The ANDICAM-SOFI Near-infrared and Optical type Ia Supernova (ASNOS) sample: Description and data release

This paper presents the ASNOS dataset, a comprehensive collection of optical and near-infrared photometry for 41 Type Ia supernovae at low redshift, detailing the sample selection, data reduction, and light-curve fitting methods used to facilitate future cosmological analysis.

The Gist

Imagine the universe is a giant, dark ocean, and astronomers are trying to map its depth and shape. To do this, they need reliable lighthouses. Type Ia Supernovae are these lighthouses: massive stellar explosions that shine with a predictable brightness, allowing scientists to calculate exactly how far away they are.

For decades, astronomers have been building a map using these lighthouses, but they've mostly been looking at them with "optical" eyes (visible light, like what we see). However, the paper you're asking about, ASNOS, argues that we've been missing a crucial piece of the puzzle: Near-Infrared (NIR) vision.

Here is the story of the ASNOS paper, explained simply:

1. The Problem: Why Look in the Dark?

Think of the universe as a dusty attic. When you look at a lighthouse through a dusty window (visible light), the dust scatters the light, making the lighthouse look dimmer and redder than it really is. This "dust extinction" makes it hard to measure the true distance.

However, if you look through the dust with Near-Infrared glasses (which see heat radiation), the dust becomes almost invisible. The light passes right through. Furthermore, in this infrared "spectrum," supernovae act like even more perfect lighthouses—they don't need as many complicated math corrections to figure out their true brightness.

The problem? While we have thousands of visible-light supernova records, we only have a few hundred infrared ones. It's like having a million photos of a city in daylight but only a handful of photos taken at night.

2. The Solution: The ASNOS Team

The authors of this paper, led by Kim Phan, decided to fix this shortage. They launched a campaign called ASNOS (ANDICAM-SOFI Near-infrared and Optical type Ia Supernova).

• The Tools: They used two main telescopes. One was the SMARTS telescope in Chile, equipped with a special camera called ANDICAM that could take pictures in both visible light and infrared simultaneously. The other was the NTT telescope, using a camera called SOFI just for infrared.
• The Harvest: Over three years, they watched 41 supernovae. They didn't just snap a photo; they watched them like a reality TV show, taking pictures every few days for months to track how they brightened and faded. This created a dataset of 1,482 individual observations (epochs).

3. The Challenge: Cleaning the Lens

Taking these pictures wasn't easy. Imagine trying to photograph a firefly (the supernova) sitting in the middle of a giant, glowing bonfire (the host galaxy). The bonfire's light would wash out the firefly.

To solve this, the team had to perform "cosmic surgery." They took images of the galaxy after the supernova had faded away (using old archives or new telescope time) and used software to subtract the galaxy's light from the new images. It's like using Photoshop to remove a background so you can see the subject clearly. They also had to deal with "cosmic rays" (tiny particles hitting the camera) and the "sky background" (the faint glow of the atmosphere), cleaning the data until it was pristine.

4. The Result: A New Map

The paper is essentially a data release manual. It's not the final destination, but it's the foundation.

• The Data: They have provided the community with a massive, high-quality library of light curves (brightness over time) for these 41 supernovae.
• The Hosts: They didn't just look at the explosions; they also analyzed the "homes" of the supernovae (the host galaxies) to understand their mass and age, which helps refine the distance calculations even further.
• The Future: This paper is the "Part 1" of a two-part story. This part says, "Here is our new, super-clean data." The next paper (the "Part 2") will use this data to redraw the map of the universe, aiming to measure the expansion rate of the cosmos with unprecedented precision.

Why Does This Matter?

By adding these 41 new "infrared lighthouses" to the mix, the ASNOS team is increasing the size of the infrared supernova database by about 10%. While that sounds small, in astronomy, every new data point is a brick in the wall of our understanding.

In a nutshell: This paper is the team saying, "We built a better camera, cleaned up the dust, and took the best infrared photos of exploding stars ever. Here are the photos. Now, let's use them to figure out exactly how fast the universe is expanding and what it's made of."

Technical Summary

Here is a detailed technical summary of the paper "The ANDICAM-SOFI Near-infrared and Optical type Ia Supernova (ASNOS) sample: Description and data release."

1. Problem Statement

Type Ia supernovae (SNe Ia) are the primary "standard candles" used to measure extragalactic distances and constrain cosmological parameters, such as the expansion rate of the Universe ($H_0$) and dark energy properties. While optical observations of SNe Ia are abundant (over 6,000 publicly available), Near-Infrared (NIR) observations remain scarce (only ~300 publicly available).

• Scientific Motivation: NIR observations offer significant advantages over optical data:
  • Reduced sensitivity to dust extinction.
  • Intrinsic standard candle behavior with less scatter in peak luminosities.
  • Minimal need for empirical corrections (e.g., color-luminosity relations).
• Gap: The lack of large, high-quality NIR datasets limits the precision of cosmological analyses and the ability to fully exploit the advantages of the NIR regime.

2. Methodology

The authors present the ASNOS (ANDICAM-SOFI Near-infrared and Optical type Ia Supernova) sample, a comprehensive dataset combining optical and NIR photometry.

