diff --git a/.gitignore b/.gitignore
index 27b2fd4..0cc7f06 100644
--- a/.gitignore
+++ b/.gitignore
@@ -25,5 +25,20 @@ Thumbs.db
 docs/candidates.json
 docs/validation_results.json
 
+# Unpublished submission materials (keep local)
+rnaas_submission/
+METHODOLOGY.md
+
+# New sector data (re-downloadable/re-generable)
+data/lightcurves_new_sectors/
+candidates_new_sectors.json
+results/new_sectors/
+
+# Temporary analysis scripts
+python/tls_remaining.py
+
+# TRILEGAL cache files
+*_TRILEGAL.csv
+
 # Keep the data directory structure
 !data/lightcurves/.gitkeep
diff --git a/CLAUDE.md b/CLAUDE.md
index 3e4bd12..24b11de 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -57,12 +57,14 @@ python3.11 -m pytest tests/ -v  # Python only (32 tests)
 ```
 
 ### Rust test modules:
+
 - `bls::tests` — BLS period recovery, SNR estimation, phase math, median (18 tests)
 - `validate::tests` — All 5 false-positive tests, scoring, integration (23 tests)
 - `crossmatch::tests` — Catalog indexing, lookup, CSV loading (13 tests)
 - `io::tests` — CSV parsing, NaN handling, file discovery (8 tests)
 
 ### Python test files:
+
 - `tests/test_validate_candidates.py` — Validation functions, scoring (24 tests)
 - `tests/test_analyze_candidates.py` — Plotting, cross-matching, binning (8 tests)
 
@@ -231,7 +233,39 @@ lc.to_csv('data/lightcurves/custom_target.csv')
 5. **Reproducibility matters** — BLS results are deterministic; running 4x gives same top candidates within ~1% SNR
 6. **Cross-reference ExoFOP** before making any claims about a specific target
 
+## Project Roadmap
+
+### Phase 1: Independent Validation Pipeline + RNAAS (DONE)
+
+- Pipeline built: download → BLS (Rust) → validate (Rust) → deep validate (Python)
+- 200 unconfirmed TOIs → 197 detections → 17 high-confidence → 3 deep-validated
+- TOI 133.01 passes all tests (TLS SDE=28.4, no centroid offset, no Gaia contaminants, no secondary eclipse)
+- TOI 210.01 upgraded to Strong (52-sector secondary eclipse = no detection)
+- TRICERATOPS run on TOI 133.01: FPP=0.566, TP most probable scenario (35.7%)
+- RNAAS LaTeX note ready in `rnaas_submission/` (local, not committed)
+- **TODO:** Submit RNAAS note at https://aas.msubmit.net, merge PR #4
+
+### Phase 2: New Planet Discovery (NEXT)
+
+- Current pipeline only re-validates existing TOIs (stars TESS already flagged)
+- To discover NEW planets: download full TESS sectors (all stars, not just TOI list)
+- Target: stars with no existing TOI designation
+- Any BLS detection on a non-TOI star = potentially new → submit as community TOI (cTOI) to ExoFOP
+- Focus on less-studied sectors or southern continuous viewing zone
+
+### Phase 3: Follow-up + Collaboration (LATER)
+
+- Run VESPA/TRICERATOPS with real TRILEGAL data on Phase 2 discoveries
+- Contact Planet Hunters TESS team (Nora Eisner) for collaboration
+- Coordinate ground-based RV follow-up for mass determination
+- Write discovery paper if new candidates are found
+
+## ExoFOP vs RNAAS
+
+- **RNAAS** (Research Notes of the AAS): Publish methodology papers. 1500-word limit, gets a DOI, editor-reviewed. For reporting what the pipeline does and its results. https://aas.msubmit.net
+- **ExoFOP-TESS**: Submit candidate observations. Upload phase-folded LCs, validation results, plots as supporting observations for existing TOIs, or submit new cTOIs. https://exofop.ipac.caltech.edu/tess/
+
 ## Dependencies
 
 - **Rust** 1.75+ with: rayon, serde, serde_json, csv, clap, indicatif, anyhow, ordered-float
-- **Python** 3.11+ with: lightkurve, astroquery, pandas, numpy, matplotlib, tqdm, scipy
+- **Python** 3.11+ with: lightkurve, astroquery, pandas, numpy, matplotlib, tqdm, scipy, transitleastsquares, triceratops
diff --git a/README.md b/README.md
index 975ef58..3ae86d7 100644
--- a/README.md
+++ b/README.md
@@ -42,13 +42,14 @@ Applied to 200 unconfirmed TESS Objects of Interest (TOIs), the pipeline:
 
 After deep validation (centroid analysis, Gaia DR3 contamination check, TLS independent confirmation, multi-sector secondary eclipse search), three candidates remain as physically plausible planet signals:
 
-| Target                         | Period   | Rp (R⊕) | TLS SDE  | Centroid | Gaia             | Sec. Eclipse     | Assessment    |
-| ------------------------------ | -------- | ------------- | -------- | -------- | ---------------- | ---------------- | ------------- |
-| **TOI 133.01** / TIC 219338557 | 8.2065 d | **1.9**       | **28.4** | Pass     | Clear            | Pass             | **Strong**    |
-| **TOI 155.01** / TIC 129637892 | 5.4504 d | **5.3**       | **20.1** | Pass     | Clear            | Marginal† | **Strong**    |
-| **TOI 210.01** / TIC 141608198 | 8.9884 d | **2.2**       | **7.1**  | Pass     | 1 faint neighbor | Marginal† | **Promising** |
-
-<sup>&dagger; Secondary eclipse depths of 0.002&ndash;0.007% are consistent with planetary thermal emission rather than eclipsing binary signatures (which produce 0.1&ndash;10% depths).</sup>
+| Target                         | Period   | Rp (R&#8853;) | TLS SDE  | Centroid | Gaia             | Sec. Eclipse     | Assessment |
+| ------------------------------ | -------- | ------------- | -------- | -------- | ---------------- | ---------------- | ---------- |
+| **TOI 133.01** / TIC 219338557 | 8.2065 d | **1.9**       | **28.4** | Pass     | Clear            | Pass             | **Strong** |
+| **TOI 155.01** / TIC 129637892 | 5.4504 d | **5.3**       | **20.1** | Pass     | Clear            | Marginal&dagger; | **Strong** |
+| **TOI 210.01** / TIC 141608198 | 8.9884 d | **2.2**       | **7.1**  | Pass     | 1 faint neighbor | Pass&Dagger;     | **Strong** |
+
+<sup>&dagger; Secondary eclipse depth of 0.002% is consistent with planetary thermal emission rather than eclipsing binary signatures (which produce 0.1&ndash;10% depths).</sup>
+<sup>&Dagger; An initial 10-sector analysis showed a marginal 3.9&sigma; secondary eclipse. A follow-up analysis using all 52 available sectors (1.8M data points) yielded no detection (&minus;2.8&sigma;), confirming the earlier signal was noise.</sup>
 
 **Pipeline validation:** TOI 125.04, a confirmed planet (CP disposition on ExoFOP), was correctly recovered and scored as high-confidence, demonstrating the pipeline produces accurate results.
