Euclid Quick Data Release (Q1): Euclid spectroscopy of quasars. 1. Identification and redshift determination of 3500 bright quasars

This paper presents the identification and redshift determination of approximately 3,500 bright quasars from the Euclid Quick Data Release using slitless NISP spectroscopy, resulting in a new homogeneous catalogue, the first Euclid quasar composite spectrum, and an analysis of host galaxy morphologies and AGN selection techniques.

The Gist

Imagine the universe as a giant, dark ocean filled with billions of islands (galaxies) and lighthouses (quasars). For a long time, astronomers have tried to map this ocean, but it's hard to see the lighthouses clearly because they are so far away and often hidden behind cosmic fog.

This paper is like a report from a new, super-powered lighthouse scanner called Euclid, which is a space telescope launched by the European Space Agency. The team behind this paper has just released their first batch of data (called "Quick Data Release 1" or Q1), and they have successfully identified 3,500 bright quasars using this new scanner.

Here is the breakdown of what they did, using some everyday analogies:

1. The Challenge: The "Blurry Prism" Problem

Usually, to identify a star or a galaxy, astronomers use a prism to split its light into a rainbow (a spectrum). This tells them what the object is made of and how fast it's moving away from us (its "redshift").

However, Euclid uses a special mode called slitless spectroscopy. Imagine taking a photo of a crowded party where everyone is holding a flashlight. Instead of isolating each person to see their light clearly, you take a picture of the whole room at once. The beams of light cross over each other, creating a messy, overlapping rainbow.

• The Problem: It's hard to tell which color belongs to which person. The light is also a bit "fuzzy" (low resolution), and sometimes the light from one person spills over onto another.
• The Solution: The team developed a clever way to untangle this mess. They combined data from three different angles (like looking at the party from three different sides) to separate the overlapping beams and create a clean, individual spectrum for each quasar.

2. The Hunt: Finding Needles in a Haystack

Before looking at the space telescope data, the team needed a list of suspects. They didn't just look randomly; they used two "wanted posters" from other telescopes:

• Gaia: A map of stars and galaxies in our own neighborhood.
• WISE: A map of infrared heat signatures from the whole sky.

They cross-referenced these lists with the Euclid data. Think of it like having a list of 10,000 people who might be the lighthouses, and then using the Euclid scanner to check their ID cards.

• The Result: Out of 9,214 candidates they checked, they confirmed 3,468 were real quasars. Even better, 2,686 of these were brand new discoveries that no one had ever spectroscopically confirmed before!

3. The "Composite" Recipe: Making a Super-Spectrum

One of the coolest things they did was create a "Composite Quasar."
Imagine you have 3,000 different photos of lighthouses. Some are blurry, some are bright, some are dim. If you stack all of them on top of each other and blend them together, the random noise (static) cancels out, and the true shape of the lighthouse becomes crystal clear.

• The Achievement: They created the first-ever "average" spectrum of a quasar using Euclid data. This is like a "master blueprint" of what a quasar looks like from the ultraviolet to the infrared. Crucially, because Euclid is in space, this blueprint is free of Earth's atmospheric interference (telluric lines), which usually acts like a dirty window that blocks certain colors.

4. The "Cosmic Zoom" and Morphology

The team also looked at what these quasars look like in the images (not just the light spectra).

• Low Redshift (Close by): When quasars are closer to us, we can see the "host galaxy" clearly. It's like seeing a lighthouse sitting on a rocky island. You can see the rocks, the waves, and the structure of the island.
• High Redshift (Far away): When they are very far away, the lighthouse is so bright and the island is so small in our view that the lighthouse light swallows the island whole. It looks like a single, perfect dot of light.
• The Insight: They found that for the far-away ones, the "dot" is so dominant that standard measuring tools get confused. They had to use a special "AI detector" (a deep-learning model) to figure out how much of the light is the lighthouse vs. the island.

5. The Limits: How Deep Can We See?

They also figured out the "limit" of their new scanner.

