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Aneesha Avasthi

Publications and source records attributed to Aneesha Avasthi.

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Gaseous forms of $^{76}$Ge, $^{82}$Se, $^{96}$Zr, $^{100}$Mo, $^{124}$Sn, and $^{130}$Te: new avenues to future $0νββ$ time projection chambers

Searches for neutrinoless double beta decay are growing larger, with tonne-scale targets in several nuclides still far from exhausting the discovery space. What's beyond ton scale? Time projection chambers (TPCs) are one option for building large (100~T or kiloton-scale) instruments, but filling them with the familiar $^{136}$Xe for a $0νββ$ search is a problem: xenon is a scarce element whose atmospheric-extraction supply chain is small and hard to grow. If future $0νββ$ searches wish to exploit TPCs' known hardware scalability, we need to fill them with non-xenon target materials. Of particular value would be a TPC that can drift electrons, rather than ions, letting us use mature readout schemes which require gas gain. In this paper, we identify a set of previously-unappreciated, affordable gases which are likely to be electropositive, allowing electron drift and gain in gas-phase TPCs sensitive to $0νββ$ with the help of track-topology background rejection. We identify candidate $^{76}$Ge, $^{82}$Se, $^{96}$Zr, $^{100}$Mo, $^{124}$Sn, and $^{130}$Te compounds suitable for gas-phase electron-drift TPCs; some may be suitable for liquid-phase TPCs as well. Using a figure-of-merit that emphasizes the need for track topology for background rejection, we argue that 100~T and kiloton-scale gas TPCs are realistic without unprecedented underground infrastructure.

physics.ins-det

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape cosmic evolution. This whitepaper presents a vision for how Artificial Intelligence (AI) can accelerate discovery in this field. We outline grand challenges that must be addressed to enable transformative breakthroughs and describe how current and planned experimental facilities can implement this vision to advance our understanding of the vast and complex physical world from the smallest to the largest scales. We show how facilities currently under construction, such as the HL-LHC, DUNE and soon EIC, can both benefit from and serve as proving grounds for this vision, while also enabling a longer-term goal for how future experiments -- like FCC-ee at CERN, IceCube-Gen2, a Muon Collider in the U.S., and smaller to mid-scale projects -- can be fully AI-native. We describe how a truly national-scale collaboration, jointly managed across large funding partners, and involving both DOE laboratories and universities, can make this happen.

hep-ex