Search

We assess the on-orbit performance of the flare event trigger for the Hinode EUV Imaging Spectrometer. Our goal is to understand the time-delay between the occurrence of a flare, as defined by a prompt rise in soft X-ray emission, and the initiation of the response observing study. Wide (266$''$) slit patrol images in the He II 256.32A spectral line are used for flare hunting, and a reponse is triggered when a pre-defined intensity threshold is reached. We use a sample of 13 $>$ M-class flares that succesfully triggered a response, and compare the timings with soft X-ray data from GOES, and hard X-ray data from RHESSI and Fermi. Excluding complex events that are difficult to interpret, the mean on orbit response time for our sample is 2 min 10 s, with an uncertainty of 84 s. These results may be useful for planning autonomous operations for future missions, and give some guidance as to how improvements could be made to capture the important impulsive phase of flares., Comment: To be published as a Brief Report in Frontiers in Astronomy and Space Sciences

We present the first result in exploring the gaseous halo and galaxy correlation using the Dark Energy Spectroscopic Instrument (DESI) survey validation data in the Cosmic Evolution Survey (COSMOS) and Hyper Suprime-Cam (HSC) field. We obtain the multiphase gaseous halo properties in the circumgalactic medium (CGM) by using 115 quasar spectra (S/N > 3). We detect MgII absorption at redshift 0.6 < z < 2.5, CIV absorption at 1.6 < z < 3.6, and HI absorption associated with the MgII and CIV. By cross-matching the COSMOS2020 catalog, we identify the MgII and CIV host galaxies in ten quasar fields at 0.9 < z < 3.1. We find that within the impact parameter of 250 kpc, a tight correlation is seen between strong MgII equivalent width and the host galaxy star formation rate. The covering fraction fc of strong MgII selected galaxies, which is the ratio of absorbing galaxy in a certain galaxy population, shows significant evolution in the main-sequence galaxies and marginal evolution in all the galaxy populations within 250 kpc at 0.9 < z < 2.2. The fc increase in the main-sequence galaxies likely suggests the co-evolution of strong MgII absorbing gas and the main-sequence galaxies at the cosmic noon. Furthermore, several MgII and CIV absorbing gas is detected out of the galaxy virial radius, tentatively indicating the feedback produced by the star formation and/or the environmental effects., Comment: 24 pages, 12 figures, 6 tables, accepted for publication in ApJ

Deep learning recommendation systems serve personalized content under diverse tail-latency targets and input-query loads. In order to do so, state-of-the-art recommendation models rely on terabyte-scale embedding tables to learn user preferences over large bodies of contents. The reliance on a fixed embedding representation of embedding tables not only imposes significant memory capacity and bandwidth requirements but also limits the scope of compatible system solutions. This paper challenges the assumption of fixed embedding representations by showing how synergies between embedding representations and hardware platforms can lead to improvements in both algorithmic- and system performance. Based on our characterization of various embedding representations, we propose a hybrid embedding representation that achieves higher quality embeddings at the cost of increased memory and compute requirements. To address the system performance challenges of the hybrid representation, we propose MP-Rec -- a co-design technique that exploits heterogeneity and dynamic selection of embedding representations and underlying hardware platforms. On real system hardware, we demonstrate how matching custom accelerators, i.e., GPUs, TPUs, and IPUs, with compatible embedding representations can lead to 16.65x performance speedup. Additionally, in query-serving scenarios, MP-Rec achieves 2.49x and 3.76x higher correct prediction throughput and 0.19% and 0.22% better model quality on a CPU-GPU system for the Kaggle and Terabyte datasets, respectively.

We report the first results of a high-redshift ($z$ >~ 5) quasar survey using the Dark Energy Spectroscopic Instrument (DESI). As a DESI secondary target program, this survey is designed to carry out a systematic search and investigation of quasars at $z$ >~ 5, up to redshift 6.8. The target selection is based on the DESI Legacy Imaging Surveys (the Legacy Surveys) DR9 photometry, combined with the Pan-STARRS1 data and $J$-band photometry from public surveys. A first quasar sample has been constructed from the DESI Survey Validation 3 (SV3) and first-year observations until May 2022. This sample includes more than 400 new quasars at redshift 4.7 <= $z$ < 6.6, down to 21.5 magnitude in the $z$ band, discovered from 35% of the entire target sample. Remarkably, there are 220 new quasars identified at $z$ >= 5, more than one third of existing quasars previously published at this redshift. The observations so far result in an average success rate of 23% at $z$ > 4.7. The current spectral dataset has already allowed analysis of interesting individual objects (e.g., quasars with damped Ly$\alpha$ absorbers and broad absorption line features), and statistical analysis will follow the survey's completion. A set of science projects will be carried out leveraging this program, including quasar luminosity function, quasar clustering, intergalactic medium, quasar spectral properties, intervening absorbers, and properties of early supermassive black holes. Additionally, a sample of 38 new quasars at $z$ ~ 3.8-5.7 discovered from a pilot survey in the DESI SV1 is also published in this paper., Comment: 28 pages, 9 figures, and 2 tables; Published in ApJS

