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Euclid: An automated system to match Rubin transient alerts to Euclid observations

C. Duffy, I. M. Hook, C. M. Gutierrez, K. Paterson, V. Petrecca, T. J. Moriya, F. Poidevin, R. Kotak, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, A. Balestra, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, G. Cañas-Herrera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, G. Castignani, S. Cavuoti, K. C. Chambers, A. Cimatti, C. Colodro-Conde, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, M. Cropper, J. -C. Cuillandre, H. Degaudenzi, G. De Lucia, H. Dole, F. Dubath, X. Dupac, S. Dusini, S. Escoffier, M. Farina, R. Farinelli, S. Ferriol, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, M. S. Holliman, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, S. Kermiche, A. Kiessling, R. Kohley, B. Kubik, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, D. Maino, E. Maiorano, O. Mansutti, S. Marcin, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, E. Medinaceli, S. Mei, Y. Mellier, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, R. Nakajima, C. Neissner, S. -M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. A. Popa, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, Z. Sakr, D. Sapone, B. Sartoris, M. Schirmer, P. Schneider, A. Secroun, G. Seidel, S. Serrano, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, J. Skottfelt, L. Stanco, J. Steinwagner, P. Tallada-Crespí, A. N. Taylor, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, L. Valenziano, J. Valiviita, T. Vassallo, Y. Wang, J. Weller, G. Zamorani, E. Zucca, J. García-Bellido, E. Jullo, J. Martín-Fleitas, A. A. Nucita, V. Scottez

TL;DR

This work presents a prototype system to automatically match Rubin transient alerts to Euclid observations, producing joint light curves and image cutouts that span optical and near-infrared wavelengths. Built around Lasair alerts (as a Rubin proxy), the pipeline integrates spatially overlapping Euclid LE2 stacks, applies a temporal overlap window, and gathers photometry—initially via forced aperture and progressively via PSF photometry using Euclid PSF models. The authors demonstrate the approach with real ZTF data, showing cases where Euclid detects transients earlier and cases where Euclid enhances host morphology and association even without a direct detection, including SN 2024pvw. Looking ahead, the work outlines a path to Rubin-based data streams, scaling to large alert volumes, API access, and integration into full joint transient DDPs, with broad potential extensions to other time-domain phenomena and data products.

Abstract

The Vera C. Rubin observatory is expected to produce 10 million transient alerts per night in ugrizy filters, whilst Euclid is a visible to near-infrared space telescope engaged in a wide field survey. We present a prototype system to automatically match the transient alerts from Rubin to Euclid observations. The system produces joint light-curves containing both visible and near-infrared photometry, and joint image cutouts. Using Zwicky Transient Facility alerts as a proxy for Rubin, we demonstrate the system in use in cases where Euclid did and did not detect the transient and highlight the value that can be added in each case. For transients detected by Euclid these benefits include identifying the supernovae (SNe) in observations taken prior to ground-based detection, thereby better constraining the explosion time, such as SN 2024pvw detected ~3 d prior to ground based detections. In cases where Euclid did not detect the transient, we demonstrate the benefit of adding Euclid observations to improve host morphology measurements and associations.

Euclid: An automated system to match Rubin transient alerts to Euclid observations

TL;DR

This work presents a prototype system to automatically match Rubin transient alerts to Euclid observations, producing joint light curves and image cutouts that span optical and near-infrared wavelengths. Built around Lasair alerts (as a Rubin proxy), the pipeline integrates spatially overlapping Euclid LE2 stacks, applies a temporal overlap window, and gathers photometry—initially via forced aperture and progressively via PSF photometry using Euclid PSF models. The authors demonstrate the approach with real ZTF data, showing cases where Euclid detects transients earlier and cases where Euclid enhances host morphology and association even without a direct detection, including SN 2024pvw. Looking ahead, the work outlines a path to Rubin-based data streams, scaling to large alert volumes, API access, and integration into full joint transient DDPs, with broad potential extensions to other time-domain phenomena and data products.

Abstract

The Vera C. Rubin observatory is expected to produce 10 million transient alerts per night in ugrizy filters, whilst Euclid is a visible to near-infrared space telescope engaged in a wide field survey. We present a prototype system to automatically match the transient alerts from Rubin to Euclid observations. The system produces joint light-curves containing both visible and near-infrared photometry, and joint image cutouts. Using Zwicky Transient Facility alerts as a proxy for Rubin, we demonstrate the system in use in cases where Euclid did and did not detect the transient and highlight the value that can be added in each case. For transients detected by Euclid these benefits include identifying the supernovae (SNe) in observations taken prior to ground-based detection, thereby better constraining the explosion time, such as SN 2024pvw detected ~3 d prior to ground based detections. In cases where Euclid did not detect the transient, we demonstrate the benefit of adding Euclid observations to improve host morphology measurements and associations.
Paper Structure (30 sections, 8 figures, 3 tables)

This paper contains 30 sections, 8 figures, 3 tables.

Figures (8)

  • Figure 1: Image: The Euclid Wide Survey shown in a Mollweide projection. The blue border bounds the entire region of interest. Colour coding denotes the year of planned observation within the 6 year survey. Bright green areas denote the Euclid Deep Fields. Red marks denote auxiliary fields. 2024EuclidI. Inset image: The Rubin Observatory’s v5.0.0 10 baseline survey coloured by visits, with special fields such as deep drilling fields (DDF) indicated in text lsst-survey-plan.
  • Figure 2: Schematic flow of operations in creating DDP following a Rubin alert. This shows the flow of operations in the situation where there exists transient alerting infrastructure. Steps and decisions on the right hand side which require transient alerts are excluded from this work. Nevertheless, they are shown here for completeness.
  • Figure 3: Example of a joint alert page showing ZTF24aawshuy.
  • Figure 4: ZTF24aawshuy: Upper left:$I_{\rm{E}}$ band $12\arcsec\times12\arcsec$ cutout at $\rm JD=2460510$. Upper right: ZTF r-band $12\arcsec\times12\arcsec$ cutout of the detection image at $\rm JD=2460513$; in both North on top and East to the left. Lower: Joint light curve, where detections are shown with filed circles and upper limits with triangles. Circles show and triangles show ZTF detections and upper limits respectively. Plus and diamonds show PSF photometry detections and upper limits respectively. Crosses show forced aperture photometry; these appear consistently brighter due to the local background contribution from the host galaxy.
  • Figure 5: ZTF24aauueye. Top:$30\arcsec\times30\arcsec$$I_{\rm{E}}$ band image at $\rm JD=2460509$ and ZTF g-band discovery image at $\rm JD=2460494$; in both North on top and East to the left. Bottom: Light curve as in Fig. \ref{['fig:SN2024pvw']}.
  • ...and 3 more figures