The First Data Release of CNIa0.02 -- A Complete Nearby (Redshift<0.02) Sample of Type Ia Supernova Light Curves

The CNIa0.02 project aims to collect a complete, nearby sample of Type Ia supernovae (SNe Ia) light curves, and the SNe are volume-limited with host-galaxy redshifts z_host<0.02. The main scientific goal is to infer the distributions of key properties (e.g., the luminosity function) of local SNe Ia in a complete and unbiased fashion in order to study SN explosion physics. We spectroscopically classify any SN candidate detected by the All-Sky Automated Survey for Supernovae (ASAS-SN) that reaches peak brightness<16.5 mag. Since ASAS-SN scans the full sky and does not target specific galaxies, our target selection is effectively unbiased by host-galaxy properties. We perform multi-band photometric observations starting from the time of discovery. In the first data release (DR1), we present the optical light curves obtained for 247 SNe from our project (including 148 SNe in the complete sample), and we derive parameters such as the peak fluxes, dm15 and s_BV.


Introduction
The explosion mechanism and progenitors of Type Ia supernovae (SNe Ia) are basic but open questions in astrophysics. There are several proposed channels, but no agreement as to which or even how many of the channels dominate (see, e.g., Maoz et al. 2014). SNe Ia span about an order of magnitude in peak luminosities and in the masses of synthesized 56 Ni that power the radiation. It is also debated whether this range in properties represents one continuous population or more than one overlapping but distinct populations. On the one hand, the main properties of SNe Ia appear to be continuous across the whole luminosity range. Phillips (1993) found that the peak luminosity of SNe Ia is tightly correlated with the light-curve shape characterized by the B-band post-peak decline rate Δm 15 (B), and this width-luminosity relation (WLR) is the foundation for using SNe Ia as cosmological distance indicators. Many properties of their light curves (see, e.g., Phillips 1993Phillips , 2012Burns et al. 2014;Bulla et al. 2020) and spectra (see, e.g., Nugent et al. 1995;Branch et al. 2009) also appear to be continuous. On the other hand, the possible existence of more than one populations of SNe Ia has long been discussed (e.g., Branch & Miller 1993), including recent claims of bimodality in the luminosity function (via the proxy of Δm 15 (B); see, e.g., Ashall et al. 2016;Hakobyan et al. 2020), existence of two classes of fast-declining SNe Ia (Dhawan et al. 2017), and distinct near-ultraviolet (NUV)-optical (Milne et al. 2013) and early-time optical (Stritzinger et al. 2018) colors.
There have been tremendous efforts to obtain high-quality multiband light curves of large samples of nearby SNe Ia (e.g., Hamuy et al. 1996;Riess et al. 1999;Jha et al. 2006;Hicken et al. 2009;Contreras et al. 2010;Ganeshalingam et al. 2010;Stritzinger et al. 2011;Hicken et al. 2012;Krisciunas et al. 2017;Foley et al. 2018;Stahl et al. 2019). However, collecting a complete and unbiased nearby sample has only been made possible recently, thanks to the advent of wide-field timedomain surveys that do not target specific galaxies, such as the All-Sky Automated Survey for SuperNovae (ASAS-SN; Shappee et al. 2014;Kochanek et al. 2017), the Gaia transient survey (Hodgkin et al. 2021), the Palomar Transient Factory (Law et al. 2009) and its successor the Zwicky Transient Facility (ZTF; Kulkarni 2016;Perley et al. 2020), the Asteroid Terrestrial-impact Last Alert System (ATLAS; Tonry 2011; Tonry et al. 2018a), the Mobile Astronomical System of TElescope Robots (MASTER; Gorbovskoy et al. 2013), OGLE Transients Detection System (OTDS; Wyrzykowski et al. 2014), the Pan-STARRS Survey for Transients (PSST; Huber et al. 2015;Chambers et al. 2016), and the Catalina Real-Time Transient Survey (CRTS; Drake et al. 2009). Compared to other untargeted surveys, ASAS-SN is a dedicated survey with the main goal to search for bright, nearby SNe scanning the entire visible sky at approximately nightly cadence (a cadence of 2-3 nights down to ∼17 mag prior to the expansion in 2017 and a nightly cadence down to ∼18.5 mag after the expansion). The Gaia transient survey has a limiting magnitude down to 20.7 mag, and it is also an all-sky transient survey, but has a very uneven cadence across the sky, which can be up to months. Most other surveys do not have full-sky coverage, while many of them have access to a large fraction of the sky at a typical cadence on the order of days with deeper limiting magnitudes (given in the parentheses following the survey names) compared with ASAS-SN: ZTF (∼20.5 mag), Pan-STARRS (∼21.8 mag), MASTER (∼20 mag), ATLAS (∼20 mag), and CRTS(∼19.5 mag). For most untargeted surveys, there is no attempt to make spectroscopic classifications for all detected candidates selected according to certain criteria to form a complete sample. Furthermore, many timedomain surveys are primarily carried out in single bands, so without additional systematic follow-up efforts, it is not possible to obtain the color information that is critical to derive host-galaxy extinction and constrain SN physics.