The shear measurement from DECaLS (Dark Energy Camera Legacy Survey) provides an excellent opportunity for galaxy-galaxy lensing study with DESI (Dark Energy Spectroscopic Instrument) galaxies, given the large ($\sim 9000$ deg$^2$) sky overlap. We explore this potential by combining the DESI 1\% survey and DECaLS DR8. With $\sim 106$ deg$^2$ sky overlap, we achieve significant detection of galaxy-galaxy lensing for BGS and LRG as lenses. Scaled to the full BGS sample, we expect the statistical errors to improve from $18(12)\%$ to a promising level of $2(1.3)\%$ at $\theta>8^{'}(<8^{'})$. This brings stronger requirements for future systematics control. To fully realize such potential, we need to control the residual multiplicative shear bias $|m|<0.01$ and the bias in the mean redshift $|\Delta z|<0.015$. We also expect significant detection of galaxy-galaxy lensing with DESI LRG/ELG full samples as lenses, and cosmic magnification of ELG through cross-correlation with low-redshift DECaLS shear. {If such systematical error control can be achieved,} we find the advantages of DECaLS, comparing with KiDS (Kilo Degree Survey) and HSC (Hyper-Suprime Cam), are at low redshift, large-scale, and in measuring the shear-ratio (to $\sigma_R\sim 0.04$) and cosmic magnification., Comment: submitted to MNRAS

The ability to accurately predict deep neural network (DNN) inference performance metrics, such as latency, power, and memory footprint, for an arbitrary DNN on a target hardware platform is essential to the design of DNN based models. This ability is critical for the (manual or automatic) design, optimization, and deployment of practical DNNs for a specific hardware deployment platform. Unfortunately, these metrics are slow to evaluate using simulators (where available) and typically require measurement on the target hardware. This work describes PerfSAGE, a novel graph neural network (GNN) that predicts inference latency, energy, and memory footprint on an arbitrary DNN TFlite graph (TFL, 2017). In contrast, previously published performance predictors can only predict latency and are restricted to pre-defined construction rules or search spaces. This paper also describes the EdgeDLPerf dataset of 134,912 DNNs randomly sampled from four task search spaces and annotated with inference performance metrics from three edge hardware platforms. Using this dataset, we train PerfSAGE and provide experimental results that demonstrate state-of-the-art prediction accuracy with a Mean Absolute Percentage Error of <5% across all targets and model search spaces. These results: (1) Outperform previous state-of-art GNN-based predictors (Dudziak et al., 2020), (2) Accurately predict performance on accelerators (a shortfall of non-GNN-based predictors (Zhang et al., 2021)), and (3) Demonstrate predictions on arbitrary input graphs without modifications to the feature extractor.

On-device machine learning (ML) inference can enable the use of private user data on user devices without revealing them to remote servers. However, a pure on-device solution to private ML inference is impractical for many applications that rely on embedding tables that are too large to be stored on-device. In particular, recommendation models typically use multiple embedding tables each on the order of 1-10 GBs of data, making them impractical to store on-device. To overcome this barrier, we propose the use of private information retrieval (PIR) to efficiently and privately retrieve embeddings from servers without sharing any private information. As off-the-shelf PIR algorithms are usually too computationally intensive to directly use for latency-sensitive inference tasks, we 1) propose novel GPU-based acceleration of PIR, and 2) co-design PIR with the downstream ML application to obtain further speedup. Our GPU acceleration strategy improves system throughput by more than $20 \times$ over an optimized CPU PIR implementation, and our PIR-ML co-design provides an over $5 \times$ additional throughput improvement at fixed model quality. Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100,000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.

