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ABSTRACT This paper provides a comprehensive overview of how fitting of baryon acoustic oscillations (BAO) is carried out within the upcoming Dark Energy Spectroscopic Instrument’s (DESI) 2024 results using its DR1 data set, and the associated systematic error budget from theory and modelling of the BAO. We derive new results showing how non-linearities in the clustering of galaxies can cause potential biases in measurements of the isotropic ($$\alpha _{\mathrm{iso}}$$) and anisotropic ($$\alpha _{\mathrm{ap}}$$) BAO distance scales, and how these can be effectively removed with an appropriate choice of reconstruction algorithm. We then demonstrate how theory leads to a clear choice for how to model the BAO and develop, implement, and validate a new model for the remaining smooth-broad-band (i.e. without BAO) component of the galaxy clustering. Finally, we explore the impact of all remaining modelling choices on the BAO constraints from DESI using a suite of high-precision simulations, arriving at a set of best practices for DESI BAO fits, and an associated theory and modelling systematic error. Overall, our results demonstrate the remarkable robustness of the BAO to all our modelling choices and motivate a combined theory and modelling systematic error contribution to the post-reconstruction DESI BAO measurements of no more than 0.1 per cent (0.2 per cent) for its isotropic (anisotropic) distance measurements. We expect the theory and best practices laid out to here to be applicable to other BAO experiments in the era of DESI and beyond. 

In this paper, we study the impact of the presence of byzantine sensors on the reduced-rank linear least squares (LS) estimator. A sensor network with N sensors makes observations of the physical phenomenon and transmits them to a fusion center which computes the LS estimate of the parameter of interest. It is well-known that rank reduction exploits the bias-variance trade-off in the full-rank estimator by putting higher priority on highly informative content of the data. The low-rank LS estimator is constructed using this highly informative content, while the remaining data can be discarded without affecting the overall performance of the estimator. We consider the scenario where a fraction of the N sensors are subject to data falsification attack from byzantine sensors, wherein an intruder injects a higher noise power (compared to the unattacked sensors) to the measurements of the attacked sensors. Our main contribution is an analytical characterization of the impact of data falsification attack of the above type on the performance of reduced-rank LS estimator. In particular, we show how optimally prioritizing the highly informative content of the data gets affected in the presence of attacks. A surprising result is that, under sensor attacks, when the elements of the data matrix are all positive the error performance of the low rank estimator experiences a phenomenon wherein the estimate of the mean-squared error comprises negative components. A complex nonlinear programming-based recipe is known to exist that resolves this undesirable effect; however, the phenomenon is oftentimes considered very objectionable in the statistical literature. On the other hand, to our advantage this effect can serve to detect cyber attacks on sensor systems. Numerical results are presented to complement the theoretical findings of the paper. 

This paper explores the feasibility of using blockchain technology to validate that measured sensor data approximately follows a known accepted model to enhance sensor data security in electricity grid systems. This provides a more robust information infrastructure that can be secured against not only failures but also malicious attacks. Such robustness is valuable in envisioned electricity grids that are distributed at a global scale including both small and large nodes. While this may be valuable, blockchain’s security benefits come at the cost of computation of cryptographic functions and the cost of reaching distributed consensus. We report experimental results showing that, for the proposed application and assumptions, the time for these computations is small enough to not negatively impact the overall system operation. From this we conclude that it is indeed worthwhile to further study the application of blockchain technology in the electricity grid, removing the assumptions we make and integrating blockchain in a much more extensive manner. To the best of our knowledge, this is the first instance where blockchain is used to validate the measured sensor data in the electricity grid thus providing security to other system operations. 

We apply artificial noise to the fingerprint embedding authentication framework to improve information-theoretic authentication for the MISO channel. Instead of optimizing for secrecy capacity, we examine the trade-off between message rate, authentication, and key security. In this case, key security aims to limit an adversary’s ability to obtain the key using a maximum likelihood decoder. 

We apply artificial noise to the fingerprint embedding authentication framework to improve information-theoretic authentication for the MISO channel. Instead of optimizing for secrecy capacity, we examine the trade-off between message rate, authentication, and key security. In this case, key security aims to limit an adversary’s ability to obtain the key using a maximum likelihood decoder. 

Abstract We present cosmological results from the measurement of baryon acoustic oscillations (BAO) in galaxy, quasar and Lyman-αforest tracers from the first year of observations from the Dark Energy Spectroscopic Instrument (DESI), to be released in the DESI Data Release 1. DESI BAO provide robust measurements of the transverse comoving distance and Hubble rate, or their combination, relative to the sound horizon, in seven redshift bins from over 6 million extragalactic objects in the redshift range 0.1 <z< 4.2. To mitigate confirmation bias, a blind analysis was implemented to measure the BAO scales. DESI BAO data alone are consistent with the standard flat ΛCDM cosmological model with a matter density Ω_m=0.295±0.015. Paired with a baryon density prior from Big Bang Nucleosynthesis and the robustly measured acoustic angular scale from the cosmic microwave background (CMB), DESI requiresH₀=(68.52±0.62) km s^-1Mpc^-1. In conjunction with CMB anisotropies fromPlanckand CMB lensing data fromPlanckand ACT, we find Ω_m=0.307± 0.005 andH₀=(67.97±0.38) km s^-1Mpc^-1. Extending the baseline model with a constant dark energy equation of state parameterw, DESI BAO alone requirew=-0.99^+0.15_-0.13. In models with a time-varying dark energy equation of state parametrised byw₀andw_a, combinations of DESI with CMB or with type Ia supernovae (SN Ia) individually preferw₀> -1 andw_a< 0. This preference is 2.6σfor the DESI+CMB combination, and persists or grows when SN Ia are added in, giving results discrepant with the ΛCDM model at the 2.5σ, 3.5σor 3.9σlevels for the addition of the Pantheon+, Union3, or DES-SN5YR supernova datasets respectively. For the flat ΛCDM model with the sum of neutrino mass ∑m_νfree, combining the DESI and CMB data yields an upper limit ∑m_ν< 0.072 (0.113) eV at 95% confidence for a ∑m_ν> 0 (∑m_ν> 0.059) eV prior. These neutrino-mass constraints are substantially relaxed if the background dynamics are allowed to deviate from flat ΛCDM.