Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh AUTONOMOUS PIPELINES David Brett, Leicester University

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Project: Why work on an autonomous classification program? WASP: Wide Angle Search for Planets telescope Leicester, St-Andrews, Cambridge, QU Belfast and Open Universities. Variable Identification: Period searching Classification System: Artificial Neural Networks Methods and Results. Methods and the Future. Talk Map

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Why do any of this? Tera-scale computing age: Volume of collected data Repetitive nature of the data reduction “Brute force” approach Creates one more layer of abstraction

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh WASP ( Wide-Angle Search for Planets) 9.5 o Four CCD chips (recently funding for five) For comparison: the INT “wide-field camera” images roughly the size of the full moon 1% photometry down to 13 th magnitude and detections down to 17 th (30s exposure) 5TB per year (raw) But what do we do with all those bits?

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Source Extraction and Data Reduction Stages: Home grown programs for “cleaning” the raw data. Use of conventional packages such as SExtractor for source extraction Variability checking programs Periodic variability locating programs Phased lightcurve recognition software Results database

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables Phase-folding: Fast to execute Easy to implement Simple to understand e.g.  2 or the L-Statistic Two Main Methods Frequency Analysis: Slower to execute Trickier to code More reliable e.g. Lomb-Scargle or Schwarzenberg-Czerny

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables Phase-Folding: 22 Maximum deviation from a constant line. Binned data, uses bin mean. Intra-bin deviation not taken into account Very quick to implement and compute. REM! Looking for a maximum, not a minimum.

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables Phase-Folding: L-Statistic Also uses binned data. Additionally considers intra-bin deviation from bin-mean. Divide  2 value by the intra-bin dispersion,  enhancing low deviation trial periods. Quick and accurate with medium to low-noise data. Created by S. Davies, 1990.

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables Frequency Analysis: Lomb-Scargle Uses the whole unbinned data time series (DTS). Created by Lomb 1976, refined by Scargle Code adapted from NR in C. Period (days) Stat

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables Frequency Analysis: Schwarzenberg-Czerny Uses the whole unbinned data time series (DTS). Created by A. Schwarzenberg- Czerny Code adapted from S- C code. Period (days) Stat

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables Choice of Trial Periods: Linear difference in period, dP. Linear difference in phase, d . Too small a dP and we may search too fine a parameter space and waste CPU time. Too large a dP and we will not search finely enough. OK 7%

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Periodic Variables Conclusions: Phase-folding methods are swiftest Frequency Analysis methods are generally more reliable Autonomous pipelines require reliability over speed Schwarzenberg-Czerny would be the method of choice In which case a better period choice method is needed

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Autonomous Classification 2 Main Stages: Memory Pattern Matching Modification of the Artificial Neural Network (ANN) INITIALFINAL

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Autonomous Classification Memory Pattern Matching: Why?

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Autonomous Classification Memory Pattern Matching: It allows us to begin grouping similar shapes together This grouping encourages self-organisation To pattern-match is the underlying goal! Finding a sensible position on the network for a pattern allows us to change the network How?

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Autonomous Classification Memory Pattern Matching: Lightcurve Pattern Node 0 Pattern Node 1 Pattern Node 0 has the lowest weight difference vector,  node 0 wins WEIGHTS

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Autonomous Classification Modification of the ANN: Modification affects an area Lessens as geometrical distance increases Area mixing encourages grouping The network can self-organise Hotspots occur

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Autonomous Classification Modification of the ANN: Adjust the weights on the network nodes so that they better represent the lightcurve.  is the learning parameter. It decreases on each learning iteration of the network.  0  0. P is the power (from the neighbour function) of the current node.

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh The Future Enhanced clustering mechanism. More precise shape-similarity evaluating methods. More dynamically adaptive choice of trial periods for period searching. Refinement of these ideas and trying other methods. Research if >2D networks are worthwhile in the current format.

Autonomous Pipelines David Brett Leicester E-Science talk Edinburgh Questions?