Engineering topics

Kamp, Nicholas [Harvard U.]

Publications and source records attributed to Kamp, Nicholas [Harvard U.].

3 records

DOE OSTI2026

Measurement of the Hubble constant with high-energy neutrinos

Measuring distances in the Universe is one of the hardest problems in physics and astronomy. Almost every distance probe relies on photons, whose propagation across cosmic distances introduces extinction, absorption, scattering, and radiative-transfer effects. Neutrinos suffer none of these and propagate unattenuated through dust, intergalactic medium, and dense source environments alike. We introduce a new distance-ladder method for measuring the Hubble constant $H_0$ using high-energy astrophysical neutrinos from point sources as standardizable candles, and report its first observational realization. Using 12 X-ray-selected Seyfert galaxies for which IceCube reports significant per-source neutrino excesses in its 14-year public point-source release, we exploit the disk-corona correlation $L_ν= κ\, L_X^β$ between neutrino and X-ray luminosities to construct a neutrino distance ladder anchored by non-redshift distances to NGC 1068 (Cepheid + TRGB). We find $H_0 = 49^{+40}_{-30}\,\mathrm{km\,s^{-1}\,Mpc^{-1}}$ and $β= 0.67^{+0.16}_{-0.25}$ (68% credible intervals), with the corona slope disfavoring the calorimetric limit $β= 1$ at ${\sim}2σ$. The result is consistent with existing $H_0$ determinations from Planck and SH0ES within 1$σ$. While the uncertainty on $H_0$ is large, the measurement is free of electromagnetic propagation systematics and demonstrates the viability of neutrinos as a novel cosmographical probe.

Herrera, Gonzalo [MIT, MKI; Harvard U.] (ORCID:000↗

DOE OSTI2026

Astrophysical Neutrino Sources as Colliders

High-energy neutrinos arise from processes at large center-of-mass energies, offering a window to test physics at comparable scales or beyond those accessible in collider experiments on Earth. Here, we present a recipe for extracting two-sided bounds on the inelastic $pp$ and $pγ$ cross sections from neutrino point-source data, by independently constraining every astrophysical input (cosmic-ray luminosities and target densities) through electromagnetic observations or theoretical arguments. The cross section is then the only remaining free parameter. Applying this framework to the IceCube associations with TXS~0506+056, NGC~1068, and the Galactic Plane, to a stacked population of eleven X-ray bright Seyfert galaxies, to the ultra-high-energy KM3NeT event KM3-230213A, and to projected observations of ultra-high-energy neutrinos, we obtain constraints that span center-of-mass energies from $\sqrt{s}\sim 1$ GeV to $\sim 10^{5}$ GeV, some of which are well beyond the reach of the LHC and, for the $pγ$ channel, beyond HERA. Several of these bounds are more stringent than unitarity limits.

Argüelles, Carlos A. [Harvard U.]↗

DOE OSTI2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML) and silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

Gonski, Julia [SLAC]↗