分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: The Dark Energy Spectroscopic Instrument (DESI) Survey has obtained a set of spectroscopic measurements of galaxies to validate the final survey design and target selections. To assist in these tasks, we visually inspect (VI) DESI spectra of approximately 2,500 bright galaxies, 3,500 luminous red galaxies (LRGs), and 10,000 emission line galaxies (ELGs), to obtain robust redshift identifications. We then utilize the VI redshift information to characterize the performance of the DESI operation. Based on the VI catalogs, our results show that the final survey design yields samples of bright galaxies, LRGs, and ELGs with purity greater than $99\%$. Moreover, we demonstrate that the precision of the redshift measurements is approximately 10 km/s for bright galaxies and ELGs and approximately 40 km/s for LRGs. The average redshift accuracy is within 10 km/s for the three types of galaxies. The VI process also helps improve the quality of the DESI data by identifying spurious spectral features introduced by the pipeline. Finally, we show examples of unexpected real astronomical objects, such as Ly$\alpha$ emitters and strong lensing candidates, identified by VI. These results demonstrate the importance and utility of visually inspecting data from incoming and upcoming surveys, especially during their early operation phases.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: A key component of the Dark Energy Spectroscopic Instrument (DESI) survey validation (SV) is a detailed visual inspection (VI) of the optical spectroscopic data to quantify key survey metrics. In this paper we present results from VI of the quasar survey using deep coadded SV spectra. We show that the majority (~70%) of the main-survey targets are spectroscopically confirmed as quasars, with ~16% galaxies, ~6% stars, and ~8% low-quality spectra lacking reliable features. A non-negligible fraction of the quasars are misidentified by the standard spectroscopic pipeline but we show that the majority can be recovered using post-pipeline "afterburner" quasar-identification approaches. We combine these "afterburners" with our standard pipeline to create a modified pipeline to improve the overall quasar yield. At the depth of the main DESI survey both pipelines achieve a good-redshift purity (reliable redshifts measured within 3000 km/s) of ~99%; however, the modified pipeline recovers ~94% of the visually inspected quasars, as compared to ~86% from the standard pipeline. We demonstrate that both pipelines achieve an median redshift precision and accuracy of ~100 km/s and ~70 km/s, respectively. We constructed composite spectra to investigate why some quasars are missed by the standard spectroscopic pipeline and find that they are more host-galaxy dominated (i.e., distant analogs of "Seyfert galaxies") and/or dust reddened than the standard-pipeline quasars. We also show example spectra to demonstrate the overall diversity of the DESI quasar sample and provide strong-lensing candidates where two targets contribute to a single spectrum.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: 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.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: We describe the spectroscopic data processing pipeline of the Dark Energy Spectroscopic Instrument (DESI), which is conducting a redshift survey of about 40 million galaxies and quasars using a purpose-built instrument on the 4-m Mayall Telescope at Kitt Peak National Observatory. The main goal of DESI is to measure with unprecedented precision the expansion history of the Universe with the Baryon Acoustic Oscillation technique and the growth rate of structure with Redshift Space Distortions. Ten spectrographs with three cameras each disperse the light from 5000 fibers onto 30 CCDs, covering the near UV to near infrared (3600 to 9800 Angstrom) with a spectral resolution ranging from 2000 to 5000. The DESI data pipeline generates wavelength- and flux-calibrated spectra of all the targets, along with spectroscopic classifications and redshift measurements. Fully processed data from each night are typically available to the DESI collaboration the following morning. We give details about the pipeline's algorithms, and provide performance results on the stability of the optics, the quality of the sky background subtraction, and the precision and accuracy of the instrumental calibration. This pipeline has been used to process the DESI Survey Validation data set, and has exceeded the project's requirements for redshift performance, with high efficiency and a purity greater than 99 percent for all target classes.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: The Dark Energy Spectroscopic Instrument (DESI) is carrying out a 5-year
survey that aims to measure the redshifts of tens of millions of galaxies and
quasars, including 8 million luminous red galaxies (LRGs) in the redshift range
of $0.4
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: 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.
分类: 天文学 >> 天文学 提交时间: 2023-02-19
摘要: Uncertainty quantification is crucial for assessing the predictive ability of AI algorithms. A large body of work (including normalizing flows and Bayesian neural networks) has been devoted to describing the entire predictive distribution (PD) of a target variable Y given input features $\mathbf{X}$. However, off-the-shelf PDs are usually far from being conditionally calibrated; i.e., the probability of occurrence of an event given input $\mathbf{X}$ can be significantly different from the predicted probability. Most current research on predictive inference (such as conformal prediction) concerns constructing calibrated prediction sets only. It is often believed that the problem of obtaining and assessing entire conditionally calibrated PDs is too challenging. In this work, we show that recalibration, as well as diagnostics of entire PDs, are indeed attainable goals in practice. Our proposed method relies on the idea of regressing probability integral transform (PIT) scores against $\mathbf{X}$. This regression gives full diagnostics of conditional coverage across the entire feature space and can be used to recalibrate misspecified PDs. We benchmark our corrected prediction bands against oracle bands and state-of-the-art predictive inference algorithms for synthetic data, including settings with a distributional shift. Finally, we produce calibrated PDs for two applications: (i) probabilistic nowcasting based on sequences of satellite images, and (ii) estimation of galaxy distances based on imaging data (photometric redshifts).