We utilize $\sim17000$ bright Luminous Red Galaxies (LRGs) from the novel Dark Energy Spectroscopic Instrument Survey Validation spectroscopic sample, leveraging its deep ($\sim2.5$ hour/galaxy exposure time) spectra to characterize the contribution of recently quenched galaxies to the massive galaxy population at $0.41$) of our sample of recently quenched galaxies represents the largest spectroscopic sample of post-starburst galaxies at that epoch. At $0.411.2$) LRGs by measuring the fraction of stellar mass each galaxy formed in the Gyr before observation, $f_\mathrm{1 Gyr}$. Although galaxies with $f_\mathrm{1 Gyr}>0.1$ are rare at $z\sim0.4$ ($\lesssim 0.5\%$ of the population), by $z\sim0.8$ they constitute $\sim3\%$ of massive galaxies. Relaxing this threshold, we find that galaxies with $f_\mathrm{1 Gyr}>5\%$ constitute $\sim10\%$ of the massive galaxy population at $z\sim0.8$. We also identify a small but significant sample of galaxies at $z=1.1-1.3$ that formed with $f_\mathrm{1 Gyr}>50\%$, implying that they may be analogues to high-redshift quiescent galaxies that formed on similar timescales. Future analysis of this unprecedented sample promises to illuminate the physical mechanisms that drive the quenching of massive galaxies after cosmic noon., Comment: Re-uploaded after acceptance to the Astrophysical Journal Letters. 14 pages, 5 figures, comments welcome!

Graph Neural Networks (GNNs) are a class of neural networks designed to extract information from the graphical structure of data. Graph Convolutional Networks (GCNs) are a widely used type of GNN for transductive graph learning problems which apply convolution to learn information from graphs. GCN is a challenging algorithm from an architecture perspective due to inherent sparsity, low data reuse, and massive memory capacity requirements. Traditional neural algorithms exploit the high compute capacity of GPUs to achieve high performance for both inference and training. The architectural decision to use a GPU for GCN inference is a question explored in this work. GCN on both CPU and GPU was characterized in order to better understand the implications of graph size, embedding dimension, and sampling on performance., Comment: 10 pages, 7 figures

We present a novel approach to the construction of mock galaxy catalogues for large-scale structure analysis based on the distribution of dark matter halos obtained with effective bias models at the field level. We aim to produce mock galaxy catalogues capable of generating accurate covariance matrices for a number of cosmological probes that are expected to be measured in current and forthcoming galaxy redshift surveys (e.g. two- and three-point statistics). We use the bias assignment method (BAM) to model the statistics of halo distribution through a learning algorithm using a few detailed $N$-body simulations, and approximated gravity solvers based on Lagrangian perturbation theory. Using specific models of halo occupation distributions, we generate galaxy mocks with the expected number density and central-satellite fraction of emission-line galaxies, which are a key target of the DESI experiment. BAM generates mock catalogues with per cent accuracy in a number of summary statistics, such as the abundance, the two- and three-point statistics of halo distributions, both in real and redshift space. In particular, the mock galaxy catalogues display $\sim 3\%-10\%$ accuracy in the multipoles of the power spectrum up to scales of $k\sim 0.4\,h^{-1}{\rm Mpc}$. We show that covariance matrices of two- and three-point statistics obtained with BAM display a similar structure to the reference simulation. BAM offers an efficient way to produce mock halo catalogues with accurate two- and three-point statistics, and is able to generate a variety of multi-tracer catalogues with precise covariance matrices of several cosmological probes. We discuss future developments of the algorithm towards mock production in DESI and other galaxy-redshift surveys. (Abridged), Comment: Accepted for publication at A&A

A key goal of the Solar Orbiter mission is to connect elemental abundance measurements of the solar wind enveloping the spacecraft with EUV spectroscopic observations of their solar sources, but this is not an easy exercise. Observations from previous missions have revealed a highly complex picture of spatial and temporal variations of elemental abundances in the solar corona. We have used coordinated observations from Hinode and Solar Orbiter to attempt new abundance measurements with the SPICE (Spectral Imaging of the Coronal Environment) instrument, and benchmark them against standard analyses from EIS (EUV Imaging Spectrometer). We use observations of several solar features in AR 12781 taken from an Earth-facing view by EIS on 2020 November 10, and SPICE data obtained one week later on 2020 November 17; when the AR had rotated into the Solar Orbiter field-of-view. We identify a range of spectral lines that are useful for determining the transition region and low coronal temperature structure with SPICE, and demonstrate that SPICE measurements are able to differentiate between photospheric and coronal Mg/Ne abundances. The combination of SPICE and EIS is able to establish the atmospheric composition structure of a fan loop/outflow area at the active region edge. We also discuss the problem of resolving the degree of elemental fractionation with SPICE, which is more challenging without further constraints on the temperature structure, and comment on what that can tell us about the sources of the solar wind and solar energetic particles., Comment: To be published in The Astrophysical Journal

