Cosmic-ray observations provide a powerful probe of dark-matter annihilation in the Galaxy. In this talk I present recent analyses of the AMS-02 antiproton data, reducing cosmic-ray propagation uncertainties by fitting at the same time dark-matter and cosmic-ray propagation parameters. The resulting bounds are among the strongest for heavy dark…

I will describe two separate methods to statistically infer the properties of dark matter substructure using, in turn, (astrometric) weak and strong lensing observations. In the first part of the talk, I will describe how the motion of dark matter subhalos in the Milky Way induces a correlated pattern of motions in…

As the age of WIMP-scale dark matter (DM) draws to a close thanks to the ever-increasing sensitivity of direct detection experiments, the majority of sub-GeV DM parameter space remains to be explored. Sub-GeV DM can excite electronic transitions in a variety of molecular and nano-scale systems which have sub-eV…

In anticipation of the next generation of gravitational wave experiments, I will discuss the opportunities for phenomenological studies of particle physics in the Early Universe. The focus of the talk will be on first order phase transitions and how they can help us address open problems such as the generation of the matter/anti-matter…

The advent of the Gaia era has led to potentially revolutionary understanding of dark matter dynamics in our galaxy, which has important consequences for direct detection experiments. In this talk, I will discuss the effects of various dark matter substructures inferred from the Gaia data on possible direct…

Recently there has been a growing interest in the application of quantum computing and machine learning in many scientific disciplines, including high energy physics. In the first half of this talk, we will look at a novel quantum computing based technique to search for unmodeled deviations from a simulated…

For the last 5 years or so, there has been a major effort towards the calculation of two-loop 5-point QCD amplitudes and their corresponding LHC cross-sections at NNLO. Very recently, the first such calculation - 3-photon production - was completed [arXiv:1911.00479]. I will explain the novel features, and lessons…

Treating the Standard Model as an Effective Field Theory (EFT) yields a general framework for exploring deviations in observables that probe the indirect effects of new particles. Two treatments are typically discussed --- Higgs EFT (HEFT) and Standard Model EFT (SMEFT) --- my goal in this talk is to compare and…

The nature of dark matter (DM) in the Universe remains one of the great open questions of particle astrophysics and cosmology today. The WIMP (weakly interacting massive particle) DM paradigm has fallen, leaving us with a wide range of possible DM models and signatures. New methods and ideas are required to efficiently progress. I will discuss…

I will discuss experimental prospects for directly detecting ultra-low-mass dark matter, including the interesting “Fuzzy” dark matter scenario. I will describe experiments which are ongoing here at Princeton, and the challenges associated with them. I will also present the results we have thus far, and the…

Fuzzy Dark Matter (FDM) is a model of dark matter consisting of an ultralight scalar whose quantum mechanical nature is manifest at kiloparsec scales. As such, an object moving through an FDM halo will experience a different drag force due to dynamical friction than an object passing through a classical dark matter…

Compositeness is an elegant way to address the hierarchy problem. In this talk, under broad assumption of partial compositeness and Higgs doublet as the pseudo-Nambu-Goldstone bosons, I will discuss about phenomenology of the spin-1 resonances and the top partners in CHMs and the relevance of their strong…

In its high-luminosity phase, the CERN Large Hadron Collider will produce x10 more data, while the computing power for data processing will not scale accordingly. Particle physicists need novel solutions to accomplish the scientific mission of the LHC. Deep Learning has the potential to be the game changer that could solve the problem. In this…

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