 
diff --git a/docs/index.html b/docs/index.html
index 040392d..a826f87 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -3,10 +3,10 @@
 <head>
 <meta charset="UTF-8">
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
-<title>Exohuntr - We Pointed a Laptop at NASA Data and Found Planet Candidates</title>
-<meta name="description" content="197 exoplanet transit candidates discovered using Rust-powered BLS detection on NASA TESS data. Open-source citizen science.">
-<meta property="og:title" content="Exohuntr - Hunting Exoplanets with Rust and NASA Data">
-<meta property="og:description" content="We analyzed 200+ TESS light curves and found 197 transit candidates. Built with Rust for speed, Python for visualization.">
+<title>Exohuntr - Open-Source Transit Detection and Validation for NASA TESS Data</title>
+<meta name="description" content="Independent transit detection and validation of 200 TESS Objects of Interest. 3 deep-validated candidates including a 1.9 R_Earth super-Earth.">
+<meta property="og:title" content="Exohuntr - Independent Transit Detection Pipeline for NASA TESS">
+<meta property="og:description" content="BLS detection in Rust, 5-test validation, deep analysis with TLS + centroid + Gaia DR3. 3 validated candidates from 200 unconfirmed TOIs.">
 <meta property="og:type" content="website">
 <meta name="twitter:card" content="summary_large_image">
 <style>
@@ -282,16 +282,16 @@
   <div class="badge"><span class="dot"></span> Live Results from NASA TESS Data</div>
   <h1>Exohuntr</h1>
   <p class="tagline">
-    An open-source pipeline that downloads NASA satellite data, scans for planetary transits
-    at <strong>50x the speed of Python</strong>, and independently rediscovered signals that
-    match NASA's own detections &mdash; including <strong>17 validated candidates</strong>
-    orbiting distant stars.
+    An open-source transit detection and validation pipeline for NASA TESS data.
+    BLS detection in Rust, false-positive validation, and deep analysis with TLS, centroid,
+    and Gaia DR3 checks &mdash; producing <strong>3 deep-validated planet candidates</strong>
+    from 200 unconfirmed TESS Objects of Interest.
   </p>
   <div class="stats-bar">
     <div class="stat"><div class="number">197</div><div class="label">Signals Detected</div></div>
-    <div class="stat"><div class="number orange">17</div><div class="label">Validated Candidates</div></div>
-    <div class="stat"><div class="number purple">5</div><div class="label">Tests Per Candidate</div></div>
-    <div class="stat"><div class="number green">70%+</div><div class="label">Period Match with NASA</div></div>
+    <div class="stat"><div class="number orange">3</div><div class="label">Deep-Validated Candidates</div></div>
+    <div class="stat"><div class="number purple">5+4</div><div class="label">Validation + Deep Tests</div></div>
+    <div class="stat"><div class="number green">200</div><div class="label">TOIs Analyzed</div></div>
   </div>
   <div class="scroll-hint">
     <svg viewBox="0 0 24 24"><path d="M12 5v14M5 12l7 7 7-7"/></svg>
@@ -301,7 +301,7 @@ <h1>Exohuntr</h1>
 <section id="how">
   <div class="container">
     <div class="section-header">
-      <h2>How We Hunt <span class="accent">Planets</span></h2>
+      <h2>How It <span class="accent">Works</span></h2>
       <p>When a planet crosses in front of its star, the star's brightness dips. We detect these dips algorithmically in NASA data.</p>
     </div>
     <div class="transit-demo">
@@ -321,7 +321,7 @@ <h3>Download <span class="tech python">Python</span></h3>
       <div class="pipeline-step">
         <div class="step-num">02</div>
         <h3>Detect <span class="tech rust">Rust</span></h3>
-        <p>Parallel BLS (Box-fitting Least Squares) scans 15,000 trial periods per star using Rayon. <strong>10-50x faster</strong> than Python.</p>
+        <p>Parallel BLS (Box-fitting Least Squares) scans 15,000 trial periods per star using Rayon for multi-core parallel processing.</p>
       </div>
       <div class="pipeline-step">
         <div class="step-num">03</div>
@@ -422,7 +422,7 @@ <h4>Data Source</h4>
       <div class="method-card">
         <h4>Performance</h4>
         <p>Rust + Rayon parallel processing. The entire 200-star dataset was scanned in under 30 seconds on 8 CPU cores.</p>
-        <div class="mono">~10-50x faster than pure Python</div>
+        <div class="mono">Parallel via Rayon (Rust)</div>
       </div>
     </div>
   </div>
@@ -467,7 +467,7 @@ <h2>Run Your Own Hunt</h2>
 <footer>
   <div class="container">
     <p>
-      Built with Rust, Python, and Claude Code. Data from NASA TESS via MAST.<br>
+      Built with Rust and Python. Data from NASA TESS via MAST.<br>
       <a href="https://github.com/humancto/exohuntr">Exohuntr</a> &mdash; Open-source exoplanet hunting.
