Search

We present the first eight months of data from our secondary target program within the ongoing Dark Energy Spectroscopic Instrument (DESI) survey. Our program uses a mid-infrared and optical colour selection to preferentially target dust-reddened QSOs that would have otherwise been missed by the nominal DESI QSO selection. So far we have obtained optical spectra for 3038 candidates, of which ~70% of the high-quality objects (those with robust redshifts) are visually confirmed to be Type 1 QSOs, consistent with the expected fraction from the main DESI QSO survey. By fitting a dust-reddened blue QSO composite to the QSO spectra, we find they are well-fitted by a normal QSO with up to Av~4 mag of line-of-sight dust extinction. Utilizing radio data from the LOFAR Two-metre Sky Survey (LoTSS) DR2, we identify a striking positive relationship between the amount of line-of-sight dust extinction towards a QSO and the radio detection fraction, that is not driven by radio-loud systems, redshift and/or luminosity effects. This demonstrates an intrinsic connection between dust reddening and the production of radio emission in QSOs, whereby the radio emission is most likely due to low-powered jets or winds/outflows causing shocks in a dusty environment. On the basis of this evidence we suggest that red QSOs may represent a transitional "blow-out" phase in the evolution of QSOs, where winds and outflows evacuate the dust and gas to reveal an unobscured blue QSO., Comment: 21 pages, 17 figures, 6 tables, accepted by MNRAS

We study the task of performing hierarchical queries based on summary reports from the {\em Attribution Reporting API} for ad conversion measurement. We demonstrate that methods from optimization and differential privacy can help cope with the noise introduced by privacy guardrails in the API. In particular, we present algorithms for (i) denoising the API outputs and ensuring consistency across different levels of the tree, and (ii) optimizing the privacy budget across different levels of the tree. We provide an experimental evaluation of the proposed algorithms on public datasets., Comment: Appeared at AdKDD 2023 workshop

This work presents a novel methodology for analysis and control of nonlinear fluid systems using neural networks. The approach is demonstrated on four different study cases being the Lorenz system, a modified version of the Kuramoto-Sivashinsky equation, a streamwise-periodic 2D channel flow, and a confined cylinder flow. Neural networks are trained as models to capture the complex system dynamics and estimate equilibrium points through a Newton method, enabled by backpropagation. These neural network surrogate models (NNSMs) are leveraged to train a second neural network, which is designed to act as a stabilizing closed-loop controller. The training process employs a recurrent approach, whereby the NNSM and the neural network controller (NNC) are chained in closed loop along a finite time horizon. By cycling through phases of combined random open-loop actuation and closed-loop control, an iterative training process is introduced to overcome the lack of data near equilibrium points. This approach improves the accuracy of the models in the most critical region for achieving stabilization. Through the use of L1 regularization within loss functions, the NNSMs can also guide optimal sensor placement, reducing the number of sensors from an initial candidate set. The datasets produced during the iterative training process are also leveraged for conducting a linear stability analysis through a modified dynamic mode decomposition approach. The results demonstrate the effectiveness of computationally inexpensive neural networks in modeling, controlling, and enabling stability analysis of nonlinear systems, providing insights into the system behaviour and offering potential for stabilization of complex fluid systems., Comment: 30 pages, 22 figures, under consideration for publication

We present the first measurements of Lyman-$\alpha$ (Ly$\alpha$) forest correlations using early data from the Dark Energy Spectroscopic Instrument (DESI). We measure the auto-correlation of Ly$\alpha$ absorption using 88,509 quasars at $z>2$, and its cross-correlation with quasars using a further 147,899 tracer quasars at $z\gtrsim1.77$. Then, we fit these correlations using a 13-parameter model based on linear perturbation theory and find that it provides a good description of the data across a broad range of scales. We detect the BAO peak with a signal-to-noise ratio of $3.8\sigma$, and show that our measurements of the auto- and cross-correlations are fully-consistent with previous measurements by the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). Even though we only use here a small fraction of the final DESI dataset, our uncertainties are only a factor of 1.7 larger than those from the final eBOSS measurement. We validate the existing analysis methods of Ly$\alpha$ correlations in preparation for making a robust measurement of the BAO scale with the first year of DESI data.