While research in the field of transformer models has primarily focused on enhancing performance metrics such as accuracy and perplexity, practical applications in industry often necessitate a rigorous consideration of inference latency constraints. Addressing this challenge, we introduce SpeedLimit, a novel Neural Architecture Search (NAS) technique that optimizes accuracy whilst adhering to an upper-bound latency constraint. Our method incorporates 8-bit integer quantization in the search process to outperform the current state-of-the-art technique. Our results underline the feasibility and efficacy of seeking an optimal balance between performance and latency, providing new avenues for deploying state-of-the-art transformer models in latency-sensitive environments.

In this white paper, we present the MegaMapper concept. The MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at $2

We measure the tidal alignment of the major axes of Luminous Red Galaxies (LRGs) from the Legacy Imaging Survey and use it to infer the artificial redshift-space distortion signature that will arise from an orientation-dependent, surface-brightness selection in the Dark Energy Spectroscopic Instrument (DESI) survey. Using photometric redshifts to down-weight the shape-density correlations due to weak lensing, we measure the intrinsic tidal alignment of LRGs. Separately, we estimate the net polarization of LRG orientations from DESI's fiber-magnitude target selection to be of order 10^-2 along the line of sight. Using these measurements and a linear tidal model, we forecast a 0.5% fractional decrease on the quadrupole of the 2-point correlation function for projected separations of 40-80 Mpc/h. We also use a halo catalog from the Abacus Summit cosmological simulation suite to reproduce this false quadrupole., Comment: 13 pages, 13 figures. Submitted to MNRAS. For an accessible summary of this paper, see https://cmlamman.github.io/doc/fakeRSD_summary.pdf

In this white paper, we present an experimental road map for spectroscopic experiments beyond DESI. DESI will be a transformative cosmological survey in the 2020s, mapping 40 million galaxies and quasars and capturing a significant fraction of the available linear modes up to z=1.2. DESI-II will pilot observations of galaxies both at much higher densities and extending to higher redshifts. A Stage-5 experiment would build out those high-density and high-redshift observations, mapping hundreds of millions of stars and galaxies in three dimensions, to address the problems of inflation, dark energy, light relativistic species, and dark matter. These spectroscopic data will also complement the next generation of weak lensing, line intensity mapping and CMB experiments and allow them to reach their full potential., Comment: Contribution to Snowmass 2021

In 2021 May, the Dark Energy Spectroscopic Instrument (DESI) began a 5 yr survey of approximately 50 million total extragalactic and Galactic targets. The primary DESI dark-time targets are emission line galaxies (ELGs), luminous red galaxies (LRGs) and quasars (QSOs). In bright time, DESI will focus on two surveys known as the Bright Galaxy Survey (BGS) and the Milky Way Survey (MWS). DESI also observes a selection of "secondary" targets for bespoke science goals. This paper gives an overview of the publicly available pipeline (desitarget) used to process targets for DESI observations. Highlights include details of the different DESI survey targeting phases, the targeting ID (TARGETID) used to define unique targets, the bitmasks used to indicate a particular type of target, the data model and structure of DESI targeting files, and examples of how to access and use the desitarget code base. This paper will also describe "supporting" DESI target classes, such as standard stars, sky locations, and random catalogs that mimic the angular selection function of DESI targets. The DESI target selection pipeline is complex and sizable; this paper attempts to summarize the most salient information required to understand and work with DESI targeting data., Comment: AJ, accepted, 27 pages, 4 figures, 10 tables, one of a suite of 8 papers detailing targeting for DESI. Minor textual updates to better match the final, accepted version. Also added two missing co-authors

