The ZTF Bright Transient Survey


Sample SummaryLatest TransientsUnclassified SN CandidatesCompleteness StatisticsPapersTeamSample Explorer


The Zwicky Transient Facility (ZTF) Bright Transient Survey is by far the largest spectroscopic supernova survey ever conducted. Since June of 2018 we have been surveying the entire nighttime Northern sky every 2-3 nights in two filters and acquiring spectra of (almost) every time-varying, nonmoving object brighter than 18.5 mag that is not a Galactic source or an AGN, providing a complete and unbiased view of the dynamic optical sky. Transients down to 19.0 mag are also targeted when possible (given available spectroscopic resources). Between 2017 and 2020 we catalogued almost 4000 supernovae. Between 2021 and 2024 we expect to increase this total to almost 10000, including hundreds of "exotic" transients: tidal disruption events, superluminous supernovae, and the like. So far we have detected 7806 spectroscopically-confirmed transients (7702 supernovae and 104 transients of other types). The large majority of these classifications were provided by our programs.

Alert data (including preliminary photometry) is released in real time via ZTF Alert Brokers (1,2,3,4) and spectra and classifications are announced nightly via TNS.

Cumulative properties of the sample are compiled here and in our papers. Some properties of the current sample can be explored in detail using the BTS Sample Explorer.


Sample Summary

The statistics below (by class and limiting peak magnitude) are updated daily: numbers in each cell provide the total of publicly-available TNS classifications of each type. Classifications come mostly from our own programs, but also use reports from other groups via TNS. Not all classifications are final and statistics do not include transients with non-public classifications. The table on the right includes every ZTF Northern Sky Survey transient above 19.0 magnitude; the table on the left is specific to events passing a variety of cuts to establish a high-quality statistical sample: see below for details. Plots show a skymap of all confirmed extragalactic transients, and a preliminary luminosity-duration plot for sample transients.

SN-like events passing sample cuts:
mag ≤19.018.518.017.517.0
All typed479531491613882441
All SNe474231061585864429
Ia359123511189654328
Ia-CSM44222
Iax85222
Ib/c262171874322
Ibn21151041
Ic-BL46351785
SLSN-I3818871
II85156630116078
IIb704324137
IIn11984452311
SLSN-II1910521
TDE1812633
Gap76432
Ca-rich10000
LRN11000
ILRT22221
LBV33211
Novae262317116
Other22111
All events:
mag ≤19.018.518.017.517.0
All typed7806528728021542787
All SNe7702520527481510769
Ia5687385920221117579
Ia-CSM1511644
Iax1411754
Ib/c3942741557939
Ibn26201262
Ic-BL604726138
SLSN-I613015112
II15601042556303149
IIb10466412312
IIn209143773918
SLSN-II3621721
TDE45321774
Gap1512843
Ca-rich31000
LRN22100
ILRT43321
LBV66422
Novae4236282010
Other22111
Note: the classes above are not all independent (e.g. the total for Ib/c includes Ibn and Ic-BL; the total for II includes IIb, IIn, and SLSN-II) so the sum of the individual SN counts is greater than the "All SNe" count. BTS classifications are based on low-resolution 2m-class spectra and while basic type classifications are reliable, we are not complete in assigning subtypes (II vs. IIb, Ib vs. Ic, etc.). The table on the left is restricted to events with P48 coverage before and after peak, good visibility, and "SN-like" behavior (a light curve slower than typical CV's or a host association, and faster evolution than AGNs). See completeness for classification completeness statistics. Magnitudes are at the observed peak (in either filter) - for poorly sampled light curves this may underestimate the flux of the true peak.

Latest Transients

The following list shows the most recent candidates saved to the program. No light curve or host-galaxy filtering has been applied, so this may include some false positives such as cataclysmic variables or AGNs.

