the molonglo cross survey virtual observatory david f. crawford crawford@physics.usyd.edu.au

The Molonglo Cross SurveyVirtual Observatory

David F. Crawford

Crawford@physics.usyd.edu.au

Molonglo Cross Survey

• A new analysis of data recorded from 16/11/1967 to 23/8/1978

• Contents1. Brief history2. Telescope parameters3. Current state of the analysis4. Some results5. Programs

Molonglo Cross History

• Designed by Prof. B. Y. Mills as a successor to the Fleurs Cross

• Early analogue (chart) data were from the East-West fan beam and then the full cross

• The first digitally recorded data that is available was on 16/11/1967

• The last data was taken on 23/8/1978

Molonglo Cross Telescope

• Mill’s Cross design with two one mile arms

• 408 MHz with 2.5 MHz bandwidth

• Single sideband with one polarization

• Transit telescope with 11 (later 33) beams

• Resolution is 2´.62 (R.A.) by 2´.86sec(Z)

• Declination range –90º to 20º

Data description

• A sample is recorded every 3 sidereal seconds

• Digital data: date, time, declination number, observer number, pulsar channel

• Analogue data: 33 pencil beams, 3 total power East-West beams, 110 MHz total power EW beams

Field calibration

• At the start of each run and every solar hour the o/p is restricted to the centre module which is switched to a load resistance and then to a noise generator.

• This is the only method used to allow for the temperature and other environmental effects.

Initial data processing

• Apply internal load and field calibration samples

• Combine Early, Centre and Late beams to produce an 11 beam output every 4 sidereal seconds in J2000 coordinates

• No interpolation is done in declination: instead the exact J2000 declination is recorded with every sample

Data calibration

• Master catalogue, generated from major surveys, with 51389 sources (whole sky) Main flux density reference is the Molonglo Reference

Catalogue (MRC)

Procedure is to calibrate long runs with the catalogue and the to cross-correlate all the runs using the overlapping data

Major problem

• The low spatial frequencies are observed by adding part of the output of the central module in the NS arm to the EW arm output

• Sometime this was not done or done with an incorrect factor

• The result is that low spatial frequencies have to be replaced by good data

Noise estimation

• There is excellent noise estimation based on the initial redundancy in the data

• Accurate flux density and position uncertainties

• Low pass filtering produces correlations between adjacent data points. The result is that the fitted flux density uncertainty is the same as the residual rms.

Noise from 34,708,860 samples

Noise/mJy

Original sample noise

Display programs

– Moldis: Shows individual runs or many combined runs as line scans.

– Molgry: displays an image as • Line scans

• Contour plot

• False colour image

• Gray scale image

Display Programs cont.

• Molfit: produces a FITS file for any region

• Fitgry: displays a FITS file as1. Gray scale image

2. Line scans

3. Contour plot

4. False colour image

Analysis programs

• Molsrz: locates and fits point sources– Either free search or it uses a source list– Can work with individual runs or with

combined data.

• Comran: Produces random position list for Monte Carlo analysis.

• Molndp: eliminates duplicate entries.

Auxiliary programs

• Hms2deg: HMS DMS degrees

• Deg2hms: degrees HMS DMS

• Molcaz: plots calibration data.

• Molint: analyses interference spikes.

• Molrun: extracts run details.