He is a member of the LIGO Scientific Collaboration (LSC) Council and the LSC sub-committee of the Indian Initiative in Gravitational wave Observations (IndIGO). Since 2013, Bose has worked on training several IndIGO scientists in gravitational wave research, particularly, using LIGO data, and on guiding their contributions in LSC science.

**In his own words:**

Currently my main research interests are gravitational waves, relativistic astrophysics, and cosmology. My primary collaborations are with members of the gravitational wave group at IUCAA, Pune, the WSU Relativity Group and the LIGO Scientific collaboration.

### Research

A brief synopsis of the various research projects I am pursuing is given below. For a more detailed description, click here.

- Searches for gravitational wave signals from compact object binaries and a stochastic gravitational wave background, and measuring their astrophysical or cosmological parameters
- Characterization of the noise in gravitational wave detectors
- Constraining the nuclear equation of state of neutron stars
- Follow-up of gravitational-wave triggers for finding their electromagnetic counterparts
- Speeding up gravitational wave data analysis computationally

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### Publications

A list of my publications is posted here.

### Students

#### Science popularization, public outreach and other community activities

A partial list of my science popularization, public outreach other community activities is included with various other news items here.

#### A synopsis of Sukanta’s research activities

**Searches for gravitational wave signals from compact object binaries and their parameter estimation**

With Sanjeev Dhurandhar and Archana Pai, I showed how to optimally combine the data from multiple ground-based interferometers to search for gravitational-wave signals from inspiraling compact object binaries. A key result was obtaining a detection statistic that handled the scanning of four of the source parameters analytically, thereby, remarkably improving the computational feasibility of the search. This method was implemented in real data (for searches of black hole ringdown signals) in collaboration with D. Talukder, S. Caudill, and P. Baker.

With the NINJA(2) collaboration, T. Dayanga and I contributed to incorporating Numerical Relativity (NR) based waveforms of binary black holes in gravitational wave data analysis.

In a work with P. Ajith, I estimated how accurately the various astrophysical parameters of binary compact objects (black holes) can be measured with earth-based GW detectors. In another work, with S. Ghosh and Ajith, we showed the extent of systematic errors that can arise in these estimates when the templates are restricted to be post-Newtonian inspirals.

**Search for a stochastic gravitational wave background**

In a series of papers, we obtained an optimal statistic for searching an anisotropic stochastic gravitational wave background using ground-based detectors, and demonstrated in simulated strain data how it can be applied to obtain sky-maps of the background. The statistic used was based on an adaptation of the radiometry technique in gravitational wave searches. It was applied to real LIGO data in D. Talukder’s PhD dissertation work to put upper limits on the GW background. Assuming that the background is produced by spinning aspherical neutron stars in our galaxy, we were able to set limits on their average ellipticity. Another interesting result found was how the discovery of a single aspherical neutron star (e.g., in a continuous wave search) and a galactic neutron star background can help estimate the number of neutron stars in the galaxy.

In the past I also contributed to the end-to-end testing of the isotropic SGWB search pipeline with hardware injections, which was important in obtaining the upper limit on the cosmological GW background published in a Nature paper.

**Characterization of the noise in gravitational wave detectors**

In a 2016 paper, we describe a tool we improved to detect excess noise in the gravitational wave channel arising from its bilinear or nonlinear coupling with fluctuations of various components of a GW interferometer and its environment. We also describe a higher-order statistics tool we developed to characterize these couplings, e.g., by unraveling the frequencies of the fluctuations contributing to such noise, and demonstrate its utility by applying it to understand nonlinear couplings in Advanced LIGO engineering data. Once such noise is detected, it is highly desirable to remove it or correct for it. Such action in the past has been shown to improve the sensitivity of the instrument in searches of astrophysical signals. If this is not possible, then steps must be taken to mitigate its influence, e.g., by characterizing its effect on astrophysical searches. We illustrate this in another 2016 paper through a study of the effect of transient sine-Gaussian and chirping sine-Gaussian noise artifacts on a compact binary coalescence template bank.

**Constraining the nuclear equation of state of neutron stars**

Bose-Einstein condensates (BECs) have been proposed as candidate states of matter for the interior of neutron stars. Specifically, Chavanis and Harko obtained the mass-radius relation for a BEC star and proposed that the recently discovered neutron stars with masses around 2M_{⊙} are BEC stars. They employed a barotropic equation of state (EOS), with one free parameter, that was first found by Colpi, Shapiro, and Wasserman (CSW), to describe them and derive stable equilibrium configurations of spinning BEC stars in General Relativity. In a 2015 work we showed that while it is true that BECs allow for compact object masses as heavy as the heaviest observed ones, such stars cannot simultaneously have radii that are small enough to be consistent with the latest observations, in spite of the flexibility available in the EOS in the form of the free parameter. In fact, our conclusion applies to any spinning relativistic boson star that obeys the CSW EOS.

**Follow-up of gravitational-wave triggers for finding their electromagnetic counterparts**

Combining information channeled through both gravitational wave and electromagnetic observations can teach us much more about their common progentiors, which may include short hard gamma-ray bursts, than what any one type of observation can do. I am working with Varun Bhalerao, Javed Rana, Bhooshan Gadre et al. in this area of research and have submitted for publication some of our findings on “An optimal method for scheduling observations of large sky error regions for finding optical counterparts to transients,” (2016).

**Speeding up gravitational wave data analysis computationally**

In a work in 2015 with Khun Sang Phukon (IIT-Kanpur) and Vihan Pandey, we demonstrated the computational speed-up that can be achieved by computing the chi-squared discriminator in compact binary coalescence (CBC) searches on Graphics Processing Units (GPUs) rather than Central Processing Units (CPUs). One of the most demanding gravitational-wave searches in high-performance and high-throughput computing is that of CBC signals in multiple detectors. This is especially true for those CBCs that have the potential to produce an electromagnetic (EM) counterpart. Such a binary system will involve at least one neutron star. The second object can be another neutron star or a relatively small stellar-mass black hole. These systems not only have long-duration signals but can require a large parameter space to define them. Multiple detectors are needed to localize them in the sky for ease of EM follow-ups. Finally, since the EM counterparts may fade quickly, the searches need to be near-real-time so as to enhance the chances of GW-EM coincidences. These various factors require that the computing involved in analyzing the GW data to find them be quick and inexpensive. We corroborate past findings, but now with more modern hardware, that GPUs can provide significant speed-ups in the computation of the chi-squared discriminator in CBC searches, in comparison to not just single CPUs but also multiple MPI processes running on a CPU cluster.