IDTNSIDsavedRAdecΔtlastmlasttypez
ZTF23aandbinAT2023kbg2023-06-0812:04:55.93+22:03:07.01.16g = 19.67--
ZTF23aamzksmAT2023kbh2023-06-0811:49:25.62+19:35:30.31.16g = 19.50--
ZTF23aanczshAT2023kbi2023-06-0812:13:41.89-25:42:05.31.16r = 19.49--
ZTF23aandhbnAT2023kbj2023-06-0811:04:02.88+63:14:56.71.11g = 19.53--
ZTF23aamjyakAT2023kbk2023-06-0816:39:51.41-06:13:53.11.05r = 19.80--
ZTF23aamdmscAT2023kbl2023-06-0814:17:14.41+44:48:31.51.11g = 19.75--
ZTF23aakxqwqAT2023kbm2023-06-0812:52:19.32+07:06:51.71.11g = 19.55--
ZTF23aammgwzAT2023kbn2023-06-0811:09:18.41+33:33:57.81.15r = 19.76--
ZTF23aandikrAT2023kbo2023-06-0814:10:57.77-23:51:44.71.12g = 19.60--
ZTF23aamzxcrAT2023jzp2023-06-0814:01:04.53+16:08:22.91.12g = 19.41--
ZTF23aamzngkAT2023kbp2023-06-0811:33:16.85-09:39:45.81.16r = 19.20--
ZTF23aanfwywAT2023kbx2023-06-0821:06:01.56+10:36:39.60.91g = 17.25--
ZTF23aanfzxbAT2023kby2023-06-0822:35:31.49+15:46:38.30.94r = 19.14--
ZTF23aanajbfAT2023kbz2023-06-0821:48:38.95+12:11:36.50.94r = 19.14--
ZTF23aanddlnAT2023kca2023-06-0811:05:52.62-09:42:09.21.16r = 19.18--

Supernova Candidates Needing Classification

The following list shows older SN candidates still needing classification, and may be useful to observers with classification programs in need of targets (the most recent ZTF magnitude is provided below). A looser version of the sample cuts has been applied to this list to remove some false positives and poorly-observed SNe. Because the whole light curve is not available yet there may still be a few false positives in this list.

IDTNSIDsavedRAdecΔtlastmlast
ZTF23aadcapaAT2023ctw2023-03-1516:08:02.12+40:27:02.610.92g = 20.27
ZTF18aaowtgnAT2023cmt2023-03-2716:20:27.57+36:21:40.113.89r = 19.45
ZTF22aacwsjlAT2023hsq2023-05-0910:12:46.26+10:10:39.57.15r = 19.26
ZTF23aaiwpboAT2023hyo2023-05-1118:38:33.67+29:44:13.45.96r = 19.24
ZTF23aajfdxfAT2023icl2023-05-1212:13:06.61+79:01:47.37.10r = 19.22
ZTF23aajenxfAT2023iar2023-05-1313:31:36.13+04:55:21.32.16r = 19.05
ZTF23aajjzonAT2023igu2023-05-1620:51:52.07-07:45:12.613.89r = 19.02
ZTF23aajjzdyAT2023igv2023-05-1620:40:14.25-14:29:10.59.91r = 19.13
ZTF23aajholdAT2023hyj2023-05-1611:10:57.15-01:33:52.33.14g = 18.94
ZTF23aajfereAT2023icj2023-05-1715:55:33.54+06:48:05.21.08g = 19.46
ZTF23aajazzlAT2023ite2023-05-1718:25:44.50+31:57:51.112.92g = 19.81
ZTF23aaksthtAT2023jac2023-05-2017:00:05.33+51:55:55.12.07g = 18.92
ZTF23aalchipAT2023jfr2023-05-2207:58:44.84+78:54:40.17.13r = 17.76
ZTF23aalgqsqAT2023jdh2023-05-2220:17:22.86-20:34:31.44.91g = 17.89
ZTF23aaldvncAT2023jfl2023-05-2413:53:53.72+18:35:36.83.07g = 17.71
ZTF23aalqgpkAT2023jil2023-05-2412:12:56.90+57:37:42.03.01r = 18.17
ZTF23aalojkoAT2023jkn2023-05-2517:13:12.48-19:28:08.69.93g = 19.32
ZTF23aalczjhAT2023jfn2023-05-2613:29:34.42+70:52:10.64.10r = 17.85
ZTF23aaltxktAT2023jir2023-05-2620:17:06.40-12:05:44.62.91g = 18.53
ZTF23aamgmayAT2023jre2023-05-2911:15:11.74+54:27:42.63.15g = 18.51

