Talks

During the Epoch of Reionisation (EoR), the ultraviolet radiation from the first stars and galaxies ionised the neutral hydrogen of the intergalactic medium, which itself can emit radiation through the 21 cm hyperfine transition. Due to this, the 21 cm signal is a direct probe of the first stars in the early Universe and a key science goal for the future Square Kilometre Array (SKA). However, observing and interpreting this signal is a notoriously difficult task.

Another high-potential probe is the patchy kinetic Sunyaev-Zel'dovich effect (pkSZ). Induced by the scattering of Cosmic Microwave Background (CMB) photons with a medium of free electrons produced during the EoR, the effect altered the small-scale CMB temperature anisotropies, imprinting information on the growth of ionising bubbles from the first galaxies. While measurements of the pkSZ angular power spectrum by Reichardt et al. (2021) have reported a 3σ constraint of D^pkSZ (l=3000) = 3.0 ± 1.0 μK2, the results are also subject to modelling uncertainties.

In this talk, we propose a simple yet effective parametric model that establishes a formal connection between the 21 cm and pkSZ power spectra. Using this model to jointly fit mock 21 cm and pkSZ data points, we confirm that these two observables exhibit complementary characteristics, leading to significantly improved constraints on reionisation compared to analysing each data set separately. Our findings demonstrate that a few well-informed low-redshift (eg., z < 8) measurements of the 21 cm power spectrum at k ≈ 0.1 cMpc^-1 and pkSZ power spectra can precisely determine the reionisation history of the Universe.

Therefore, even in the early stages of observations with the SKA, we can begin to constrain cosmic reionisation by performing a combined analysis of the 21 cm power spectrum with the pkSZ observations.

The standard way to do this is to use the chain rule to backpropagate gradients through layers of neurons. I shall briefly review a few of the engineering successes of backpropagation and then describe a very different way of getting the gradients that, for a while, seemed a lot more plausible as a model of how the brain gets gradients. Consider a system composed of binary neurons that can be active or inactive with weighted pairwise couplings between pairs of neurons, including long range couplings. If the neurons represent pixels in a binary image, we can store a set of binary training images by adjusting the coupling weights so that the images are local minima of a Hopfield energy function which is minus the sum over all pairs of active neurons of their coupling weights. But this energy function can only capture pairwise correlations. It cannot represent the kinds of complicated higher-order correlations that occur in images. Now suppose that in addition to the "visible" neurons that represent the pixel intensities, we also have a large set of hidden neurons that have weighted couplings with each other and with the visible neurons. Suppose also that all of the neurons are asynchronous and stochastic: They adopt the active state with a log odds that is equal to the difference in the energy function when the neuron is inactive versus active. Given a set of training images, is there a simple way to set the weights on all of the couplings so that the training images are local minima of the free energy function obtained by integrating out the states of the hidden neurons? The Boltzmann machine learning algorithm solved this problem in an elegant way. It was proof of principle that learning in neural networks with hidden neurons was possible using only locally available information, contrary to what was generally believed at the time.

The observations of the redshifted 21-cm signal contain a wealth of cosmological and astrophysical information. The study of this signal from the high redshift Universe provides a unique opportunity to learn about the properties of the first stars and galaxies. However, the problem is particularly challenging due to the presence of foregrounds and system noise. In this talk, I will talk about different statistical estimators to measure the cosmological 21-cm signal from radio interferometric observations. Also, I will present our recent results towards detecting this faint 21-cm signal using the uGMRT and the MWA low-frequency observations.

The Canadian Hydrogen Intensity Mapping Experiment (CHIME) is a drift-scan radio interferometer located at the Dominion Radio Astrophysical Observatory (DRAO) in Penticton, British Columbia, Canada. CHIME, operating between 400 and 800 MHz, will map the redshifted 21 cm emission of neutral hydrogen between redshifts z = 0.8 − 2.5 across the northern sky. The 21cm line is a tracer of the large-scale structure of matter, whose statistics encode a well-understood standard ruler, the baryon acoustic oscillation scale. By detecting and tracking the evolution of this scale with redshift, CHIME aims to constrain the expansion history of the Universe over this crucial redshift epoch when the overall energy density of the Universe is expected to have become dominated by dark energy.

However, measuring this cosmological signal is challenging due to bright astrophysical foregrounds, which are about 4-5 orders of magnitude brighter than the cosmological HI signal.

In principle, these two signals can be separated due to their different spectral features, in which, the foreground signal is smooth in frequency, whereas the cosmological signal has spectral structure. However, this separation requires an instrument calibration at the sub-percent level accuracy. Recently, the CHIME collaboration reported the detection of cosmological 21 cm emission from a large-scale structure between redshift 0.78 and 1.43 in cross-correlation with eBOSS galaxy and quasar catalogs. In this cross-correlation measurement, a high-pass filter is applied to the frequency axis of each map pixel to remove foregrounds, which results in a measurement of the cosmological signal at the non-linear scales. I will discuss recent results in measuring the 21 cm signal in auto-correlation and some of the challenges that we have encountered. I will describe the improvements in the data processing and various data quality cuts required to measure the signal in auto-correlation. I will show the first results of the power spectrum using CHIME data.

The study of the Universe's large-scale structure and evolution has entered a new era with the emergence of neutral hydrogen intensity mapping as a powerful observational technique. HI intensity mapping (IM) offers a unique and innovative approach to cosmological research by probing the distribution of neutral hydrogen on vast scales. This technique uses radio telescopes like MeerKAT to detect the cumulative 21cm emission from neutral hydrogen. In this talk, I will introduce our current HI IM experiment with MeerKAT in the post-reionization universe and discuss our recent breakthroughs in the field. Additionally, I will present the on-the-fly (OTF) interferometric imaging capabilities with MeerKAT. Our aim is to explore cosmology through single-dish HI intensity mapping while simultaneously generating continuum images via the interferometer. This technique allows for commensal observing for both intensity mapping and interferometric imaging. We plan to survey an extensive 10,000 square degrees in the UHF band, achieving a sensitivity of 25 uJy/beam rms and a resolution of 13’’. Our partial observations have achieved an image sensitivity of approximately 140 uJy, with a goal of reaching 30 uJy using existing data. These advanced techniques are propelling the joint studies of cosmology and radio astronomy to new heights, and with the emergence of the Square Kilometre Array (SKA), the future of this field looks exceptionally promising.

The Square Kilometre Array (SKA) promises groundbreaking advances in key scientific areas, including ultra-sensitive continuum imaging and probing the cosmos through redshifted spectral lines from the epochs of cosmic dawn and reionization. However, achieving these ambitious goals requires overcoming significant challenges, particularly at low frequencies. These include ionospheric distortions, wide fields of view, complex antenna layouts, and the massive data volumes generated—all of which make calibration and imaging exceptionally difficult.

While precursor instruments have demonstrated steady progress and highlighted SKA’s immense scientific potential, the complexity of the SKA's mission demands innovative, independent approaches to deliver robust results. This unique landscape presents unparalleled opportunities for developing novel methods to address calibration and imaging challenges at scale.

In this talk, I will showcase promising advancements in tools designed for calibration and imaging. These methods offer a glimpse into how we can tackle SKA's challenges and harness its full potential, paving the way for transformative discoveries in astronomy.