     </p>
   </div>
diff --git a/python/download_new_sectors.py b/python/download_new_sectors.py
new file mode 100644
index 0000000..c168472
--- /dev/null
+++ b/python/download_new_sectors.py
@@ -0,0 +1,136 @@
+#!/usr/bin/env python3.11
+"""Download light curves from less-studied TESS sectors (80-96) for new detections.
+
+Strategy: Query ExoFOP TOI list, filter for TOIs observed in sectors 80-96,
+and download those that are still unconfirmed (PC disposition).
+"""
+if __name__ == '__main__':
+    import warnings
+    warnings.filterwarnings('ignore')
+    import os
+    import sys
+    import numpy as np
+    import pandas as pd
+    from pathlib import Path
+    from tqdm import tqdm
+
+    OUTPUT_DIR = Path('data/lightcurves_new_sectors')
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    SECTOR_MIN = 80
+    SECTOR_MAX = 96
+    LIMIT = 200
+
+    print("=" * 60)
+    print(f"Downloading TOIs from TESS Sectors {SECTOR_MIN}-{SECTOR_MAX}")
+    print("=" * 60)
+
+    # Step 1: Get TOI catalog from ExoFOP
+    print("\n[1/3] Fetching TOI catalog from ExoFOP...", flush=True)
+    try:
+        toi_url = "https://exofop.ipac.caltech.edu/tess/download_toi.php?sort=toi&output=csv"
+        tois = pd.read_csv(toi_url, comment="#")
+        print(f"  Total TOIs in catalog: {len(tois)}", flush=True)
+    except Exception as e:
+        print(f"  ERROR: Could not fetch TOI list: {e}", flush=True)
+        sys.exit(1)
+
+    # Step 2: Filter for unconfirmed candidates in target sectors
+    print(f"\n[2/3] Filtering for unconfirmed TOIs in sectors {SECTOR_MIN}-{SECTOR_MAX}...", flush=True)
+
+    # Keep only Planet Candidates
+    candidates = tois[tois["TFOPWG Disposition"].isin(["PC", ""])]
+    print(f"  Unconfirmed candidates: {len(candidates)}", flush=True)
+
+    # Filter by sector — the "Sectors" column contains comma-separated sector numbers
+    def in_target_sectors(sectors_str):
+        try:
+            sectors = [int(s.strip()) for s in str(sectors_str).split(',')]
+            return any(SECTOR_MIN <= s <= SECTOR_MAX for s in sectors)
+        except (ValueError, AttributeError):
+            return False
+
+    if 'Sectors' in candidates.columns:
+        target_candidates = candidates[candidates['Sectors'].apply(in_target_sectors)]
+    elif 'Sector' in candidates.columns:
+        target_candidates = candidates[candidates['Sector'].apply(in_target_sectors)]
+    else:
+        # Try to find the right column
+        print(f"  Available columns: {list(candidates.columns)}", flush=True)
+        print("  WARNING: No 'Sectors' column found. Downloading general unconfirmed TOIs.", flush=True)
+        # Fall back: take TOIs with high TOI numbers (newer, likely from later sectors)
+        candidates_sorted = candidates.sort_values('TOI', ascending=False)
+        target_candidates = candidates_sorted.head(LIMIT)
+
+    target_candidates = target_candidates.head(LIMIT)
+    print(f"  TOIs in sectors {SECTOR_MIN}-{SECTOR_MAX}: {len(target_candidates)}", flush=True)
+
+    if len(target_candidates) == 0:
+        print("  No TOIs found in target sectors. Trying newest unconfirmed TOIs instead...", flush=True)
+        candidates_sorted = candidates.sort_values('TOI', ascending=False)
+        target_candidates = candidates_sorted.head(LIMIT)
+        print(f"  Using {len(target_candidates)} newest unconfirmed TOIs", flush=True)
+
+    # Step 3: Download light curves
+    print(f"\n[3/3] Downloading {len(target_candidates)} light curves...", flush=True)
+    import lightkurve as lk
+
+    downloaded = 0
+    failed = 0
+    already_have = 0
+
+    for _, row in tqdm(target_candidates.iterrows(), total=len(target_candidates), desc="  Downloading"):
+        try:
+            tic_id = f"TIC {int(row['TIC ID'])}"
+            toi_num = str(row.get('TOI', 'unknown'))
+
+            # Check if we already have this
+            filename = f"TOI_{toi_num}_{tic_id.replace(' ', '_')}.csv"
+            filepath = OUTPUT_DIR / filename
+
+            # Also check the original data dir
+            orig_path = Path('data/lightcurves') / filename
+            if filepath.exists() or orig_path.exists():
+                already_have += 1
+                continue
+
+            search = lk.search_lightcurve(tic_id, mission='TESS', author='SPOC')
+            if len(search) == 0:
+                failed += 1
+                continue
+
+            lc = search[0].download(quality_bitmask='hardest')
+            if lc is None:
+                failed += 1
+                continue
+
+            lc = lc.remove_nans().remove_outliers(sigma=5).normalize()
+            if len(lc.time.value) < 100:
+                failed += 1
+                continue
+
+            flux_err = lc.flux_err.value if lc.flux_err is not None else np.full(len(lc.time.value), 0.001)
+            df = pd.DataFrame({
+                'time': lc.time.value,
+                'flux': lc.flux.value,
+                'flux_err': flux_err,
+            })
+            df.to_csv(filepath, index=False)
+            downloaded += 1
+
+        except Exception as e:
+            failed += 1
+            continue
+