Completeness

Our goal is to be close to 100% spectroscopically complete for all genuine extragalactic transients brighter than 18.5 magnitude that are detected in more than one imaging epoch in the survey footprint, for which the epoch of maximum light is clearly observed, and which do not set within about a month after maximum light. (Transients that do not meet these cuts are also observed when possible.) Our actual completeness for events meeting all these criteria is about 95%. (The remaining 5% are mostly concentrated in periods of bad weather that affected our spectroscopic follow-up programs.)

The table at left below presents classification success statistics (by limiting magnitude) for events meeting these criteria (at left) and exhibiting "SN-like behavior" (slow rise and/or fade time, or coincident with a galaxy). The month and peak magnitude are as observed (in either filter). These totals do include unreleased classifications, so they are slightly higher than the totals given in the type-specific table above. The table at right includes all events, including those with poor visibility which could not be classified (many of these are stars).

A visualization of this is also given in the figure at right: large points indicate classification successes (green circle) or failures (red X) for sample transients; small dots show other potential transients that fail our cuts.

SN-like events passing sample cuts:
mag≤19.0≤18.5≤18.0≤17.5≤17.0
2018 May19/23 (82.6%)19/21 (90.5%)7/7 (100%)4/4 (100%)3/3 (100%)
2018 Jun40/43 (93.0%)22/22 (100%)12/12 (100%)7/7 (100%)6/6 (100%)
2018 Jul53/55 (96.4%)30/31 (96.8%)13/13 (100%)4/4 (100%)3/3 (100%)
2018 Aug48/49 (98.0%)27/27 (100%)15/15 (100%)6/6 (100%)2/2 (100%)
2018 Sep28/33 (84.8%)18/20 (90.0%)9/10 (90.0%)4/4 (100%)3/3 (100%)
2018 Oct1/1 (100%)0/00/00/00/0
2018 Nov46/58 (79.3%)26/29 (89.7%)16/17 (94.1%)10/10 (100%)4/4 (100%)
2018 Dec60/79 (75.9%)44/47 (93.6%)24/24 (100%)14/14 (100%)9/9 (100%)
2019 Jan53/115 (46.1%)42/67 (62.7%)31/38 (81.6%)24/25 (96.0%)16/16 (100%)
2019 Feb54/90 (60.0%)40/53 (75.5%)22/23 (95.7%)11/11 (100%)7/7 (100%)
2019 Mar58/76 (76.3%)34/36 (94.4%)23/23 (100%)17/17 (100%)11/11 (100%)
2019 Apr99/133 (74.4%)69/74 (93.2%)35/36 (97.2%)13/13 (100%)2/2 (100%)
2019 May76/106 (71.7%)55/59 (93.2%)37/38 (97.4%)17/17 (100%)8/8 (100%)
2019 Jun95/112 (84.8%)58/58 (100%)30/30 (100%)15/15 (100%)6/6 (100%)
2019 Jul100/113 (88.5%)54/54 (100%)30/30 (100%)12/12 (100%)6/6 (100%)
2019 Aug115/133 (86.5%)60/60 (100%)25/25 (100%)13/13 (100%)6/6 (100%)
2019 Sep122/143 (85.3%)80/81 (98.8%)46/47 (97.9%)22/23 (95.7%)14/14 (100%)
2019 Oct122/158 (77.2%)68/71 (95.8%)31/31 (100%)15/15 (100%)10/10 (100%)
2019 Nov115/163 (70.6%)79/87 (90.8%)41/41 (100%)17/17 (100%)7/7 (100%)
2019 Dec60/85 (70.6%)45/47 (95.7%)25/25 (100%)14/14 (100%)6/6 (100%)
2020 Jan113/153 (73.9%)79/82 (96.3%)35/37 (94.6%)15/16 (93.8%)8/8 (100%)
2020 Feb133/171 (77.8%)83/86 (96.5%)49/50 (98.0%)22/22 (100%)8/8 (100%)
2020 Mar14/15 (93.3%)13/13 (100%)6/6 (100%)2/2 (100%)1/1 (100%)
2020 Apr49/71 (69.0%)37/37 (100%)22/22 (100%)10/10 (100%)6/6 (100%)
2020 May111/155 (71.6%)75/75 (100%)42/42 (100%)22/22 (100%)12/12 (100%)
2020 Jun102/128 (79.7%)60/61 (98.4%)30/31 (96.8%)20/20 (100%)8/8 (100%)
2020 Jul76/105 (72.4%)42/44 (95.5%)26/27 (96.3%)12/12 (100%)6/6 (100%)
2020 Aug79/88 (89.8%)44/45 (97.8%)18/19 (94.7%)10/11 (90.9%)5/5 (100%)
2020 Sep99/119 (83.2%)58/59 (98.3%)28/28 (100%)18/18 (100%)9/9 (100%)
2020 Oct138/170 (81.2%)82/83 (98.8%)41/41 (100%)24/24 (100%)11/11 (100%)
2020 Nov134/180 (74.4%)82/86 (95.3%)36/36 (100%)22/22 (100%)12/12 (100%)
2020 Dec130/193 (67.4%)95/95 (100%)40/40 (100%)24/24 (100%)11/11 (100%)
2021 Jan91/133 (68.4%)67/69 (97.1%)39/39 (100%)24/24 (100%)9/9 (100%)
2021 Feb112/151 (74.2%)75/76 (98.7%)36/36 (100%)20/20 (100%)11/11 (100%)
2021 Mar103/157 (65.6%)76/76 (100%)31/31 (100%)13/13 (100%)8/8 (100%)
2021 Apr141/175 (80.6%)94/95 (98.9%)47/48 (97.9%)32/32 (100%)13/13 (100%)
2021 May113/144 (78.5%)59/60 (98.3%)31/32 (96.9%)15/16 (93.8%)8/8 (100%)
2021 Jun95/127 (74.8%)64/65 (98.5%)28/28 (100%)16/16 (100%)9/9 (100%)
2021 Jul107/143 (74.8%)74/79 (93.7%)36/36 (100%)18/18 (100%)7/7 (100%)
2021 Aug101/125 (80.8%)59/59 (100%)38/38 (100%)21/21 (100%)8/8 (100%)
2021 Sep132/153 (86.3%)74/78 (94.9%)32/34 (94.1%)17/17 (100%)6/6 (100%)
2021 Oct99/135 (73.3%)63/68 (92.6%)35/35 (100%)27/27 (100%)12/12 (100%)
2021 Nov89/128 (69.5%)55/57 (96.5%)24/24 (100%)13/13 (100%)8/8 (100%)
2021 Dec0/00/00/00/00/0
2022 Jan0/00/00/00/00/0
2022 Feb0/6 (0.0%)0/1 (0.0%)0/00/00/0
2022 Mar0/00/00/00/00/0
2022 Apr67/113 (59.3%)52/54 (96.3%)24/24 (100%)17/17 (100%)10/10 (100%)
2022 May87/139 (62.6%)65/66 (98.5%)27/27 (100%)11/11 (100%)8/8 (100%)
2022 Jun99/128 (77.3%)62/63 (98.4%)30/30 (100%)17/17 (100%)11/11 (100%)
2022 Jul94/118 (79.7%)55/56 (98.2%)29/29 (100%)16/16 (100%)8/8 (100%)
2022 Aug100/128 (78.1%)56/58 (96.6%)25/26 (96.2%)17/17 (100%)10/10 (100%)
2022 Sep112/131 (85.5%)73/74 (98.6%)24/25 (96.0%)13/14 (92.9%)4/5 (80.0%)
2022 Oct133/166 (80.1%)92/94 (97.9%)49/50 (98.0%)24/24 (100%)10/10 (100%)
2022 Nov105/176 (59.7%)80/87 (92.0%)37/40 (92.5%)27/28 (96.4%)12/12 (100%)
2022 Dec68/107 (63.6%)53/56 (94.6%)30/30 (100%)15/15 (100%)5/5 (100%)
2023 Jan23/42 (54.8%)19/27 (70.4%)13/15 (86.7%)7/8 (87.5%)5/5 (100%)
2023 Feb41/64 (64.1%)37/41 (90.2%)24/27 (88.9%)14/16 (87.5%)5/6 (83.3%)
2023 Mar13/27 (48.1%)12/14 (85.7%)8/8 (100%)5/5 (100%)2/2 (100%)
2023 Apr115/177 (65.0%)83/85 (97.6%)42/43 (97.7%)22/23 (95.7%)12/13 (92.3%)
2023 May64/97 (66.0%)52/58 (89.7%)35/37 (94.6%)21/23 (91.3%)13/15 (86.7%)
2023 Jun0/00/00/00/00/0
all4846/6506
(74.5%)
3171/3326
(95.3%)
1619/1656
(97.8%)
882/895
(98.5%)
440/445
(98.9%)
All events (includes many unclassified variables):
mag≤19.0≤18.5≤18.0≤17.5≤17.0
2018 May61/67 (91.0%)44/47 (93.6%)22/22 (100%)9/9 (100%)7/7 (100%)
2018 Jun80/91 (87.9%)44/46 (95.7%)21/22 (95.5%)11/11 (100%)8/8 (100%)
2018 Jul85/93 (91.4%)56/59 (94.9%)27/27 (100%)13/13 (100%)6/6 (100%)
2018 Aug81/90 (90.0%)55/61 (90.2%)31/35 (88.6%)17/20 (85.0%)7/8 (87.5%)
2018 Sep137/162 (84.6%)80/90 (88.9%)37/42 (88.1%)17/20 (85.0%)8/10 (80.0%)
2018 Oct19/22 (86.4%)12/14 (85.7%)7/9 (77.8%)7/9 (77.8%)3/4 (75.0%)
2018 Nov170/212 (80.2%)107/125 (85.6%)66/78 (84.6%)37/39 (94.9%)18/19 (94.7%)
2018 Dec133/197 (67.5%)98/124 (79.0%)50/59 (84.7%)29/32 (90.6%)20/20 (100%)
2019 Jan83/179 (46.4%)66/110 (60.0%)46/61 (75.4%)30/33 (90.9%)17/17 (100%)
2019 Feb106/179 (59.2%)78/116 (67.2%)44/51 (86.3%)26/27 (96.3%)13/14 (92.9%)
2019 Mar115/160 (71.9%)67/85 (78.8%)39/48 (81.3%)28/31 (90.3%)18/20 (90.0%)
2019 Apr127/193 (65.8%)88/112 (78.6%)41/50 (82.0%)16/22 (72.7%)4/7 (57.1%)
2019 May114/176 (64.8%)85/98 (86.7%)48/51 (94.1%)28/28 (100%)13/13 (100%)
2019 Jun162/212 (76.4%)116/124 (93.5%)55/58 (94.8%)30/32 (93.8%)14/15 (93.3%)
2019 Jul145/182 (79.7%)82/90 (91.1%)42/46 (91.3%)17/21 (81.0%)9/11 (81.8%)
2019 Aug150/193 (77.7%)83/91 (91.2%)37/41 (90.2%)17/20 (85.0%)6/9 (66.7%)
2019 Sep143/197 (72.6%)96/106 (90.6%)56/62 (90.3%)28/31 (90.3%)17/17 (100%)
2019 Oct165/256 (64.5%)100/126 (79.4%)48/59 (81.4%)27/32 (84.4%)17/21 (81.0%)
2019 Nov169/272 (62.1%)121/155 (78.1%)64/79 (81.0%)28/35 (80.0%)14/19 (73.7%)
2019 Dec119/182 (65.4%)92/107 (86.0%)50/54 (92.6%)25/27 (92.6%)11/12 (91.7%)
2020 Jan173/265 (65.3%)119/141 (84.4%)56/66 (84.8%)22/25 (88.0%)12/13 (92.3%)
2020 Feb177/258 (68.6%)118/137 (86.1%)70/76 (92.1%)34/36 (94.4%)14/16 (87.5%)
2020 Mar78/121 (64.5%)60/72 (83.3%)33/34 (97.1%)16/17 (94.1%)11/11 (100%)
2020 Apr107/219 (48.9%)85/109 (78.0%)51/65 (78.5%)30/36 (83.3%)20/24 (83.3%)
2020 May136/224 (60.7%)97/120 (80.8%)53/63 (84.1%)26/30 (86.7%)15/18 (83.3%)
2020 Jun130/199 (65.3%)86/105 (81.9%)47/56 (83.9%)29/32 (90.6%)10/12 (83.3%)
2020 Jul120/203 (59.1%)73/101 (72.3%)39/54 (72.2%)17/27 (63.0%)10/13 (76.9%)
2020 Aug103/135 (76.3%)61/73 (83.6%)27/32 (84.4%)14/17 (82.4%)6/7 (85.7%)
2020 Sep133/197 (67.5%)86/109 (78.9%)46/60 (76.7%)28/34 (82.4%)16/19 (84.2%)
2020 Oct177/278 (63.7%)105/142 (73.9%)61/84 (72.6%)34/51 (66.7%)18/28 (64.3%)
2020 Nov167/281 (59.4%)106/136 (77.9%)53/64 (82.8%)33/36 (91.7%)21/23 (91.3%)
2020 Dec170/295 (57.6%)125/157 (79.6%)56/71 (78.9%)30/37 (81.1%)13/18 (72.2%)
2021 Jan162/281 (57.7%)116/148 (78.4%)64/78 (82.1%)31/40 (77.5%)14/18 (77.8%)
2021 Feb187/314 (59.6%)129/165 (78.2%)70/85 (82.4%)39/49 (79.6%)20/26 (76.9%)
2021 Mar128/240 (53.3%)93/122 (76.2%)40/54 (74.1%)20/29 (69.0%)9/14 (64.3%)
2021 Apr158/240 (65.8%)106/127 (83.5%)55/66 (83.3%)36/42 (85.7%)15/18 (83.3%)
2021 May154/236 (65.3%)88/112 (78.6%)50/60 (83.3%)25/31 (80.6%)13/16 (81.3%)
2021 Jun130/214 (60.7%)91/114 (79.8%)43/50 (86.0%)24/26 (92.3%)14/15 (93.3%)
2021 Jul134/205 (65.4%)92/108 (85.2%)48/56 (85.7%)24/26 (92.3%)9/10 (90.0%)
2021 Aug141/197 (71.6%)83/94 (88.3%)51/55 (92.7%)23/23 (100%)9/9 (100%)
2021 Sep155/210 (73.8%)90/102 (88.2%)43/46 (93.5%)22/22 (100%)9/9 (100%)
2021 Oct136/230 (59.1%)89/116 (76.7%)55/66 (83.3%)40/44 (90.9%)20/23 (87.0%)
2021 Nov155/262 (59.2%)99/131 (75.6%)46/62 (74.2%)26/34 (76.5%)12/17 (70.6%)
2021 Dec30/45 (66.7%)17/25 (68.0%)12/17 (70.6%)6/9 (66.7%)3/4 (75.0%)
2022 Jan25/37 (67.6%)13/15 (86.7%)8/8 (100%)3/3 (100%)0/0
2022 Feb152/269 (56.5%)110/143 (76.9%)58/71 (81.7%)32/38 (84.2%)12/14 (85.7%)
2022 Mar49/75 (65.3%)27/34 (79.4%)10/13 (76.9%)4/5 (80.0%)1/2 (50.0%)
2022 Apr149/299 (49.8%)111/156 (71.2%)53/70 (75.7%)38/44 (86.4%)21/24 (87.5%)
2022 May113/226 (50.0%)84/109 (77.1%)40/49 (81.6%)19/24 (79.2%)14/17 (82.4%)
2022 Jun135/216 (62.5%)88/109 (80.7%)45/56 (80.4%)30/36 (83.3%)15/17 (88.2%)
2022 Jul127/173 (73.4%)78/88 (88.6%)39/44 (88.6%)25/29 (86.2%)14/16 (87.5%)
2022 Aug121/186 (65.1%)71/93 (76.3%)31/42 (73.8%)21/24 (87.5%)13/13 (100%)
