Advantages of a wide Field-of-view
camera for galaxy survey and its data
reduction techniques.
Pranshu Mandal
30/11/2020

Contents
I.Introduction
II.Data reduction pipeline and image synthesis
III.Sky removal
IV. Introducing new ChunkPCA algorithm
V. Beam characteristics of the MKID camera
VI. Advantages of the pipeline
VII.Conclusion

Introduction
MKID camera: A superconducting based on microwave kinetic inductance detectors toobserve 100 GHz bands with Nobeyama 45-m camera.
Objective ●Survey the massive star forming regions of the Galactic plane in 100 GHz.
●Distant galaxy survey to understand the star-formation, history and evolution
process of galaxies.
Peak freq. Of BB emission (from dust) isshifted for high redshift.
100 GHz is good freq for high Z

Optical (color) : near galaxy
350 GHz : distant galaxy

Blain et al. 20013

Introduction
Objective Galactic plane survey
Observation of massive star forming region with large FoV and high spatialresolution.
●Free-free emission is dominant at the the 100-GHz continuum observation➢Good tracer of the massive star forming region (HII region)●Physical properties will be determined by comparing the synchrotron and dustemissions●Credit: Csengeri+2016

GBT ( 3.5cm), Law + 2014

APEX (The ATLASGAL survey @ 350 GHz

4Nobeyama 45m telescope
Altitude1350 mDiameter of antenna

45 m

Surface accuracy

0.1mm

Frequency

20 〜 150 GHz

Angular resolution

0.004°/14.4”

Weight700 tons

5Camera Optics
Camera was installed in the Nobeyama
45-m telescope from 10th May to 1stJune, 2018.

Optics of 45-m telescope & MKID camera

The Nobeyama 45-m telescope has anoptics leading the beam to the camera.
Works by raster scan

T. Nitta et al., 2018

Camera cryostat at theReceiver Cabin

T. Nitta et al., 20186Cryostat and Focal Plane Array
The refractive type cold re-imaging optics has two silicon lenses.
Purpose- Coupling from a telescope focus of F/5.6 to a focal plane of F/2.
Field of view (FoV) of the camera is around 3’, which is limited by the telescope
optics.
The cold optics is composed of ●Two Si lenses 154mm(1K) and 300mm(4K),●
Zitex coated PTFE and Nylon filters,●
Cold baffle for reducing the stray light●Metal-mesh filters with 120 and 300GHz cutoff●
1K cold stop

T. Nitta et al., 2018

Camera cryostat

7

Cryostat and Focal Plane Array
The focal plane array of the camera●
109 pixels antenna-coupled Microwave Kinetic Inductance Detectors (MKID) array and theglass-beads coated silicon (Si) lens array. 3 inch area.
MKID consists of●
coplanar-waveguide λ/4 resonator●
double-slot antenna.
●
150nm Al film is deposited on the Si wafer

Readout

109 pixel MKIDS array and detector

An Individual MKID pixel

Si lens array – diameter - 5.7mm, glass beads coating anti-reflection coating. (30% reflec)
Purpose - to produce low side-lobe beam pattern..

8T. Nitta et al., 2018

MKID Working Principle
K. Hattori et al., 2013

The Microwave Kinetic Inductance
Detector(MKIDs) consists of:●●A feedline,
Several resonators(specific resonantfrequency)

T. Nitta et al., 2018

P.K. Day et al., 2003

Resonators fed with resonant frequency
through a capacitive coupling with thefeed line.

Frequency[Hz]Temperature dependence on frequency

A typical S21 from a MKID readout.

9T. Nitta et al., 2018

Data Acquisition System

10II). Pipeline Workflow
Antennapointing
log

Preprocessing

Data

Pointing
Corrections

MKID
Readout
dataPixelselection

Interpolation

Gridding Algorithm

Baseline
removalCalibration

Beammaps

Sky maps

Beamcharacteristics

11

This thesis presents all the work that has been done shown in Green here.

a). Baseline Removal
Fluctuations in observed data caused by variations in Sky due toweather and atmospheric changes, as well as variation in the electronic
readout system.

Baseline removal using median filter:

A median filter is a method where a ’window’ is defined and themedian is calculated for the window.
●
Extensively used for denoising. Being a very fast method toimplement, it is used to preview the data in some cases.
●Easy and fast to use.
Cons:
Not enough control on
baseline determiantion.
(low-high freq. noise)

Frequency shift(MHz)Pros:- median- raw data
- subtracted12
Data points

PCA
Principal Component Analysis(PCA) is a method of transforming correlated dataset ofobservations into linearly uncorrelated variables. The uncorrelated variables are then used on
the observations to represent the dataset in reduced dimensions.
Explanation:n: data points per sample(number = lengthof observation * sampling rate)
p: Time Order Data(number = number ofpixels)
Step 2: The mean of each column issubtracted from all the values in thatcolumn such that every column has a meanof 0. (Z)Then the covariance, Σ is calculated

elements

Time order data

Step 1: Arrange the dataset.

Covariance matrix:

13

PCA
Step 3: Eigen decomposition of Σ.
Σ = PDP-1
P is the Matrix of Eigen vectors.
D is the diagonal matrix of corresponding
eigen values. (eigen vectors areindipendent of one another)
Step 4: D, the diagonal matrix is then sorted
according to the eigen value from highest tolowest. And P is also modified accordingly
to give P*.
Step 5: Z*, is calculated.
Z* is a standardized, centered version of X,
with the weights determined by the eigenvectors.
(Since P* is independent of one another, Z* will
also be independent of one another)14

Mars observation

PCA Decorrelation

Decorrelated using PCA

Correlation of raw data

15

Simulation data
To test and demonstrate the behavior of thedecorrelation algorithm, a dataset is generated.
Process:●A common mode is first defined●Gaussian signal peaks added at intervals●Random noise is added.

