Nikhil Sarin

Nikhil Sarin

Nordita Fellow working on various aspects of gravitational-wave multi-messenger astronomy

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Analying multiple datasets with a joint likelihood

less than 1 minute read

Published:

Suppose you have multiple datasets that share one/two or more parameters, you want to combine the data and analyse them together to make the best possible measurement for the parameters shared by the multiple different data sets. A joint likelihood is the natural way to do such an analysis. One massive benefit of such a method is that not only this allows one to do model selection, it also gives a better measurement of the parameters than multiplying the individual posteriors would. This can also help constrain the other non-common parameters too, particularly if the additional data set can break a degeneracy. In this example, I show how you can use a joint likelihood for a simple system involving two data sets; noisy observations of a linear model. I show how such data can be fit in a joint likelihood with bilby. Specifically, in this example the gradient is a joint parameter while the intercept is unique to each data set.

Using PyStan to fit a model with X and Y errors

less than 1 minute read

Published:

After writing my first post. I was recommended Stan to fit more complicated models to data with X and Y uncertainties. Stan and Hamiltonian Monte Carlo (HMC) is naturally suited to problems like this as the simplest way to treat X errors is to sample over the true x values and then marginalise. This adds a dimension for each data point which breaks most samplers, except HMC. This example is for a simple problem like in the first post, but instead using Stan to sample. Shoutout to Andy Casey for help with Stan.

Fitting a line to data with X and Y errors

less than 1 minute read

Published:

I wrote this post as a reference for myself and anyone else looking to determine how to fit a model to data with both x and y uncertainties. Commonly, in Astrophysics even when fitting data with Bayesian inference, x errors are commonly ignored and only y errors are included leading to a significant underestimation of model uncertainty. In this tutorial, I explore this problem with Bilby.

portfolio

publications

talks

ANITA workshop 2018

Published:

My first ever conference talk on using X-ray afterglows to constrain gravitational-wave searches for post-merger remnants.

ASA 2018 Talk

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Gave a talk on the X-ray afterglows of short gamma-ray bursts. I won the runner-up prize in the best-student talk competition with this talk!

ANITA workshop 2019

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Gave a talk on the X-ray afterglows of short gamma-ray bursts and the nature of binary neutron star post-merger remnants.

Yukawa, YITP 2019

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Gave a talk on my work on the population properties of long-lived neutron star post-merger remnants at the Yukawa institute for theoretical astrophysics. This talk was at the long-term YITP workshop.

teaching

Tutor

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.