We describe the Milky Way Survey (MWS) that will be undertaken with the Dark Energy Spectroscopic Instrument (DESI) on the Mayall 4m telescope at the Kitt Peak National Observatory. Over the next 5 yr DESI MWS will observe approximately seven million stars at Galactic latitudes |b|>20 degrees, with an inclusive target selection scheme focused on the thick disk and stellar halo. MWS will also include several high-completeness samples of rare stellar types, including white dwarfs, low-mass stars within 100pc of the Sun, and horizontal branch stars. We summarize the potential of DESI to advance understanding of Galactic structure and stellar evolution. We introduce the final definitions of the main MWS target classes and estimate the number of stars in each class that will be observed. We describe our pipelines for deriving radial velocities, atmospheric parameters, and chemical abundances. We use ~500,000 spectra of unique stellar targets from the DESI Survey Validation program (SV) to demonstrate that our pipelines can measure radial velocities to ~1 km/s and [Fe/H] accurate to ~0.2 dex for typical stars in our main sample. We find the stellar parameter distributions from ~100 sq. deg of SV observations with >90% completeness on our main sample are in good agreement with expectations from mock catalogs and previous surveys., Comment: Accepted for publication in ApJ, 44 pages, 25 figures, 4 tables, one of a suite of 8 papers detailing targeting for DESI; v2 added links to data shown in figures, added citations to other DESI papers, corrected author list and minor typos; v3 fixed minor errors in Fig. 6 and clarified associated text; v4 updated to include minor changes in response to review

Over the next five years, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage-IV dark energy galaxy survey. At $z < 0.6$, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the Universe during the dark energy dominated epoch with redshifts of >10 million galaxies over 14,000 deg$^2$. In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target a $r < 19.5$ magnitude-limited sample (BGS Bright); a fainter $19.5 < r < 20.175$ sample, color-selected to have high redshift efficiency (BGS Faint); and a smaller low-z quasar sample. BGS will observe these targets using exposure times, scaled to achieve uniform completeness, and visit each point on the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with realistic simulations to show that BGS can complete its strategy and make optimal use of `bright' time. We demonstrate that BGS targets have stellar contamination <1% and that their densities do not depend strongly on imaging properties. We also confirm that BGS Bright will achieve >80% fiber assignment efficiency. Finally, we show that BGS Bright and Faint will achieve >95% redshift success rates with no significant dependence on observing conditions. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise Baryon Acoustic Oscillations and Redshift-Space Distortions measurements at $z < 0.4$. It also presents opportunities to exploit new methods that require highly complete and dense galaxy samples (e.g. N-point statistics, multi-tracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter., Comment: AJ, submitted, 34 pages, 22 figures, one of a suite of 8 papers detailing targeting for DESI

The Dark Energy Spectroscopic Instrument (DESI) survey will measure large-scale structures using quasars as direct tracers of dark matter in the redshift range 0.92.1. We present several methods to select candidate quasars for DESI, using input photometric imaging in three optical bands (g, r, z) from the DESI Legacy Imaging Surveys and two infrared bands (W1, W2) from the Wide-field Infrared Explorer (WISE). These methods were extensively tested during the Survey Validation of DESI. In this paper, we report on the results obtained with the different methods and present the selection we optimized for the DESI main survey. The final quasar target selection is based on a Random Forest algorithm and selects quasars in the magnitude range 16.599% purity for a nominal effective exposure time of ~1000s. With a 310 per sq. deg. target density, the main selection allows DESI to select more than 200 QSOs per sq. deg. (including 60 quasars with z>2.1), exceeding the project requirements by 20%. The redshift distribution of the selected quasars is in excellent agreement with quasar luminosity function predictions., Comment: 21 pages, 21 figures, typos corrected, references added