Fault-tolerant complexes describe surface-code fault-tolerant protocols from a single geometric object. We first introduce fusion complexes that define a general family of fusion-based quantum computing (FBQC) fault-tolerant quantum protocols based on surface codes. We show that any 3-dimensional cell complex where each edge has four incident faces gives a valid fusion complex. This construction enables an automated search for fault tolerance schemes, allowing us to identify 627 examples within a moderate search time. We implement this using the open-source software tool Gavrog and present threshold results for a variety of schemes, finding fusion networks with higher erasure and Pauli thresholds than those existing in the literature. We then define more general structures we call fault-tolerant complexes that provide a homological description of fault tolerance from a large family of low-level error models, which include circuit-based computation, floquet-based computation, and FBQC with multi-qubit measurements. This extends the applicability of homological descriptions of fault tolerance, and enables the generation of many new schemes which have not been previously identified. We also define families of fault-tolerant complexes for color codes and 3d single-shot subsystem codes, which enables similar constructive methods, and we present several new examples of each.

The prediction of the wind wave spectrum of the ocean using numerical models are an important tool for researchers, engineers, and communities living in coastal areas. The governing equation of the wind wave models, the Wave Action Balance Equation, presents unique challenges for implementing reliable numerical models because it is highly advective, highly nonlinear and high dimensional. Historically, most operational models have utilized finite difference methods, others have used finite volume methods but relatively few attempts at using finite element methods. In this work, we seek to fill this gap by investigating several different finite element discretizations of the Wave Action Balance Equation. The methods, which include streamline upwind Petrov-Galerkin (SUPG), least squares, and discontinuous Galerkin, are implemented and convergence properties are examined for some simplified 2-D test cases. Then, a new spectral wind wave model, WAVEx, is formulated and implemented for the full problem setting. WAVEx uses continuous finite elements along with SUPG stabilization in geographic/spectral space that allows for fully unstructured triangular meshes in both geographic and spectral space. For propagation in time, a second order fully implicit finite difference method is used. When source terms are active, a second order operator splitting scheme is used to linearize the problem. In the splitting scheme, propagation is solved using the implicit method and the nonlinear source terms are treated explicitly. Several test cases, including analytic tests and laboratory experiments, are demonstrated and results are compared to analytic solutions, observations, as well as output from another model that is used operationally.

Flood inundation forecast provides critical information for emergency planning before and during flood events. Real time flood inundation forecast tools are still lacking. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. Here we present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction. We used the Fourier neural operator (FNO), a highly efficient ML method, for surrogate modeling. The FNO model is demonstrated over an urban area in Houston (Texas, U.S.) by training using simulated water depths (in 15-min intervals) from six historical storm events and then tested over two holdout events. Results show FNO outperforms the baseline U-Net model. It maintains high predictability at all lead times tested (up to 3 hrs) and performs well when applying to new sites, suggesting strong generalization skill., Comment: Artificial Intelligence (AI) and Humanitarian Assistance and Disaster Recovery (HADR) workshop, ICCV 2023 in Paris, France

Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a Halo Occupation Distribution (HOD) model upon the \textsc{FastPM} simulations, such that the resulting galaxy clustering reproduces high resolution $N$-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements -- monopole and quadrupole -- to a precision required by the DESI Year 1 Emission Line Galaxy sample down to non-linear scales, i.e. $k<0.5\,h\mathrm{Mpc}$ or $s>10\,\mathrm{Mpc}/h$. Furthermore, we compute covariance matrices based on the resulting \textsc{FastPM} galaxy clustering -- monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate [e.g. power spectrum] on the covariance matrix of the Fourier counterpart [e.g. correlation function]. We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between two to ten per cent Consequently, using the current pipeline we can precisely reproduce the clustering of $N$-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies., Comment: 21 pages, 18 figures, submitted to MNRAS, Zenodo link https://doi.org/10.5281/zenodo.8185822

Recent tropical cyclones, e.g., Hurricane Harvey (2017), have lead to significant rainfall and resulting runoff with accompanying flooding. When the runoff interacts with storm surge, the resulting floods can be greatly amplified and lead to effects that cannot be modeled by simple superposition of its distinctive sources. In an effort to develop accurate numerical simulations of runoff, surge, and compounding floods, we develop a local discontinuous Galerkin method for modified shallow water equations. In this modification, nonzero sources to the continuity equation are included to incorporate rainfall into the model using parametric rainfall models from literature as well as hindcast data. The discontinuous Galerkin spatial discretization is accompanied with a strong stability preserving explicit Runge Kutta time integrator. Hence, temporal stability is ensured through the CFL condition and we exploit the embarrassingly parallel nature of the developed method using MPI parallelization. We demonstrate the capabilities of the developed method though a sequence of physically relevant numerical tests, including small scale test cases based on laboratory measurements and large scale experiments with Hurricane Harvey in the Gulf of Mexico. The results highlight the conservation properties and robustness of the developed method and show the potential of compound flood modeling using our approach.