We carry out the CNIa0.02 project to collect a complete, nearby, and effectively unbiased sample of Type Ia SNe at host-galaxy redshifts z host < 0.02 with well-observed multiband light curves. Our follow-up observations started in 2015 January and ended in 2020 January, and the SNe observed between 2015 September 17 and 2019 January 31 followed the selection criteria of the complete sample discussed below. The main goal for constructing a complete sample that is unbiased toward host-galaxy properties is to enable a reliable statistical inference on the distributions of photometric properties of SNe Ia (e.g., luminosity, color, light-curve shape, and derived physical parameters) in the local universe and also to study their dependence on host-galaxy properties.
To our knowledge, collecting and studying a complete sample in astronomy can be traced back to Schmidt (1968), who studied a complete sample of quasars defined with an observed flux density limit to derive their spatial distribution and luminosity function. Since then, complete samples have been widely applied in many areas of astronomy, and for instance, the LOSS survey produced one of the most influential complete samples of SNe from targeted searches (Leaman et al. 2011;Li et al. 2011aLi et al. , 2011b. Such a complete sample is defined to include all objects that meet a certain set of welldefined selection criteria on observables, making it possible to derive quantitative completeness corrections to infer the statistical distribution of intrinsic properties such as the luminosity function. For the complete sample of CNIa0.02, we adopt the following observational selection criteria: (a) host-galaxy redshifts z < 0.02, (b) peak brightness V peak < 16.5 mag, and (c) detection by the ASAS-SN survey, that is, we not only include SNe discovered by ASAS-SN, but also SNe that were discovered first by others and were later detected by ASAS-SN. The ASAS-SN detections are nearly 100% complete for SNe with peak brightness <16.5 mag (see Appendix C), and the ASAS-SN sample also has minimal bias in host-galaxy properties or SN locations inside the hosts (Holoien et al. 2017a(Holoien et al. , 2017b(Holoien et al. , 2017c(Holoien et al. , 2019. All of the SNe in DR1 have been spectroscopically classified by ASAS-SN or other groups. The complete sample includes all spectroscopic subclasses that are known to follow the WLR of the SNe Ia population. These include Ia-91bg and Ia-91T subtypes, but exclude SNe Iax and other peculiar SNe Ia-like objects that deviate from the WLR of SNe Ia (see Appendix D for a detailed discussion). The redshifts derived from SN classification spectra generally have too large uncertainties for our purpose, so we adopt host-galaxy spectroscopic redshifts for our complete sample selection. Where host-galaxy redshifts were unavailable in the NASA/IPAC Extragalactic Database 41 (NED), we have also measured the host-galaxy redshifts directly to determine whether the SNe Ia belong to the complete sample. We do not exclude SN candidates without apparent hosts from our selection (i.e., the "hostless" SN). In our project, ASASSN-18nt is the only hostless SN, which is an intracluster SN Ia located in the galaxy cluster Abell 0194 (z = 0.018), and its peak brightness (16.66 ± 0.02) does not meet our selection criterion for the complete sample. All of the SNe were followed photometrically, mainly in the optical bands (primarily BVri), but with near-infrared (IR) and Swift NUV observations of some objects as well. In this first data release (DR1) of CNIa0.02, we present optical light curves for 247 SNe Ia observed between 2015 and 2020. CNIa0.02 DR1 includes some SNe Ia that are not in the complete sample, and the complete sample has 148 SNe in total. We describe the overall project and the sample in Section 2, the data processing in Section 3, and the resulting light curves in Section 4. Our present results are summarized in Section 5.

Program Description and the Sample
We select our targets primarily based on ASAS-SN detections, and the complete sample was collected between 2015 September 17 and 2019 January 31. We have also observed a few SNe Ia before (since 2015 January) and after this period (until 2020 January), and they are included in DR1, but are not part of the complete sample. In the early phase of the complete sample collection, we attempted to observe all SNe Ia with z < 0.034 and a peak magnitude of V peak < 17. Between 2016 October and 2019 January, we restricted the complete sample to focus on SNe Ia with z < 0.02 and a peak magnitude of V peak < 16.5, as shown in Figure 4 and discussed in Appendix A. The detection efficiency of the ASAS-SN survey has been evolving mainly owing to upgrades in hardware, and since 2015, the detection efficiency has been almost 100% complete to <16.5 mag (see Appendix C for a detailed discussion of the sample completeness).
In Table 1 we give the general information (names given by the survey groups, IAU names, equatorial coordinates, discovery dates, host-galaxy names, and heliocentric host redshifts) for all objects in the CNIa0.02 DR1, which includes objects that have follow-up data (regardless of whether they belong to the complete sample) or have been considered for follow-up observations (regardless of whether such data are obtained). The host-galaxy redshifts are either from NED or are new measurements presented in Table 2. There are four SNe whose host-galaxy spectroscopic redshifts are not yet available, and for them, the redshifts determined from the SN spectra are given in Table 1 and are indicated with asterisks. Note that for all those four SNe, their peak magnitudes are fainter than 16.5, so they do not belong to the complete sample. We also provide additional information of V-band peak magnitudes (see Section 4.2 for how they are measured) and whether they were detected by ASAS-SN in Table 1. The complete sample includes 148 SNe. Figure 1 shows the cumulative distribution of host-galaxy redshifts of all SNe and those in the complete sample as blue and black histograms, respectively, and the latter roughly follows the expectation for a volume-limited complete sample (shown with the red line) when the peculiar velocity is negligible compared to the Hubble expansion velocity (at z  0.01). Note that our complete sample includes all SNe Ia selected by the observational criteria of V peak < 16.5 and z < 0.02. It does not include all SNe Ia at the dim end of the luminosity function (−18.2) near z = 0.02, therefore it is not expected to exactly follow the distribution of a volumelimited complete sample covering the full luminosity range.
CNIa0.02 DR1 includes V-band and g-band photometry from the 14 cm telescopes used to conduct the ASAS-SN survey. Immediately after the discovery of an SN candidate that met our magnitude criteria, we started multiband photometric observations, regardless of whether a spectroscopic classification was available then. For most objects, this data release contains followup photometry ending around 40-60 days after the optical peak. For objects with bright galaxy backgrounds that require image subtractions, we took template images at least 300 days after Bband peak, when the SN is typically more than 7 mag below the peak. We have performed photometric follow-up observations using a number of telescopes ranging from ∼0.3 m to ∼2 m. In this data release, most data are in BVri bands observed by 1 m telescopes of the Las Cumbres Observatory Global Telescope network (LCOGT; Brown et al. 2013) distributed over four sites covering both hemispheres, two 0.6 m telescopes in Sierra Remote Observatories (CA, USA) and Mayhill (NM, USA) of the Post Observatory (PO), and the 1.3 m telescope of Small & Moderate Aperture Research Telescope System (SMARTS; Subasavage et al. 2010). For SNe found between 2016 October and 2018 March, we carried out a follow-up program using the Ultra-Violet/Optical Telescope (UVOT; Roming et al. 2005) on the Neil Gehrels Swift Observatory (Swift; Gehrels et al. 2004), and the UVOT bv-band data from that program are included in DR1. We also include some photometric data obtained from the 2 m Liverpool Telescope (LT), 0.5 m DEdicated MONitor of EXotransits and Transients (DEMONEXT; Villanueva et al. 2018), the 1 m telescope at WeiHai observatory of Shandong University (WHO; Hu et al. 2014), a 0.41 m telescope at A77 observatory, the Ohio State Multi-Object Spectrograph (OSMOS) on the 2.4 m Hiltner Telescope at the MDM observatory, the Wide Field reimaging CCD (WFCCD) camera and directimaging CCD camera SITe2K on the 2.5 m du Pont telescope, and Alhambra Faint Object Spectrograph and Camera (ALFOSC) on the 2.56 m Nordic Optical Telescope (NOT). The instrument specifications for the above facilities are described in Appendix B. We plan to make other follow-up data collected by our project available in the future.

Data Processing
This data release contains the results of processing over 20,000 images from ground-based observations and also Swift-UVOT images. For ground-based data, we developed the 41 https://ned.ipac.caltech.edu       Table 2. If the host-galaxy spectroscopic redshift is not available, then the SN spectroscopic redshift is displayed here instead and is indicated with an asterisk. ASASSN-18nt (2018ctv) was discovered in the galaxy cluster Abell 0194 (Chen et al. 2018), which was found to be not associated with any obvious galaxy in the cluster, but is located in the intracluster light appearing to bridge between the galaxy pair NGC545+547 and NGC541 (Moral-Pombo et al. 2018). Here we adopt the redshift of the galaxy cluster for ASASSN-18nt (Struble & Rood 1999 photometric pipeline PmPyeasy to automatically process the images and obtain the photometry. The pipeline uses several external software packages that are all wrapped in a Python interface. The pipeline runs automatically by default, but allows manual operations at any point when necessary. The pipeline uses pyds9 42 to facilitate human inspections through XPA messaging to SAOImageDS9. 43 It takes images that have already been preprocessed, including bias removal and flat-fielding. Below we outline our procedures, and at the end of the section, we summarize our reduction of the UVOT data.

Image Registration and Source Detection
The pipeline distributes all the images to object-specific folders and adds information such as the filter, exposure time, and epoch to a database. Next, it removes cosmic rays using an implementation of the L. A.Cosmic algorithm (van Dokkum 2001), measures the FWHM of the stellar profiles, and estimates the background value for each image. It then employs PyRAF daofind to generate a source catalog for each image.

PSF Photometry and Image Subtraction
We perform point-spread function (PSF) photometry for SNe that have negligible host-galaxy contaminations using DoPHOT Alonso-García et al. 2012). For each image, DoPHOT generates a PSF model automatically and yields magnitudes for point sources.
A large number of targets (102 out of 247 SNe) have significant host-galaxy background fluxes and require image subtraction. To perform image subtraction, the pipeline first matches point sources detected on the science image with those on the template image, and then the science image is astrometrically aligned to the same reference frame of the template image using the matched sources and resampled. The image subtraction is done with the High Order Transform of PSF ANd Template Subtraction package (HOTPANTS; Becker 2015). The FWHMs of the template and resampled science image are used to determine the convolution direction: images with better seeings are convolved with the kernel for subtraction. We configured HOTPANTS to normalize the fluxes measured on all subtracted images to the template's flux scale. To perform photometry for targets after image subtraction, the pipeline first identifies isolated stars with high signalto-noise ratios on the template image, and these stars are used to build a PSF model for each convolved image. Then PSF photometry is performed at the SN position on the subtracted image and for all the sources on the template image using the PyRAF daophot task.
In some cases, host-galaxy flux subtraction is required, but image subtraction is not feasible when too few reference stars are available in the observed field or when template images are not available. If an SN is under such a circumstance and its host galaxy has a smooth profile that can be characterized by an isophote model (e.g., an elliptical galaxy), we devise a method to subtract the host-galaxy flux by incorporating an ellipse isophote modeling of the host galaxy. We adopt the following steps: (1) perform the usual PSF photometry with PyRAF/daophot for point sources (including the SN) within the region to be fitted by an isophote model; (2) subtract the point sources from the image, and then use the isophote/ellipse task from PyRAF/stsdas package to model the host-galaxy flux on the point-source-subtracted image; (3) subtract the best-fit isophote model from the original image and then perform PSF photometry for the stellar objects on the galaxy-flux-subtracted image; (4) steps (2) and (3) are then performed iteratively for three more times. In each iteration, the isophote model for the galaxy and the PSF photometry for the stellar objects are refined. This method has been used for the following targets with corresponding telescope/instruments given in the parentheses:

Photometric Calibration
For photometric calibration, we transform our photometry to the standard Johnson magnitudes (BV ) in Vega system and SDSS magnitudes (ri) in AB magnitude system, respectively, using the reference stars with available calibrated magnitudes in the field. Since our targets cover the full sky, the preferred sources for reference stars should be an all-sky catalog with homogeneous photometric calibrations. We use the photometric system defined by the Pan-STARRS1 (PS1) survey (Chambers et al. 2016), which has a well-characterized photometric system, with transformations to other standard photometric systems available in Tonry et al. (2012). The PS1 3π Steradian Survey (Chambers et al. 2016) has multiband (grizy P1 ) coverage of the sky with declinations >−30°, and we use photometry given in the Pan-STARRS1 DR1 MeanObject database (Flewelling et al. 2020). For the remaining quarter of the sky, we use the ATLAS All-Sky Stellar Reference Catalog (Refcat2), which was assembled from a variety of sources and brought onto the the same photometric system as Pan-STARRS1 (Tonry et al. 2018b). Before being used for photometric calibrations of our targets, the PS1 (or Refcat2) magnitudes of the reference stars in the fields are first converted into Johnson BV and SDSS ri bands adopting the following transformations

