adsabs/AstroCLIMB huggingface dataset:
Paper: The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects
Authors: J. Lee, M. Acevedo, M. Sako, M. Vincenzi, D. Brout, B. Sanchez, R. Chen, T. M. Davis, M. Jarvis, D. Scolnic, H. Qu, L. Galbany, R. Kessler, J. Lasker, M. Sullivan, P. Wiseman, M. Aguena, S. Allam, O. Alves, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, N. Kuropatkin, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, M. Raveri, K. Reil, M. Rodriguez-Monroy, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, C. To, N. Weaverdyck
Figure caption: Magnitude corrections histogram for the SHAPE effect; x-axis is the magnitude correction needed while y-axis is the number of observations. About 7.1% of corrections needed are larger than 0.005 mag.
Paper: The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects
Authors: J. Lee, M. Acevedo, M. Sako, M. Vincenzi, D. Brout, B. Sanchez, R. Chen, T. M. Davis, M. Jarvis, D. Scolnic, H. Qu, L. Galbany, R. Kessler, J. Lasker, M. Sullivan, P. Wiseman, M. Aguena, S. Allam, O. Alves, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, N. Kuropatkin, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, M. Raveri, K. Reil, M. Rodriguez-Monroy, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, C. To, N. Weaverdyck
Figure caption: Magnitude corrections histogram for the COORD effect; corrections are always negative and about 1.3% of corrections in the g band are larger than 0.005 mag in size. Note that the horizontal axis is in units of mag and the vertical axis is logarithmic.
Paper: The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects
Authors: J. Lee, M. Acevedo, M. Sako, M. Vincenzi, D. Brout, B. Sanchez, R. Chen, T. M. Davis, M. Jarvis, D. Scolnic, H. Qu, L. Galbany, R. Kessler, J. Lasker, M. Sullivan, P. Wiseman, M. Aguena, S. Allam, O. Alves, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, N. Kuropatkin, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, M. Raveri, K. Reil, M. Rodriguez-Monroy, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, C. To, N. Weaverdyck
Figure caption: (Blinded) distance modulus differences (with corrections − without corrections) due to λ-dependent effects; λ-dependent effects (ALL λ) shift pseudoμ by about 1 to 2 mmag at z between 0.2 and 0.4, and by about −2 to −1 mmag at z between 0.6 and 0.7 as the magnitude corrections for the SHAPE effect reverse from negative to positive. A shift of w by ±0.005 cause Δpseudo μ  to shift by ∓0.0007 to ∓0.002. At z > 0.7, we do not provide corrections due to the lack of g magnitudes in that regime.
Paper: The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects
Authors: J. Lee, M. Acevedo, M. Sako, M. Vincenzi, D. Brout, B. Sanchez, R. Chen, T. M. Davis, M. Jarvis, D. Scolnic, H. Qu, L. Galbany, R. Kessler, J. Lasker, M. Sullivan, P. Wiseman, M. Aguena, S. Allam, O. Alves, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, N. Kuropatkin, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, M. Raveri, K. Reil, M. Rodriguez-Monroy, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, C. To, N. Weaverdyck
Figure caption: Magnitude corrections vs. redshift (left) and rest-frame epoch (right), unbinned; for the SHAPE effect (in blue), magnitudes corrections increase as SNe are redshifted, with flux being underestimated at z < 0.4 and overestimated at z > 0.5 due to the SHAPE effect. On the right, we also see a trend for the SHAPE effect with SNe epoch, as the SEDs of SNe change throughout its evolution.
Paper: The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects
Authors: J. Lee, M. Acevedo, M. Sako, M. Vincenzi, D. Brout, B. Sanchez, R. Chen, T. M. Davis, M. Jarvis, D. Scolnic, H. Qu, L. Galbany, R. Kessler, J. Lasker, M. Sullivan, P. Wiseman, M. Aguena, S. Allam, O. Alves, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, N. Kuropatkin, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, M. Raveri, K. Reil, M. Rodriguez-Monroy, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, C. To, N. Weaverdyck
Figure caption: Magnitude corrections vs. redshift (left) and rest-frame epoch (right), same as Figure 10 but binned and showing the mean and errors on the mean—the trends are much more clear for the SHAPE effect. Note that the MAGCOR scales are different from Figure 10.
Paper: The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects
Authors: J. Lee, M. Acevedo, M. Sako, M. Vincenzi, D. Brout, B. Sanchez, R. Chen, T. M. Davis, M. Jarvis, D. Scolnic, H. Qu, L. Galbany, R. Kessler, J. Lasker, M. Sullivan, P. Wiseman, M. Aguena, S. Allam, O. Alves, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, N. Kuropatkin, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, M. Raveri, K. Reil, M. Rodriguez-Monroy, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, C. To, N. Weaverdyck
Figure caption: Magnitude corrections histogram for both COORD and SHAPE effects; the mean corrections required are about 3 mmag and −2 mmag for the SHAPE and COORD effects, respectively, with the mean of the total corrections needed being about 1 mmag.
