Buchumschlag

Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)

Fuente: arXiv

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Bibliographische Detailangaben
Hauptverfasser:	Zhang, T., Charles, E., Crenshaw, J. F., Schmidt, S. J., Adari, P., Gschwend, J., Mau, S., Andrews, B., Aubourg, E., Bains, Y., Bechtol, K., Boucaud, A., Boutigny, D., Burchat, P., Chevalier, J., Chiang, J., Chiang, H. -F., Clowe, D., Cohen-Tanugi, J., Combet, C., Connolly, A., Dagoret-Campagne, S., Daly, P. N., Daruich, F., Daubard, G., De Vicente, J., Drass, H., Fanning, K., Gawiser, E., Graham, M., Guy, L. P., Hang, Q., Ingraham, P., Ilbert, O., Jarvis, M., Jee, M. J., Jenness, T., Johnson, A., Juramy-Gilles, C., Kahn, S. M., Kalmbach, J. B., Kang, Y., Kannawadi, A., Kelvin, L. S., Liang, S., Lynn, O., Lust, N. B., Lutfi, M., Malz, A., Mandelbaum, R., Marshall, S., Meyers, J., Migliore, M., Moniez, M., Neveu, J., Newman, J. A., Nourbakhsh, E., Oldag, D., Park, H., Pelesky, S., Malagón, A. A. Plazas, Quint, B., Rahman, M., Rasmussen, A., Reil, K., Roby, W., Roodman, A., Roucelle, C., Salvato, M., Sánchez, B., Sanmartim, D., Schindler, R. H., Scora, J., Sebag, J., Sedaghat, N., Sevilla-Noarbe, I., Shirley, R., Shugart, A., Solomon, R., Taranu, D., Thayer, G., Cipriano, L. Toribio San, Urbach, E., Utsumi, Y., van Reeven, W., von der Linden, A., Walter, C. W., Wood-Vasey, W. M., Zuntz, J., Collaboration, LSST Dark Energy Science
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
Online-Zugang:	Acceder al recurso
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_version_	1866911199391645696
author	Zhang, T. Charles, E. Crenshaw, J. F. Schmidt, S. J. Adari, P. Gschwend, J. Mau, S. Andrews, B. Aubourg, E. Bains, Y. Bechtol, K. Boucaud, A. Boutigny, D. Burchat, P. Chevalier, J. Chiang, J. Chiang, H. -F. Clowe, D. Cohen-Tanugi, J. Combet, C. Connolly, A. Dagoret-Campagne, S. Daly, P. N. Daruich, F. Daubard, G. De Vicente, J. Drass, H. Fanning, K. Gawiser, E. Graham, M. Guy, L. P. Hang, Q. Ingraham, P. Ilbert, O. Jarvis, M. Jee, M. J. Jenness, T. Johnson, A. Juramy-Gilles, C. Kahn, S. M. Kalmbach, J. B. Kang, Y. Kannawadi, A. Kelvin, L. S. Liang, S. Lynn, O. Lust, N. B. Lutfi, M. Malz, A. Mandelbaum, R. Marshall, S. Meyers, J. Migliore, M. Moniez, M. Neveu, J. Newman, J. A. Nourbakhsh, E. Oldag, D. Park, H. Pelesky, S. Malagón, A. A. Plazas Quint, B. Rahman, M. Rasmussen, A. Reil, K. Roby, W. Roodman, A. Roucelle, C. Salvato, M. Sánchez, B. Sanmartim, D. Schindler, R. H. Scora, J. Sebag, J. Sedaghat, N. Sevilla-Noarbe, I. Shirley, R. Shugart, A. Solomon, R. Taranu, D. Thayer, G. Cipriano, L. Toribio San Urbach, E. Utsumi, Y. van Reeven, W. von der Linden, A. Walter, C. W. Wood-Vasey, W. M. Zuntz, J. Collaboration, LSST Dark Energy Science
author_facet	Zhang, T. Charles, E. Crenshaw, J. F. Schmidt, S. J. Adari, P. Gschwend, J. Mau, S. Andrews, B. Aubourg, E. Bains, Y. Bechtol, K. Boucaud, A. Boutigny, D. Burchat, P. Chevalier, J. Chiang, J. Chiang, H. -F. Clowe, D. Cohen-Tanugi, J. Combet, C. Connolly, A. Dagoret-Campagne, S. Daly, P. N. Daruich, F. Daubard, G. De