• If a quasar is too faint (like a candle in a storm), the scanner can't distinguish it from the background noise. They found that for the Wide Field Survey, quasars fainter than a certain brightness (roughly magnitude 21.5) become too hard to identify reliably with this first batch of data. It's like trying to read a book in the dark; once the light gets too dim, you just can't make out the words anymore.

Why Does This Matter?

• New Map: They have added thousands of new points to the map of the universe, helping us understand how galaxies and black holes are distributed.
• Better Tools: The "Composite Spectrum" they built is a new standard tool. Future astronomers will use this "master blueprint" to study new quasars more accurately.
• Future Proofing: This paper is just the appetizer. As Euclid collects more data over the coming years, it will find even fainter, more distant quasars, helping us understand the very early universe and the nature of dark energy.

In short: The Euclid team took a messy, overlapping rainbow of light, cleaned it up, and used it to find thousands of cosmic lighthouses, creating a new "average" picture of what a quasar looks like and proving that their new space telescope is a powerful tool for exploring the deep universe.

Technical Summary

Here is a detailed technical summary of the paper "Euclid spectroscopy of quasars. 1. Identification and redshift determination of 3500 bright quasars".

1. Problem Statement

The Euclid space mission aims to probe dark matter and dark energy by surveying a large fraction of the sky. A key instrument, the Near-Infrared Spectrometer and Photometer (NISP), operates in a slitless spectroscopy mode with a wide field of view (0.57 deg²). While this enables efficient large-scale spectroscopic surveys, it introduces specific challenges:

• Low Spectral Resolution: The resolving power ($R > 480$) is lower than traditional slit spectroscopy.
• Spectral Overlap: In a slitless mode, spectra from multiple sources can overlap, complicating source isolation.
• Contamination and Noise: The convolution of the intrinsic spectrum with the spatial light profile leads to upturns at spectral ends and lower signal-to-noise ratios (S/N).
• Redshift Degeneracy: Without high-resolution features, determining redshifts for objects with weak or single emission lines is difficult.

The paper addresses the need to validate the efficiency of Euclid's slitless spectroscopy for identifying Active Galactic Nuclei (AGNs) and determining their redshifts, specifically focusing on bright quasars in the first Quick Data Release (Q1).

2. Methodology

Data Selection and Input Sample

• Input Candidates: The authors constructed a high-purity input sample of 10,201 quasar candidates by cross-matching the Euclid Q1 photometric catalogue with:
  • Gaia DR3: Purified subsets (Quaia, CatNorth, CatSouth) totaling ~1.9 million candidates, selected via machine learning and variability analysis.
  • WISE (AllWISE): The R90 AGN candidate catalogue (90% reliability), selected using mid-infrared colors ($W1-W2$).
• Spectroscopic Data: The study utilized 1D slitless spectra from the NISP red grisms (RGE band: 1206–1892 nm). Spectra were trimmed to the useful range (12,047–18,734 Å) to avoid edge artifacts.

Identification and Redshift Determination

1. Template Matching (PCF): An initial redshift estimate ($z_{temp}$) was derived using a Pearson Correlation Function (PCF) comparing observed spectra against a rest-frame quasar template.
2. Visual Inspection: An interactive tool (PGSpecPlot) was used to manually verify spectra.
   • Cross-Validation: When available, Gaia redshifts ($z_{Gaia}$) were compared with $z_{temp}$. A match within $|z_{temp} - z_{Gaia}|/(1+z_{temp}) < 0.15$ was considered secure.
   • Line Identification: Redshifts were determined based on prominent emission lines visible in the RGE band:
     • $z < 0.89$: [S III], Pa$\delta$, He I, Pa$\gamma$, Pa$\beta$.
     • $0.89 < z < 1.83$: H$\alpha$ (strongest line).
     • $1.83 < z < 2.85$: H$\beta$, [O III], H$\gamma$.
     • $z > 3.3$: Mg II.
3. Spurious Rejection: Sources with inconsistent morphologies (high concentration parameter $\mu_{max} - \text{mag}$) at high redshifts were flagged as spurious and removed.
4. External Validation: The final sample was cross-matched with DESI DR1 and the Million Quasar Catalogue (Milliquas) to identify new discoveries and verify redshift accuracy.