16

Result

Loss of amplitude by using PCA
Pros: Decorrelates data. Better method than median filter.
Cons: For astronomical TODs, it inherits artifacts

17

New ChunkPCA algorithm
Scan areapixels
Typical Radio telescope sky removal

Cam
eraFoVSource

Advantage of Multiarray large Field of View camera

A new algorithm is created to take advantage of the large FoV of theMKID camera.
Uses off-source pixels only for baseline calculation using PCA.

18

Masking on-source signal1). source detection using sigma clipping

An astropy method
called ‘sigma_clip’ isused to do the sigmaclipping.

Scan area2). Predefining a mask wher the source isexpected.

Masked area

Source19

Chunk Matrix●●

Divide the data intochunks.

Create the chunk matrix
ChunksIf it has mask, chunkmatrix value = 1.
If no mask, then 0.

20

ChunkPCA Algorithm
Input: masked 2D dataset
Extract the mask andDivide into chunks

noAnalyze elements inthe chunk using PCA

If the elementin the chunk has
any maskyes

Use the PCA baseline
To subtract from the raw
data
repeatedover allchunks

Get the baseline
Subtract the baselinefrom the raw data
Clean data

CombineClean data21

ChunkPCA Result

No loss of amplitude by using ChunkPCA
22

PCA - ChunkPCA Compare
Preservation of signal amplitude
Amplitude by ChunkPCA

Amplitude

Amplitude by PCA

Sample data points

Baseline obtained by ChunkPCA compared with Conventional PCA.
The residue(shown in red) shows the improvement in baseline.

23

PCA - ChunkPCA Compare
The algorithms are implemented on a real observation data of Mars observation.
The same TOD has been used for both the PCA, and Chunk-PCA analysis.
Subtracting the two, the residue shows the rippling artifact caused by the signals has been
removedThe “rippling” artifacts in the baseline are successfully removed.
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Amplitude

RMS with amplitudeSample data points

Amplitude of signal is 1 as shown here

The RMS of the subtracted data iscalculated.
As expected, higher the amplitude,higher the ripples in the baseline forPCA.
ChunkPCA shows lower RMS

RMS value

This amplitude in the sample data isincreased step by step upto 10.

Amplitude25

Intensity calculation• To calculate the temperature of observed region we calibrate
the data with
• Load measurement (f_load / f_ref)• On source measurement (f_on)• Off source measurement/empty sky measurement(f_off)Frequency shift (MHz)As a special case for doing Radio
Astronomy with MKID data, weneed to calculate Ta* from thefrequency shift data.

sample26

Chopper wheel temperature calculation27

Making Beam maps
I have developed A gridding system to work with the Nobeyama antenna
pointing log files and MKID readout files for generating several kinds ofmaps.
It’s based on an algorithm called Cygrid.

A single element Beammap

28

Beam shape and position
Identifying the low noise readouts.
●
Syncing signal with antenna-log
during observation.
●
Define the Beamwidth(bw),kernelsize(~bw/2-3), andpixelsize(bw/4-5)●
Offsets are noted from these beampositions.
●Beam position

Manufactured positions ofMKID Array29Beam size
Beam size from individual element beammap

Beam position from individual element beammap

The Beam size averaged, is very closed to the expected value.

30

Antenna temperature (Mars)
Antenna temperature calculated plotted using thepipeline commands

31

Chopper wheel temperature calculation
Main beam efficiency

Main beam temperature

Aperture efficiency

32

Efficiencies (Mars)
Efficiencies calculated plotted using the pipelinecommands

33

Efficiencies (Mars)
Efficiencies compared to reported data.

34

Combined Beammaps
The offset of each Beam from the central pixel is corrected toget a Combined Beammap

Combined BeamMap in dB of the Mars
observation

Combined BeamMap of the Mars
observation

35

Reduction in RMS by combininng

As more maps are combined, the RMS value followed an expected decline.
36Skymap●●●The antenna log is used to
extract the RA-Deccoordinates.
Cygrid can handle World
Coordinate System very
well, hence gridding
becomes very easy.
The coordinates
calculated matches theoriginal position of Marsin the sky duringobservation.

Skymap in RA-Dec coordinates.
Colorbar is Main-beam temperature (K)37Conclusion●●●

With the wide FoV, larger areas can be scanned
relatively quicker with less noise.
Baseline removal can utilize larger FoV fordecorrelation.
A new pipeline developed for data reduction ofMKID 45m 100GHz camera.

Thank youextraInterpolation of coordinates
Interpolation of coordinates:
Difference in sampling rate of the Antenna pointing log file andintensity log files.
●
Interpolated using linear interpolation to get the values to match innumber of data points.
●41Data compilation and Gridding
Objectives:●●●●

Intensity – antenna pointing coordinate relation.
Intensity integration management.
To manage different projections properly
Survey observation data management.

HEALPix gridding:Pros and cons of using HEALPIX
Pros:
One step solution for positioning andintensity averaging of multiple observations,
including rotation of the camera
Merging different observations is convenientCons:
Resolution goes up in discreet steps, it could
create problems in finding the optimum
resolution in which the data is processed.

Credit: Mark R. Calabretta et al.

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