+    print(f"\n  Downloaded: {downloaded}")
+    print(f"  Already had: {already_have}")
+    print(f"  Failed: {failed}")
+    print(f"  Output: {OUTPUT_DIR}/")
+
+    if downloaded > 0:
+        print(f"\n  Next: Run BLS on these:")
+        print(f"  cargo build --release")
+        print(f"  ./target/release/hunt search -i {OUTPUT_DIR} -o candidates_new_sectors.json --snr-threshold 6.0")
+        print(f"  ./target/release/hunt validate -i candidates_new_sectors.json -l {OUTPUT_DIR} -o results/")
+
+    print("\nDone!", flush=True)
diff --git a/python/run_triceratops.py b/python/run_triceratops.py
new file mode 100644
index 0000000..e4e0b32
--- /dev/null
+++ b/python/run_triceratops.py
@@ -0,0 +1,183 @@
+#!/usr/bin/env python3.11
+"""Run TRICERATOPS statistical validation on TOI 133.01 (TIC 219338557)"""
+if __name__ == '__main__':
+    import warnings
+    warnings.filterwarnings('ignore')
+    import os
+    os.environ['OMP_NUM_THREADS'] = '1'
+
+    import numpy as np
+    import lightkurve as lk
+    import triceratops.triceratops as tr
+
+    TIC_ID = 219338557
+    PERIOD = 8.2065  # days (BLS period)
+    T0 = None  # will get from TLS or data
+    TDEPTH = 0.000707  # fractional depth = (Rp/Rs)^2 = 0.0266^2
+
+    DEEP_DIR = 'results/deep_analysis'
+    os.makedirs(DEEP_DIR, exist_ok=True)
+
+    print("=" * 60)
+    print("TRICERATOPS Validation — TOI 133.01 / TIC 219338557")
+    print("=" * 60)
+
+    # Step 1: Download light curve for phase-folding
+    print("\n[1/5] Downloading light curve...", flush=True)
+    search = lk.search_lightcurve(f'TIC {TIC_ID}', mission='TESS', author='SPOC')
+    print(f"  Found {len(search)} sectors", flush=True)
+
+    # Use first 3 sectors for the phase-folded LC
+    n_download = min(3, len(search))
+    lc_collection = search[:n_download].download_all()
+    lc = lc_collection.stitch().remove_nans().remove_outliers(sigma=5)
+    lc = lc.normalize()
+
+    time_raw = lc.time.value
+    flux_raw = lc.flux.value
+
+    # Get sector numbers for TRICERATOPS
+    import re
+    sector_numbers = []
+    for s in search[:n_download]:
+        mission_str = str(s.mission)
+        nums = re.findall(r'\d+', mission_str)
+        if nums:
+            sector_numbers.append(int(nums[0]))
+    if not sector_numbers:
+        # Fallback: try table column
+        try:
+            for row in search[:n_download].table:
+                if 'sequence_number' in row.colnames:
+                    sector_numbers.append(int(row['sequence_number']))
+        except Exception:
+            pass
+    if not sector_numbers:
+        sector_numbers = [1]  # final fallback
+    print(f"  Sectors: {sector_numbers}", flush=True)
+
+    # Phase-fold the light curve
+    print("\n[2/5] Phase-folding light curve...", flush=True)
+    # Find T0 by finding the deepest dip
+    phase = time_raw % PERIOD
+    n_bins = 200
+    bin_edges = np.linspace(0, PERIOD, n_bins + 1)
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+    bin_flux = np.array([np.nanmedian(flux_raw[(phase >= bin_edges[i]) & (phase < bin_edges[i+1])])
+                         for i in range(n_bins)])
+    valid = np.isfinite(bin_flux)
+    t0_phase = bin_centers[valid][np.argmin(bin_flux[valid])]
+    T0 = time_raw[0] + t0_phase
+    print(f"  T0 = {T0:.4f} (phase of minimum = {t0_phase:.4f}d)", flush=True)
+
+    # Phase-fold centered on transit
+    phase_fold = (time_raw - T0 + PERIOD / 2) % PERIOD - PERIOD / 2
+    # Keep only points within +/- 0.25 * period of transit for efficiency
+    transit_window = 0.25 * PERIOD
+    mask = np.abs(phase_fold) < transit_window
+    time_folded = phase_fold[mask]
+    flux_folded = flux_raw[mask]
+
+    # Sort by phase
+    sort_idx = np.argsort(time_folded)
+    time_folded = time_folded[sort_idx]
+    flux_folded = flux_folded[sort_idx]
+
+    flux_err = float(np.nanstd(flux_raw[np.abs(phase_fold) > 0.1 * PERIOD]))
+    print(f"  Phase-folded: {len(time_folded)} points, flux_err = {flux_err:.6f}", flush=True)
+
+    # Step 3: Create TRICERATOPS target
+    print("\n[3/5] Creating TRICERATOPS target (querying TIC + TESSCut)...", flush=True)
+    sectors = np.array(sector_numbers)
+    try:
+        target = tr.target(ID=TIC_ID, sectors=sectors, search_radius=10)
+        print(f"  Found {len(target.stars)} nearby stars", flush=True)
+        print(f"  Target star: Tmag={target.stars.iloc[0].get('Tmag', 'N/A')}", flush=True)
+    except Exception as e:
+        print(f"  ERROR creating target: {e}", flush=True)
+        print("  Trying with single sector...", flush=True)
+        target = tr.target(ID=TIC_ID, sectors=np.array([sectors[0]]), search_radius=10)
+        print(f"  Found {len(target.stars)} nearby stars", flush=True)
+
+    # Check and fill missing stellar properties
+    star = target.stars.iloc[0]
+    print(f"  Star properties: mass={star.get('mass', 'N/A')}, "
+          f"rad={star.get('rad', 'N/A')}, Teff={star.get('Teff', 'N/A')}", flush=True)
+
+    for param in ['mass', 'rad', 'Teff']:
+        val = star.get(param, None)
+        if val is None or (isinstance(val, float) and np.isnan(val)):
+            defaults = {'mass': 0.85, 'rad': 0.82, 'Teff': 5100.0}  # K-dwarf defaults for TOI 133
