The High cadence Transient Survey (HiTS)

Between 2013 and 2015 we led a ZTF/LSST precursor experiment with a small team at the Center for Mathematical Modelling and the Millennium Institute of Astrophysics. In the High cadence Transient Survey (HiTS) we looked for supernova explosions in real-time using the Dark Energy Camera. We optimized the survey (see this video) to find the elusive supernova shock breakout in red supergiant stars, designing and implementing all the image processing tools from scratch in the National Laboratory for High Performance Computing, our own real/bogus machine learning classification tools, and visualization tools to aid the discoveries during the observation campaign of about one week in early 2013, 2014 and 2015.

This experiment led us to form an interdisciplinary group focused on astroinformatics. It also made us realize the importance of processing large alert streams such as those produced by ZTF and LSST, leading to the creation of the ALeRCE broker.

The main scientific results from HiTS were the following:

• A deep (24th mag), large field of view (320 deg2 in total), high cadence (2/1.6 hr) optical survey (Förster et al. 2016, ApJ).. See Figure 1.
• 1st real-time survey using DECam data, leading to 125 candidate SNe (Förster et al. 2016, ApJ). See Figure 2.
• Evidence for the absence of red supergiant “envelope” shock breakouts (Förster et al. 2016, ApJ). See Figure 3.
• 1st deep learning real/bogus classifier (Cabrera-Vives et al. 2016, IEEE IJCNN, Cabrera-Vives et al. 2017, ApJ; Reyes et al. 2018, IEEE WCCI). See Figure 4.
• 18 distant RR Lyrae stars used to understand our galaxy’s halo (Medina et al. 2017, 2018, ApJ). See Figure 5.
• Thousands of new asteroids discovered (Peña et al. 2018, 2020, AJ). See Figure 6.
• A machine learning classified catalog of variable sources (Martínez-Palomera et al. 2018, AJ). See Figure 7.
• Discovery that most SNe II undergo a phase of confined circumstellar material shock breakout (Förster et al. 2018, Nat. Ast.). See Figure 8.
• 1st combination of hydrodynamical model grids with Markov Chain Monte Carlo inference methods for supernova studies (Förster et al. 2018, Nat. Ast.). See Figure 9.
• 1st recurrent convolutional neural network for the classification of image stamp time series (Carrasco-Davis et al. 2019, PASP). See Figure 10.
• Discovery of new population of intermediate mass black hole candidates (Martínez-Palomera et al. 2020, ApJ). See Figure 11.