Morphological Analysis

• VIS Imaging: The Visible Camera (VIS) data was used to analyze host galaxy structures.
• Metrics:
  • Sérsic Index ($n_{VIS}$): To fit light profiles.
  • CAS Parameters: Concentration, Asymmetry, and Clumpiness.
  • Deep Learning PSF Fraction ($f_{PSF}$): A model-independent metric to quantify the fraction of light dominated by the point-like nucleus vs. the extended host.

Composite Spectrum Generation

• A "golden sample" of 2,868 high-quality quasars was selected (S/N > 3, minimal invalid pixels).
• A 1D Drizzle algorithm was applied to rebin spectra onto a common rest-frame grid ($\Delta\lambda = 4$ Å) to create a composite spectrum free of telluric absorption lines.

3. Key Contributions

1. Large Homogeneous Sample: Identification of 3,468 bright quasars with reliable spectroscopic redshifts in the range $0 < z \lesssim 4.8$.
2. New Discoveries: 2,686 of these quasars are new spectroscopic identifications not present in existing public compilations (DESI, Milliquas).
3. First Euclid Composite Spectrum: Generation of the first composite quasar spectrum covering rest-frame NUV to NIR (0.32–1.48 $\mu$m) using Euclid data, which is free from telluric absorption lines that plague ground-based composites.
4. Empirical Depth Limits: Established the practical magnitude limits for reliable slitless spectroscopic identification in the Wide Field Survey: $J_E \lesssim 21.5$ and $H_E \lesssim 21.3$. Beyond these limits, secure identification rates drop sharply.
5. Morphological Insights: Demonstrated the redshift dependence of quasar host morphology and the limitations of single-Sérsic fits for high-redshift, nucleus-dominated sources.

4. Key Results

• Success Rate: The spectroscopic identification success rate for the input sample was 37.6% (3,468 quasars out of 9,214 sources with spectra).
• Redshift Distribution: The sample peaks at $0.89 \lesssim z \lesssim 1.83$(where H$\alpha$is visible), with a sharp drop-off when H$\alpha$ moves out of the spectral window.
• Morphology vs. Redshift:
  • Low Redshift ($z < 0.5$): Host galaxies are frequently resolved. Single-Sérsic models fit ~50% of sources, though many show high Sérsic indices ($n > 5.45$) indicating bright cores.
  • Intermediate Redshift ($0.5 < z < 2$):** The nuclear component dominates. **90$n \approx 5.5$), rendering single-component fits unreliable. In this regime, the **$f_{PSF}$ metric (derived from deep learning) is a superior measure of compactness, showing a peak around 0.8 (nucleus-dominated).
• Color Space: The non-Gaia subset (selected via WISE) occupies redder color spaces in Euclid diagrams ($Y_E - H_E$, $J_E - H_E$), confirming the survey's ability to detect dust-reddened quasars missed by optical surveys.
• Spectral Properties: The composite spectrum shows a flat NIR continuum slope ($\alpha_\lambda \approx -0.73$), likely due to host galaxy light contributions from low-redshift AGNs in the sample.

5. Significance

• Validation of Euclid Capabilities: The paper proves that Euclid's slitless spectroscopy is a powerful tool for large-scale quasar censuses, overcoming many limitations of ground-based slitless surveys through space-based sensitivity and resolution.
• Cosmological Probes: The large, homogeneous sample of quasars with known redshifts will be crucial for mapping the large-scale structure of the universe and studying baryon acoustic oscillations (BAO).
• AGN Selection Techniques: The study highlights the synergy between mid-infrared (WISE) and optical/NIR (Euclid) photometry for selecting obscured and red quasars, providing a blueprint for future AGN selection strategies.
• Benchmark Data: The release of the composite spectrum and the detailed catalogue provides a vital reference for future spectral analysis, template generation, and machine learning training for AGN identification in upcoming Euclid data releases.
• Methodological Advancement: The successful application of the 1D Drizzle technique and deep-learning-based morphological decomposition sets a precedent for analyzing slitless spectroscopic data from space telescopes.