Effects of standard and modified gravity on interplanetary ranges

We numerically investigate the impact on the two-body range by several Newtonian and non-Newtonian dynamical effects for some Earth-planet (Mercury, Venus, Mars, Jupiter, Saturn) pairs in view of the expected cm-level accuracy in some future planned or proposed interplanetary ranging operations (abridged).Comment: LaTex, World Scientific style, 46 pages, 55 figures, 1 table, 57 references. Version in press in International Journal of Modern Physics D (IJMPD

The Kolmogorov-Smirnov test for the CMB

We investigate the statistics of the cosmic microwave background using the Kolmogorov-Smirnov test. We show that, when we correctly de-correlate the data, the partition function of the Kolmogorov stochasticity parameter is compatible with the Kolmogorov distribution and, contrary to previous claims, the CMB data are compatible with Gaussian fluctuations with the correlation function given by standard Lambda-CDM. We then use the Kolmogorov-Smirnov test to derive upper bounds on residual point source power in the CMB, and indicate the promise of this statistics for further datasets, especially Planck, to search for deviations from Gaussianity and for detecting point sources and Galactic foregrounds.Comment: Improved significance of the results (which remain unchanged) by using patches instead of ring segments in the analysis. Added sky maps of the Kolmogorov-parameter for original and de-correlated CMB ma

Laying the Groundwork for the Development of the Data Archive of the New Robotic Telescope

The Liverpool Telescope has been in fully autonomous operation since 2004. The supporting data archive facility has largely been untouched. The data provision service has not been an issue although some modernisation of the system is desirable. This project is timely. Not only does it suit the upgrade of the current LT data archive, it is in line with the design phase of the New Robotic Telescope which will be online in the early-2020s; and with the development of a new data archive facility for a range of telescopes at the National Astronomical Research Institute of Thailand. The Newton Fund enabled us to collaborate in designing a new versatile generic system that serves all purposes. In the end, we conclude that a single system would not meet the needs of all parties and only adopt similar front-ends while the back-ends are bespoke to our respective systems and data-flows

Photometric Selection of Emission Line Galaxies, Clustering Analysis and a Search for the ISW effect

We investigate the use of simple colour cuts applied to the SDSS optical imaging to perform photometric selections of emission line galaxies out to z<1. From colour-cuts using the SDSS g, r and i bands, we obtain mean photometric redshifts of z=0.32+-0.08, z=0.44+-0.12 and z=0.65+-0.21. We further calibrate our high redshift selection using spectroscopic observations with the AAOmega spectrograph on the 4m Anglo-Australian Telescope (AAT), observing ~50-200 galaxy candidates in 4 separate fields. With just 1-hour of integration time and with seeing of ~1.6", we successfully determined redshifts for ~65% of the targeted candidates. We calculate the angular correlation functions of the samples and find correlation lengths of r0=2.64 h-1 Mpc, r0=3.62 h-1 Mpc and r0=5.88 h-1 Mpc for the low, mid and high redshift samples respectively. Comparing these results with predicted dark matter clustering, we estimate the bias parameter for each sample to be b=0.70, b=0.92 and b=1.46. We calculate the 2-point redshift-space correlation function at z~0.6 and find a clustering amplitude of s0=6.4 h-1 Mpc. Finally, we use our photometric sample to search for the Integrated Sachs-Wolfe signal in the WMAP 5yr data. We cross-correlate our three redshift samples with the WMAP W, V, Q and K bands and find an overall trend for a positive signal similar to that expected from models. However, the signal in each is relatively weak. Combining all three galaxy samples we find a signal of wTg(<100')=0.20+-0.12 microK in the WMAP W-band, a significance of 1.7sigma.Comment: 14 pages, 17 figures, submitted to MNRA

Kolmogorov analysis detecting radio and Fermi gamma-ray sources in cosmic microwave background maps

The Kolmogorov stochasticity parameter is shown to act as a tool to detect point sources in the cosmic microwave background (CMB) radiation temperature maps. Kolmogorov CMB map constructed for the WMAP's 7-year datasets reveals tiny structures which in part coincide with point radio and Fermi/LAT gamma-ray sources. In the first application of this method, we identified several sources not present in the then available 0FGL Fermi catalog. Subsequently they were confirmed in the more recent and more complete 1FGL catalog, thus strengthening the evidence for the power of this methodology.Comment: 4 pages, 3 figs, 1 Table; to match the published versio

Thai national telescope studies of ultraluminous X-ray sources

Ultraluminous X-ray sources (ULXs) are extra-galactic, non-nuclear sources with X-ray luminosity in excess of 10^39 erg s^–1. It has been thought that the majority of ULX populations are stellar-mass objects accreting matter at a super-Eddington rate. Although ULX studies are often focused in the X-ray regime, this work studied the ULXs in the optical regime, identified as the ULX counterparts (CTPs). The optical variability of nine CTPs were observed using the 2.4-m Thai National Telescope. Out of the nine ULXs, we detected three ULXs exhibiting strong variability up to ~1 magnitude, suggesting that the CTP light does not come from the donor star's emission. The paper discusses the physical origins of the variability which potentially explain the observed light curves

Clustering of 2PIGG galaxy groups with 2dFGRS galaxies

Prompted by indications from QSO lensing that there may be more mass associated with galaxy groups than expected, we have made new dynamical infall estimates of the masses associated with 2PIGG groups and clusters. We have analysed the redshift distortions in the cluster-galaxy cross-correlation function as a function of cluster membership, cross-correlating z<0.12 2PIGG clusters and groups with the full 2dF galaxy catalogue. We have made estimates of the dynamical infall parameter beta and new estimates of the group velocity dispersions. We first find that the amplitude of the full 3-D redshift space cross-correlation function, xi_{cg}, rises monotonically with group membership. We use a simple linear-theory infall model to fit xi(sigma, pi) in the range 5<s<40h^{-1}Mpc. We find that the beta versus membership relation for the data shows a minimum at intermediate group membership n~20 or L~2x10^11h^-2Lsun, implying that the bias and hence M/L ratios rise by a significant factor (~5x) both for small groups and rich clusters. However, the mocks show a systematic shift between the location of the beta minimum and the M/L minimum at L~10^10h^-2Lsun given by direct calculation using the known DM distribution. Our overall conclusion is that bias estimates from dynamical infall appear to support the minimum in star-formation efficiency at intermediate halo masses. Nevertheless, there may still be significant systematic problems arising from measuring beta~1/b using large-scale infall rather than M/L using small-scale velocity dispersions.Comment: 20 pages, 32 figures, 9 table

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy, however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO), whose primary science objective is the optical follow-up of Gravitational Wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near real-time to fully exploit the time-domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this "off-the-shelf" pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to sub-pixel levels, and that measured L-band photometries are accurate to ∼50 mmag at mL∼16 and ∼200 mmag at mL∼18. These values compare favourably to those obtained using GOTO's primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic "obs package" that others can build-upon should they wish to use the LSST Science Pipelines to process data from other facilities

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations