For thousands of years, astrophysical transients were discovered only by chance. It was Zwicky (1938) who began the first systematic search for extragalactic astro-physical transients, which evolved into the Palomar Supernova Search, discovering over 100 supernovae (SNe) in the following few decades. Systematic searches of the Southern sky started in the 1980s (Maza 1980) , essentially doubling capabilities. Combining charge-coupled devices (CCDs) with robotic telescopes to automatically search for SNe was the next major innovation (Kare et al. 1981) , although successful searches started about arXiv:2010.09724v1 [astro-ph.HE] 19 Oct 2020 a decade after initial tries (Perlmutter 1989; Filippenko 1992; Richmond et al. 1993) .
The discovery of SN 1987A (Kunkel et al. 1987 ) caused a resurgence of interest in SN science and at a similar time, advances in calibrating SNe Ia, building upon work of Kowal (1968) , Rust (1974) , and Pskovskii (1977) , indicated that SNe Ia would be precise distance indicators capable of measuring the expansion history of the Universe (Phillips 1993) . The innovative Calan/Tololo survey (Hamuy et al. 1993) , which used photometric plates for discovery and CCDs for follow-up observations, was key to a significant increase in SN discovery. Quickly, pencil-beam surveys designed to discover highredshift SNe overtook the discovery rate (Norgaard-Nielsen et al. 1989; Perlmutter et al. 1997; Schmidt et al. 1998) . Rolling searches, where the search epochs and follow-up observations are combined, became an efficient method for observing many transients (Barris et al. 2004 ), and we still use this strategy today.
In the late 1990s, the Lick Observatory Supernova Search (LOSS; Filippenko 2005) and extremely sophisticated amateur astronomers (e.g., Evans 1994) continued to increase the discovery rate of nearby SNe. Additional scientific discoveries such as detection of SN progenitor stars in pre-explosion images (e.g., Woosley et al. 1987; Aldering et al. 1994) , the connection between SNe and long-duration gamma-ray bursts (Galama et al. 1998; Hjorth et al. 2003; Stanek et al. 2003) , and the discovery of the accelerating expansion of the Universe (Riess et al. 1998; Perlmutter et al. 1999 ) made transient discovery even more valuable.
In the last two decades, the discovery rate of astrophysical transients has been increasing at an exponential rate 1 . This is mainly the result of systematic searches for high-redshift SNe (Supernova Cosmology Project, Perlmutter et al. 1997; High-Z Supernova Search, Schmidt et al. 1998; Deep Lens Survey, Becker et al. 2004; Supernova Legacy Survey, Astier et al. 2006; Guy et al. 2010; ESSENCE, Miknaitis et al. 2007; Narayan et al. 2016; SDSS-II, Frieman et al. 2008; Kessler et al. 2009; Sako et al. 2018 , Dark Energy Survey, Bernstein et al. 2012 Dark Energy Survey Collaboration et al. 2016; Abbott et al. 2019; Brout et al. 2019 ; Pan-STARRS Medium-Deep Survey, Rest et al. 2014; Jones et al. 2018a; Villar et al. 2020 ; HST surveys CANDELS, CLASH, and the Frontier Fields, Graur et al. 2014; Rodney et al. 2014; Kelly et al. 2015 , Subaru Hyper Suprime-Cam Transient Survey, Tanaka et al. 2016 ) and low-redshift SNe (SNFactory, Aldering et al. 2002; Texas Supernova Search, Quimby 2006; SkyMapper, Keller et al. 2007; Scalzo et al. 2017; Catalina Real-Time Transient Survey, Drake et al. 2009 ; (i)PTF, Law et al. 2009; CHASE, Pignata et al. 2009; MASTER, Lipunov et al. 2010; ATLAS, Tonry 2011; Tonry et al. 2018; La Silla QUEST, Baltay et al. 2013 ; ASAS-SN, Shappee et al. 2014; PSST, Huber et al. 2015; DLT40 Valenti et al. 2017 ; ZTF Bellm et al. 2019b) . Recent low-redshift all-sky surveys such as ATLAS, ASAS-SN and PSST in particular were critical to increasing the transient discovery rate in the last few years, with ZTF now augmenting the rate even further. This recent wealth of time domain data has led to significant advancement in our understanding of stellar evolution, SN explosion mechanisms, black holes, the chemical enrichment of galaxies and the fundamental physics of our universe (Campana et al. 2006; Lorimer et al. 2007; Smith et al. 2007; Smartt et al. 2009; Nomoto et al. 2013; Riess et al. 2016; Abbott et al. 2017; Scolnic et al. 2018 ). In the coming decade, the Vera C. Rubin Observatory will further increase the rate of transient discoveries by an order of magnitude, potentially finding 10 5 new transients per year (LSST Science Collaboration et al. 2009) .
A number of smaller-scale time domain surveys are complementing these efforts with multi-wavelength and fast-cadence transient searches. Surveys including the Vista Infrared Extragalactic Legacy Survey (Hönig et al. 2017 ) and the GALEX time domain survey (Gezari et al. 2013 ) undertook transient searches in the nearinfrared and ultraviolet, respectively. Current fastcadence searches, including the ZTF one-day survey, DLT40 , the Evryscope (Law et al. 2015) , the Korea Microlensing Telescope Network (Kim et al. 2016 ), Kepler (K2; Howell et al. 2014 ) and TESS (Fausnaugh et al. 2019 ) have pioneered new techniques to understand transient and variable phenomena on short timescales. However, a number of key questions remain unanswered by current surveys due to limited wavelength range, area, cadence, depth, or photometric calibration.
Here, we describe the Young Supernova Experiment (YSE), a three-year survey for transients that focuses on discovering statistical samples of young transients with additional emphasis on discovering rare and red transients, measuring cosmological parameters, and preparing for the Rubin Observatory. YSE began on November 24th, 2019, and is currently using 7% of the observing time on Pan-STARRS1 to survey 750 deg 2 of sky with a 3-day cadence to a depth of gri ≈ 21.5 mag and z ≈ 20.5 mag; we plan to double the survey area to ∼1500 deg 2 in the near future. Our survey strategy emphasizes increased coverage in iz and improved depth to distinguish our transient discovery demographics from other ongoing surveys. It will also leverage the excellent photometric calibration of PS1 for SN Ia cosmology. When possible, we attempt to interleave our observations with those of ZTF for an alternating one-or two-day combined cadence, further improving our ability to identify young transients.
Below, in Section 2, we discuss key open questions and challenges in transient astrophysics that can be addressed through a new wide-angle time domain survey such as YSE − in particular, understanding SN progenitors through observations of young supernovae, building a census of faint, fast, and red transients, measuring cosmological parameters, understanding black hole variability and tidal disruption events (TDEs), and preparing for Rubin Observatory science. We will use these goals to motivate the YSE survey strategy in Section 3. YSE vetting and follow-up procedures are described in Section 4 and an overview of the YSE survey status and discoveries to date are described in Section 5.
In the hours after a stellar explosion, a SN is still roughly the size of its progenitor star. The shock breakout, which produces a large X-ray and UV photon flux, will "flash ionize" the immediate circumstellar medium (CSM; e.g., Gal-Yam et al. 2014) . During this time, the SN shock will also interact with nearby CSM and any potential companion star, ablating material from its surface. As the ejecta expands, it will smooth out the initial inhomogeneities caused by asymmetric explosions and the initial conditions of the progenitor star.
Flash ionization provides a unique window to examine the CSM before the SN ejecta sweeps it up. If the ionizing spectrum is known and the CSM is optically thin, one can convert measured fluxes from lines of different atomic species into densities and abundances at different radii. Combined with multi-wavelength observations (e.g., Margutti et al. 2012; Chomiuk et al. 2012) , one can create a holistic picture of the progenitor star's circumstellar environment (Jacobson-Galán et al. 2020b) .
Any radioactive elements in the outermost layers of the SN ejecta will also result in additional flux before energy produced in the deeper layers of the ejecta has time to diffuse out. As a result, this can produce "excess" flux relative to the later rising light curve (Piro & Nakar 2013) . Excess flux has been seen for several SNe Ia (Marion et al. 2016; Hosseinzadeh et al. 2017; Dimitriadis et al. 2019a; Shappee et al. 2019; Miller et al. 2020 ) and other peculiar thermonuclear transients (Cao et al. 2015; Jiang et al. 2017 ), but either because of a limited number of data points or limited color information, the interpretation is often unclear (e.g., Dimitriadis et al. 2019b; Tucker et al. 2019) . Earlier detections, higher cadence, additional color information, and early spectroscopy could break such degeneracies.
Luminous mass-loss episodes prior to the SN explosion may also be detectable given a survey with sufficient depth and cadence ; for a review, see Smith 2014). A particularly spectacular example was a luminous outburst two years before the explosion of SN Ibn 2006jc Pastorello et al. 2007) . Slightly more common are outbursts likely associated with luminous blue variables (LBVs) before a terminal explosion, such as in SN 2009ip (Smith et al. 2010b; Foley et al. 2011; Pastorello et al. 2013; Margutti et al. 2014 ) and 2015bh (Elias-Rosa et al. 2016; Thöne et al. 2017) . Early bumps in the light curves of superluminous SNe (SLSNe) have also been found (e.g., Leloudas et al. 2012; Nicholl et al. 2015; Smith et al. 2016; Nicholl et al. 2017) , and may be due to outbursts prior to the explosion or interaction between the SN and shells of CSM. A survey with deep time-domain imaging would provide enormous legacy value for analysis of future transients -perhaps discovered even decades later -by enabling deep searches for such outbursts.
There is considerable uncertainty in our understanding of the nature of rare classes and subclasses of transients, including peculiar thermonuclear SNe (e.g., SN Iax; SN Ia-CSM; Hamuy et al. 2003; Ca-rich SNe; Perets et al. 2010; SN 2000cx-like; Li et al. 2001; SN 2002es-like; Ganeshalingam et al. 2010; SN 2006bt-like; Foley et al. 2010; various He-shell explosions De et al. 2019; Jacobson-Galán et al. 2020a; Miller et al. 2020; Siebert et al. 2020) , superluminous supernovae (e.g., Gal-Yam 2012), tidal disruption events (Gezari et al. 2013) , low-luminosity transients (e.g., Foley et al. 2009; Valenti et al. 2009; Rodney et al. 2018; Srivastav et al. 2020) , fast-evolving blue optical transients (FBOTs; Drout et al. 2014) , LBV outbursts (e.g., Van Dyk et al. 2000; Kilpatrick et al. 2018b) , and kilonovae (Coulter et al. 2017; Drout et al. 2017; Tanvir et al. 2017) .
Transients from many of these classes are rarely discovered because they are intrinsically low luminosity, fast evolving, or red (Siebert et al. 2017) . Traditional surveys such as LOSS (Leaman et al. 2011) were de-signed to detect common SNe and so had cadences of 5-15 days with limiting magnitudes set to detect SNe II in targeted galaxies. These surveys were usually performed with blue-sensitive filters. Such a survey would therefore miss many transients of these uncommon classes even if the intrinsic volumetric rate was high. Transients such as intermediate luminosity red transients (ILRTs; Bond et al. 2009 ) and luminous red novae (LRN; e.g., Mould et al. 1990; Mason et al. 2010; Nicholls et al. 2013) , associated with stellar outbursts and mergers, are some of the faint transient classes that may have been frequently missed by previous surveys.
Additionally, these small field of view (FoV) surveys targeted luminous galaxies to increase their overall discovery yield and rejected potential transients at the centers of galaxies to avoid subtraction false positives and active galactic nucleus (AGN) activity. However some rare classes occur preferentially in low-luminosity galaxies (Lunnan et al. 2015) and nuclear regions , and previous surveys would miss these transients even if they were luminous, blue, and long-lived.
A new, untargeted, red-sensitive survey would not only increase rare transient discoveries from individual epochs but would also have the potential to increase discoveries of faint but long-duration transients by stacking multiple search epochs obtained over a period of several nights. This would have a significant impact, for example, on the discovery of gravitationally lensed SNe (glSNe) and superluminous SNe (SLSNe), where the SNe are luminous and long-lived but their volumetric rates are low. Both classes are predominantly found at high redshift and extend the cosmological epochs that can be probed spectroscopically. Red bands are advantageous for glSN discoveries in particular (Wojtak et al. 2019 ) and systematic detections of glSNe could pave the way for constraining the Hubble constant (H 0 ) from gravitational time delay measurements (Refsdal 1964) . For SLSNe (Knop et al. 1999; Smith et al. 2010a; Quimby et al. 2011) , many questions remain about their progenitors, explosion mechanisms, luminosity sources, intrinsic rates, interaction with circumstellar material, and how feedback from these sources affects their host galaxies (Gal-Yam 2019). Surveys capable of discovering SLSNe before peak with high-cadence multi-band light curves will enable rapid identification, providing sources for multi-wavelength follow-up and a large statistical sample.
It is counter-intuitively the nearest SN Ia samples that are responsible for most of the systematic uncertainty on measurements of the dark energy equation-of-state parameter, w (Brout et al. 2018; Jones et al. 2019 ). Most existing low-z SN Ia observations were compiled on more than 13 different, though partially correlated, photometric systems at a time when cosmological analyses were not yet limited by mmag-level uncertainties. It is critical to replace these legacy data with large, wellcalibrated samples of low-z SNe Ia for next-generation cosmological analyses. Large statistical samples of low-z SNe are also ideal for measuring the growth of structure from their peculiar velocities to test general relativity (Howlett et al. 2017; Huterer et al. 2017; Boruah et al. 2019; Kim et al. 2019 ) and reducing the statistical uncertainty of H 0 (Riess et al. 2016) .
The 2020s will have two groundbreaking facilities for measuring cosmological parameters with SNe Ia: the Rubin Observatory and the Roman Space Telescope. However, neither will provide an optimal low-z sample for cosmological parameter measurements. Roman will find few low-z SNe (Hounsell et al. 2018) , while the nominal-cadence strategy of Rubin will create large (∼20-day) single-filter gaps in their low-z light curves that would make SN Ia standardization less accurate. Therefore, cosmological measurements with either observatory will likely rely on external low-z data sets. Large, high-cadence, unbiased low-z samples could also be used to refine and extend new models for SN Ia standardization (e.g., Guy et al. 2007; Burns et al. 2011; Saunders et al. 2018; Léget et al. 2020; Mandel et al. 2020) , to better constrain relationships between SN Ia distance measurements and host galaxy properties (Rigault et al. 2013; Jones et al. 2018b; Roman et al. 2018; Rigault et al. 2018) , to improve our understanding of common sources of CC SN contamination in SN Ia cosmology measurements (Jones et al. 2018a) , and to increase the number of independent photometric systems on which SNe are observed in order to reduce photometric calibration uncertainties -typically the dominant systematic uncertainty in dark energy measurements.
Varying accretion rates in active galactic nuclei (AGN) and QSOs are reflected in their optical variability. By discovering and monitoring a large number of quasars, it is possible to revisit tests of the dampedrandom walk model of quasar variability (Kelly et al. 2009; MacLeod et al. 2010 ) and study correlations between quasar variability and physical properties (e.g., Sánchez-Sáez et al. 2018; Kimura et al. 2020 ). Optical variability is also a useful tool for identifying the relatively elusive AGN in low-mass galaxies (e.g., Butler & Bloom 2011; Baldassare et al. 2018; Sánchez-Sáez et al. 2019; Baldassare et al. 2020) .
A small fraction of these AGN and QSOs will evolve dramatically over a period of a few years (MacLeod et al. 2016; Ruan et al. 2016) , changing their brightness, hardness of their spectrum, ionization state, and presence of broad spectral lines (LaMassa et al. 2015; Runnoe et al. 2016) . Prompt spectroscopic observations immediately after photometric changes are detected may then reveal the underlying physics of these "changing-look quasars" (e.g., MacLeod et al. 2019) . By discovering changinglook AGN in the early stages of their transition, one can monitor the change in black hole accretion physics and the possible build up of an accretion disc .
A survey that can reliably find transients in the cores of galaxies and identify them early also allows TDE discoveries. Detailed multi-wavelength follow-up observations of TDEs can trace their full evolution, including the formation of accretion disks (Hung et al. 2020) , the formation of jets (Zauderer et al. 2011) , and the properties of the black holes themselves (e.g., Auchettl et al. 2017; Hung et al. 2017; Holoien et al. 2019; Mockler et al. 2019; Alexander et al. 2020; van Velzen et al. 2020) . Studying the diversity of TDEs constrains the black hole mass function and extends it to lower masses than is possible with AGN observations alone (MacLeod et al. 2012; Kochanek 2016; French et al. 2020) . Observations of AGN or TDEs from wandering black holes could constrain their dynamics, a key phase before the formation of black hole binaries, and track the ways in which SMBHs settle to the centers of their host galaxies (Bellovary et al. 2019; Reines et al. 2020 ).
One of the key transient science challenges of the forthcoming Rubin Observatory era is how to identify unusual or scientifically valuable transients from samples of hundreds of thousands. This could include classifications within the first days or hours of discovery, allowing subsequent spectroscopic or photometric followup observations, or full light-curve classifications, necessary for a census of SN rates and luminosity functions as well as for cosmological parameter measurements with SNe Ia. Recent efforts to classify a diverse sample of transients have been successful (e.g., Boone 2019; Muthukrishna et al. 2019; Villar et al. 2019; Möller & de Boissière 2020) including those that exclusively use contextual host galaxy information (Foley & Mandel 2013; Baldeschi et al. 2020; Gagliano et al. 2020 ), but the sensitivity of these classifiers to biases and nonrepresentative training samples is often unclear. Train-ing data sets built from existing transient surveys will typically have significantly different photometric filters or cadences than Rubin. Multi-color light curves for thousands of transients within a magnitude-limited but otherwise unbiased discovery space would serve as a vital test bed for training transient brokers and designing efficient follow-up strategies. Light curves with iz coverage in particular are missing from most current time-domain surveys. SNe from the Pan-STARRS medium deep survey will assist with these goals Villar et al. 2020) , but even that sample lacks examples of several rare classes and rarely has detections within a few days of explosion.
In spite of thousands of new SN discoveries per year, the rates and luminosity functions of many CC SN classes -especially fainter and redder classes -are highly uncertain (Li et al. 2011b; Perley et al. 2020) . This propagates to uncertainty in photometric classification of SNe (Jones et al. 2017) , stellar evolution, the physics of SN explosions, and the chemical enrichment of the Universe (Maoz & Graur 2017) . The most often used local rate measurements were measured from a galaxytargeted survey (Li et al. 2011b) , and therefore the rates and luminosity functions are heavily biased by the correlations between SNe and their galaxy environments Quimby et al. 2011; Sanders et al. 2012; Taggart & Perley 2019) . Blue-sensitive surveys will also not discover highly reddened transients, resulting in a systematic uncertainty for their rate. Since some SNe, particularly those with short-lived progenitor stars, will preferentially occur in dusty environments, the level of bias will be different for each class.
A number of teams, including ASAS-SN ) and the ZTF Bright Transient Survey , are carrying out large surveys to spectroscopically classify all transients within a given magnitude for an improved census of transients that does not rely on galaxy associations, redshift catalogs, and assumed distances. However, a survey with greater sensitivity at redder wavelengths would increase the demographics of SNe that can be discovered and a volumelimited survey, which would require some distance information, would ensure that low-luminosity transients are well represented in the measured rates and luminosity functions. Combining magnitude-and volume-limited surveys leverages the statistics of luminous events in the magnitude-limited sample while preserving the lowluminosity transients in the volume-limited sample. The combined sample would constrain the stellar initial mass function and the progenitor systems of transient classes and subclasses (Strolger et al. 2015) .
The science goals above motivate the YSE survey design, field selection and strategy: we wish to design a wide-angle, deep survey with multi-color light curves, including significant coverage at redder wavelengths, and with a survey strategy that facilitates obtaining a statistical sample of young SN detections. Below, we give an overview of the Pan-STARRS telescopes and photometric system, discuss and motivate the YSE survey properties, and use survey simulations to estimate the SN yields from the chosen survey design. YSE fields chosen to date are shown in Figure 1 and basic survey characteristics are given in Table 1 .
Photometric System, and Data Processing Pipeline YSE observations use the two 1.8-meter Pan-STARRS telescopes (Pan-STARRS1 and Pan-STARRS2), each with 1.4 gigapixel cameras (GPC1 and GPC2; Kaiser et al. 2002) . The Pan-STARRS telescopes have an approximately seven deg 2 FoV, with that of Pan-STARRS2 (PS2) slightly larger due to additional CCD chips at the corners of the FoV and the GPC2 detector's reduced masking compared to GPC1. Pan-STARRS1 (PS1) commenced formal survey operation in May of 2010 and has currently imaged over 3π steradians of the sky . PS2 recently finished commissioning. There are sub-percent color transformations between the PS1 and PS2 photometric systems.
Pan-STARRS observations are taken through one of six broadband filters, grizyw P 1 (hereafter grizyw). The filter transmissions and total system throughput for these filters have been measured via a calibrated photodiode and tunable laser (Tonry et al. 2012) . Combined with the well-measured filter functions, the 3π steradians of Pan-STARRS sky coverage allowed Schlafly et al. (2012) to solve for the relative calibration of the PS1 photometric system to a precision of better than 5 mmag. Scolnic et al. (2015) then improved the absolute calibration of PS1 by comparing observations of secondary standard stars across multiple photometric systems. The excellent photometric calibration of PS1 is critical for SN cosmology and helpful for many of the other YSE science goals discussed in Section 2.
Pan-STARRS data are processed using the Image Processing Pipeline (IPP) at the University of Hawaii's Institute for Astronomy ). The IPP is used to download, process and archive all Pan-STARRS images, and includes a difference imaging pipeline to search for transients. Once data are taken and reduced, and a template image is convolved and subtracted from the data, the Transient Science Server at Queens University Belfast (Section 4; Smith et al. 2020) uses a combination of massive catalog cross matching and a machine learning algorithm to discover transient phenomena in the survey images.
The Pan-STARRS design and infrastructure has enabled a number of innovative transient science programs, notably the Medium Deep Survey (MDS), the Pan-STARRS survey for Transients (PSST; Huber et al. 2015) , and recently the Pan-STARRS Search for Kilonovae (McBrien et al. 2020 ). The Medium Deep Survey observed 70 deg 2 of sky at an average of six observations per ten days from 2010 to 2014, discovering over 5,000 SNe. Approximately 500 of these SNe were classified Figure 1 . YSE fields chosen prior to October 1st, 2020 (blue). The red fields correspond to the two daily Virgo pointings. Lighter (white) background regions correspond to a higher value for the YSE field selection metric (Section 3.3). In addition to this metric, we typically require fields to be at least 20 degrees from the ecliptic plane (dashed line). The labeled high-metric white squares are at the locations of the Pan-STARRS medium deep fields.
spectroscopically (Rest et al. 2014; Scolnic et al. 2018) and spectroscopic host galaxy redshifts for over 3,000 were obtained (Jones et al. 2017) . Science highlights from these data include cosmological parameter measurements (Rest et al. 2014; Jones et al. 2018a) , tidal disruption event discoveries (Gezari et al. 2013; Chornock et al. 2014) , fast-evolving luminous SNe (Drout et al. 2014) , SLSNe (Chomiuk et al. 2011; Lunnan et al. 2013; McCrum et al. 2014) , SNe Iax (Narayan et al. 2011 ) and transient classification tools for the Rubin Observatory (Villar et al. 2019) .
PSST commenced after the end of the MDS and used the data taken on PS1 through NASA's near earth object (NEO) observation program to discover new transients. PSST combines the IPP difference imaging pipeline at the IfA in Hawaii and the Transient Science Server at Queen's University Belfast to search for transients. The PSST team has discovered the majority of the ∼9000 publicly reported PS1 transients. The Pan-STARRS Search for Kilonovae is using these same NEO observations to conduct a volume-limited search for intrinsically faint transients within ∼200 Mpc (McBrien et al. 2020) . Though YSE will not carry out LIGO counterpart searches, Pan-STARRS is also a powerful facility for searching for gravitational wave counterparts, due to both the field of view and the existence of reference sky templates for immediate difference images above δ ≈ −30. Pan-STARRS has has been employed for this work during the LIGO first to third observing runs (e.g., Smartt et al. 2016; Ackley et al. 2020 ).
The Pan-STARRS telescopes allow for a large-area, high-cadence, well-calibrated survey, but place a number of constraints on the exposure time and area coverage of YSE. Our team considered only exposure times greater than 15 sec; for practical purposes, this is the minimum allowed exposure time to limit systematic uncertainties in the photometry due to the GPC1 shutter. However, the Pan-STARRS cameras also have a relatively high read noise of 8 e − ) and an overhead of approximately 11 sec per exposure, which makes it advantageous to increase the YSE exposure time beyond the 15-sec minimum. Figure 2 demonstrates the trade-off between area and volume for different PS1 exposure times. The read noise and sky noise for PS1 during dark time are given by Chambers et al. (2016) , and agree with measured depths from Foundation Supernova Survey data ) and PSST. We wish to survey a large volume while still covering a large enough area to discover many brighter, nearby events, and while surveying a significant fraction of the ZTF area. Overlap with ZTF enables high-cadence light curves from combined YSE and ZTF data. a Area per cadence cycle. b Dark time, when available. c Using the bluest available band. d "Cyan" and "Orange" bands, combinations of g + r and r + i, respectively. e Or ∼30000 deg 2 every 2-3 days. f Separate dark/bright time limits were not given by Holoien et al. (2017) , with the ASAS-SN website (http://www. astronomy.ohio-state.edu/ ∼ assassin/index.shtml) quoting limits down to 18th magnitude. g The PSST cadence is irregular, with ∼1-4 return visits for a given field typically scheduled within 15 days after the initial observation. After evaluating a number of different strategies (Appendix A), our team chose griz exposure times of 27 sec. This choice of exposure time satisfies a number of useful criteria. First, YSE observations are ∼0.4-0.8 mag deeper than ZTF, enabling day-before observationsboth detections and non-detections -that put useful limits on the ages of newly discovered SNe. Second, while YSE data are read-noise dominated in g observations taken during dark time, this exposure time ensures that the rest of the data will be background-dominated. Finally, a 27-sec exposure time nearly maximizes the possible YSE survey volume (Figure 2 ), but still covers a large area such that more than 100 SNe/year will have r < 18.5 mag, making them easy to follow spectroscopically using 2-and 3-meter-class telescopes from the ground.
We considered several different filter sequences for YSE, a number of which are discussed in Appendix A. We decided on a strategy that emphasizes the iz filters while also having at least one g or r band observation per epoch, as gr observations allow us to measure rise times for young SNe from the combination of YSE and ZTF data without any assumptions on the color of the event. YSE observes in only two filters per night to maximize the survey area while still preserving color information for transient observations. During dark time, we alter- nate observations of gr and gi, and in bright time, due to the ∼1 mag lower g-band depth, we alternate ri and rz observations. During brief periods of grey time (moon illumination between 33% and 66%) we alternate gi and gz.
Finally, YSE observes each field with a three-day cadence to increase synergy with the ZTF observing strategy and to allow well-sampled light curves for relatively fast-evolving transients.
In Table 2 we compare the YSE survey characteristics to those of other ongoing time-domain surveys. Each of these surveys has unique advantages, with ASAS-SN discovering nearby SNe with extremely fast cadence, ATLAS having a slightly slower cadence compared to ASAS-SN but increased depth and excellent calibration, and ZTF covering less area than ASAS-SN/ATLAS but having greater depth and both high-cadence and i-band sub-surveys. In terms of area observed at a given time, YSE will observe significantly less area than ASAS-SN, ATLAS, and ZTF. However, the depth is 0.4-0.8 mag deeper than any other survey and therefore YSE will cover more volume than ASAS-SN, 50-70% as much volume as ATLAS, depending on the transient luminosity, and ∼20% as much volume as ZTF. In terms of the photometric calibration in particular, the excellent PS1 calibration outperforms other systems (and PS2 calibration will be tied to PS1 for comparable accuracy), which is particularly important for SN Ia cosmology. The oversampled PS1 point spread function (PSF) may allow for improved image subtraction, which benefits black hole variability and TDE studies by facilitating discoveries near the centers of galaxies. The combined ZTF/YSE cadence is particularly advantageous for fast-cadence studies, helping with discoveries of young SNe, and the z band coverage is unique to YSE and will aide in the discovery of very red transients.
We select YSE survey fields based on the following criteria:
• To increase transient discovery volume, we choose fields with high Galactic latitude and low Milky Way extinction.
• We prioritize fields with a large amount of available archival data, primarily redshifts and deep multi-wavelength imaging.
• We more heavily prioritize equatorial fields, as they can be observed with follow-up facilities from both hemispheres. However, in most cases we require fields to be at least 20 degrees from the ecliptic plane to minimize observing gaps caused by the Pan-STARRS moon avoidance angle of 30 degrees.
• We avoid fields with declinations less than −30 degrees as YSE requires deep template images from previous Pan-STARRS observations, which are scarce at large negative declinations.
• We choose fields that, given their history of observations, ZTF will typically observe on a three-day cadence or greater and in two filters.
• Our observations are interwoven with Pan-STARRS near earth object observations, requiring us to space our fields in right ascension so that they can be observed throughout the night.
• When possible, we place pointings in minimum schedulable blocks of six pointings each, as Pan-STARRS slews of more than 15 degrees require approximately one minute of overhead to refocus the telescope.
• We prioritize new fields with rising or scientifically interesting transients that have been discovered by other time-domain surveys and for which YSE data would be beneficial. We will attempt to follow such transients using as many fields as possible in the first year of the survey and using 50% of fields in subsequent years. The remaining 50% will be kept the same across multiple years for long-term monitoring.
• We prioritize fields that have a larger number of nearby galaxies within 150 Mpc, with galaxies at <10 Mpc given the highest weight. This will result in a slight bias to the nearby rates in exchange for an increase in SN discoveries but we expect to be able to correct for this bias in future analyses given the large fraction of galaxies that are untargeted.
We use a semi-arbitrary field selection metric to take these priorities into account, which is discussed in Appendix B. We consider the total amount of HST exposure time when selecting fields, which is important for identifying or constraining progenitor systems in preexplosion imaging (e.g., Li et al. 2011a; McCully et al. 2014; Foley et al. 2015; Kilpatrick et al. 2018a; Kilpatrick & Foley 2018) , as well as the number of active SNeparticularly SNe before maximum light -in a given field, but do not formally include these quantities in the metric. The initial set of YSE pointings is shown in Figure 1 .
When the Virgo Cluster is observable at airmass < 1.5, YSE will also dedicate two Pan-STARRS pointings to a daily survey of Virgo. Our team may choose to undertake similar mini-surveys of other nearby clusters, such as Coma, in the future. The Virgo region of the sky produces an extraordinary number of SNe; in the last 15 years, 30 SNe were spectroscopically classified within the radius of the two YSE Virgo pointings our team has adopted 2 (not accounting for detector masking). Most recently, this includes three SNe Ia (SNe 2018bgb, 2018axs, and 2017eea) and one SN IIn (SN2017jfs).
YSE will be able to detect pre-explosion outbursts for Virgo transients to an approximate absolute magnitude of −10. We have chosen our pointings to maximize the archival HST exposure time, thereby simultaneously including the majority of Virgo galaxies. A four-deg 2 deep stack from the first season of observation for our daily M87 Virgo field is shown in Figure 3 along with the locations of six transients observed in the field (five are located in the background of Virgo and one is in the cluster). These are YSE-observed SN 2020pf (a ZTF discovery but with simultaneous YSE observations), and YSEdiscovered SNe 2019yub, 2020ndg, 2020lhy, 2020mvx, and finally AT 2020iuy, which is likely a stellar outburst with peak M i ≈ −11.9 mag.
Although we wish to keep 50% of our fields fixed for long-term monitoring, the other 50% of YSE fields can be adjusted each year to follow new transients that meet our science objectives. We hope to obtain targeted observations of ∼15 SNe per month. This includes rare transients, young transients, or low-z SNe Ia to build an anchor sample for next-generation cosmology surveys. Of these 50% that can be adjusted, our survey requirements specify that 40% of new fields should be within 15 degrees of existing fields to limit refocus overheads. To move an existing YSE survey field to target a new SN, the survey field should meet these criteria (below, a setting field must have airmass greater than 1.5):
1. To relocate an existing field to a new location, the existing field should either have a) no clearly rising transients and be within ten days of setting, or b) it should have no transients within either two weeks of maximum light or within a month of discovery. Certain rising transients that would have very poor light-curve coverage (e.g., if the field is near setting) would also be reasonable candidate fields to move. Conversely, a current field with a post-max SN that is of high scientific value to the collaboration should not be discontinued unless it is near setting.
2. If YSE science will substantially benefit from continued follow-up observations of the transients in the existing field, the field will not be moved.
3. The location of the new field should be within 15 degrees of existing survey fields unless the target is of exceptionally high priority.
4. In most cases, a new field should be three months or more from setting.
To maximize discoveries of young SNe and increase the effective cadence for many YSE and ZTF transients, we attempt to schedule YSE observations such that they precede ZTF observations by one calendar day. Because our survey is ∼0.4-0.8 mag deeper than ZTF during dark time, we hope to discover fast-rising SNe or newly exploded SNe by observing a >1 mag rise between the Pan-STARRS observation and the ZTF observation approximately 21 hours later. As ZTF reports transient discoveries with a median delay of just ∼10 minutes from the time the image is taken, our team is able to quickly combine the data streams to trigger rapid spectroscopic and multi-wavelength follow-up observations of fast-rising SNe.
In practice, predicting ZTF observations can be nontrivial, as ZTF uses a complex scheduling metric to avoid large coverage gaps rather than a fixed threeday cadence and weather makes it difficult to anticipate whether an observation will be scheduled or carried out on a given night. However, ZTF generally prioritizes fields by those with the longest time since they were last observed (Bellm et al. 2019b) . Over the first three months of YSE, during full nights of observing with clear weather at both Haleakala and Palomar, 61% of our observations were taken a day before ZTF observations of the same field (compared to 33% for uncoordinated observations; see Section 5 for examples of our data). However, we find that during longer periods of good weather, we are more able to more successfully predict the ZTF observations. Near the beginning of YSE, most fields were also concentrated within a narrow right ascension window, and these may be less likely to have ZTF observations evenly spread over three days.
YSE reports all discoveries of likely transients, omitting likely variable stars or AGN, to the Transient Name Server 3 (TNS), the official International Astronomical Union mechanism for recording new transient discoveries. Our discovery reports include the first epoch of photometry for each transient (we will provide the first two photometric epochs in the near future), and whenever a team member obtains a spectroscopic classification of an unclassified YSE transient, the spectrum and classification is sent to TNS as well.
Obtaining a second epoch before reporting a discovery is sometimes necessary for asteroid rejection; to avoid substantial ∼1-minute refocusing overheads for Pan-STARRS when slewing more than 15 degrees, our observations in different filters are spaced only ∼4 minutes apart. This makes asteroid rejection more difficult, occasionally necessitating a second observation or con-3 https://wis-tns.weizmann.ac.il/.
firmation from a second survey in order to confirm that a YSE alert is a bona fide transient. When YSE observations for a new field begins, AGN contamination is also significant; as our team has gained vetting experience we have become more conservative in promoting candidates during the first epochs of a given field to avoid reporting AGN. In most cases, however, the colors and presence of nearby host galaxies make transient discoveries unambiguous. In cases where it is unclear if a transient is a true SN versus an asteroid, variable star, or image artifact, we wait to send these events to TNS until additional epochs of data can clarify whether or not they are bona fide transient phenomena.
To evaluate the effectiveness and expected SN discoveries from the survey strategy presented above, we simulated the YSE survey using the SNANA software (Kessler et al. 2010 ). We include models of different SN types and subtypes from the PLAsTiCC SN classification challenge , including SN Ia, SN Iax, SN II, SN Ib/c, ILOT, and CART. We omit TDEs, SLSN-I and kilonovae due to large uncertainties on their rates, as well as purely theoretical models and non-transient models (e.g., variable stars and AGN). We use anticipated survey depths from Chambers et al. (2016) , moon phase-dependent sky noise to match 3π observations, SN rates from Strolger et al. (2015) and Dilday et al. (2008) , normalization of those rates at z = 0 from PLAsTiCC (and references therein; most rates originate from Li et al. 2011b) , and a nominal ZTF survey to match their reported depths with ZTF observations scheduled 21 hours after YSE. Details of the simulation methodology and the effect of choosing alternate survey strategies are given in Appendix A.
Summary statistics for the nominal simulated YSE survey design are shown in Figure 4 . Due to its depth -YSE is able to find SNe Ia at redshifts of nearly ∼0.3 -simulations predict 5149 SNe per year having at least three detections with a signal-to-noise ratio (S/N) > 5, including 3920 SNe Ia, 277 SNe Ib/c, 705 SNe II, and 67 SNe IIn. Our magnitude-and volume-limited samples, comprising SNe with m < 18.5 mag and D < 250 Mpc, respectively, are expected to contain a total of 217 SNe per year ( Figure 5 ; 49 SNe are members of only the magnitude-limited survey, 109 are members of only the volume-limited survey, and 59 are members of both).
In Appendix A, we discuss the results of several other survey designs explored by our team, including variations in exposure time, three or four filters per day, and a survey design focused on either blue or red filters. We caution that some simulated yields are affected by small- . We predict 3920 SNe Ia, 277 SNe Ib/c, 705 SNe II, and 67 SNe IIn each year of full survey operations. We note that PLAsTiCC templates for certain models, including a subset of SNe II and SNe Ib/c, were not given subtypes and thus do not have subtypes here. number statistics. As exposure time increases, the number of discovered SNe tends to increase, but begins to flatten after 25 seconds, as would be expected from Figure 2. Surveys consisting of three or four filters per day would be excellent for some science cases, but would drastically reduce the discovery space for transients, resulting in a 40-50% reduction in the number of transient discoveries. A blue-focused survey was primarily ruled out because it offers a less unique discovery space compared to existing time-domain surveys, while a survey focusing only on riz observations would be expected to reduce young SN yields as most young SNe are expected to be blue. Our nominal survey design − 27s exposures with a gr, gi observing sequence during dark time − was settled on as the best choice for YSE, especially given the characteristics of the Pan-STARRS system and our allocated 7% fraction of time on each telescope. Finally, Figure 6 and Table 5 show the anticipated yields of SNe less than three days old assuming that PLAsTiCC models are correct realizations of young SN brightness and color. For example, PLAsTiCC simulations of SNe Ia are based on the SALT2 model, which has a slower rise at early times than observations of SNe 2017cbv, 2018oh, and a number of SNe discovered by Jiang et al. (2020) . Since SNe IIb often show luminous shock breakout cooling (e.g., Richmond et al. 1994; Arcavi et al. 2011; Kilpatrick et al. 2017; Fremling et al. 2019) , they are often easy to detect early as well, yet there are no SN IIb templates in the PLAsTiCC set. It is therefore difficult to truly predict the number of young Figure 4 , but for YSE SNe per year meeting the criteria of our volume-or magnitude-limited surveys, <250 Mpc or r < 18.5 mag (a magnitude at which we expect to be able to obtain spectroscopic classifications). We predict 94 SNe Ia, 34 SNe Ib/c, 77 SNe II, and 3 SNe IIn per year. SN models are from PLAsTiCC and references therein). We note that PLAsTiCC templates for certain models, including a subset of SN II and Ibc, were not given subtypes.
SNe that will be discovered by YSE, but we note that our team has already observed a handful of young SN candidates, including SNe Ia 2020pf, 2020fci, 2020ioz, 2020juq, 2020nbo and CC SNe 2020pni, 2020kpz, and 2020tlf that are almost certainly within three days of explosion or less (see Section 5). Figure 7 also shows the simulated colors of young SNe; we find that a simple color cut could identify a subset of young SNe with ∼40% accuracy, which is a reasonably high confidence given the follow-up resources available. We find that g − r colors are useful for separating young versus old CC SNe and YSE r − i colors appear to be excellent indicators of young SNe Ia, complementing g − r observations from YSE and ZTF. Our team is working on more sophisticated classification methods using existing observations of young SNe.
While these simulations are state of the art for transient survey planning, they still lack several details for precise estimates. Although early survey yields are returning the expected number of volume-and magnitudelimited SNe, just 26% of transients are above the median predicted survey redshift of 0.19. This is likely because our algorithm for measuring YSE photometry has been obtaining inflated uncertainties, effectively reducing the S/N of YSE photometry and our detection efficiency. Figure 4 , but for YSE SNe per year discovered within three days of explosion. Simulations yield 13 SN Ia, 3 SN Ib/c, and 11 SN II. We expect the simulation to underestimate to true number of young SNe since the SALT2 model for SNe Ia has a slower early rise than, for example, the SNe discovered by Jiang et al. (2020) , and few PLAsTiCC CC SN models include a shock breakout cooling stage.
We identified this issue by implementing a second photometric reduction algorithm and are currently working to implement a fix to our pipeline photometry. Other contributing factors may include biases in the photo-z determinations, as reliable photo-z's are more difficult to estimate for faint or undetected host galaxies. Additional stacked observations may also reduce the noise in the template images, which simulations assume to be negligible. Finally, efficiency losses in the machinelearning "real/bogus" algorithm (Section 4) may also reduce the number of discovered SNe as a function of S/N. Nevertheless, we have observed or discovered 916 transients over the first ten months of the survey while observing at half of our nominal area and experiencing ∼2 months of down time.
Given the YSE survey design and simulations described above, we briefly summarize a number of ways in which the YSE data will address the science drivers identified in Section 2:
• Young SNe. YSE will discover at least 2 SNe within three days of explosion per month, with a larger sample likely due to known deficiencies in the PLAsTiCC models used to generate YSE simulations. Many of these SNe will have i or z Figure 7 . Color-color diagram for "noise-free" YSE SNe as simulated by SNANA with colored points representing the time since explosion as described by the above color bar. The black arrow indicates the direction and magnitude an object would experience if its dust reddening were increased by 1 mag (with RV = 3.1). The black line defines a relatively simple cut that adequately identifies SNe within three days of explosion, with 38% of all objects to the left of the line being young, while only 5% of those to the right of the line are young. We highlight the path a typical SN Ia and SN II (star symbols) travel as they evolve from explosion. We note that the g − r and r − i colors are particularly useful for selecting young SNe II and SNe Ia from the entire population, respectively. SN 2020pf at an epoch of ∼2 days after explosion from YSE and ZTF data is displayed as a star with error bars; it is consistent with the simulations and in the defined "early" region. To the top and right are histograms of the g − r and r − i colors for SNe before +3 days from explosion (blue) and after +3 days from explosion (orange).
photometry at very early times. Several examples from early YSE data are discussed in Section 5.
• Red and Rare Transients. The iz coverage of YSE will aid the identification of very red transients. Within our volume-limited sample out to 250 Mpc, we will discover such transients down to M i ≈ −15.5 mag and M z ≈ −16.5 mag. We also expect YSE to observe two glSNe per year in regular sky scans and up to five glSNe per year in stacked images based on projections from Wojtak et al. (2019). YSE fields to date include ∼1400 galaxies near enough for YSE to discover luminous red novae, though their volumetric rate is not well known (Pastorello et al. 2019) . York et al. (2000) , Chambers et al. (2016) , Abbott et al. (2018) , and Kuijken et al. (2019) , respectively.
• Cosmology. The YSE SN Ia sample will be a mix of targeted SN Ia observations to increase the z 0.05 sample size, using a strategy modeled after the Foundation Supernova Survey , and untargeted observations, which will have a median redshift of z ≈ 0.12 (Appendix A). The untargeted sample will include up to 350 z < 0.1 SNe per year with a wellunderstood, magnitude-limited selection and lower uncertainties from peculiar velocities than other low-z samples. The large sample with z > 0.1 will serve as an important sample for measuring w, testing general relativity, and helping to understand CC SN contamination in future cosmology analyses.
We did not include TDEs in survey simulations as their redshift-dependent rates are uncertain, as is the efficiency of detecting them in the bright cores of galaxies; based on the survey magnitude limit alone with the rates and luminosities from van Velzen et al. (2020) , and including an approximate mean M g = −17.5 mag, we would expect ∼20 per year -and YSE has already observed two TDEs in untargeted observations just in early survey data (a third, AT 2020nov, was targeted; Section 5) -but due to the difficulty of detect-ing transients in the cores of galaxies, it is unclear if the expected number from these orderof-magnitude estimates is overly optimistic. YSE survey data will also contain tens of thousands of AGN for variability studies.
• Preparation for the Rubin Observatory. YSE will measure griz light curves for hundreds of spectroscopically classified transients and thousands of transients in total, including hundreds of detections within the first few days of explosion. This will allow YSE to serve as an invaluable training sample for the Rubin Observatory, adding fidelity to early time and full light curve classifications and constraining the rates of rare classes that will exist in Rubin survey data.
• Magnitude-and Volume-limited Census of SNe. The iz coverage of YSE will improve the census of red transients and the YSE redshift range will allow a longer lever arm on the redshiftdependent rates of many transients compared to other ongoing surveys. We plan to spectroscopically classify every SN that is either brighter than r ≈ 18.5 mag at peak or nearer than 250 Mpc, a sample of ≈217 SNe per year (Section 3.7) to constrain both the rates and luminosity functions of SN types and subtypes.
In addition to addressing these time-domain science questions, YSE will create one-year stacked images over approximately 5,000 deg 2 of sky and three-year stacked images over an additional ∼2000 deg 2 of sky. Using the typical read noise and moon-dependent sky noise values from Chambers et al. (2016) and assuming 30% loss due to weather and 24% due to pixel masking, we roughly estimate that in one-year stacks, YSE+3π will reach depths of griz ≈ 23.6, 23.7, 23.6, 22.8 mag. In the three-year stacks, YSE+3π will reach depths of griz ≈ 24.0, 24.2, 24.1, 23.3 mag. Comparisons to the depths of several other large-area surveys are shown in Table 3 .
Efficiently working with large time-domain data sets to identify transients of interest for follow-up observations and facilitating large statistical analyses with the data are key challenges of transient discovery searches (Bloom et al. 2012; Kasliwal et al. 2019; van der Walt et al. 2019) . Efficiently separating data artifacts from bona fide transients (e.g., Duev et al. 2019 ) is also critical. YSE in particular requires a robust and efficient broker process for discovering and following transients.
Once YSE data are taken, they are first processed by the IPP difference image pipeline and sent to the Transient Science Server at Queens University Belfast . The Transient Science Server then determines the likelihood that each SN is a bona fide transient, including a visual inspection stage performed by our team. From there, possible transients are sent to a transient database and web application housed at UC Santa Cruz, which we refer to as "YSE-PZ." The full YSE broker process is summarized in Figure 8 and the details of this process are discussed below.
The Transient Science Server removes YSE detections from known AGN, variable stars, and moving objects using a library of catalogs (see Smith et al. 2020 for details) and employs both pixel-based and catalog-based machine learning algorithms -i.e., the algorithm is based on both the pixels themselves and on photometric metadata such as PSF shape − to remove likely image artifacts, and assign a "real-bogus" score to each detection. Candidates are included on the by-eye vetting Figure 9 . Example "transient summary" page from the YSE-PZ web application. Transient summary pages show YSE image stamps, spectra, photometry, archival images, and metadata, with a more extensive set of data and metadata available via the "detail view." list if the "real-bogus" score considers them to be real with at least 32% confidence; we choose this threshold because the training procedure estimates it will yield a high fraction of real candidates (75%), while rejecting just 1% of bona fide transients. We require 3.5-σ detections in at least two separate images (possibly over multiple epochs) to add a source to the list of candidates to be vetted. The machine-learning algorithm is trained using slow-moving asteroids as the "good" objects; we plan to retrain using real transients in more realistic host-galaxy environments in the future. Additional details of the algorithm are given in Smith et al. (2020) .
If members of our team determine that a candidate is likely a bona fide transient, they are immediately sent to the Transient Name Server along with the first epoch of photometric data. If we are unsure, the transients remain on a "possible" list until more data have been collected. Both the transients on the "good" and "possible" lists are ingested to YSE-PZ for combining with data from other surveys and making follow-up decisions.
The YSE-PZ application is designed to ingest every transient reported to the Transient Name Server 4 to facilitate queries of the data, combine YSE transients with external data, store follow-up data obtained by our team, and enable easy communication among team members for vetting and following new transients. YSE-PZ is a Django-based application built on a MySQL database. The code base is publicly available at https: //github.com/davecoulter/YSE PZ and we encourage collaboration and new contributors. An example of the web application interface is shown in Figure 9 .
YSE-PZ will be described in detail in Coulter et al. (in prep.) . Here, we summarize key features that allow our team to prioritize and organize follow-up observations of transients:
1. Data ingestion from ZTF and TNS. We ingest ZTF alerts using the ANTARES broker (Narayan et al. 2018) for events in our survey fields that have not yet been reported to TNS. We also ingest every transient reported to TNS using the TNS API. For transients that have been reported to TNS, we ingest ZTF photometry from MARS 5 .
Transients are ingested into YSE-PZ with a status of "New" and then parsed into categories of "Watch," "Interesting," "FollowupRequested," "Following," or "Ignore" based on characteristics such as brightness, redshift, classification, or color. Transients that meet the YSE volume-or magnitude-limited survey criteria are prioritized for followup by moving them to "Interesting" or "FollowupRequested" and subsequent queries are used to flag transients that brighten to the point where they are included in the magnitude-limited criteria. YSE-PZ compiles a large amount of data and metadata to aid in the vetting process, including survey images, archival images, other external data (e.g., HST or Chandra imaging), Milky Way extinction, photometry, spectra, classifications, and discovery details. Our team sorts through new transient discoveries daily.
3. Follow-up Requests. YSE-PZ allows follow-up observations to be requested on any telescope for which our team has access. Follow-up requests are added to a custom page for a given observing night and resource, where observers can prioritize the requests and schedule observations. Finder charts Transient discovery statistics between Nov. 24th, 2019 and Oct. 1st, 2020. We have been at 50% of our full observing allocation since early January, but lost ∼2 months due to weather and telescope malfunctions.
using Pan-STARRS 3π imaging may be generated through the web interface and we plan to add additional tools to facilitate scheduling in the near future.
4. Queries. YSE-PZ allows users to query on any field in the database. A query, once completed and saved, can be added to a user's personal "dashboard" to allow each user to be alerted to transients that are relevant to their science interests. Example queries include the YSE volume-limited sample of transients within 250 Mpc, or transients rising by more than 0.5 mag in a day in a given filter. As new transients are ingested, users can choose to be alerted via email or text message to transients that match a given query to enable extremely rapid follow-up observations. YSE-PZ is under active development, and new features are continually being added. A well-developed, open-source, robust transient management framework is vital to our science goals and a key part of developing tools for the next decade of transient science.
YSE has discovered 5.1% of the likely transients reported to TNS so far in 2020 and after including discoveries from other sources, YSE has discovered or observed a total or 8.3% of IAU-reported 2020 transients within our survey fields. YSE has announced a total of 778 discoveries as of October 1st, 2020. Though early discovery announcements included significant AGN contamination, our team has become much more accurate over time at removing AGN, asteroids, and artifacts (burns, persistence, etc.) that passed initial cuts; after inspecting the full YSE light curves to remove these objects, we estimate that YSE discovered or observed 916 bona Figure 10 . Examples of YSE SN light curves by increasing redshift, from z = 0.02 to z = 0.2. For the SNe Ia, we also display the best-fit SALT2.4 template light curves (Guy et al. 2010; Betoule et al. 2014 ) for illustration. ZTF gr light curves are displayed as lightly shaded diamonds when available, which effectively doubles the cadence of nearby SNe in our sample (though low-z SN Ic 2020lbn, left, has no public ZTF data). We note that CC SNe 2020hrw and 2020koc are somewhat unusual SNe, as SN 2020hrw is likely a SN 1987A-like SN and SN 2020koc is unusually luminous for a SN II (although perhaps not quite luminous enough to be considered a SLSN).
fide transients prior to October 1st, 2020. A summary of our discovery and follow-up statistics are reported in Table 4 and sample light curves are shown in Figure 10 . Magnitude and redshift histograms for YSE transients to date are shown in Figure 11 .
To date, YSE observations have a median PSF full width at half maximum (FWHM) of 1.3 arcsec and 5-σ detection limits of gri ≈ 21.5, 21.7, 21.4 mag during dark time and riz ≈ 20.9, 20.9, 20.5 mag during bright time (Table 1) . After weather loss, we observe with a median cadence of 3.9 days. The median phase of first S/N > 3 observation for YSE transients (based on estimates of the time of maximum light) is −6.4 days 6 .
YSE observations include 138 spectroscopically classified transients ( Figure 12 ); highlights include two SNe Iax (2020inp and 2020sck), one super-Chandrasekhar SN Ia (2020esm), four SNe IIb (2020fqv, 2020ikq, 2020ivg, 2020tkc) , one SN Ic-BL (2020fhj), and three TDEs: 2020neh, 2020nov, and 2020opy, with AT 2020neh being particularly unusual in its rapid evolution 7 . We have observed several SNe Ia within 2-3 days of explosion, including SNe 2020fci, 2020ioz, 2020juq, 2020nbo, and 2020pf, among other candidates. We also observed flash ionization features in Keck spectra of CC SNe 2020pni and 2020tlf and, for 2020pni, we observed a strong UV peak in Swift photometry at approximately 1.9 days after explosion thanks to ZTF detections within one day of explosion and deep YSE non-detections two days prior ( Figure 13 ). The early rise of SN Ia 2020pf is shown in Figure 14 , with the first detection occurring at approximately 19 days before B-band maximum light as determined from a fit of the light curve using SALT2.
We have obtained targeted YSE survey observations of nearby stripped-envelope SNe 2019yvr (Ib), 2020oi (Ic), and 2020nxt (Ibn) as well as four very nearby SNe Ia, SNe 2020jgl and 2020uxz and Virgoadjacent SNe 2020ue and 2020nlb; analyses of these exciting objects are forthcoming. SNe 2020ees, 2020fqv, and 2020nxt have contemporaneous HST observations, while an additional ten YSE transients have HST preexplosion imaging. We are working with ongoing programs such as "Supernovae in the near-Infrared avec Hubble" (SIRAH; PI Jha) to share promising young supernovae for coordinated observations with HST and other facilities. We welcome external collaborators; our external scientist policy together with a guide to the application can be found at https://yse.ucsc.edu/ collaborate/.
Given telescope down time, we have effectively observed ∼7 months to date, which equates to approximately 3.5 months of the survey simulations in Section 3.7 as they assume double the area coverage. By this estimation we are slightly under-performing our expected yields of ∼5,000 SNe per year, but this is expected as early survey photometry has overestimated photometric uncertainties by factors of 1.7-1.9 (see Section 3.7). Our current magnitude-limited sample of 125 SNe is much larger than expected from the 108 predicted SNe per year in our simulations, though a handful of targeted SNe and survey edge effects as we catch Figure 11 . Top: Approximate peak and discovery magnitudes for YSE transients to date, including targeted followup observations, with the filter combinations used at each discovery epoch or first observation shown in the pie chart. Histograms of 2020 ZTF discoveries, re-scaled to match the total number of YSE discoveries, are shown with dashed lines. Estimated peak magnitudes are taken from the brightest epoch, regardless of filter, and include publicly reported observations when available. Discovery magnitudes are more often from the bluer of the two filters at the discovery epoch, as the bluer observation is typically performed first. Bottom: redshift distribution (using an internal photo-z estimate trained on SDSS data, when necessary) for the YSE sample to date. The normalized ZTF redshift histogram for 2020 discoveries to date is shown in grey. Lines show Gaussian kernel density estimates for discoveries in each filter with colors corresponding to the pie chart in the top plot. Photo-z outliers may artificially increase the numbers in some highredshift bins.
slow-declining SNe when we begin observations of new fields have likely boosted this number significantly. Our volume-limited sample also exceeds that expected from simulations, though this number may also be impacted by photo-z uncertainties.
Our team aims to obtain a spectrum of every unclassified transient with peak r < 18.5 mag, D < 250 Mpc, or a detection within two days of explosion for an estimated total of 217 per year ( Figure 5 ). Our secondary priority will be to spectroscopically classify transients that appear unusual based on their photometric properties. Our current magnitude-limited sample is 91% spectroscopically complete. We examined the failures and the two most common reasons for a lack of a spectrum are lack of spectroscopic resources because of COVID-19 shutdowns and the transient being discovered shortly before it sets. Therefore, we are optimistic that our completeness can increase as the survey continues.
6. CONCLUSIONS YSE is a transient survey that will use 7% of the time on both the Pan-STARRS1 and Pan-STARRS2 telescopes to survey 1512 deg 2 in griz with a 3-day cadence and two filters per epoch. YSE began on November 24th, 2019, and has been running for the past several months using only Pan-STARRS1 but will soon double its observing time by using Pan-STARRS2. Detailed simulations indicate that at full capacity, YSE will discover ∼5,000 SNe per year. YSE images reach limiting magnitudes of ∼21.5 in gri and ∼20.5 in z, with dark-and bright-time observations taken in combinations of gri and riz, respectively. The YSE survey strategy avoids the Galactic plane and prioritizes equatorial fields with a high volume of archival and, when possible, multi-wavelength data. YSE is currently planning to carry out a three-year survey; survey updates and contact information for members of our team can be found at https://yse.ucsc.edu.
The primary science drivers of YSE include opening a new discovery space for faint and red transients, building a census of transients in the nearby universe, understanding black hole variability and tidal disruption events, and assembling a legacy high-cadence, lowz SN Ia cosmology sample to complement high-redshift samples from the Rubin Observatory, and the Roman Space Telescope. YSE has discovered or observed 916 SNe to date and 8.3% of the likely transients announced so far in 2020. When Pan-STARRS2 observations commence, we anticipate that the YSE survey volume will be approximately ∼20% that of ZTF. We will have YSE data for approximately 10% of ZTF transients in the public survey, giving thousands of light curves with combined observations every 1-2 days.
The Young Supernova Experiment's datasets are yielding new insights into transient physics and are complementary to other ongoing surveys. YSE's multiwavelength light curves and our team's follow-up spectroscopy are forming a rich dataset that will improve our understanding of the time-domain universe and prepare the community for the forthcoming Rubin Observatory and its Legacy Survey of Space and Time. (Kessler et al. 2010) . SNANA generates catalog-based simulations that use real survey noise properties, detection efficiencies, SN rates, and luminosity functions, as well as the transient SED models used for the PLAsTiCC SN classification challenge Malz et al. 2019) . We base the YSE simulations on simulations that were originally generated for the Foundation Supernova Survey and used for the cosmological parameter measurements in Jones et al. (2019) . Because Foundation took predominantly 15-second exposures, we were able to generate an accurate YSE survey realization simply by scaling the exposure times to match the 5-σ detection limits shown in Figure 2 . This may introduce small errors in the relation between S/N and brightness, but these should be negligible as our analysis focuses on the number of detected SNe in the simulations (with the exception of cosmologically useful SNe Ia in Table 5 , which may scale up or down slightly depending on the true survey noise properties).
The PLAsTiCC transient SED models are described in detail in Kessler et al. (2019) . We do not simulate any variable sources (e.g., stars and AGN) or purely theoretical models (e.g., pair-instability SNe or cosmic strings). However, we simulate the full range of available SN Ia and CC SN subtypes, as well as SNe Iax, Ca-rich transients, and intermediate luminosity optical transients (ILOTs). The PLAsTiCC models come from a wide variety of sources; we note that at early times in particular, they are based on scant observational data.
For the simulated SN rates, we again follow PLAsTiCC, who use SN Ia rates from Dilday et al. (2008) : 
also describes the adopted redshift-dependent rates of peculiar classes of events. While the PLAsTiCC models and SNANA simulations suffer from a number of uncertainties in both the underlying models and in the rates and luminosity functions of both rare and relatively common SN subtypes, PLAsTiCC is the most reliable currently available compilation combining rates, luminosity functions, and spectrophotometric transient models (however, see Vincenzi et al. 2019 for a recent update to a number of CC SN templates). As a sanity check, we integrate these rates over our expected 1512 deg 2 survey area and within volumes where we would expect to discover nearly every SN given the survey depth. Assuming 24% loss for detector masking and an additional 30% for weather, we predict a discovery rate of ∼2300 SNe Ia per year to a distance of 1 Gpc and 76 CC SNe per year to a distance of 250 Mpc, distances corresponding to where a 27-second PS1 exposure should be sufficiently deep to detect nearly all SNe Ia and CC SNe. Our baseline simulation predicts that we should detect 1840 SNe Ia per year within 1 Gpc and 116 CC SN per year within 250 Mpc, consistent with these estimated numbers, with the increase in CC SNe likely due to the longer duration of many of these events.
We conservatively assume 24% masking of the GPC1 and GPC2 cameras, which will likely give a slight underestimate of the true SN yields and the area shown in Table 5 includes these losses. Per-month estimates of weather loss are taken from PS1 data from the summit of Haleakala between 2010 and 2014 and are directly included in the simulation to account for correlations in downtime. We also varied the simulated survey depth as a function of lunar phase to match what we observe from Foundation data and the 3π survey; gri depths are estimated to vary by approximately 1.0, 0.7, and 0.2 mag between dark time and bright, while z depths are assumed to remain constant. Typical night-to-night variation in depth is included. We also simulated an approximate ZTF survey by assuming an average depth of 21.1 mag in dark time that again varies to the same degree with moon phase as PS1 and we assumed that ZTF observations (when simulated Palomar and Haleakala weather allows) will always take place 21 hours after YSE observations. ZTF weather is estimated from monthly averages of weather loss at Palomar from the first year of ZTF observations. A small number of second-order effects are not included in these simulations. First, we do not include loss due to the Pan-STARRS moon avoidance angle (30-35 • ) in the simulations. Because we prioritize fields far from the ecliptic, this loss can usually be limited to approximately five days per month depending on the field. We also do not simulate the changing of the telescope position angle, which changes the location of masked pixels on the sky and is effectively a 20% weather-like reduction in the sampling of light curves (but not the area on sky). Finally, though fields will typically be observed for approximately 4-6 months per year, we do not include edge effects from rising or setting fields. We also do not simulate the Virgo one-day survey. However, none of these effects should cause substantial reductions in the numbers or demographics of simulated SNe, which are our primary goals in this section.
The nominal survey strategy along with several alternate strategies considered by our team are shown in Table 5 . We caution that some of the numbers, particularly discoveries of young SNe, are affected by small-number statistics. Due to the limited filter choices during bright time to avoid the drastic reduction in g-band depth, we simulated a ri + rz filter sequence during bright time for all two-filter survey designs. We found that as exposure time increases, the predicted number of SN discoveries also tends to increase but begins to flatten after 25s, which is unsurprising given the volume as a function of exposure time shown in Figure 2 . Choosing lower exposure times increases the number of transient discoveries for which we can obtain spectroscopic follow-up observations, while higher exposure time increases the total number of transients. We decided on a survey with two filters per epoch because the survey designs featuring three and four filters per epoch would be very valuable for some science cases but would substantially reduce the transient discovery volume. Finally, a blue-focused survey would probe a more similar discovery space to other ongoing surveys such as ZTF, while a red-focused survey would be expected to reduce the number of young SNe discovered as these are expected to be blue. For these reasons, we chose a two-filter survey with a gr + gi filter sequence during bright time with exposure times in each band of 27 seconds. Each of these individual metrics is parameterized and scaled to ensure reasonable weights and a final range from 0 to 1. m MW is set to zero for MW E(B −V ) greater than 0.2 and is proportional to the inverse extinction [1+E(B −V )] −1 . The depth metric m d uses r-band depths from 20.5 (m d = 0) to 22.5 mag (m d = 1) scaled linearly; fields with PS1 MDS coverage are given a metric of 1, and the depth of either 3π or SDSS for non-MDS regions of sky is used depending on which is deeper (typically PS1, except for Stripe 82). For the nearby galaxies metric m ng , we weight by the number of galaxies at < 150, < 40, < 20, and < 10 Mpc from the NASA/IPAC Extragalactic Database (NED) and combine each of these sets; e.g., galaxies at < 10 Mpc contribute to the number of galaxies in all four maps and therefore have four times the effective weight of 150 Mpc galaxies. The nearby galaxies metric is added to the redshifts metric m z , which is the number of redshifts from the combination of SDSS and NED. The resulting map is clipped at the 98th percentile to reduce the influence of large outliers. The DECaLS depth metric m DECaLS is computed in the same manner as m d but given half the weight as the other surveys, and not multiplied by the MW E(B-V), given its predominantly z-band coverage. The follow-up observation metric m f is arbitrarily determined as the product of the airmass and hours of observation per night from La Silla observatory in Chile averaged over a year. Most of the high-metric fields are in the Northern hemisphere and this choice weights YSE survey fields for which spectroscopic follow-up observations from Figure 15 . Components of the YSE field selection metric (top six panels) and combined metric map (bottom panel) with lighter colors corresponding to higher values of the metric. We note that a number of the input maps shown here are restricted to the 3π coverage area (δ > −30 • ). We also prioritize the amount of HST exposure time, particularly interesting galaxies/clusters, and fields with newly discovered SNe from other surveys, but do not formally include these in the metric. High-priority fields that do not pass our cut on proximity to the ecliptic are sometimes included in YSE. ) and requiring total distance modulus uncertainty (including intrinsic dispersion) < 0.15 mag. b These filter combinations are for dark time; in bright time, we simulate combinations of ri and rz for all two-filter survey strategies due to limited filter options that would achieve reasonable depths. In the case of three and four filters per day, the same strategies are used for both bright and dark time observations. The "gr+gi" sequence indicates that our survey design alternates these two filter combinations with the full sequence repeating every six days. c Area after assuming 24% masking of the GPC detectors per epoch. d The small number is largely because SALT2 does not include the rest-frame iz bands in light-curve fitting.
Chile are possible. Finally, unless a field is of particular scientific interest we exclude those fields within 20 deg of the ecliptic plane; due to the Pan-STARRS moon avoidance angle of 30 deg, fields near the ecliptic may have 8-10 day gaps in their light curves. The metric map is then normalized between 0 and 1. The combination of individual metric quantities along with the final map are shown in Figure 15 .