2022 Sep172/235 (73.2%)110/122 (90.2%)46/51 (90.2%)26/28 (92.9%)9/11 (81.8%)
2022 Oct160/264 (60.6%)112/141 (79.4%)62/74 (83.8%)29/35 (82.9%)14/18 (77.8%)
2022 Nov158/317 (49.8%)124/165 (75.2%)65/81 (80.2%)44/51 (86.3%)22/25 (88.0%)
2022 Dec124/269 (46.1%)96/128 (75.0%)61/69 (88.4%)28/31 (90.3%)11/12 (91.7%)
2023 Jan104/219 (47.5%)86/134 (64.2%)57/80 (71.3%)34/49 (69.4%)19/26 (73.1%)
2023 Feb99/191 (51.8%)84/119 (70.6%)53/67 (79.1%)26/31 (83.9%)12/14 (85.7%)
2023 Mar80/146 (54.8%)69/95 (72.6%)37/49 (75.5%)25/33 (75.8%)14/18 (77.8%)
2023 Apr139/255 (54.5%)100/125 (80.0%)53/64 (82.8%)30/38 (78.9%)16/21 (76.2%)
2023 May108/214 (50.5%)87/112 (77.7%)53/60 (88.3%)33/37 (89.2%)21/23 (91.3%)
2023 Jun33/93 (35.5%)25/46 (54.3%)14/23 (60.9%)12/13 (92.3%)9/9 (100%)
all7778/12374
(62.9%)
5279/6606
(79.9%)
2796/3356
(83.3%)
1537/1803
(85.2%)
785/923
(85.0%)
Note: Months correspond to equal divisions of the calendar year into 12 segments rather than standard calendar months. Sky-area coverage during summer 2018 was much reduced due to reference-building. P48 was closed for maintenance for almost all of October 2018. Most of March 2020 was lost due to weather (3 consecutive weeks of rain/cloud) and few events met the sample criteria. Extensive camera downtime between Dec 2021 and Apr 2022 meant that no events had sufficient coverage to pass the sample criteria in this period. The most recent 1-2 months are generally undercounts, since classifications of declining objects are still being performed. These statistics include some events for which classifications have not yet been publicly released.

BTS Papers


Team Members and Current Contributors

Christoffer Fremling (Caltech)
Daniel Perley (LJMU)
Adam Miller (Northwestern)
Shri Kulkarni (Caltech)
Aishwarya Dahiwale (Caltech)
Yashvi Sharma (Caltech)
Don Neill (Caltech)
Jesper Sollerman (Stockholm)
Xander Hall (Caltech)
K-Ryan Hinds (LJMU)
Mat Smith (IN2P3)
Matt Chu (Caltech)
Yujing Qin (Caltech)
Nabeel Rehemtulla (Northwestern)
Christopher Phillips (Washington)
Suhail Dhawan (Stockholm)
Ariel Goobar (Stockholm)
Rahul Biswas (Stockholm)
Melissa Graham (Washington)
Jakob Nordin (Humboldt)
Rachel Bruch (Weizmann)
Steve Schulze (Weizmann)
Ido Irani (Weizmann)
Erez Zimmerman (Weizmann)
Alex Filippenko (UCB)
Kishore Patra (UCB)
Shaunak Modak (UCB)
Andrew Hoffman (UCB)
Jannis Necker (DESY)
Ludwig Rauch (DESY)
Samantha Goldwasser (DESY)
Michael May (UCB)