Cosmological analyses with type Ia supernovae (SNe Ia) often assume a single empirical relation between color and luminosity ($\beta$) and do not account for varying host-galaxy dust properties. However, from studies of dust in large samples of galaxies, it is known that dust attenuation can vary significantly. Here we take advantage of state-of-the-art modeling of galaxy properties to characterize dust parameters (dust attenuation $A_V$, and a parameter describing the dust law slope $R_V$) for the Dark Energy Survey (DES) SN Ia host galaxies using the publicly available \texttt{BAGPIPES} code. Utilizing optical and infrared data of the hosts alone, we find three key aspects of host dust that impact SN Ia cosmology: 1) there exists a large range ($\sim1-6$) of host $R_V$ 2) high stellar mass hosts have $R_V$ on average $\sim0.7$ lower than that of low-mass hosts 3) there is a significant ($>3\sigma$) correlation between the Hubble diagram residuals of red SNe Ia that when corrected for reduces scatter by $\sim13\%$ and the significance of the ``mass step'' to $\sim1\sigma$. These represent independent confirmations of recent predictions based on dust that attempted to explain the puzzling ``mass step'' and intrinsic scatter ($\sigma_{\rm int}$) in SN Ia analyses. We also find that red-sequence galaxies have both lower and more peaked dust law slope distributions on average in comparison to non red-sequence galaxies. We find that the SN Ia $\beta$ and $\sigma_{\rm int}$ both differ by $>3\sigma$ when determined separately for red-sequence galaxy and all other galaxy hosts. The agreement between fitted host-$R_V$ and SN Ia $\beta$ \& $\sigma_{\rm int}$ suggests that host dust properties play a major role in SN Ia color-luminosity standardization and supports the claim that SN Ia intrinsic scatter is driven by $R_V$ variation., Comment: 22 pages. Submitted to MNRAS

The outer atmosphere of the Sun is composed of plasma heated to temperatures well in excess of the visible surface. We investigate short cool and warm (<1 MK) loops seen in the core of an active region to address the role of field-line braiding in energising these structures. We report observations from the High-resolution Coronal imager (Hi-C) that have been acquired in a coordinated campaign with the Interface Region Imaging Spectrograph (IRIS). In the core of the active region, the 172 A band of Hi-C and the 1400 A channel of IRIS show plasma loops at different temperatures that run in parallel. There is a small but detectable spatial offset of less than 1 arcsec between the loops seen in the two bands. Most importantly, we do not see observational signatures that these loops might be twisted around each other. Considering the scenario of magnetic braiding, our observations of parallel loops imply that the stresses put into the magnetic field have to relax while the braiding is applied: the magnetic field never reaches a highly braided state on these length-scales comparable to the separation of the loops. This supports recent numerical 3D models of loop braiding in which the effective dissipation is sufficiently large that it keeps the magnetic field from getting highly twisted within a loop., Comment: Accepted for publication in the Astrophysical Journal, 24 pages, 18 figures

A system of 5,020 robotic fiber positioners was installed in 2019 on the Mayall Telescope, at Kitt Peak National Observatory. The robots automatically re-target their optical fibers every 10 - 20 minutes, each to a precision of several microns, with a reconfiguration time less than 2 minutes. Over the next five years, they will enable the newly-constructed Dark Energy Spectroscopic Instrument (DESI) to measure the spectra of 35 million galaxies and quasars. DESI will produce the largest 3D map of the universe to date and measure the expansion history of the cosmos. In addition to the 5,020 robotic positioners and optical fibers, DESI's Focal Plane System includes 6 guide cameras, 4 wavefront cameras, 123 fiducial point sources, and a metrology camera mounted at the primary mirror. The system also includes associated structural, thermal, and electrical systems. In all, it contains over 675,000 individual parts. We discuss the design, construction, quality control, and integration of all these components. We include a summary of the key requirements, the review and acceptance process, on-sky validations of requirements, and lessons learned for future multi-object, fiber-fed spectrographs., Comment: 51 pages, 41 figures

Using synthetic Lyman-$\alpha$ forests from the Dark Energy Spectroscopic Instrument (DESI) survey, we present a study of the impact of errors in the estimation of quasar redshift on the Lyman-$\alpha$ correlation functions. Estimates of quasar redshift have large uncertainties of a few hundred $\text{km s}^{-1}\,$ due to the broadness of the emission lines and the intrinsic shifts from other emission lines. We inject Gaussian random redshift errors into the mock quasar catalogues, and measure the auto-correlation and the Lyman-$\alpha$-quasar cross-correlation functions. We find a smearing of the BAO feature in the radial direction, but changes in the peak position are negligible. However, we see a significant unphysical correlation for small separations transverse to the line of sight which increases with the amplitude of the redshift errors. We interpret this contamination as a result of the broadening of emission lines in the measured mean continuum, caused by quasar redshift errors, combined with the unrealistically strong clustering of the simulated quasars on small scales., Comment: 13 pages, 8 figures, submitted to MNRAS

This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. Impala builds upon recent solutions that combine the complementary strengths of homomorphic encryption (HE) and secure multi-party computation (MPC). A series of protocol optimizations are developed to reduce both communication and performance bottlenecks. First, we remove MPC's overwhelmingly high communication cost from the client by introducing a proxy server and developing a low-overhead key switching technique. Key switching reduces the clients bandwidth by multiple orders of magnitude, however the communication between the proxy and cloud is still excessive. Second, to we develop an optimized garbled circuit that leverages truncated secret shares for faster evaluation and less proxy-cloud communication. Finally, we propose sparse HE convolution to reduce the computational bottleneck of using HE. Compared to the state-of-the-art, these optimizations provide a bandwidth savings of over 3X and speedup of 4X for private deep learning inference.