Paper: The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects
Authors: J. Lee, M. Acevedo, M. Sako, M. Vincenzi, D. Brout, B. Sanchez, R. Chen, T. M. Davis, M. Jarvis, D. Scolnic, H. Qu, L. Galbany, R. Kessler, J. Lasker, M. Sullivan, P. Wiseman, M. Aguena, S. Allam, O. Alves, F. Andrade-Oliveira, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, H. T. Diehl, P. Doel, S. Everett, I. Ferrero, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, N. Kuropatkin, J. Mena-Fernández, R. Miquel, R. L. C. Ogando, A. Palmese, A. Pieres, A. A. Plazas Malagón, M. Raveri, K. Reil, M. Rodriguez-Monroy, E. Sanchez, V. Scarpine, I. Sevilla-Noarbe, M. Smith, E. Suchyta, G. Tarle, C. To, N. Weaverdyck
Figure caption: Northern half of the celestial sphere; the HA is defined as the angle XPZ and q is the parallactic angle.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: Simulation procedure in an instant time. As shown in this figure, our method will continuously call the telescope model, the celestial object model, and the observation environment model to generate realistic observation results.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The telescope array simulator, which consists of three main components: the telescope array model, the observation environment model, and the celestial object model. We use the STK package to visualize results obtained by telescope arrays with different observation strategies.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The location information of the telescope. Observation sites in black represent the configurations of the observation sites in the training set, and observation sites in red represent the configurations of the observations sites in the test set.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The performance of the framework in detection of space debris, when we use reward to evaluate its performance. The blue line represents rewards in each step, and the orange line represents mean rewards in each round.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: Simulation procedure in continuous time. As shown in this figure, the environment will update its state at each time step, and the control algorithm will update its parameters accordingly.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The flowchart of the framework for optimization of the observation strategy for distributed telescope arrays.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The performance of the framework in detection of space debris, when we use number of detected and tracked space debris to evaluate its performance.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: Schematic diagram of the DQN and the simulator in our framework.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: Schematic draw of the action in DRL used for observation strategy optimization. Satellites represent moving targets; six-point stars represent stars, quasars, and galaxies; the blue circle represents exposure time; and orange arrows represent moving directions.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: Schematic diagram of the DQN used in this paper.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The schematic diagram of the space debris track and observation track of the telescope.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: Schematic draw of the state in DRL used for observation strategy optimization.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The performance of the strategy obtained by our framework and that obtained by the Sky Survey in a Four-Group Environment, when evaluated with tracked or newly discovered celestial objects.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The performance of the strategy obtained by our framework and that obtained by the Sky Survey in a Four-Group Environment, when evaluated with reward value.
Paper: Observation Strategy Optimization for Distributed Telescope Arrays with Deep Reinforcement Learning
Authors: Peng Jia, Qiwei Jia, Tiancheng Jiang, Jifeng Liu
Figure caption: The performance of the framework in detection of space debris, when we use right action rates to evaluate its performance. The blue line represents the ratio of the number of valid observations to the number of all actions in each step, and the orange line represents mean right action rates in each round.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Our fit to the Strömgren (b + y)/2 photometry from Lothringer et al. (2018), mimicking their Figure 1. Left: data with a sinusoidal fit to the 7.75 ± 0.10 yr activity cycle period, with the linear trend overlaid. The line thickness represents the uncertainty of the fit. Right: data with the activity cycle sinusoid and linear trend subtracted, folded onto the best-fit rotation period of 44.09 ± 1.16 days and binned by groups of 30. The rotation model sinusoid is overlaid, with the translucent region representing the uncertainty of the fit. The error bars show the sample errors on the means of the binned points.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Cumulative frequency distribution of the GJ 436 flares in the FUV130 band. The gray regions give uncertainty envelopes from fits to a compendium of M-dwarf flares in L18. To highlight the cliff in the GJ 436 flares, the figure also shows random draws of event equivalent durations and energies, assuming the same number of events above 500 s and 2.5 × 1028 erg and an inverse power-law slope, α, equal to the median observed by L18. The gray horizontal line represents the limit where one event would be expected to occur within the cumulative observing time.