A search for time-directional coincidences of ultra-high-energy (UHE) photons above 10 EeV with gravitational wave (GW) events from the LIGO/Virgo runs O1 to O3 is conducted with the Pierre Auger Observatory. Due to the distinctive properties of photon interactions and to the background expected from hadronic showers, a subset of the most interesting GW events is selected based on their localization quality and distance. Time periods of 1000 s around and 1 day after the GW events are analyzed. No coincidences are observed. Upper limits on the UHE photon fluence from a GW event are derived that are typically at $\sim$7 MeV cm$^{-2}$ (time period 1000~s) and $\sim$35 MeV cm$^{-2}$ (time period 1 day). Due to the proximity of the binary neutron star merger GW170817, the energy of the source transferred into UHE photons above 40 EeV is constrained to be less than 20% of its total gravitational wave energy. These are the first limits on UHE photons from GW sources., Comment: 15 pages

In this paper, we investigate the conditions for the HI-to-H$_{2}$ transition in the solar neighborhood by analyzing HI emission and absorption measurements toward 58 Galactic lines of sight (LOSs) along with $^{12}$CO(1$-$0) (CO) and dust data. Based on the accurate column densities of the cold and warm neutral medium (CNM and WNM), we first perform a decomposition of gas into atomic and molecular phases and show that the observed LOSs are mostly HI-dominated. In addition, we find that the CO-dark H$_{2}$, not the optically thick HI, is a major ingredient of the dark gas in the solar neighborhood. To examine the conditions for the formation of CO-bright molecular gas, we analyze the kinematic association between HI and CO and find that the CNM is kinematically more closely associated with CO than the WNM. When CNM components within CO line widths are isolated, we find the following characteristics: spin temperature $<$ 200 K, peak optical depth $>$ 0.1, CNM fraction of $\sim$0.6, and $V$-band dust extinction $>$ 0.5 mag. These results suggest that CO-bright molecular gas preferentially forms in environments with high column densities where the CNM becomes colder and more abundant. Finally, we confront the observed CNM properties with the steady-state H$_{2}$ formation model of Sternberg et al. and infer that the CNM must be clumpy with a small volume filling factor. Another possibility would be that missing processes in the model, such as cosmic-rays and gas dynamics, play an important role in the HI-to-H$_{2}$ transition., Comment: 25 pages, 12 figures, 6 tables, Accepted for publication in ApJ

Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnostic machine learning techniques. In this work, we introduce GOKU-UI, an evolution of the SciML generative model GOKU-nets. GOKU-UI not only broadens the original model's spectrum to incorporate other classes of differential equations, such as Stochastic Differential Equations (SDEs), but also integrates attention mechanisms and a novel multiple shooting training strategy in the latent space. These modifications have led to a significant increase in its performance in both reconstruction and forecast tasks, as demonstrated by our evaluation of simulated and empirical data. Specifically, GOKU-UI outperformed all baseline models on synthetic datasets even with a training set 16-fold smaller, underscoring its remarkable data efficiency. Furthermore, when applied to empirical human brain data, while incorporating stochastic Stuart-Landau oscillators into its dynamical core, our proposed enhancements markedly increased the model's effectiveness in capturing complex brain dynamics. This augmented version not only surpassed all baseline methods in the reconstruction task, but also demonstrated lower prediction error of future brain activity up to 15 seconds ahead. By training GOKU-UI on resting state fMRI data, we encoded whole-brain dynamics into a latent representation, learning a low-dimensional dynamical system model that could offer insights into brain functionality and open avenues for practical applications such as the classification of mental states or psychiatric conditions. Ultimately, our research provides further impetus for the field of Scientific Machine Learning, showcasing the potential for advancements when established scientific insights are interwoven with modern machine learning.

A variety of living and non-living systems exhibit collective motion. From swarm robotics to bacterial swarms, and tissue wound healing to human crowds, examples of collective motion are highly diverse but all of them share the common necessary ingredient of moving and interacting agents. While collective motion has been extensively studied in non-proliferating systems, how the proliferation of constituent agents affects their collective behavior is not well understood. Here, we focus on growing active agents as a model for cells and study how the interplay between noise in their direction of movement and proliferation determines the overall spatial pattern of collective motion. In this agent-based model, motile cells possess the ability to adhere to each other through cell-cell adhesion, grow in size and divide. Cell-cell interactions influence not only the direction of cell movement but also cell growth through a force-dependent mechanical feedback process. We show that noise in the direction of a cell's motion has striking effects on the emergent spatial distribution of cell collectives and proliferation. While higher noise strength leads to a random spatial distribution of cells, we also observe increased cell proliferation. On the other hand, low noise strength leads to a ring-like spatial distribution of cell collectives together with lower proliferation. Our findings provide insight into how noise in the direction of cell motion determines the local spatial organization of cells with consequent mechanical feedback on cell division impacting cell proliferation due to the formation of cell clusters.