+ -
In practice, we use reference stars brighter than 19 mag in the field. For a target using PSF photometry, our measured magnitudes of the references are matched to standard Figure 1. The cumulative redshift distribution of all SNe Ia (blue histogram) in DR1 and those in the complete sample (black histogram) from the CNIa0.02 project. The redshift limit of z = 0.02 for the complete sample is indicated with the dashed vertical blue line. An illustrative N ∝ z 3 is plotted with the red line to indicate a simplified expectation from a volume-limited sample covering the full luminosity range by assuming a linear relation between distance and redshift. The distribution approximately follows the expectation for a volumelimited sample at z  0.01, for which peculiar velocities are negligible compared to the Hubble expansion velocity. The apparent excess of SNe with 0.005  z  0.013 with respect to the volume-limited expectation is probably contributed by the effects of peculiar velocities at low redshift and/or fluctuations due to small number statistics. magnitudes to derive a zeropoint offset for each image. For a target using image subtractions, the flux scale of the template is calibrated using the references, and then all measured magnitudes are scaled to the same photometric system as the template. The photometric uncertainties are estimated by quadratically combining the photometric errors reported by DoPHOT or PyRAF daophot with those of the zeropoint calibrations into the standard systems. The typical uncertainty of our calibrated photometry is ∼0.05 mag.

Swift UVOT Photometry
In this section, we briefly describe how we perform Swift UVOT bv photometry, and detailed discussions and results of our full Swift SNe Ia campaign will be given in a future paper. Processed Swift UVOT images are downloaded from the Swift Archive. 44 We follow the same basic photometric procedures as described in Brown et al. (2014). We use the calibration database (CALDB) version released on 2020 December 15, which includes the revised photometric zeropoints (Breeveld et al. 2011) and latest time-dependent detector sensitivity. We follow the Swift UVOT standard photometric calibrations (Poole et al. 2008; to extract the source counts on the science images and the host-galaxy counts on the template images with an aperture with radius of 5″. We subtract the host-galaxy contributions and then convert the source count rates into magnitudes in the UVOT-Vega system.

Light-curve Data
In this section, we present the optical light curves of 247 SNe Ia. Most of them have ASAS-SN Vg-band light curves using image subtractions (see Jayasinghe et al. 2018 for descriptions of the ASAS-SN image-subtraction photometry). For 219 SNe, we conducted BVri follow-up observations with the LCOGT 1 m and PO telescopes, and the light curves for all of them are included in DR1. BV light curves for 24 SNe obtained with SMARTS 1.3 m telescope are included in this data release. We also include BVri light curves for several targets obtained from the LCOGT 2 m telescope, LT, DEMONEXT, and A77 as well as relatively late-phase data for a small number of targets from Hiltner, du Pont, and NOT. The light curves are given in Table 3; they are the main result of CNIa0.02 DR1. In Figure 2 we show the multiband light curves up to 80 days past B-band peak (or the time of discovery if peak time is not available).

Light-curve Parameters
As discussed in Section 2, the V-band peak magnitude V peak < 16.5 is one of the criteria for the complete sample of CNIa0.02. To obtain the V-band peak magnitudes of SNe Ia presented in Table 1, we used the SNooPy 45 (Burns et al. 2011) software to fit (using the "max_model") the observed light curves with SNe Ia template light curves. The light curves are shifted in both phase and brightness to find the best match with a set of template light curves characterized by the color-stretch parameter s BV , which is found to be tightly correlated with the peak luminosity across the full range of SN Ia decline rate (Burns et al. 2014). s BV , B-band peak time t peak (B), and the peak magnitudes in all bands involved are free parameters. Swift UVOT bv data are not included in our fitting, except for two SNe (2017emq and 2017fbj) whose UVOT light curves have the essential coverage missed by other sites. Since the follow-up V-band data are generally more precise and have better coverage than ASAS-SN, we only include ASAS-SN Vband data in cases where follow-up V-band data are unavailable. During the fitting process, > 5σ outliers from the model were removed iteratively. The best-fit parameters (t peak (B), s BV , B peak , g peak , V peak , r peak , and i peak ) for 232 SNe Ia in CNIa0.02 are given in the max_model section of Table 4, and the corresponding best-fit models are displayed in Figure 2.
