Research

A wordcloud of my research

Below is an incomplete list of my works...

Faraday Tomography

Cooray, S.; Takeuchi, T. T.; Ideguhi, S.; Akahori, T.; Miyashita, Y.; Takahashi, K.; "Wavelets and sparsity for Faraday tomography", arxiv.org/abs/2112.01444

Cooray, S.; Takeuchi, T. T.; Akahori, T.; Miyashita, Y.; Ideguhi, S.; Takahashi, K.; Ichiki, K.; "An Iterative Reconstruction Algorithm for Faraday Tomography", https://doi.org/10.1093/mnras/staa3580

Faraday tomography offers crucial information on the magnetized astronomical objects, such as quasars, galaxies, or galaxy clusters, by observing its magnetoionic media. The observed linear polarization spectrum is inverse Fourier transformed to obtain the Faraday dispersion function (FDF), providing us a tomographic distribution of the magnetoionic media along the line of sight. However, this transform gives a poor reconstruction of the FDF because of the instrument’s limited wavelength coverage. The current Faraday tomography techniques’ inability to reliably solve the above inverse problem has noticeably plagued cosmic magnetism studies. We proposed a new algorithm inspired by the well-studied area of signal restoration, called the Constraining and Restoring iterative Algorithm for Faraday Tomography (CRAFT). This iterative model-independent algorithm is computationally inexpensive and only requires weak physically motivated assumptions to produce high fidelity FDF reconstructions.

Since the original algorithm, we have updated CRAFT to include wavelet sparsity. Below shows the reconstruction performance against the popular technique of Rotation Measure (RM) Synthesis.

A comparison of reconstructions of a realistic galaxy FDF for observations with 3 frequency coverage cases. The three columns corresponds to 700 [MHz] - 1800 [MHz], 350 [MHz] - 1760 [MHz], and 50 [MHz] - 1760 [MHz], frequency ranges, respectively. Black solid line is the original model FDF (noiseless) and the three rows from the top correspond to the reconstruction with RM Synthesis (green dotted), CRAFT (blue dashed), and the new technique proposed in this work, CRAFT + WS (red dash dotted). In each panel, the upper part shows the amplitude and the bottom part shows the polarization angle.

Signal Reconstruction

Schematic diagram of the reconstruction algorithm.

Cooray, S.; Takeuchi, T. T.; Yoda, M.; Sorai, K, "A Method for Unmasking Incomplete Astronomical Signals: Application to CO Multi-line Imaging of Nearby Galaxies Project" (arxiv.org/abs/2004.06979)

I have presented a algorithm for reconstructing partial astronomical signals. The method is an iterative extrapolation algorithm that with some assumed smoothness of the signal can reconstruct missing regions.

The method was applied to restore calibration images in radio observations.

Set of images of CO12 and CO13 observations that were affected by artifacts.

Set of images that were restored using the algorithm

BAO Measurement with Linear Point

Found linear point for a particular correlation function realization

Measuring the Baryonic Acoustic Oscillation (BAO) in the clustering of galaxies provides a standard ruler to infer cosmological distances and also a way to constrain cosmological models. Due to this effect, we expect a "bump" in two-point correlation function in the clustering of galaxies. But, the measured BAO signature is not what we expect from linear structure growth due to the non-linear growth in smaller scales. This results in smaering the bump we measure. To counter this, we apply a method called the linear point for our analysis.

The linear point (LP) is the midpoint of the observed peak and the first dip before the peak. This was shown by Stefano Anselmi et al. 2017 (Anselmi17) as a measurement of the BAO signature that is largely insensitive to the non-linear effects. We applied the above methodology to the TAIPAN survey mocks.

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