We report on the spectroscopic confirmation of 68 new bright ($G=13.5-17.2$ mag) and blue (pre-)white dwarfs (WDs). This finding has allowed us to almost double the number of the hottest ($T_{\mathrm{eff}} \geq 60$kK) known WDs brighter than $G=16$ mag. We increased the number of known ultra-high excitation (UHE) WDs by 20%, found one unambiguous close binary system consisting of one DA WD with an irradiated low-mass companion, one DAO, and one DOA WD that are likely in their transformation phase of becoming pure DA WDs, one rare, naked O(H) star, two DA and two DAO WDs with $T_{\mathrm{eff}}$ possibly in excess of 100kK, three new DOZ WDs, and three of our targets are central stars of (possible) planetary nebulae. Using non-local thermodynamic equilibrium models, we derived the atmospheric parameters of these stars and by fitting their spectral energy distribution we derived their radii, luminosities, and gravity masses. In addition, we derived their masses in the Kiel and Hertzsprung-Russell diagram (HRD). We find that Kiel, HRD, and gravity mass agree only in half of the cases. This is not unexpected and we attribute this to the neglect of metal opacities, possibly stratified atmospheres, as well as possible uncertainties of the parallax zero point determination. Furthermore, we carried out a search for photometric variability in our targets using archival data, finding that 26% of our targets are variable. This includes 15 new variable stars, with only one of them being clearly an irradiation effect system. Strikingly, the majority of the variable stars exhibit non-sinusoidal light-curve shapes, which are unlikely explained in terms of close binary systems. We propose that a significant fraction of all (not just UHE) WDs develop spots when entering the WD cooling phase. We suggest that this could be related to the on-set of weak magnetic fields and possibly diffusion., Comment: Accepted for publication in A&A

We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter $\fnl$. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range $0.2< z < 1.35$. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulations with and without $\fnl$ and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $\fnl = 34^{+24(+50)}_{-44(-73)}$ at 68\%(95\%) confidence. We apply a series of robustness tests (e.g., cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power-spectrum and degrades the $\fnl$ constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid over-correction, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of $\fnl$ with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the $\fnl$ uncertainty., Comment: 21 pages, 17 figures, 7 tables (Appendix excluded). Published in MNRAS

Although there are estimated to be 100 million isolated black holes (BHs) in the Milky Way, only one has been found so far, resulting in significant uncertainty about their properties. The Galactic Bulge Time Domain Survey provides the only opportunity in the coming decades to grow this catalog by order(s) of magnitude. This can be achieved if 1) Roman's astrometric potential is fully realized in the observation strategy and software pipelines, 2) Roman's observational gaps of the Bulge are minimized, and 3) observations with ground-based facilities are taken of the Bulge to fill in gaps during non-Bulge seasons. A large sample of isolated BHs will enable a broad range of astrophysical questions to be answered, such as massive stellar evolution, origin of gravitational wave sources, supernova physics, and the growth of supermassive BHs, maximizing Roman's scientific return., Comment: 20 pages. Submitted in response to Nancy Grace Roman Space Telescope white paper call: https://roman.gsfc.nasa.gov/science/ccs_white_papers.html. v2 fixes a typo in Figure 5 axis label (days --> year)

Effective light extraction from optically active solid-state spin centres inside high-index semiconductor host crystals is an important factor in integrating these pseudo-atomic centres in wider quantum systems. Here we report increased fluorescent light collection efficiency from laser-written nitrogen vacancy centers (NV) in bulk diamond facilitated by micro-transfer printed GaN solid immersion lenses. Both laser-writing of NV centres and transfer printing of micro-lens structures are compatible with high spatial resolution, enabling deterministic fabrication routes towards future scalable systems development. The micro-lenses are integrated in a non-invasive manner, as they are added on top of the unstructured diamond surface and bond by Van-der-Waals forces. For emitters at 5 micrometer depth, we find approximately 2x improvement of fluorescent light collection using an air objective with a numerical aperture of NA = 0.95 in good agreement with simulations. Similarly, the solid immersion lenses strongly enhance light collection when using an objective with NA = 0.5, significantly improving the signal-to-noise ratio of the NV center emission while maintaining the NV's quantum properties after integration., Comment: 21 pages, 13 figures