+            print(f"  WARNING: {param} is missing, setting to {defaults[param]}", flush=True)
+            target.update_star(ID=TIC_ID, param=param, value=defaults[param])
+
+    # Step 4: Calculate depths
+    print("\n[4/5] Calculating transit depths for all nearby stars...", flush=True)
+    try:
+        apertures = target.get_spoc_apertures()
+        target.calc_depths(tdepth=TDEPTH, all_ap_pixels=apertures)
+    except Exception as e:
+        print(f"  SPOC apertures failed ({e}), using default aperture", flush=True)
+        target.calc_depths(tdepth=TDEPTH)
+
+    # Step 5: Calculate FPP
+    print("\n[5/5] Running TRICERATOPS FPP calculation (N=50000)...", flush=True)
+    print("  This may take 10-30 minutes...", flush=True)
+    try:
+        target.calc_probs(
+            time=time_folded,
+            flux_0=flux_folded,
+            flux_err_0=flux_err,
+            P_orb=PERIOD,
+            N=50000,  # reduced from 1M for speed
+        )
+
+        fpp = target.FPP
+        nfpp = target.NFPP
+        probs = target.probs
+
+        print("\n" + "=" * 60)
+        print("TRICERATOPS RESULTS — TOI 133.01")
+        print("=" * 60)
+        print(f"  FPP  = {fpp:.6f} ({fpp*100:.4f}%)")
+        print(f"  NFPP = {nfpp:.6f} ({nfpp*100:.4f}%)")
+        print()
+
+        if fpp < 0.015 and nfpp < 0.001:
+            verdict = "STATISTICALLY VALIDATED PLANET (FPP < 1.5%, NFPP < 0.1%)"
+        elif fpp < 0.5 and nfpp < 0.1:
+            verdict = "LIKELY PLANET (FPP < 50%, NFPP < 10%)"
+        else:
+            verdict = "NOT VALIDATED (FPP too high)"
+        print(f"  Verdict: {verdict}")
+        print()
+
+        # Print scenario probabilities
+        print("  Scenario probabilities:")
+        for _, row in probs.iterrows():
+            if row['prob'] > 0.001:
+                print(f"    {row['scenario']:12s}: {row['prob']:.6f} ({row['prob']*100:.4f}%)")
+
+        # Save results
+        results_file = os.path.join(DEEP_DIR, 'triceratops_TOI_133_01.txt')
+        with open(results_file, 'w') as f:
+            f.write("TRICERATOPS Statistical Validation — TOI 133.01 / TIC 219338557\n")
+            f.write("=" * 60 + "\n\n")
+            f.write(f"Period: {PERIOD} d\n")
+            f.write(f"Transit depth: {TDEPTH} (fractional, = {TDEPTH*1e6:.0f} ppm)\n")
+            f.write(f"Sectors used: {sector_numbers}\n")
+            f.write(f"N (MC draws): 50000\n\n")
+            f.write(f"FPP  = {fpp:.6f} ({fpp*100:.4f}%)\n")
+            f.write(f"NFPP = {nfpp:.6f} ({nfpp*100:.4f}%)\n\n")
+            f.write(f"Verdict: {verdict}\n\n")
+            f.write("Scenario probabilities:\n")
+            for _, row in probs.iterrows():
+                f.write(f"  {row['scenario']:12s}: {row['prob']:.6f}\n")
+        print(f"\n  Saved: {results_file}")
+
+    except Exception as e:
+        print(f"\n  ERROR in calc_probs: {e}", flush=True)
+        import traceback
+        traceback.print_exc()
+
+    print("\nTRICERATOPS complete!", flush=True)
diff --git a/python/tls_single.py b/python/tls_single.py
new file mode 100644
index 0000000..1bb0a1c
--- /dev/null
+++ b/python/tls_single.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3.11
+"""Run TLS on TOI 210.01 and TOI 155.01 — single-threaded to avoid spawn issues"""
+if __name__ == '__main__':
+    import warnings
+    warnings.filterwarnings('ignore')
+    import os
+    os.environ['OMP_NUM_THREADS'] = '1'
+
+    import numpy as np
+    import lightkurve as lk
+    import matplotlib
+    matplotlib.use('Agg')
+    import matplotlib.pyplot as plt
+    from transitleastsquares import transitleastsquares
+
+    DEEP_DIR = 'results/deep_analysis'
+    os.makedirs(DEEP_DIR, exist_ok=True)
+
+    targets = [
+        {"toi": "TOI 210.01", "tic": "TIC 141608198", "period": 8.9884},
+        {"toi": "TOI 155.01", "tic": "TIC 129637892", "period": 5.4504},
+    ]
+
+    for t in targets:
+        name = t['toi']
+        print(f"\n=== {name} ===", flush=True)
+        search = lk.search_lightcurve(t['tic'], mission='TESS', author='SPOC')
+        if len(search) == 0:
+            search = lk.search_lightcurve(t['tic'], mission='TESS')
+
+        print(f"Found {len(search)} light curves", flush=True)
+
+        # Only 2 sectors
+        n_download = min(2, len(search))
+        print(f"Downloading {n_download} sectors...", flush=True)
+        lc_collection = search[:n_download].download_all()
+        lc = lc_collection.stitch()
+        lc = lc.remove_nans().remove_outliers(sigma=5)
+
+        time = lc.time.value
+        flux = lc.flux.value
+        flux = flux / np.nanmedian(flux)
+
+        # Aggressive binning to keep fast
+        if len(time) > 50000:
+            print(f"Binning {len(time)} points...", flush=True)
+            lc_norm = lc.normalize()
+            lc_binned = lc_norm.bin(time_bin_size=0.02)  # ~30 min bins
+            time = lc_binned.time.value
+            flux = lc_binned.flux.value
+            mask = np.isfinite(time) & np.isfinite(flux)
+            time = time[mask]
+            flux = flux[mask]
+
+        print(f"Running TLS on {len(time)} data points (single-threaded)...", flush=True)
+        model = transitleastsquares(time, flux)
+        results = model.power(
+            period_min=t['period'] * 0.9,
+            period_max=t['period'] * 1.1,
+            oversampling_factor=2,
+            duration_grid_step=1.15,
+            use_threads=1,
+        )
+
+        tls_period = float(results.period)
+        tls_sde = float(results.SDE)
+        tls_rp_rs = float(results.rp_rs) if hasattr(results, 'rp_rs') else None
+        tls_t0 = float(results.T0) if hasattr(results, 'T0') else None
+        period_diff_pct = abs(tls_period - t['period']) / t['period'] * 100
+
+        if period_diff_pct < 1.0 and tls_sde > 6.0:
+            status = "CONFIRMED"
+        elif period_diff_pct < 1.0:
+            status = "WEAK_CONFIRM"
+        else:
+            status = "DISAGREE"
+
+        print(f"Result: {status}", flush=True)
+        print(f"  TLS period:  {tls_period:.4f}d", flush=True)
+        print(f"  BLS period:  {t['period']:.4f}d", flush=True)
+        print(f"  Period diff: {period_diff_pct:.3f}%", flush=True)
+        print(f"  SDE:         {tls_sde:.1f}", flush=True)
+        if tls_rp_rs:
+            print(f"  Rp/Rs:       {tls_rp_rs:.5f}", flush=True)
+
+        # Plot
+        fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+        fig.suptitle(f'{name} — Transit Least Squares', fontsize=14)
+        axes[0].plot(results.periods, results.power, 'k-', lw=0.5)
+        axes[0].axvline(tls_period, color='red', ls='--', label=f'TLS P={tls_period:.4f}d')
+        axes[0].axvline(t['period'], color='blue', ls=':', label=f'BLS P={t["period"]:.4f}d')
+        axes[0].set_xlabel('Period (days)')
+        axes[0].set_ylabel('SDE')
+        axes[0].legend(fontsize=9)
+
+        if tls_t0:
+            phase = (time - tls_t0 + tls_period/2) % tls_period - tls_period/2
+        else:
+            phase = (time % tls_period)
+        axes[1].scatter(phase * 24, flux, s=1, alpha=0.3, c='gray')
+        n_bins = 100
+        be = np.linspace(phase.min(), phase.max(), n_bins + 1)
+        bc = (be[:-1] + be[1:]) / 2
+        bf = np.array([np.nanmedian(flux[(phase >= be[i]) & (phase < be[i+1])]) for i in range(n_bins)])
+        v = np.isfinite(bf)
+        axes[1].plot(bc[v] * 24, bf[v], 'r-', lw=2, label='Binned')
+        axes[1].set_xlabel('Hours from mid-transit')
+        axes[1].set_ylabel('Normalized flux')
+        axes[1].set_xlim(-5, 5)
+        axes[1].legend(fontsize=9)
+        plt.tight_layout()
+        pp = os.path.join(DEEP_DIR, 'tls_%s.png' % name.replace(" ", "_").replace(".", "_"))
+        plt.savefig(pp, dpi=150, bbox_inches='tight')
+        plt.close()
+        print(f"  Saved: {pp}", flush=True)
+
+    print("\nAll TLS complete!", flush=True)
diff --git a/python/toi210_full_sectors.py b/python/toi210_full_sectors.py
new file mode 100644
index 0000000..7227d79
--- /dev/null
+++ b/python/toi210_full_sectors.py
@@ -0,0 +1,193 @@
+#!/usr/bin/env python3.11
+"""Download all available sectors for TOI 210.01 and run definitive secondary eclipse search"""
+if __name__ == '__main__':
+    import warnings
+    warnings.filterwarnings('ignore')
+    import os
+    os.environ['OMP_NUM_THREADS'] = '1'
+
+    import numpy as np
+    import lightkurve as lk
+    import matplotlib
+    matplotlib.use('Agg')
+    import matplotlib.pyplot as plt
+    from scipy import stats
+
+    TIC_ID = 'TIC 141608198'
+    PERIOD = 8.9884  # days
+    TOI_NAME = 'TOI 210.01'
+
+    DEEP_DIR = 'results/deep_analysis'
+    os.makedirs(DEEP_DIR, exist_ok=True)
+
+    print("=" * 60)
+    print(f"Full-Sector Secondary Eclipse — {TOI_NAME} / {TIC_ID}")
+    print("=" * 60)
+
+    # Step 1: Find all available sectors
+    print("\n[1/4] Searching for all available sectors...", flush=True)
+    search = lk.search_lightcurve(TIC_ID, mission='TESS', author='SPOC')
+    print(f"  Found {len(search)} sectors", flush=True)
+
+    # Download in batches to avoid memory issues
+    batch_size = 10
+    all_time = []
+    all_flux = []
+    n_sectors_downloaded = 0
+
+    print(f"\n[2/4] Downloading in batches of {batch_size}...", flush=True)
+    for batch_start in range(0, len(search), batch_size):
+        batch_end = min(batch_start + batch_size, len(search))
+        print(f"  Downloading sectors {batch_start+1}-{batch_end} of {len(search)}...", flush=True)
+        try:
+            batch = search[batch_start:batch_end].download_all()
+            for lc in batch:
+                lc = lc.remove_nans().remove_outliers(sigma=5).normalize()
+                t = lc.time.value
+                f = lc.flux.value
+                mask = np.isfinite(t) & np.isfinite(f)
+                all_time.append(t[mask])
+                all_flux.append(f[mask])
+                n_sectors_downloaded += 1
+        except Exception as e:
+            print(f"  WARNING: Batch failed: {e}", flush=True)
+            # Try individual downloads
+            for i in range(batch_start, batch_end):
+                try:
+                    lc = search[i].download()
+                    lc = lc.remove_nans().remove_outliers(sigma=5).normalize()
+                    t = lc.time.value
+                    f = lc.flux.value
+                    mask = np.isfinite(t) & np.isfinite(f)
+                    all_time.append(t[mask])
+                    all_flux.append(f[mask])
+                    n_sectors_downloaded += 1
+                except Exception as e2:
+                    print(f"    Sector {i+1} failed: {e2}", flush=True)
+
+    time = np.concatenate(all_time)
+    flux = np.concatenate(all_flux)
+    print(f"\n  Downloaded {n_sectors_downloaded} sectors, {len(time)} total data points", flush=True)
+
+    # Step 3: Secondary eclipse search
+    print("\n[3/4] Searching for secondary eclipse at phase 0.5...", flush=True)
+
+    # Phase-fold
+    # First find T0 from the primary transit
+    phase = time % PERIOD
+    n_bins = 500
+    bin_edges = np.linspace(0, PERIOD, n_bins + 1)
+    bin_centers = (bin_edges[:-1] + bin_edges[1:]) / 2
+    bin_flux = np.array([np.nanmedian(flux[(phase >= bin_edges[i]) & (phase < bin_edges[i+1])])
+                         for i in range(n_bins)])
+    valid = np.isfinite(bin_flux)
+    t0_phase = bin_centers[valid][np.argmin(bin_flux[valid])]
+
+    # Phase-fold centered on primary transit
+    phase_fold = (time - time[0] - t0_phase + PERIOD / 2) % PERIOD - PERIOD / 2
+
+    # Secondary eclipse region: phase 0.4-0.6 of period (centered on 0.5)
+    sec_phase = phase_fold + PERIOD / 2  # shift so secondary is at center
+    sec_phase = (sec_phase + PERIOD / 2) % PERIOD - PERIOD / 2
+
+    # Compare in-eclipse vs out-of-eclipse
+    eclipse_half_width = 0.02 * PERIOD  # ~2% of period
+    in_eclipse = np.abs(sec_phase) < eclipse_half_width
+    out_eclipse = (np.abs(sec_phase) > 0.1 * PERIOD) & (np.abs(sec_phase) < 0.4 * PERIOD)
+
+    flux_in = flux[in_eclipse]
+    flux_out = flux[out_eclipse]
+
+    if len(flux_in) > 10 and len(flux_out) > 10:
+        depth = np.nanmedian(flux_out) - np.nanmedian(flux_in)
+        # Bootstrap uncertainty
+        n_boot = 10000
+        depths_boot = np.zeros(n_boot)
+        for b in range(n_boot):
+            fi = np.random.choice(flux_in, size=len(flux_in), replace=True)
+            fo = np.random.choice(flux_out, size=len(flux_out), replace=True)
+            depths_boot[b] = np.nanmedian(fo) - np.nanmedian(fi)
+        depth_err = np.std(depths_boot)
+        depth_sigma = depth / depth_err if depth_err > 0 else 0
+
+        print(f"  In-eclipse points:  {len(flux_in)}")
+        print(f"  Out-eclipse points: {len(flux_out)}")
+        print(f"  Secondary depth:    {depth:.8f} ({depth*100:.6f}%)")
+        print(f"  Depth uncertainty:  {depth_err:.8f}")
+        print(f"  Significance:       {depth_sigma:.1f} sigma")
+
+        if depth_sigma < 3:
+            verdict = "NO SECONDARY ECLIPSE DETECTED (< 3 sigma)"
+        elif depth_sigma < 5:
+            verdict = f"MARGINAL DETECTION ({depth_sigma:.1f} sigma)"
+        else:
+            if depth * 100 < 0.01:
+                verdict = f"SHALLOW DETECTION ({depth_sigma:.1f} sigma, {depth*100:.4f}%) — consistent with planetary thermal emission"
+            else:
+                verdict = f"SIGNIFICANT DETECTION ({depth_sigma:.1f} sigma, {depth*100:.4f}%) — possible binary"
+    else:
+        depth = 0
+        depth_err = 0
+        depth_sigma = 0
+        verdict = "INSUFFICIENT DATA"
+
+    print(f"\n  Verdict: {verdict}")
+
+    # Step 4: Plot
+    print("\n[4/4] Generating plot...", flush=True)
+    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+    fig.suptitle(f'{TOI_NAME} — Full-Sector Secondary Eclipse Search ({n_sectors_downloaded} sectors, {len(time):,} points)', fontsize=12)
+
+    # Left: full phase-folded LC
+    phase_hours = phase_fold * 24
+    axes[0].scatter(phase_hours, flux, s=0.1, alpha=0.1, c='gray')
+    # Binned
+    n_plot_bins = 300
+    be = np.linspace(phase_fold.min(), phase_fold.max(), n_plot_bins + 1)
+    bc = (be[:-1] + be[1:]) / 2
+    bf = np.array([np.nanmedian(flux[(phase_fold >= be[i]) & (phase_fold < be[i+1])]) for i in range(n_plot_bins)])
+    v = np.isfinite(bf)
+    axes[0].plot(bc[v] * 24, bf[v], 'r-', lw=1.5, label='Binned median')
+    axes[0].set_xlabel('Hours from mid-transit')
+    axes[0].set_ylabel('Normalized flux')
+    axes[0].set_title('Primary transit')
+    axes[0].set_xlim(-8, 8)
+    axes[0].legend(fontsize=9)
+
+    # Right: secondary eclipse region
+    sec_hours = sec_phase * 24
+    axes[1].scatter(sec_hours, flux, s=0.1, alpha=0.1, c='gray')
+    be2 = np.linspace(sec_phase.min(), sec_phase.max(), n_plot_bins + 1)
+    bc2 = (be2[:-1] + be2[1:]) / 2
+    bf2 = np.array([np.nanmedian(flux[(sec_phase >= be2[i]) & (sec_phase < be2[i+1])]) for i in range(n_plot_bins)])
+    v2 = np.isfinite(bf2)
+    axes[1].plot(bc2[v2] * 24, bf2[v2], 'b-', lw=1.5, label='Binned median')
+    axes[1].axhline(1.0, color='gray', ls='--', alpha=0.5)
+    axes[1].axvspan(-eclipse_half_width * 24, eclipse_half_width * 24, alpha=0.2, color='red', label='Eclipse window')
+    axes[1].set_xlabel('Hours from expected secondary')
+    axes[1].set_ylabel('Normalized flux')
+    axes[1].set_title(f'Secondary eclipse ({depth_sigma:.1f}σ, depth={depth*100:.4f}%)')
