 CN eruption model, we deduce a global M31 CN rate of 65+16−15 yr−1, a value much higher than found by previous surveys. Using the global rate, we derive a M31 bulge rate of 38+15−12 yr−1 and a disc rate of 27+19−15 yr−1. Given our understanding of the completeness and an analysis of other sources of error, we conclude that the true global nova rate of M31 is at least 50 per cent higher than was previously thought and this has consequent implications for the presumed CN rate in the Milky Way. We deduce a Galactic bulge rate of 14+6−5 yr−1, a disc rate of 20+14−11 yr−1 and a global Galactic rate of 34+15−12 yr−1, consistent with the Galactic global rate derived elsewhere by independent methods.

The POINT-AGAPE collaboration is carrying out a search for gravitational microlensing toward M 31 to reveal galactic dark matter in the form of MACHOs (Massive Astrophysical Compact Halo Objects) in the halos of the Milky Way and M 31. A high-threshold analysis of 3 years of data yields 6 bright, short-duration microlensing events, which are confronted to a simulation of the observations and the analysis. The observed signal is much larger than expected from self lensing alone and we conclude, at the 95% confidence level, that at least 20% of the halo mass in the direction of M 31 must be in the form of MACHOs if their average mass lies in the range 0.5-1 M . This lower bound drops to 8% for MACHOs with masses ∼0.01 M . In addition, we discuss a likely binary microlensing candidate with caustic crossing. Its location, some 32' away from the centre of M 31, supports our conclusion that we are detecting a MACHO signal in the direction of M 31.

Synthetic biology is a complex discipline that involves creating detailed, purpose-built designs from genetic parts. This process is often phrased as a Design-Build-Test-Learn loop, where iterative design improvements can be made, implemented, measured, and analyzed. Automation can potentially improve both the end-to-end duration of the process and the utility of data produced by the process. One of the most important considerations for the development of effective automation and quality data is a rigorous description of implicit knowledge encoded as a formal knowledge representation. The development of knowledge representation for the process poses a number of challenges, including developing effective human−machine interfaces, protecting against and repairing user error, providing flexibility for terminological mismatches, and supporting extensibility to new experimental types. We address these challenges with the DARPA SD2 Round Trip software architecture. The Round Trip is an open architecture that automates many of the key steps in the Test and Learn phases of a Design-Build-Test-Learn loop for highthroughput laboratory science. The primary contribution of the Round Trip is to assist with and otherwise automate metadata creation, curation, standardization, and linkage with experimental data. The Round Trip's focus on metadata supports fast, automated, and replicable analysis of experiments as well as experimental situational awareness and experimental interpretability. We highlight the major software components and data representations that enable the Round Trip to speed up the design and analysis of experiments by 2 orders of magnitude over prior ad hoc methods. These contributions support a number of experimental protocols and experimental types, demonstrating the Round Trip's breadth and extensibility. We describe both an illustrative use case using the Round Trip for an on-the-loop experimental campaign and overall contributions to reducing experimental analysis time and increasing data product volume in the SD2 program.

Computational tools addressing various components of design-build-test-learn loops (DBTL) for the construction of synthetic genetic networks exist, but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called DART (Design Assemble Round Trip). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection, and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions.

An automated search is carried out for microlensing events using a catalogue of 44 554 variable superpixel light curves derived from our 3‐yr monitoring programme of M31. Each step of our candidate selection is objective and reproducible by a computer. Our search is unrestricted, in the sense that it has no explicit time‐scale cut. So, it must overcome the awkward problem of distinguishing long time‐scale microlensing events from long‐period stellar variables. The basis of the selection algorithm is the fitting of the superpixel light curves to two different theoretical models, using variable star and blended microlensing templates. Only if microlensing is preferred is an event retained as a possible candidate. Further cuts are made with regard to: (i) sampling, (ii) goodness of fit of the peak to a Paczyński curve, (iii) consistency of the microlensing hypothesis with the absence of a resolved source, (iv) achromaticity, (v) position in the colour–magnitude diagram and (vi) signal‐to‐noise ratio. Our results are reported in terms of first‐level candidates, which are the most trustworthy, and second‐level candidates, which are possible microlensing events but have a lower signal‐to‐noise ratio and are more questionable. The pipeline leaves just three first‐level candidates, all of which have very short full‐width at half‐maximum time‐scales (t1/2 < 5 d) and three second‐level candidates, which have time‐scales of t1/2= 31, 36 and 51 d. We also show 16 third‐level light curves, as an illustration of the events that just fail the threshold for designation as microlensing candidates. They are almost certainly mainly variable stars. Two of the three first‐level candidates correspond to known events (PA 00‐S3 and 00‐S4) already reported by the POINT‐AGAPE project. The remaining first‐level candidate is new. This algorithm does not find short time‐scale events that are contaminated with flux from nearby variable stars (such as PA 99‐N1).