For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. Recent advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, existing approaches either translate the natural language directly into robot trajectories or factor the inference process by decomposing language into task sub-goals and relying on a motion planner to execute each sub-goal. When complex environmental and temporal constraints are involved, inference over planning tasks must be performed jointly with motion plans using traditional task-and-motion planning (TAMP) algorithms, making factorization into subgoals untenable. Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan. To improve translation, we automatically detect and correct both syntactic and semantic errors via autoregressive re-prompting, resulting in significant improvements in task completion. We show that our approach outperforms several methods using LLMs as planners in complex task domains. See our project website https://yongchao98.github.io/MIT-REALM-AutoTAMP/ for prompts, videos, and code., Comment: 8 pages, 4 figures

We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of Luminous Red Galaxies (LRG) data collected during the initial two months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). We run the pipeline on multiple effective Zel'dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and compare the results with the mock sample covariance to assess the accuracy and its fluctuations. We propose an extension of the previously developed formalism for catalogs processed with standard reconstruction algorithms. We consider methods for comparing covariance matrices in detail, highlighting their interpretation and statistical properties caused by sample variance, in particular, nontrivial expectation values of certain metrics even when the external covariance estimate is perfect. With improved mocks and validation techniques, we confirm a good agreement between our predictions and sample covariance. This allows one to generate covariance matrices for comparable datasets without the need to create numerous mock galaxy catalogs with matching clustering, only requiring 2PCF measurements from the data itself. The code used in this paper is publicly available at https://github.com/oliverphilcox/RascalC., Comment: 19 pages, 1 figure. Published in MNRAS. Code available at https://github.com/oliverphilcox/RascalC, table and figure data available at https://dx.doi.org/10.5281/zenodo.7750637

The One-Percent survey of the Dark Energy Spectroscopic Instrument collected ~ 270k emission line galaxies (ELGs) at 0.8 < z < 1.6. The high completeness of the sample allowed the clustering to be measured down to scales never probed before, 0.04 Mpc/h in rp for the projected 2-point correlation function (2PCF) and 0.17 Mpc/h in galaxy pair separation s for the 2PCF monopole and quadrupole. The most striking feature of the measurements is a strong signal at the smallest scales, below 0.2 Mpc/h in rp and 1 Mpc/h in s. We analyze these data in the halo occupation distribution framework. We consider different distributions for central galaxies, a standard power law for satellites with no condition on the presence of a central galaxy and explore several extensions of these models. For all considered models, the mean halo mass of the sample is found to be log10 ~ 11.9. We obtain a satellite mean occupation function which agrees with physically motivated ELG models only if we introduce central-satellite conformity, meaning that the satellite occupation is conditioned by the presence of central galaxies of the same type. To achieve in addition a good modeling of the clustering between 0.1 and 1 Mpc/h in rp, we allow for ELG positioning outside of the halo virial radius and find 0.5% of ELGs residing in the outskirts of halos. Furthermore, the satellite velocity dispersion inside halos is found to be ~ 30% larger than that of the halo dark matter particles. These are the main findings of our work. We investigate assembly bias as a function of halo concentration, local density or local density anisotropies and observe no significant change in our results. We split the data sample in two redshift bins and report no significant evolution with redshift. Lastly, changing the cosmology in the modeling impacts only slightly our results.

In the current Dark Energy Spectroscopic Instrument (DESI) survey, emission line galaxies (ELGs) and luminous red galaxies (LRGs) are essential for mapping the dark matter distribution at $z \sim 1$. We measure the auto and cross correlation functions of ELGs and LRGs at $0.80.1$ $\mathrm{Mpc}\,h^{-1}$. We can also reproduce the auto correlations of ELGs at $r_{\mathrm{p}}>0.3$ $\mathrm{Mpc}\,h^{-1}$ ($s>1$ $\mathrm{Mpc}\,h^{-1}$) in real (redshift) space. Although our model has only seven parameters, we show that it can be extended to higher redshifts and reproduces the observed auto correlations of ELGs in the whole range of $0.8

The one-dimensional power spectrum $P_{\mathrm{1D}}$ of the Ly$\alpha$ forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of $P_{\mathrm{1D}}$ with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of $54~600$ quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with two-dimensional image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Ly$\alpha$ emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and implications for the upcoming DESI Year 1 analysis., Comment: 23 pages, 20 figures. To be published in MNRAS