Astrophysics

Effective field theory is a computationally powerful and flexible theoretical framework, finding application in many areas of physics. In particle physics, Weinberg’s folk theorem also promises that any theory that reproduces the predictions of the Standard Model will, at low energies, look like an effective field theory. In one sense the power of this framework should be a cause for pride in the progress of physics and discovery of real structural features of the world. But given the inability to fully unite quantum theory and gravity into a consistent theoretical picture, there is also cause for pessimism: indirect tests of candidate theories of quantum gravity will ultimately reduce to something like an effective field theory, undermining efforts to find low-energy windows into new physics. One way around this pessimism is to look at where and why effective field theory breaks down in current physical theories. I will point to familiar breakdowns (the cosmological constant, inflation, the hierarchy problem), offering a take on what these breakdowns tell us about the shape of physics beyond the Standard Model. Cracks in the wall of effective field theory allow for a dim glimpse of what might lie beyond.

The astrophysical stochastic gravitational wave background (SGWB) originates from numerous faint sub-threshold  gravitational wave (GW) signals arising from the coalescing binary compact objects. This background is expected to be discovered from the current (or next-generation) network of GW detectors by cross-correlating the signal between multiple pairs of GW detectors. However, detecting this signal is challenging and the correlation is only detectable at low frequencies due to the arrival time delay between different detectors. In this work, we propose a novel technique, Spectrogram Correlated Stacking (or SpeCS), which goes beyond the usual cross-correlation (and to higher frequencies)  by exploiting the higher-order statistics in the time-frequency domain which accounts for the chirping nature of the individual events that comprise SGWB.

We show that SpeCS improves the signal-to-noise for the detection of SGWB by up to an order of magnitude, compared to standard optimal cross-correlation methods which are tuned to measure only the power spectrum of the SGWB signal. SpeCS can probe beyond the power spectrum and its application to the GW data available from the current and next-generation GW detectors would speed up the SGWB discovery. 

based on work with Ramit Dey, Luis Longo, and Suvodip Mukherjee

Zoom link:   https://pitp.zoom.us/j/97091817158?pwd=MHNkdjFQT0plVzJJY2lsOHRxdDdwdz09

The fundamental nature of dark matter (DM) so far eludes direct detection experiments, but it has left its imprint in the large-scale structure (LSS) of the Universe. I will present a search using cosmic microwave background (CMB) and galaxy surveys for ultra-light DM particle candidates called axions that are well motivated from high energy theory. In combining these datasets, I will discuss how the presence of axions can improve consistency between these probes and, in particular, help alleviate the S_8 cosmological parameter tension (the discrepancy in the amplitude of density fluctuations as inferred from CMB and galaxy data). I will then present complementary searches for ultra-light and light (sub-GeV) DM using a LSS probe called the Lyman-alpha forest. By combining complementary large- and small-scale structure probes, I will demonstrate how current and forthcoming cosmological data will systematically test the nature of DM. In order to model novel DM physics accurately and efficiently in CMB and LSS probes, I will present new machine learning approaches using neural network "emulators" to accelerate DM parameter inference from days to seconds and active learning to reduce massively the computational expense.

Zoom Link: TBD