+    axes[1].set_xlim(-8, 8)
+    axes[1].legend(fontsize=9)
+
+    plt.tight_layout()
+    plot_path = os.path.join(DEEP_DIR, 'secondary_TOI_210_01_full.png')
+    plt.savefig(plot_path, dpi=150, bbox_inches='tight')
+    plt.close()
+    print(f"  Saved: {plot_path}", flush=True)
+
+    # Save results
+    results_file = os.path.join(DEEP_DIR, 'toi210_full_secondary.txt')
+    with open(results_file, 'w') as f:
+        f.write(f"Full-Sector Secondary Eclipse Search — {TOI_NAME} / {TIC_ID}\n")
+        f.write("=" * 60 + "\n\n")
+        f.write(f"Period: {PERIOD} d\n")
+        f.write(f"Sectors downloaded: {n_sectors_downloaded}\n")
+        f.write(f"Total data points: {len(time)}\n\n")
+        f.write(f"Secondary eclipse depth: {depth:.8f} ({depth*100:.6f}%)\n")
+        f.write(f"Depth uncertainty: {depth_err:.8f}\n")
+        f.write(f"Significance: {depth_sigma:.1f} sigma\n\n")
+        f.write(f"Verdict: {verdict}\n")
+    print(f"  Saved: {results_file}")
+
+    print(f"\nDone! {n_sectors_downloaded} sectors analyzed.", flush=True)
diff --git a/results/DEEP_ANALYSIS.md b/results/DEEP_ANALYSIS.md
index 112b4c5..be361d9 100644
--- a/results/DEEP_ANALYSIS.md
+++ b/results/DEEP_ANALYSIS.md
@@ -88,11 +88,11 @@ We selected the 3 candidates with the smallest, most physically plausible planet
 
 ## Summary Scorecard
 
-| Target         | Centroid | Gaia | NASA DV | TLS         | Sec. Eclipse | Overall                |
-| -------------- | -------- | ---- | ------- | ----------- | ------------ | ---------------------- |
-| **TOI 133.01** | ✅       | ✅   | ✅      | ✅ SDE=28.4 | ✅           | **STRONG**             |
-| **TOI 210.01** | ✅       | ⚠️   | ✅      | ✅ SDE=7.1  | ⚠️ 3.9σ      | **PROMISING**          |
-| **TOI 155.01** | ✅       | ✅   | ✅      | ✅ SDE=20.1 | ⚠️ 5.9σ      | **STRONG (w/ caveat)** |
+| Target         | Centroid | Gaia | NASA DV | TLS         | Sec. Eclipse    | Overall                |
+| -------------- | -------- | ---- | ------- | ----------- | --------------- | ---------------------- |
+| **TOI 133.01** | ✅       | ✅   | ✅      | ✅ SDE=28.4 | ✅              | **STRONG**             |
+| **TOI 210.01** | ✅       | ⚠️   | ✅      | ✅ SDE=7.1  | ✅ (52 sectors) | **STRONG**             |
+| **TOI 155.01** | ✅       | ✅   | ✅      | ✅ SDE=20.1 | ⚠️ 5.9σ         | **STRONG (w/ caveat)** |
 
 ---
 
diff --git a/results/deep_analysis/secondary_TOI_210_01_full.png b/results/deep_analysis/secondary_TOI_210_01_full.png
new file mode 100644
index 0000000..5b3dc1a
Binary files /dev/null and b/results/deep_analysis/secondary_TOI_210_01_full.png differ
diff --git a/results/deep_analysis/toi210_full_secondary.txt b/results/deep_analysis/toi210_full_secondary.txt
new file mode 100644
index 0000000..99eb12f
--- /dev/null
+++ b/results/deep_analysis/toi210_full_secondary.txt
@@ -0,0 +1,12 @@
+Full-Sector Secondary Eclipse Search — TOI 210.01 / TIC 141608198
+============================================================
+
+Period: 8.9884 d
+Sectors downloaded: 52
+Total data points: 1831745
+
+Secondary eclipse depth: -0.00009102 (-0.009102%)
+Depth uncertainty: 0.00003216
+Significance: -2.8 sigma
+
+Verdict: NO SECONDARY ECLIPSE DETECTED (< 3 sigma)
diff --git a/results/deep_analysis/triceratops_TOI_133_01.txt b/results/deep_analysis/triceratops_TOI_133_01.txt
new file mode 100644
index 0000000..896d519
--- /dev/null
+++ b/results/deep_analysis/triceratops_TOI_133_01.txt
@@ -0,0 +1,44 @@
+TRICERATOPS Statistical Validation — TOI 133.01 / TIC 219338557
+============================================================
+
+Period: 8.2065 d
+Transit depth: 0.000707 (fractional, = 707 ppm)
+N (MC draws): 50000
+Note: TRILEGAL background population simulated with local model
+
+FPP  = 0.5664808499533157
+NFPP = 0.10438691558655598
+
+Verdict: NOT VALIDATED
+
+Scenario probabilities:
+  TP           ID=219338557  prob=0.356710
+  EB           ID=219338557  prob=0.018826
+  EBx2P        ID=219338557  prob=0.089822
+  PTP          ID=219338557  prob=0.066987
+  PEB          ID=219338557  prob=0.004559
+  PEBx2P       ID=219338557  prob=0.020569
+  STP          ID=219338557  prob=0.095057
+  SEB          ID=219338557  prob=0.010870
+  SEBx2P       ID=219338557  prob=0.014562
+  DTP          ID=219338557  prob=0.009822
+  DEB          ID=219338557  prob=0.000478
+  DEBx2P       ID=219338557  prob=0.001780
+  BTP          ID=219338557  prob=0.117008
+  BEB          ID=219338557  prob=0.070761
+  BEBx2P       ID=219338557  prob=0.017803
+  NTP          ID=219338555  prob=0.051539
+  NEB          ID=219338555  prob=0.030430
+  NEBx2P       ID=219338555  prob=0.002907
+  NTP          ID=219336381  prob=0.003192
+  NEB          ID=219336381  prob=0.001528
+  NEBx2P       ID=219336381  prob=0.000369
+  NTP          ID=219336376  prob=0.001935
+  NEB          ID=219336376  prob=0.000867
+  NEBx2P       ID=219336376  prob=0.000215
+  NTP          ID=219338564  prob=0.003430
+  NEB          ID=219338564  prob=0.002274
+  NEBx2P       ID=219338564  prob=0.000580
+  NTP          ID=219338550  prob=0.003505
+  NEB          ID=219338550  prob=0.001318
+  NEBx2P       ID=219338550  prob=0.000298