We also fit the data using SNooPy's "EBV_model2", which can derive host-galaxy extinctions. The EBV_model2 method fit the light curves with the templates as described below, where m X is the observed magnitude in band X, T X (f, s BV ) is the template light curve as a function of rest-frame phase f, and s BV , M X (s BV ) is the peak absolute magnitude of the given s BV , μ is the distance modulus in magnitudes, K XY is the cross-band kcorrection from Y band to the observed X band, E(B − V ) gal and E(B − V ) host are galactic and host-galaxy color excess due to extinction, and R X gal and R X host are the ratios of total to selective extinction for the Milky Way and the host galaxy, respectively. Among the parameters listed above, M X (s BV ), K XY , , R X MW , R X host are predetermined and provided by SNooPy, and t peak (B), s BV , E B V host ( ) - , and μ are free parameters in the fitting. E B V MW ( ) is obtained from the results of Schlafly & Finkbeiner (2011), and the canonical R 3.1 V MW = is adopted for the Milky Way. SNooPy has different sets of calibration results of the peak luminosity of SNe Ia, and we adopt R 1.729 V host = (corresponding to calibration = 5 in SNooPy), which is the result of calibration by using SNe Ia covering the full range of s BV (Burns et al. 2014). Our data set generally has the best coverage in BVri, and light curves in these bands are used in the EBV_model2 fitting for all objects, except for four objects (2018hkq, 2018htw, 2018kmu, and 2019swh). When the g-band light curves provide coverage missed by other bands, they are also used in the fitting. We obtain the best-fit parameters (t peak (B), s BV , , and μ) for 212 SNe Ia, and they are listed in the EBV_model2 section of Table 4. We also perform model-independent fitting to the wellcovered SN Ia light curves to directly derive parameters including the times and magnitudes of peak brightness and the decline rates in the B and V bands. The decline rate Δm 15 (X) (Phillips 1993) refers to the magnitude decline within 15 days after peak brightness in a given filter X. We measure these parameters directly from the interpolated light curves in B and V band using a Gaussian process regression method, which has the advantage of allowing for the inclusion of uncertainty information and producing relatively unbiased estimates of interpolated values (see, e.g., Lochner et al. 2016). The results are given in Table 5. Note that the fitting is performed without making host-galaxy extinction corrections, which may affect the derived Δm 15 for objects with high extinction (Phillips et al. 1999

Summary
CNIa0.02 aims to obtain a homogeneous and unbiased sample of nearby SNe Ia with multiband light curves to study the SNe Ia population. In CNIa0.02 DR1, we present 247 SNe with optical light curves, including 148 SNe in the complete sample. DR1 offers large and homogenous optical photometric data sets to systematically study the SNe Ia population. In this paper, we present the first analysis of our data set by extracting        parameters such as Δm 15 (B). We plan to publish near-UV, near-IR, and late-phase photometric data in the future. Our multiband light curves also allow us to derive host-galaxy extinction and luminosity, and in a forthcoming publication, we plan to make a completeness correction and study the SN Ia luminosity function. CNIa0.02 provides a large and homogeneous data set to infer the intrinsic distribution properties of SNe Ia in the local universe to help answer basic questions regarding SN Ia progenitor systems and explosion mechanisms. We used two 24 inch CDK24 telescopes operated by the Post Observatory (PO) mainly for following-up northern objects. One is located at the Sierra Remote Observatories (SRO 51 ; CA, USA) and the other at Post Observatory Mayhill (NM, USA). We used two types of cameras: an Apogee Alta U230 camera and an Apogee Alta U47 camera. Both cameras are back-illuminated, with similar quantum efficiency >90% over a broad region. The U230 camera was used by default at both sites. The telescope at SRO used the U230 for almost all images, and the Mayhill site had the U47 camera for a long period of time when the U230 camera was unavailable because its damaged shutter was being repaired. Astrodon Photometrics BV 52 and Sloan ri 53 filters were used. The 1.3 m telescope of the Small and Moderate Aperture Research Telescope System (SMARTS; Subasavage et al. 2010) is located at Cerro Tololo Inter-American Observatory (CTIO). It is equipped with A Novel Dual Imaging CAMera (ANDICAM; DePoy et al. 2003). The optical CCD for ANDICAM is a Fairchild 447 2048 × 2048 pixel CCD. The IR Array for the ANDICAM is a Rockwell 1024x1024 HgCdTe "Hawaii" Array with 18 μm pixels. SMARTS/ ANDICAM is equipped with standard KPNO-recipe Johnson-Kron-Cousins BVRI filters and standard CIT/CTIO JHK filters. SNe were observed in BVRI and JH-bands with ANDICAM. In this data release, we publish BV data. The RI and JH data taken by ANDICAM will be published in a future data release.
