Atacama Large Millimeter/submillimeter Array Expanded Very Large Array Robert C. Byrd Green Bank Telescope Very Long Baseline Array Observing Scripts Basic Reduction Scott Schnee (NRAO) 25 – April

The General Idea: Amplitudes and Phases ALMA Data Workshop – Dec 1 st, b 1  = /b phase The visibility is a complex quantity: - amplitude tells “how much” of a certain frequency component - phase tells “where” this component is located

The General Idea: Amplitudes and Phases ALMA Data Workshop – Dec 1 st, Each pair of antennas will generate a visibility (amplitude and phase) – Every integration: time interval – Every channel: frequency interval Goal of calibration is to correct these amplitudes and phases for atmospheric and instrumental effects Phase corrections are additive Amplitude corrections are multiplicative Measurements are baseline-based, but corrections are antenna-based (usually)

The General Idea: Corrupted Data ALMA Data Workshop – Dec 1 st, Variations in the amount of precipitable water vapor (PWV) cause phase fluctuations and result in –Low coherence (loss of sensitivity) –Radio “seeing”, typically 1  at 1 mm –Anomalous pointing offsets –Anomalous delay offsets Patches of air with different water vapor content (and hence index of refraction) affect the incoming wave front differently.

The General Idea: Corrupted Data ALMA Data Workshop – Dec 1 st, The atmosphere can absorb/emit significantly at (sub)millimeter wavelengths, creating phase and amplitude variations that need to be removed from measurement sets The antennas and other parts of the array also introduce noise into data sets

The General Idea: Calibration ALMA Data Workshop – Dec 1 st, Basic calibration involves observing “calibrators” of known brightness and morphology Quasars (bright point sources) Solar system objects (well-characterized, so easily modeled) Determine corrections that make the observations fit the model Derive the changes to amplitude and phase (complex gain) vs frequency and time Apply the corrections from the calibration data to the science target data Interpolating the derived calibration solutions

What Goes into ALMA Observations? ALMA Data Workshop – Dec 1 st, TimeSourceIntent 09:29: :30:26.3J CALIBRATE_POINTING 09:32: :32:43.3J CALIBRATE_ATMOSPHERE 09:32: :38:07.5J CALIBRATE_BANDPASS 09:38: :39:45.6J CALIBRATE_POINTING 09:41: :41:26.1MarsCALIBRATE_ATMOSPHERE 09:41: :44:11.9MarsCALIBRATE_AMPLI 09:44: :45:11.5J CALIBRATE_ATMOSPHERE 09:45: :45:43.8J CALIBRATE_PHASE 09:47: :54:16.6Science Targets 09:54: :54:56.2J CALIBRATE_PHASE Repeat for ~1 hourQuasar – Science loop

How to choose calibrators ALMA Data Workshop – Dec 1 st, Bandpass calibrator Corrects amplitude & phase vs. frequency Choose brightest quasar in the sky (Sometimes) assume that corrections are constant in time Amplitude calibrator Sets absolute flux of all other sources in observation Choose something bright, compact, and very well known Phase calibrator Corrects amplitude and phase vs. time Choose quasar that is: Bright enough to get reasonable signal to noise in (a few) minutes As close as possible to science target

Other Calibration ALMA Data Workshop – Dec 1 st, Focus observations Done automatically by ALMA observatory Data not included in observations delivered to PI Baseline observations Done after antennas are moved Determine the x,y,z position of each antenna in the array Pointing observations Done at beginning of observations and after each large (10s of degrees) sky slew For CARMA, pointing repeated every 2 – 4 hours WVR observation Removes short timescale phase fluctuations caused by water vapor in the atmosphere

ALMA WVR Correction 10 Data WVR Residual Two different baselines Jan 4, 2010 There are 4 “channels” flanking the peak of the 183 GHz water line Matching data from opposite sides are averaged Data taken every second, and are written to the ASDM (science data file) The four channels allow flexibility for avoiding saturation Next challenges are to perfect models for relating the WVR data to the correction for the data to reduce residual phase noise prior to performing the traditional calibration steps.

ALMA WVR Correction 11

First Look at Your Data In CASA: listobs(vis=‘my_data.ms’) 12 ALMA Data Workshop – Dec 1 st, 2011

Bandpass Phase vs. Frequency (Before) 13

Bandpass Phase vs. Frequency (Model) 14

Bandpass Phase vs. Frequency (Solutions) 15

Bandpass Phase vs. Frequency (After) 16

Phasecal Phase vs. Time (Before) 17

Phasecal Phase vs. Time (Model) 18

Phasecal Phase vs. Time (Solutions) 19

Phasecal Phase vs. Time (After) 20

Ampcal Amplitude vs. uv-distance (Before) 21

22 Ampcal Amplitude vs. uv-distance (Model)

23 Ampcal Amplitude vs. uv-distance (After)

Phasecal Amplitude vs. Time (Before) 24

Phasecal Amplitude vs. Time (Model) 25

Phasecal Amplitude vs. Time (Solutions) 26

Phasecal Amplitude vs. Time (Solutions) 27

Phasecal Amplitude vs. Time (After) 28

Task Names setjy – Set the model for your calibrator gaincal – Amplitude and phase vs. time solutions bandpass – Amplitude and phase vs. frequency solutions fluxscale – Overall amplitude scaling applycal – Apply solutions from gaincal, bandpass, & fluxscale plotcal – Plot the amplitude & phase solutions plotms – Plot the UV data (raw, model, corrected) 29