Vicente, J. Drass, H. Fanning, K. Gawiser, E. Graham, M. Guy, L. P. Hang, Q. Ingraham, P. Ilbert, O. Jarvis, M. Jee, M. J. Jenness, T. Johnson, A. Juramy-Gilles, C. Kahn, S. M. Kalmbach, J. B. Kang, Y. Kannawadi, A. Kelvin, L. S. Liang, S. Lynn, O. Lust, N. B. Lutfi, M. Malz, A. Mandelbaum, R. Marshall, S. Meyers, J. Migliore, M. Moniez, M. Neveu, J. Newman, J. A. Nourbakhsh, E. Oldag, D. Park, H. Pelesky, S. Malagón, A. A. Plazas Quint, B. Rahman, M. Rasmussen, A. Reil, K. Roby, W. Roodman, A. Roucelle, C. Salvato, M. Sánchez, B. Sanmartim, D. Schindler, R. H. Scora, J. Sebag, J. Sedaghat, N. Sevilla-Noarbe, I. Shirley, R. Shugart, A. Solomon, R. Taranu, D. Thayer, G. Cipriano, L. Toribio San Urbach, E. Utsumi, Y. van Reeven, W. von der Linden, A. Walter, C. W. Wood-Vasey, W. M. Zuntz, J. Collaboration, LSST Dark Energy Science
contents	We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep ugrizy coverage in the Extended Chandra Deep Field South (ECDFS) field and griz data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalog from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of $σ_{\rm NMAD} \sim 0.03$ and outlier fractions around 10% in the 6-band data, with performance degrading at faint magnitudes and z>1.2. The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution n(z). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at z>1.2. Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.
format	Preprint
id	arxiv_https___arxiv_org_abs_2510_07370
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL) Zhang, T. Charles, E. Crenshaw, J. F. Schmidt, S. J. Adari, P. Gschwend, J. Mau, S. Andrews, B. Aubourg, E. Bains, Y. Bechtol, K. Boucaud, A. Boutigny, D. Burchat, P. Chevalier, J. Chiang, J. Chiang, H. -F. Clowe, D. Cohen-Tanugi, J. Combet, C. Connolly, A. Dagoret-Campagne, S. Daly, P. N. Daruich, F. Daubard, G. De Vicente, J. Drass, H. Fanning, K. Gawiser, E. Graham, M. Guy, L. P. Hang, Q. Ingraham, P. Ilbert, O. Jarvis, M. Jee, M. J. Jenness, T. Johnson, A. Juramy-Gilles, C. Kahn, S. M. Kalmbach, J. B. Kang, Y. Kannawadi, A. Kelvin, L. S. Liang, S. Lynn, O. Lust, N. B. Lutfi, M. Malz, A. Mandelbaum, R. Marshall, S. Meyers, J. Migliore, M. Moniez, M. Neveu, J. Newman, J. A. Nourbakhsh, E. Oldag, D. Park, H. Pelesky, S. Malagón, A. A. Plazas Quint, B. Rahman, M. Rasmussen, A. Reil, K. Roby, W. Roodman, A. Roucelle, C. Salvato, M. Sánchez, B. Sanmartim, D. Schindler, R. H. Scora, J. Sebag, J. Sedaghat, N. Sevilla-Noarbe, I. Shirley, R. Shugart, A. Solomon, R. Taranu, D. Thayer, G. Cipriano, L. Toribio San Urbach, E. Utsumi, Y. van Reeven, W. von der Linden, A. Walter, C. W. Wood-Vasey, W. M. Zuntz, J. Collaboration, LSST Dark Energy Science Instrumentation and Methods for Astrophysics Astrophysics of Galaxies We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep ugrizy coverage in the Extended Chandra Deep Field South (ECDFS) field and griz data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalog from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of $σ_{\rm NMAD} \sim 0.03$ and outlier fractions around 10% in the 6-band data, with performance degrading at faint magnitudes and z>1.2. The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution n(z). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at z>1.2. Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.
title	Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)
topic	Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
url	https://arxiv.org/abs/2510.07370