The instruments described above are the primary ones used for DR1. Their instrument specifications are listed in Table 6. The filter set used for observations in DR1 is compared to Landolt BV (Landolt 1992) and the SDSS ri (Fukugita et al. 1996) standard bandpasses in Figure 5.
DEdicated MONitor of EXotransits and Transients (DEMO-NEXT; Villanueva et al. 2018) is a 0.5 m PlaneWave CDK20 f/6.8 Corrected Dall-Kirkham Astrograph telescope at Winer Observatory in Sonoita, Arizona. DEMONEXT has a 2048 × 2048 pixel FLI Proline CCD3041 camera, with a 30 30 ¢´¢ field of view (FOV) and a pixel scale of 0 9 pixel −1 . DEMONEXT has a full suite of Bessel BVRI and SDSS griz filters. BVri data for four SNe (2016hli, 2016gou, 2016gxp, and 2017isq) are included in DR1. We also include photometry for three SNe (2017ghu, 2017hle, and 2018ast) obtained with the Liverpool Telescope (LT) IO:O instrument in DR1. IO:O is the optical-imaging component of the IO (Infrared-Optical) suite of instruments. It is equipped with a 4096 × 4112 pixel e2V CCD 231-84, with a 10 10 ¢´¢ FoV and a pixel scale of ∼ 0 3 pixel −1 with 2 × 2 binning. The 1 m telescope at WeiHai Observatory of Shandong University (WHO; Hu et al. 2014) was used to obtain BVri images for ASASSN-15uh. It has a back-illuminated PIXIS 2048B CCD camera at the Cassegrain focus, providing a 12 12 ¢´¢ FoV and a pixel scale of 0 35 pixel −1 . The 0.41 m f/3.3 reflector telescope at A77 observatory (Dauban, 04150 Banon, France) was used to obtain some BV images for 2015ar. The telescope is equipped with an ST8XME CCD, and its pixel scale is 1 4 pixel −1 .
We used instruments mounted on the du Pont 2.5 m telescope, the 2.4 m Hiltner telescope and the 2.56 m Nordic Optical Telescope (NOT) to image a number of SNe. We used two cameras mounted on du Pont. One is called "CCD", which is a direct-imaging camera with a 2048 × 2048 pixel SITe2K CCD with plate scale of 0 259 pixel −1 and an FoV of 8.85 8.85 ¢´¢ . The other is the WF4K CCD for the Wide Field Reimaging CCD Camera (WFCCD), which has a 4064 × 4064 pixel CCD with a plate scale of 0 484 pixel −1 and an FoV of 25¢ in diameter. For Hiltner, we used Ohio State Multi-Object Spectrograph (OSMOS), which is a wide-field imager and multi-object spectrograph. For our imaging observation, a 4096 × 4096 pixel STA0500A CCD was used, which has a scale of 0 273 pixel −1 and an FoV with 20¢ in diameter. On NOT, we used the Alhambra Faint Object Spectrograph and Camera (ALFOSC), which has both spectroscopic and imaging capabilities. The ALFOSC imaging was performed using a CCD231-42-g-F61 back-illuminated CCD with an FoV of 6.4 6.4 ¢´¢ and a plate scale of 0 2138 pixel −1 in the imaging mode. Note. a The central 1024 × 1024 pixels were used. Figure 5. The filter bandpasses used to obtain the majority of data released in this paper (solid lines). The standard Landolt BV and SDSS ri bandpasses (dashed lines) are shown for comparison. 51 https://www.sierra-remote.com/ 52 https://astrodon.com/products/astrodon-photometrics-uvbri-filters/ 53 https://astrodon.com/products/astrodon-photometrics-sloan-filters/