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Lightcurve of the summed emission line flux at a 10 s cadence. We shortened the gaps in the data to a logarithmic scaling of their original lengths, as labeled in the plots. The labels below each epoch of data indicate the date range covered and the program(s) from which the observations originated. The red points indicate flares detected at >5σ, with assigned labels above them in red. The orange points indicate anomalous (>3σ) runs of data that were excluded when estimating the quiescence for the purpose of flare detection.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Coadded spectrum of GJ 436. The range of the plot matches that of the FUV130 band. The gray regions delineate regions of airglow contamination and a detector gap that falls near 1300 Å that we masked from the FUV130 band. The labels indicate the strong emission lines, which we also analyzed for variability, and the regions between the labeled lines and gray bands represent the pseudocontinuum.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Fits to the rotational modulations of the flux in each of the analyzed bands during the 2017/2018 epoch of observations. The data have been cleaned of flares. The fits were made to data binned at a 10 minute cadence, but to minimize clutter, the plotted data are binned to the full exposure length (typically around 45 minutes). The fits corresponding to alternative periods are shown as the dotted and dashed lines in the second panel from the top (see the text). The slight downward trends in the UV fits are due to the simultaneous fits to the activity cycle.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Fluxes plotted against planetary orbital phase. The planet’s optical transit spans a narrow range of ±0.008 in phase. Top: data from the 2017/2018 epoch, binned by exposure, with the best-fit rotational modulation subtracted. The blue points exclude and the red points include flares. The black solid line shows the 2σ upper-limit amplitude for a truncated sine SPI model fit to the data with flares included. Bottom: data at a 10 s cadence, to show individual flares. The blue (quiescent) and red (flare) data are from the 2017/2018 epoch, whereas the green (quiescent) and orange (flare) data are from the earlier 2012 and 2015 epochs. We speculate that the lack of flares when the planet is near eclipse (phase 0.5) could suggest that a magnetic SPI is triggering the star’s frequent small flares, but with energies too weak to register as a detection using the truncated sine model.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Correlations between the variability in each of the analyzed bands. The FUV130 and summed line bands have been omitted, since they are compendia of all the other bands (thus not independent). The gray lines indicate a 1:1 correlation. The p-value from a Spearman rank order correlation test is given in each panel. Most band pairs are correlated to very high confidence, with the exception of some of the correlations with the pseudocontinuum. The largest excursions from the means are driven by flares, indicated by the red colorings of the points. Notably, neither N V nor the pseudocontinuum seem to follow 1:1 relationships with the other bands, although the statistical significances of the relationships remain very high, despite its shallower slope.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Summary of contributions from various sources to the FUV variability of GJ 436. The arrows indicate the upper or lower limits (see the text). In the parentheses below each label are the timescales at which we measured each form of variability.
Paper: Flares, Rotation, Activity Cycles, and a Magnetic Star–Planet Interaction Hypothesis for the Far-ultraviolet Emission of GJ 436
Authors: R. O. Parke Loyd, P. C. Schneider, James A. G. Jackman, Kevin France, Evgenya L. Shkolnik, Nicole Arulanantham, P. Wilson Cauley, Joe Llama, Adam C. Schneider
Figure caption: Summed line flux (blue points), binned by HST visit, across all three epochs of HST data, fit with a sinusoidal model for activity cycle variations (orange curve) fixed to the optical cycle period, and shown alongside the (rescaled) fit to the optical cycle (green curve), to compare phases.
Paper: The Target-selection Pipeline for the Dark Energy Spectroscopic Instrument
Authors: Adam D. Myers, John Moustakas, Stephen Bailey, Benjamin A. Weaver, Andrew P. Cooper, Jaime E. Forero-Romero, Bela Abolfathi, David M. Alexander, David Brooks, Edmond Chaussidon, Chia-Hsun Chuang, Kyle Dawson, Arjun Dey, Biprateep Dey, Govinda Dhungana, Peter Doel, Kevin Fanning, Enrique Gaztañaga, Satya Gontcho A Gontcho, Alma X. Gonzalez-Morales, ChangHoon Hahn, Hiram K. Herrera-Alcantar, Klaus Honscheid, Mustapha Ishak, Tanveer Karim, David Kirkby, Theodore Kisner, Sergey E. Koposov, Anthony Kremin, Ting-Wen Lan, Martin Landriau, Dustin Lang, Michael E. Levi, Christophe Magneville, Lucas Napolitano, Paul Martini, Aaron Meisner, Jeffrey A. Newman, Nathalie Palanque-Delabrouille, Will Percival, Claire Poppett, Francisco Prada, Anand Raichoor, Ashley J. Ross, Edward F. Schlafly, David Schlegel, Michael Schubnell, Ting Tan, Gregory Tarle, Michael J. Wilson, Christophe Yèche, Rongpu Zhou, Zhimin Zhou, Hu Zou
Figure caption: The bits that comprise a positive DESI TARGETID. The least significant bit (i.e., 20) is depicted to the right of this diagram. The 58 least significant bits (20–257) record information from the Legacy Imaging Surveys. The next two most significant bits are Boolean flags that indicate whether a target is drawn from a mock or random catalog (258) and whether a target is a blank-sky location (259). The remaining two populated bits (GAIADR), which are used to indicate targets that are selected solely using Gaia information, store the Gaia Data Release number (260–261). Bit 62 is not used. Note that DESI also uses a differently structured negative TARGETID to distinguish some sky locations (see Section 3.3.2).