Data Acquisition

• Primary Instrument: ANDICAM (A Novel Dual Imaging CAMera) on the 1.3-meter SMARTS telescope at Cerro Tololo Inter-American Observatory (CTIO).
  • Campaign: 2018A–2019A (3 semesters).
  • Filters: Optical ($B, V, R, I$) and NIR ($Y, J, H$).
  • Sample: 41 SNe Ia (29 normal, 8 1991T-like, 4 peculiar/subtypes) at redshifts $z < 0.085$.
• Supplementary Instruments:
  • SOFI (Son OF ISAAC) on the 3.58-meter New Technology Telescope (NTT) at La Silla: Provided additional $J, H, K_s$ epochs via the PESSTO collaboration.
  • ATLAS & ZTF: Provided forced optical photometry to fill gaps and extend light curves.
• Host Galaxy Data: Template images from DES, Pan-STARRS, SkyMapper, UKIRT, and NOTCam were used to subtract host galaxy light.

Data Reduction Pipeline

The authors developed a custom reduction pipeline using Python packages (ccdproc, photutils, ImageMatch):

1. Pre-processing: Bias and dark subtraction, flat-fielding (using master flats/darks from the SMARTS archive).
2. Cosmic Ray Removal: Using ccdproc.cosmicray_lacosmic.
3. Quadrant Correction: Addressing background level differences between detector quadrants.
4. Sky Subtraction: Creating sky backgrounds from dithered images, masking bright sources (host galaxies) to prevent contamination.
5. World Coordinate System (WCS): Assigned using Astrometry.net (with fallback to relative host positions for difficult cases).
6. Template Subtraction: Used ImageMatch to geometrically align and convolve template images with science images to remove host galaxy light.
7. Photometry:
   • Aperture Photometry: Performed on local sequence stars and SNe using circular apertures optimized to $0.673 \times \text{FWHM}$.
   • Calibration: Used the ATLAS-REFCAT2 catalog. An iterative process was employed to determine zeropoints and color-term coefficients to transform catalog magnitudes to the instrument's natural system.
   • Extinction: Corrected for Milky Way extinction; atmospheric extinction coefficients were applied for optical bands but assumed zero for NIR bands due to low values at the observatory sites.

Analysis & Fitting

Three independent light-curve fitting tools were applied to the data:

1. SALT3-NIR: An extension of the SALT3 model trained on optical and NIR data, implemented via SNCosmo.
2. SNooPy: A tool calibrated on CSP light curves, using $\Delta m_{15}$ and color-stretch parameters.
3. BayeSN: A hierarchical Bayesian model for SNe Ia Spectral Energy Distributions (SEDs).

Host Galaxy Characterization

• HostPhot: Used to construct low-resolution SEDs for global and local (1–3 kpc) environments of the SNe.
• Prospector: Used for stellar population synthesis to derive host galaxy properties, including stellar mass ($M_*$), metallicity ($Z$), and star formation rates (SFR) over different timescales.

3. Key Contributions

• Data Release: The paper releases a dataset of 1,482 epochs in optical/NIR filters for 41 SNe Ia, significantly expanding the public NIR sample (increasing it by ~10%).
• Methodological Framework: Detailed documentation of a robust reduction pipeline specifically tailored for dual-imaging NIR/optical data, including handling of quadrant background variations and iterative color-term calibration.
• Multi-Tool Comparison: Provides a direct comparison of light-curve parameters derived from SALT3-NIR, SNooPy, and BayeSN, highlighting systematic differences (e.g., BayeSN yielding systematically brighter $B_{max}$ than SNooPy).
• Host Environment Data: Releases global and local host galaxy photometry and derived physical parameters (mass, metallicity, SFR) essential for correcting the "mass-step" in cosmological analyses.

4. Results

• Sample Composition: The sample includes 29 normal SNe Ia, 8 1991T-like objects, 2 1991bg-like, 1 2002cx-like, and 1 Ic-broad line event.
• Photometric Quality:
  • Template subtraction was shown to be critical for SNe located deep within their host galaxies ($d_{DLR} < 1$), reducing contamination significantly.
  • Color-term coefficients were successfully derived for all filters, though the $Y$-band showed larger uncertainties due to a limited number of local sequence stars.
  • $K_s$-band data from SOFI was found to be too noisy for some targets and was largely excluded from the final fitting.
• Fitting Consistency:
  • The three fitters generally agreed within $\sim0.1$ mag on peak magnitudes.
  • Discrepancies were noted in specific cases (e.g., ATLAS data incompatibility with ANDICAM in the $B$-band for some objects), leading to the exclusion of ATLAS data for those specific fits to ensure accuracy.
• Host Properties: Stellar masses for the hosts range widely, allowing for future analysis of the mass-step correction in the NIR.

5. Significance

• Cosmological Impact: This dataset serves as the foundation for a companion paper focused on precision cosmology. By combining these high-quality NIR observations with literature data, the authors aim to construct Hubble diagrams with reduced systematic errors associated with dust and intrinsic scatter.
• Future Projects: The methods and data presented here form the basis for the Aarhus-Barcelona cosmic FLOWS project, which aims to observe over 700 SNe Ia in the NIR.
• Community Resource: The release of the ASNOS sample addresses the critical bottleneck of NIR data scarcity, enabling other researchers to test cosmological models and SN physics in the infrared regime.

Conclusion: The ASNOS paper is a foundational data release that bridges the gap between optical and NIR supernova cosmology. By providing a rigorously reduced, multi-instrument dataset with comprehensive host galaxy characterization, it enables more precise measurements of extragalactic distances and paves the way for next-generation cosmological constraints.