The chemical composition of the solar corona is different from that of the solar photosphere, with the strongest variation being observed in active regions (ARs). Using data from the Extreme Ultraviolet (EUV) Imaging Spectrometer (EIS) on Hinode, we present a survey of coronal elemental composition as expressed in the first ionisation potential (FIP) bias in 28 ARs of different ages and magnetic flux content, which are at different stages in their evolution. We find no correlation between the FIP bias of an AR and its total unsigned magnetic flux or age. However, there is a weak dependence of FIP bias on the evolutionary stage, decreasing from 1.9-2.2 in ARs with spots to 1.5-1.6 in ARs that are at more advanced stages of the decay phase. FIP bias shows an increasing trend with average magnetic flux density up to 200 G but this trend does not continue at higher values. The FIP bias distribution within ARs has a spread between 0.4 and 1. The largest spread is observed in very dispersed ARs. We attribute this to a range of physical processes taking place in these ARs including processes associated with filament channel formation. These findings indicate that, while some general trends can be observed, the processes influencing the composition of an AR are complex and specific to its evolution, magnetic configuration or environment. The spread of FIP bias values in ARs shows a broad match with that previously observed in situ in the slow solar wind., Comment: 20 pages, 14 figures

Autonomous machines (e.g., vehicles, mobile robots, drones) require sophisticated 3D mapping to perceive the dynamic environment. However, maintaining a real-time 3D map is expensive both in terms of compute and memory requirements, especially for resource-constrained edge machines. Probabilistic OctoMap is a reliable and memory-efficient 3D dense map model to represent the full environment, with dynamic voxel node pruning and expansion capacity. This paper presents the first efficient accelerator solution, i.e. OMU, to enable real-time probabilistic 3D mapping at the edge. To improve the performance, the input map voxels are updated via parallel PE units for data parallelism. Within each PE, the voxels are stored using a specially developed data structure in parallel memory banks. In addition, a pruning address manager is designed within each PE unit to reuse the pruned memory addresses. The proposed 3D mapping accelerator is implemented and evaluated using a commercial 12 nm technology. Compared to the ARM Cortex-A57 CPU in the Nvidia Jetson TX2 platform, the proposed accelerator achieves up to 62$\times$ performance and 708$\times$ energy efficiency improvement. Furthermore, the accelerator provides 63 FPS throughput, more than 2$\times$ higher than a real-time requirement, enabling real-time perception for 3D mapping., Comment: 2022 Design Automation and Test in Europe Conference (DATE), March 14-23, 2022, Virtual

The elemental composition of the Sun's hot atmosphere, the corona, shows a distinctive pattern that is different than the underlying surface, or photosphere (Pottasch 1963). Elements that are easy to ionize in the chromosphere are enhanced in abundance in the corona compared to their photospheric values. A similar pattern of behavior is often observed in the slow speed (< 500 km/s) solar wind (Meyer 1985), and in solar-like stellar coronae (Drake et al. 1997), while a reversed effect is seen in M-dwarfs (Liefke et al. 2008). Studies of the inverse effect have been hampered in the past because only unresolved (point source) spectroscopic data were available for these stellar targets. Here we report the discovery of several inverse events observed in-situ in the slow solar wind using particle counting techniques. These very rare events all occur during periods of high solar activity that mimic conditions more widespread on M-dwarfs. The detections allow a new way of connecting the slow wind to its solar source, and are broadly consistent with theoretical models of abundance variations due to chromospheric fast mode waves with amplitudes of 8-10 km/s; sufficient to accelerate the solar wind. The results imply that M-dwarf winds are dominated by plasma depleted in easily ionized elements, and lend credence to previous spectroscopic measurements., Comment: To be published in The Astrophysical Journal Letters